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Nash J, Debono S, Whittington B, Kaczynski J, Clark T, Macnaught G, Semple S, van Beek EJR, Tavares A, Dey D, Williams MC, Slomka PJ, Newby DE, Dweck MR, Fletcher AJ. Thoracic aortic microcalcification activity in combined positron emission tomography and magnetic resonance imaging. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06670-5. [PMID: 38456972 DOI: 10.1007/s00259-024-06670-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Non-invasive detection of pathological changes in thoracic aortic disease remains an unmet clinical need particularly for patients with congenital heart disease. Positron emission tomography combined with magnetic resonance imaging (PET-MRI) could provide a valuable low-radiation method of aortic surveillance in high-risk groups. Quantification of aortic microcalcification activity using sodium [18F]fluoride holds promise in the assessment of thoracic aortopathies. We sought to evaluate aortic sodium [18F]fluoride uptake in PET-MRI using three methods of attenuation correction compared to positron emission tomography computed tomography (PET-CT) in patients with bicuspid aortic valve, METHODS: Thirty asymptomatic patients under surveillance for bicuspid aortic valve disease underwent sodium [18F]fluoride PET-CT and PET-MRI of the ascending thoracic aorta during a single visit. PET-MRI data were reconstructed using three iterations of attenuation correction (Dixon, radial gradient recalled echo with two [RadialVIBE-2] or four [RadialVIBE-4] tissue segmentation). Images were qualitatively and quantitatively analysed for aortic sodium [18F]fluoride uptake on PET-CT and PET-MRI. RESULTS Aortic sodium [18F]fluoride uptake on PET-MRI was visually comparable with PET-CT using each reconstruction and total aortic standardised uptake values on PET-CT strongly correlated with each PET-MRI attenuation correction method (Dixon R = 0.70; RadialVIBE-2 R = 0.63; RadialVIBE-4 R = 0.64; p < 0.001 for all). Breathing related artefact between soft tissue and lung were detected using Dixon and RadialVIBE-4 but not RadialVIBE-2 reconstructions, with the presence of this artefact adjacent to the atria leading to variations in blood pool activity estimates. Consequently, quantitative agreements between radiotracer activity on PET-CT and PET-MRI were most consistent with RadialVIBE-2. CONCLUSION Ascending aortic microcalcification analysis in PET-MRI is feasible with comparable findings to PET-CT. RadialVIBE-2 tissue attenuation correction correlates best with the reference standard of PET-CT and is less susceptible to artefact. There remain challenges in segmenting tissue types in PET-MRI reconstructions, and improved attenuation correction methods are required.
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Affiliation(s)
- Jennifer Nash
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
| | - Samuel Debono
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Beth Whittington
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Jakub Kaczynski
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Tim Clark
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Gillian Macnaught
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Department of Medical Physics, NHS Lothian, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Scott Semple
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging Facility Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Edwin J R van Beek
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging Facility Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Adriana Tavares
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Damini Dey
- Departments of Medicine, Division of Artificial Intelligence) and Biomedical Imaging Research Institute, Cedars-Sinai Medical Centre, Los Angeles, USA
| | - Michelle C Williams
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Piotr J Slomka
- Departments of Medicine, Division of Artificial Intelligence) and Biomedical Imaging Research Institute, Cedars-Sinai Medical Centre, Los Angeles, USA
| | - David E Newby
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Marc R Dweck
- The University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Room SU.305, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Alexander J Fletcher
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
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Miyai M, Fukui R, Nakashima M, Goto S. Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study. Radiol Phys Technol 2024; 17:165-175. [PMID: 38032506 DOI: 10.1007/s12194-023-00762-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/15/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023]
Abstract
This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.
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Affiliation(s)
- Masahiro Miyai
- Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-Shi, Okayama, 700-8558, Japan.
- Department of Radiology, Kawasaki Medical School General Medical Center, 2-6-1 Nakasange, Kita-Ku, Okayama-shi, Okayama, 700-8505, Japan.
| | - Ryohei Fukui
- Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan
| | - Masahiro Nakashima
- Division of Radiological Technology, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan
| | - Sachiko Goto
- Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan
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Nakashima M, Yamazaki Y. Evaluation of attenuation correction method for head holder in brain perfusion single-photon emission computed tomography. Radiol Phys Technol 2024; 17:322-328. [PMID: 38332240 DOI: 10.1007/s12194-024-00778-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 02/10/2024]
Abstract
Head holder attenuation affects brain perfusion single-photon emission computed tomography (SPECT) image quality. Here, we proposed a head holder-attenuation correction (AC) method using attenuation coefficient maps calculated by Chang's method from CT images. Then, we evaluated the effectiveness of the head holder-AC method by numerical phantom and clinical cerebral perfusion SPECT studies. In the numerical phantom, the posterior counts were 10.7% lower than the anterior counts without head holder-AC method. However, by performing head holder-AC, the posterior count recovered by approximately 6.8%, approaching the true value. In the clinical study, the normalized count ratio was significantly increased by performing the head holder-AC method in the posterior-middle cerebral artery, posterior cerebral artery and cerebellum regions. There were no significant increases in other regions. The head holder-AC method can correct the counts attenuated by the head holder.
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Affiliation(s)
- Masahiro Nakashima
- Division of Radiological Technology, Okayama University Hospital, 2-5-1 Shikatacho, Kitaku, Okayama, 700-8558, Japan.
| | - Yuta Yamazaki
- Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara-Shi, Tochigi, 324-8550, Japan
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Kawakubo M, Nagao M, Kaimoto Y, Nakao R, Yamamoto A, Kawasaki H, Iwaguchi T, Matsuo Y, Kaneko K, Sakai A, Sakai S. Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging. Ann Nucl Med 2024; 38:199-209. [PMID: 38151588 PMCID: PMC10884131 DOI: 10.1007/s12149-023-01889-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/23/2023] [Indexed: 12/29/2023]
Abstract
OBJECTIVE Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECTSPT) against PET in 17 segments according to the American Heart Association (AHA). METHODS This retrospective study evaluated the patient-to-patient stress, resting SPECT, and PET datasets of 71 patients. The SPECTSPT generation model was trained (stress: 979 image pairs, rest: 987 image pairs) and validated (stress: 421 image pairs, rest: 425 image pairs) using 31 cases of SPECT and PET image pairs using an image-to-image translation network. Forty of 71 cases of left ventricular base-to-apex short-axis images were translated to SPECTSPT in the stress and resting state (stress: 1830 images, rest: 1856 images). Representative voxel values of SPECT and SPECTSPT in the 17 AHA segments against PET were compared. The stress, resting, and difference scores of 40 cases of SPECT and SPECTSPT were also compared in each of the 17 segments. RESULTS For AHA 17-segment-wise analysis, stressed SPECT but not SPECTSPT voxel values showed significant error from PET at basal anterior regions (segments #1, #6), and at mid inferoseptal regions (segments #8, #9, and #10). SPECT, but not SPECTSPT, voxel values at resting state showed significant error at basal anterior regions (segments #1, #2, and #6), and at mid inferior regions (segments #8, #9, and #11). Significant SPECT overscoring was observed against PET in basal-to-apical inferior regions (segments #4, #10, and #15) during stress. No significant overscoring was observed in SPECTSPT at stress, and only moderate over and underscoring in the basal inferior region (segment #4) was found in the resting and difference states. CONCLUSIONS Our PET-supervised deep learning model is a new approach to correct well-known inferior wall attenuation in SPECT myocardial perfusion imaging. As standalone SPECT systems are used worldwide, the SPECTSPT generation model may be applied as a low-cost and practical clinical tool that provides powerful auxiliary information for the diagnosis of myocardial blood flow.
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Affiliation(s)
- Masateru Kawakubo
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Michinobu Nagao
- Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.
| | - Yoko Kaimoto
- Department of Radiology, Tokyo Women's Medical University, Tokyo, Japan
| | - Risako Nakao
- Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan
| | - Atsushi Yamamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan
- Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan
| | - Hiroshi Kawasaki
- Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Takafumi Iwaguchi
- Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Yuka Matsuo
- Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan
| | - Koichiro Kaneko
- Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan
| | - Akiko Sakai
- Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shuji Sakai
- Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan
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Salas-Ramirez M, Leube J, Lassmann M, Tran-Gia J. Effect of kilovoltage and quality reference mAs on CT-based attenuation correction in 177Lu SPECT/CT imaging: a phantom study. EJNMMI Phys 2024; 11:21. [PMID: 38407672 DOI: 10.1186/s40658-024-00622-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/09/2024] [Indexed: 02/27/2024] Open
Abstract
INTRODUCTION CT-based attenuation correction (CT-AC) plays a major role in accurate activity quantification by SPECT/CT imaging. However, the effect of kilovoltage peak (kVp) and quality-reference mAs (QRM) on the attenuation coefficient image (μ-map) and volume CT dose index (CTDIvol) have not yet been systematically evaluated. Therefore, the aim of this study was to fill this gap and investigate the influence of kVp and QRM on CT-AC in 177Lu SPECT/CT imaging. METHODS Seventy low-dose CT acquisitions of an Electron Density Phantom (seventeen inserts of nine tissue-equivalent materials) were acquired using various kVp and QRM combinations on a Siemens Symbia Intevo Bold SPECT/CT system. Using manufacturer reconstruction software, 177Lu μ-maps were generated for each CT image, and three low-dose CT related aspects were examined. First, the μ-map-based attenuation values (μmeasured) were compared with theoretical values (μtheoretical). Second, changes in 177Lu activity expected due to changes in the μ-map were calculated using a modified Chang method. Third, the noise in the μ-map was assessed by measuring the coefficient of variation in a volume of interest in the homogeneous section of the Electron Density Phantom. Lastly, two phantoms were designed to simulate attenuation in four tissue-equivalent materials for two different source geometries (1-mL and 10-mL syringes). 177Lu SPECT/CT imaging was performed using three different reconstruction algorithms (xSPECT Quant, Flash3D, STIR), and the SPECT-based activities were compared against the nominal activities in the sources. RESULTS The largest relative errors between μmeasured and μtheoretical were observed in the lung inhale insert (range: 18%-36%), while it remained below 6% for all other inserts. The resulting changes in 177Lu activity quantification were -3.5% in the lung inhale insert and less than -2.3% in all other inserts. Coefficient of variation and CTDIvol ranged from 0.3% and 3.6 mGy (130 kVp, 35 mAs) to 0.4% and 0.9 mGy (80 kVp, 20 mAs), respectively. The SPECT-based activity quantification using xSPECT Quant reconstructions outperformed all other reconstruction algorithms. CONCLUSION This study shows that kVp and QRM values in low-dose CT imaging have a minimum effect on quantitative 177Lu SPECT/CT imaging, while the selection of low values of kVp and QRM reduce the CTDIvol.
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Affiliation(s)
- Maikol Salas-Ramirez
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany.
| | - Julian Leube
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| | - Michael Lassmann
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
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Feher A, Pieszko K, Shanbhag A, Lemley M, Bednarski B, Miller RJH, Huang C, Miras L, Liu YH, Sinusas AJ, Slomka PJ, Miller EJ. CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients. Eur J Nucl Med Mol Imaging 2024; 51:695-706. [PMID: 37924340 DOI: 10.1007/s00259-023-06484-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.
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Affiliation(s)
- Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bryan Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Leonidas Miras
- Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA
| | - Yi-Hwa Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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Kobayashi T, Shigeki Y, Yamakawa Y, Tsutsumida Y, Mizuta T, Hanaoka K, Watanabe S, Morimoto-Ishikawa D, Yamada T, Kaida H, Ishii K. Generating PET Attenuation Maps via Sim2Real Deep Learning-Based Tissue Composition Estimation Combined with MLACF. J Imaging Inform Med 2024; 37:167-179. [PMID: 38343219 DOI: 10.1007/s10278-023-00902-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/20/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
Deep learning (DL) has recently attracted attention for data processing in positron emission tomography (PET). Attenuation correction (AC) without computed tomography (CT) data is one of the interests. Here, we present, to our knowledge, the first attempt to generate an attenuation map of the human head via Sim2Real DL-based tissue composition estimation from model training using only the simulated PET dataset. The DL model accepts a two-dimensional non-attenuation-corrected PET image as input and outputs a four-channel tissue-composition map of soft tissue, bone, cavity, and background. Then, an attenuation map is generated by a linear combination of the tissue composition maps and, finally, used as input for scatter+random estimation and as an initial estimate for attenuation map reconstruction by the maximum likelihood attenuation correction factor (MLACF), i.e., the DL estimate is refined by the MLACF. Preliminary results using clinical brain PET data showed that the proposed DL model tended to estimate anatomical details inaccurately, especially in the neck-side slices. However, it succeeded in estimating overall anatomical structures, and the PET quantitative accuracy with DL-based AC was comparable to that with CT-based AC. Thus, the proposed DL-based approach combined with the MLACF is also a promising CT-less AC approach.
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Affiliation(s)
- Tetsuya Kobayashi
- Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan.
| | - Yui Shigeki
- Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan
| | - Yoshiyuki Yamakawa
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Yumi Tsutsumida
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Tetsuro Mizuta
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Daisuke Morimoto-Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hayato Kaida
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Faculty of Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Faculty of Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
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Izadi S, Shiri I, F Uribe C, Geramifar P, Zaidi H, Rahmim A, Hamarneh G. Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks. Z Med Phys 2024:S0939-3889(24)00002-3. [PMID: 38302292 DOI: 10.1016/j.zemedi.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 02/03/2024]
Abstract
In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.
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Affiliation(s)
- Saeed Izadi
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Carlos F Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark; University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada.
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9
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Wyatt JJ, Kaushik S, Cozzini C, Pearson RA, Petrides G, Wiesinger F, McCallum HM, Maxwell RJ. Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis. EJNMMI Phys 2024; 11:10. [PMID: 38282050 DOI: 10.1186/s40658-024-00617-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/15/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Positron emission tomography-magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis. METHODS Ten patients being treated with ano-rectal radiotherapy received a [Formula: see text]F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of [Formula: see text]. Equivalence margins of [Formula: see text] were used. RESULTS Mean whole-image SUV differences were -0.02% (sCTAC) compared to -3.0% (MRAC), with larger differences in the bone regions (-0.5% to -16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients [Formula: see text]. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in [Formula: see text] were [Formula: see text] (± standard error, sCTAC) and [Formula: see text] (MRAC), and [Formula: see text] (sCTAC) and [Formula: see text] (MRAC) in [Formula: see text]. The sCTAC was statistically equivalent to CTAC within a [Formula: see text] equivalence margin for [Formula: see text] and [Formula: see text] ([Formula: see text] and [Formula: see text]), whereas the MRAC was not ([Formula: see text] and [Formula: see text]). CONCLUSION Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.
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Affiliation(s)
- Jonathan J Wyatt
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
| | - Sandeep Kaushik
- GE Healthcare, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | | | - Rachel A Pearson
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - George Petrides
- Nuclear Medicine Department, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | | | - Hazel M McCallum
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Ross J Maxwell
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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Fallahpoor M, Chakraborty S, Pradhan B, Faust O, Barua PD, Chegeni H, Acharya R. Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space. Comput Methods Programs Biomed 2024; 243:107880. [PMID: 37924769 DOI: 10.1016/j.cmpb.2023.107880] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.
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Affiliation(s)
- Maryam Fallahpoor
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Prabal Datta Barua
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | | | - Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
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Iwao Y, Akamatsu G, Tashima H, Takahashi M, Yamaya T. Pre-acquired CT-based attenuation correction with automated headrest removal for a brain-dedicated PET system. Radiol Phys Technol 2023; 16:552-559. [PMID: 37819445 DOI: 10.1007/s12194-023-00744-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023]
Abstract
Attenuation correction (AC) is essential for quantitative positron emission tomography (PET) images. Attenuation coefficient maps (μ-maps) are usually generated from computed tomography (CT) images when PET-CT combined systems are used. If CT has been performed prior to PET imaging, pre-acquired CT can be used for brain PET AC, because the human head is almost rigid. This pre-acquired CT-based AC approach is suitable for stand-alone brain-dedicated PET, such as VRAIN (ATOX Co. Ltd., Tokyo, Japan). However, the headrest of PET is different from the headrest in pre-acquired CT images, which may degrade the PET image quality. In this study, we prepared three different types of μ-maps: (1) based on the pre-acquired CT, where namely the headrest is different from the PET system (μ-map-diffHr); (2) manually removing the headrest from the pre-acquired CT (μ-map-noHr); and (3) artificially replacing the headrest region with the headrest of the PET system (μ-map-sameHr). Phantom images by VRAIN using each μ-map were investigated for uniformity, noise, and quantitative accuracy. Consequently, only the uniformity of the images using μ-map-diffHr was out of the acceptance criteria. We then proposed an automated method for removing the headrest from pre-acquired CT images. In comparisons of standardized uptake values in nine major brain regions from the 18F-fluoro-2-deoxy-D-glucose-PET of 10 healthy volunteers, no significant differences were found between the μ-map-noHr and the μ-map-sameHr. In conclusion, pre-acquired CT-based AC with automated headrest removal is useful for brain-dedicated PET such as VRAIN.
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Affiliation(s)
- Yuma Iwao
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan.
| | - Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan.
| | - Hideaki Tashima
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan
| | - Miwako Takahashi
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan
| | - Taiga Yamaya
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan.
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12
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Arabi H, Zaidi H. Recent Advances in Positron Emission Tomography/Magnetic Resonance Imaging Technology. Magn Reson Imaging Clin N Am 2023; 31:503-515. [PMID: 37741638 DOI: 10.1016/j.mric.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Abstract
More than a decade has passed since the clinical deployment of the first commercial whole-body hybrid PET/MR scanner in the clinic. The major advantages and limitations of this technology have been investigated from technical and medical perspectives. Despite the remarkable advantages associated with hybrid PET/MR imaging, such as reduced radiation dose and fully simultaneous functional and structural imaging, this technology faced major challenges in terms of mutual interference between MRI and PET components, in addition to the complexity of achieving quantitative imaging owing to the intricate MRI-guided attenuation correction in PET/MRI. In this review, the latest technical developments in PET/MRI technology as well as the state-of-the-art solutions to the major challenges of quantitative PET/MR imaging are discussed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva CH-1205, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense 500, Denmark.
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13
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Bebbington NA, Christensen KB, Østergård LL, Holdgaard PC. Ultra-low-dose CT for attenuation correction: dose savings and effect on PET quantification for protocols with and without tin filter. EJNMMI Phys 2023; 10:66. [PMID: 37861887 PMCID: PMC10589162 DOI: 10.1186/s40658-023-00585-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/12/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Ultra-low-dose (ULD) computed tomography (CT) scans should be used when CT is performed only for attenuation correction (AC) of positron emission tomography (PET) data. A tin filter can be used in addition to the standard aluminium bowtie filter to reduce CT radiation dose to patients. The aim was to determine how low CT doses can be, when utilised for PET AC, with and without the tin filter, whilst providing adequate PET quantification. METHODS A water-filled NEMA image quality phantom was imaged in three configurations with 18F-FDG: (1) water only (0HU); (2) with cylindrical insert containing homogenous mix of sand, flour and water (SFW, approximately 475HU); (3) with cylindrical insert containing sand (approximately 1100HU). Each underwent one-bed-position (26.3 cm) PET-CT comprising 1 PET and 13 CT acquisitions. CT acquisitions with tube current modulation were performed at 120 kV/50 mAs-ref (reference standard), 100 kV/7 mAs-ref (standard ULDCT for PET AC protocol), Sn140kV (mAs range 7-50-ref) and Sn100kV (mAs range 12-400-ref). PET data were reconstructed with μ-maps provided by each CT dataset, and PET activity concentration measured in each reconstruction. Differences in CT dose length product (DLP) and PET quantification were determined relative to the reference standard. RESULTS At each tube voltage, changes in PET quantification were greater with increasing density and reducing mAs. Compared with the reference standard, differences in PET quantification for the standard ULDCT protocol for the three phantoms were ≤ 1.7%, with the water phantom providing a DLP of 7mGy.cm. With tin filter at Sn100kV, differences in PET quantification were negligible (≤ 1.2%) for all phantoms down to 50mAs-ref, proving a DLP of 2.8mGy.cm, at 60% dose reduction compared with standard ULDCT protocol. Below 50mAs-ref, differences in PET quantification were > 2% for at least one phantom (2.3% at 25mAs-ref in SFW; 6.4% at 12mAs-ref in sand). At Sn140kV/7mAs-ref, quantification differences were ≤ 0.6% in water, giving 3.8mGy.cm DLP, but increased to > 2% at bone-equivalent densities. CONCLUSIONS CT protocols for PET AC can provide ultra-low doses with adequate PET quantification. The tin filter can allow 60-87% lower dose than the standard ULDCT protocol for PET AC, depending on tissue density and accepted change in PET quantification.
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Affiliation(s)
| | - Kenneth Boye Christensen
- Department of Nuclear Medicine, Lillebaelt Hospital - University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark
| | - Lone Lange Østergård
- Department of Nuclear Medicine, Lillebaelt Hospital - University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark
| | - Paw Christian Holdgaard
- Department of Nuclear Medicine, Lillebaelt Hospital - University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark
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Abstract
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT, 06520, USA.
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15
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Sun H, Wang F, Yang Y, Hong X, Xu W, Wang S, Mok GSP, Lu L. Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network. Eur J Nucl Med Mol Imaging 2023; 50:3630-3646. [PMID: 37474736 DOI: 10.1007/s00259-023-06343-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. METHODS We recruited 60 subjects for rest-only scans and 14 subjects for rest-stress scans, all of whom underwent [13N]ammonia cardiac PET/CT examinations to acquire static and dynamic frames with both 3D NAC and CT-based AC (CTAC) PET images. We developed a 3D pix2pix deep learning AC (DLAC) framework via a U-net + ResNet-based generator and a convolutional neural network-based discriminator. Paired static or dynamic NAC and CTAC PET images from 60 rest-only subjects were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) training, respectively. The pre-trained S-DLAC network was then fine-tuned by paired dynamic NAC and CTAC PET frames of 60 rest-only subjects to derive an improved D-DLAC-FT for dynamic PET images. The 14 rest-stress subjects were used as an internal testing dataset and separately tested on different network models without training. The proposed methods were evaluated using visual quality and quantitative metrics. RESULTS The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods were consistent with clinical CTAC in terms of various images and quantitative metrics. The S-DLAC (slope = 0.9423, R2 = 0.947) showed a higher correlation with the reference static CTAC as compared to static NAC (slope = 0.0992, R2 = 0.654). D-DLAC-FT yielded lower myocardial blood flow (MBF) errors in the whole left ventricular myocardium than D-DLAC, but with no significant difference, both for the 60 rest-state subjects (6.63 ± 5.05% vs. 7.00 ± 6.84%, p = 0.7593) and the 14 stress-state subjects (1.97 ± 2.28% vs. 3.21 ± 3.89%, p = 0.8595). CONCLUSION The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods achieve comparable performance with clinical CTAC. Transfer learning shows promising potential for dynamic MP PET.
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Affiliation(s)
- Hao Sun
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Fanghu Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuling Yang
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Xiaotong Hong
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Weiping Xu
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shuxia Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Pazhou Lab, Guangzhou, 510330, China.
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Krokos G, MacKewn J, Dunn J, Marsden P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys 2023; 10:52. [PMID: 37695384 PMCID: PMC10495310 DOI: 10.1186/s40658-023-00569-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
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Affiliation(s)
- Georgios Krokos
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Jane MacKewn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
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Schiebler T, Apostolova I, Mathies FL, Lange C, Klutmann S, Buchert R. No impact of attenuation and scatter correction on the interpretation of dopamine transporter SPECT in patients with clinically uncertain parkinsonian syndrome. Eur J Nucl Med Mol Imaging 2023; 50:3302-3312. [PMID: 37328621 PMCID: PMC10541531 DOI: 10.1007/s00259-023-06293-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 06/05/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE The benefit from attenuation and scatter correction (ASC) of dopamine transporter (DAT)-SPECT for the detection of nigrostriatal degeneration in clinical routine is still a matter of debate. The current study evaluated the impact of ASC on visual interpretation and semi-quantitative analysis of DAT-SPECT in a large patient sample. METHODS One thousand seven hundred forty consecutive DAT-SPECT with 123I-FP-CIT from clinical routine were included retrospectively. SPECT images were reconstructed iteratively without and with ASC. Attenuation correction was based on uniform attenuation maps, scatter correction on simulation. All SPECT images were categorized with respect to the presence versus the absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake by three independent readers. Image reading was performed twice to assess intra-reader variability. The specific 123I-FP-CIT binding ratio (SBR) was used for automatic categorization, separately with and without ASC. RESULTS The mean proportion of cases with discrepant categorization by the same reader between the two reading sessions was practically the same without and with ASC, about 2.2%. The proportion of DAT-SPECT with discrepant categorization without versus with ASC by the same reader was 1.66% ± 0.50% (1.09-1.95%), not exceeding the benchmark of 2.2% from intra-reader variability. This also applied to automatic categorization of the DAT-SPECT images based on the putamen SBR (1.78% discrepant cases between without versus with ASC). CONCLUSION Given the large sample size, the current findings provide strong evidence against a relevant impact of ASC with uniform attenuation and simulation-based scatter correction on the clinical utility of DAT-SPECT to detect nigrostriatal degeneration in patients with clinically uncertain parkinsonian syndrome.
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Affiliation(s)
- Tassilo Schiebler
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr, 52, 20246, Hamburg, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr, 52, 20246, Hamburg, Germany
| | - Franziska Lara Mathies
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr, 52, 20246, Hamburg, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr, 52, 20246, Hamburg, Germany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr, 52, 20246, Hamburg, Germany.
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Huxohl T, Patel G, Zabel R, Burchert W. Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text]SPECT collimator. EJNMMI Phys 2023; 10:49. [PMID: 37639082 PMCID: PMC10462587 DOI: 10.1186/s40658-023-00568-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/07/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND The use of CT images for attenuation correction of myocardial perfusion imaging with single photon emission computer tomography (SPECT) increases diagnostic confidence. However, acquiring a CT image registered to a SPECT image is often not possible because most scanners are SPECT-only. It is possible to approximate attenuation maps using deep learning, but this has not yet been shown to work for a SPECT scanner with an IQ[Formula: see text]SPECT collimator. This study investigates whether it is possible to approximate attenuation maps from non-attenuation-corrected (nAC) reconstructions acquired with a SPECT scanner equipped with an IQ[Formula: see text]SPECT collimator. METHODS Attenuation maps and reconstructions were acquired retrospectively for 150 studies. A U-Net was trained to predict attenuation maps from nAC reconstructions using the conditional generative adversarial network framework. Predicted attenuation maps are compared to real attenuation maps using the normalized mean absolute error (NMAE). Attenuation-corrected reconstructions were computed, and the resulting polar maps were compared by pixel and by average perfusion per segment using the absolute percent error (APE). The training and evaluation code is available at https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map . RESULTS Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020±0.007. The same is true for polar maps generated from reconstructions with predicted attenuation maps compared to CT-based attenuation maps. Their pixel-wise absolute distance is 3.095±3.199, and the segment-wise APE is 1.155±0.769. CONCLUSIONS It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ[Formula: see text]SPECT collimator using deep learning.
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Affiliation(s)
- Tamino Huxohl
- Institute of Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, University Hospital of the Ruhr University Bochum, Bad Oeynhausen, Germany
| | - Gopesh Patel
- Institute of Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, University Hospital of the Ruhr University Bochum, Bad Oeynhausen, Germany
| | - Reinhard Zabel
- Institute of Nuclear Medicine, Hospital Lippe, Lippe, Germany
| | - Wolfgang Burchert
- Institute of Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, University Hospital of the Ruhr University Bochum, Bad Oeynhausen, Germany
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Gandia-Ferrero MT, Torres-Espallardo I, Martínez-Sanchis B, Morera-Ballester C, Muñoz E, Sopena-Novales P, González-Pavón G, Martí-Bonmatí L. Objective Image Quality Comparison Between Brain-Dedicated PET and PET/CT Scanners. J Med Syst 2023; 47:88. [PMID: 37589893 DOI: 10.1007/s10916-023-01984-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/02/2023] [Indexed: 08/18/2023]
Abstract
As part of a clinical validation of a new brain-dedicated PET system (CMB), image quality of this scanner has been compared to that of a whole-body PET/CT scanner. To that goal, Hoffman phantom and patient data were obtined with both devices. Since CMB does not use a CT for attenuation correction (AC) which is crucial for PET images quality, this study includes the evaluation of CMB PET images using emission-based or CT-based attenuation maps. PET images were compared using 34 image quality metrics. Moreover, a neural network was used to evaluate the degree of agreement between both devices on the patients diagnosis prediction. Overall, results showed that CMB images have higher contrast and recovery coefficient but higher noise than PET/CT images. Although SUVr values presented statistically significant differences in many brain regions, relative differences were low. An asymmetry between left and right hemispheres, however, was identified. Even so, the variations between the two devices were minor. Finally, there is a greater similarity between PET/CT and CMB CT-based AC PET images than between PET/CT and the CMB emission-based AC PET images.
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Affiliation(s)
- Maria Teresa Gandia-Ferrero
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain.
| | - Irene Torres-Espallardo
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | - Begoña Martínez-Sanchis
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | | | - Enrique Muñoz
- Oncovision, Carrer de Jeroni de Montsoriu, 92, València, 46022, Spain
| | - Pablo Sopena-Novales
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | | | - Luis Martí-Bonmatí
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain
- Radiology Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
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Schaefferkoetter J, Shah V, Hayden C, Prior JO, Zuehlsdorff S. Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data. Eur J Nucl Med Mol Imaging 2023; 50:2292-2304. [PMID: 36882577 DOI: 10.1007/s00259-023-06181-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 03/09/2023]
Abstract
BACKGROUND For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images. PURPOSE This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion. MATERIALS AND METHODS A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications. RESULTS A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors. CONCLUSION This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging.
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Affiliation(s)
| | - Vijay Shah
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA
| | - Charles Hayden
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA
| | - John O Prior
- Department of Nuclear and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Sven Zuehlsdorff
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA
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Bishop A, King S, Stace S, Elliott J. Can retrospectively fusing SPECT to CT images reduce radiation doses in myocardial perfusion imaging? Radiography (Lond) 2023; 29:327-332. [PMID: 36706601 DOI: 10.1016/j.radi.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/07/2023] [Accepted: 01/11/2023] [Indexed: 01/26/2023]
Abstract
INTRODUCTION To establish if the CT dataset acquired during the stress element of myocardial perfusion imaging can be fused to the subsequent rest scan to reduce radiation doses from these procedures. METHODS 86 rest scans were processed and evaluated using a self-designed project specific tool. Recording processing time, the time between the two data sets selected for fusion and assessing radiographic reports to ensure produced images were of diagnostic quality. RESULTS 70% of fused scans were acquired 6-7 days apart; the mean (SD) processing time was calculated as 2.03 (0.36) minutes. The Pearson's correlation between these two variables was determined to be 0.22, showing a slight positive correlation although not statistically significant. 100% of the images produced were of diagnostic quality. CONCLUSION Rest scans can be fused to a previously acquired CT, careful consideration should be given when positioning the patient and to the time interval between acquiring the two data sets, departmental guidelines can assist with this. Staff training may also be beneficial to ensure staff can assess if data sets are fusible prior to completing a scan. IMPLICATIONS FOR PRACTICE This data provides evidence that retrospective fusion can reduce patient radiation doses in myocardial perfusion imaging without compromising diagnostic outcomes. Dose optimisation is an essential part of the ionising radiation (medical exposure) regulations therefore retrospective fusion should be considered in practice to ensure departmental compliance, although it is noteworthy this study is solely based in a single centred one camera department.
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Affiliation(s)
- A Bishop
- Hywel Dda University Health Board Pembrokeshire, UK: UWE, Bristol, UK: Cardiff University, Cardiff, UK.
| | | | - S Stace
- Hywel Dda University Health Board Pembrokeshire, UK
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Hagio T, Moody JB, Poitrasson-Rivière A, Renaud JM, Pierce L, Buckley C, Ficaro EP, Murthy VL. Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial. Eur J Nucl Med Mol Imaging 2023; 50:1028-33. [PMID: 36401636 DOI: 10.1007/s00259-022-06045-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/13/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnostic accuracy for detection of coronary artery disease (CAD) beyond non-attenuation-corrected (NAC) polar maps in a large single center study. However, the generalizability of this approach to other institutions with different scanner models and protocols is uncertain. In this study, we evaluated the diagnostic performance of DLAC compared to NAC for detection of CAD as defined by invasive coronary angiography (ICA) in a large multi-center trial. METHODS During the phase 3 flurpiridaz multi-center diagnostic clinical trial, conducted over 74 international sites, patients with known or suspected CAD who were referred for a clinically indicated ICA were enrolled. Using receiver operating characteristic (ROC) analysis, we evaluated the detectability of obstructive CAD, defined by quantitative coronary angiography by a core laboratory, using total perfusion deficit (TPD) as an integrated measure of defect extent and severity on DLAC polar maps compared to NAC polar maps. This was also compared against the visual scoring of three expert core lab readers. RESULTS Out of 755 patients, 722 (69% male) had evaluable SPECT and ICA for this study. ROC analysis demonstrated significant improvement in detecting per-patient obstructive CAD with DLAC over NAC with area under the curve (AUC) of 0.752 (95% CI: 0.711-0.792) for DLAC compared to 0.717 (0.675-0.759) for NAC (p value = 0.016). Compared to the consensus of expert readers AUC = 0.743 (0.701-0.784), DLAC was comparable (p value = 0.913), whereas NAC underperformed (p value = 0.051). CONCLUSION DL-based attenuation correction improves diagnostic performance of SPECT MPI for detecting CAD in data from a large multi-center clinical trial regardless of SPECT camera model or protocol. TRIAL REGISTRATION A Phase 3 Multi-center Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD, ClinicalTrials.gov Identifier: NCT01347710, registered on 4 May 2011. https://clinicaltrials.gov/ct2/show/study/NCT01347710.
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Shiri I, Vafaei Sadr A, Akhavan A, Salimi Y, Sanaat A, Amini M, Razeghi B, Saberi A, Arabi H, Ferdowsi S, Voloshynovskiy S, Gündüz D, Rahmim A, Zaidi H. Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning. Eur J Nucl Med Mol Imaging 2023; 50:1034-1050. [PMID: 36508026 PMCID: PMC9742659 DOI: 10.1007/s00259-022-06053-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 11/18/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. METHODS Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). RESULTS In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. CONCLUSION Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Alireza Vafaei Sadr
- Department of Theoretical Physics and Center for Astroparticle Physics, University of Geneva, Geneva, Switzerland.,Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Azadeh Akhavan
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | | | | | - Deniz Gündüz
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Raymond C, Jurkiewicz MT, Orunmuyi A, Liu L, Dada MO, Ladefoged CN, Teuho J, Anazodo UC. The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis. J Neuroradiol 2023; 50:315-326. [PMID: 36738990 DOI: 10.1016/j.neurad.2023.01.157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
PURPOSE This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards. METHODS Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classification performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively. RESULTS A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 ± 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 ± 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 ± 0.1 / 0.95 ± 0.03 / 0.85 ± 0.14. CONCLUSIONS In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical implementation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available.
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Affiliation(s)
- Confidence Raymond
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada
| | - Michael T Jurkiewicz
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada
| | - Akintunde Orunmuyi
- Kenyatta University Teaching, Research and Referral Hospital, Nairobi, Kenya
| | - Linshan Liu
- Lawson Health Research Institute, London, ON, Canada
| | | | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | - Jarmo Teuho
- Turku PET Centre, Turku University, Turku, Finland; Turku University Hospital, Turku, Finland
| | - Udunna C Anazodo
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada; Montreal Neurological Institute, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
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Tadesse GF, Geramifar P, Abbasi M, Tsegaw EM, Amin M, Salimi A, Mohammadi M, Teimourianfard B, Ay MR. Attenuation Correction for Dedicated Cardiac SPECT Imaging Without Using Transmission Data. Mol Imaging Radionucl Ther 2023; 32:42-53. [PMID: 36818953 PMCID: PMC9950684 DOI: 10.4274/mirt.galenos.2022.55476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Objectives Attenuation correction (AC) using transmission scanning-like computed tomography (CT) is the standard method to increase the accuracy of cardiac single-photon emission computed tomography (SPECT) images. Recently developed dedicated cardiac SPECT do not support CT, and thus, scans on these systems are vulnerable to attenuation artifacts. This study presented a new method for generating an attenuation map directly from emission data by segmentation of precisely non-rigid registration extended cardiac-torso (XCAT)-digital phantom with cardiac SPECT images. Methods In-house developed non-rigid registration algorithm automatically aligns the XCAT- phantom with cardiac SPECT image to precisely segment the contour of organs. Pre-defined attenuation coefficients for given photon energies were assigned to generate attenuation maps. The CT-based attenuation maps were used for validation with which cardiac SPECT/CT data of 38 patients were included. Segmental myocardial counts of a 17-segment model from these databases were compared based on the basis of the paired t-test. Results The mean, and standard deviation of the mean square error and structural similarity index measure of the female stress phase between the proposed attenuation maps and the CT attenuation maps were 6.99±1.23% and 92±2.0%, of the male stress were 6.87±3.8% and 96±1.0%. Proposed attenuation correction and computed tomography based attenuation correction average myocardial perfusion count was significantly higher than that in non-AC in the mid-inferior, mid-lateral, basal-inferior, and lateral regions (p<0.001). Conclusion The proposed attenuation maps showed good agreement with the CT-based attenuation map. Therefore, it is feasible to enable AC for a dedicated cardiac SPECT or SPECT standalone scanners.
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Affiliation(s)
- Getu Ferenji Tadesse
- Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran,Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran,St. Paul’s Hospital Millennium Medical College, Department of Internal Medicine, Addis Ababa, Ethiopia
| | - Parham Geramifar
- Tehran University of Medical Sciences, Shariati Hospital, Research Center for Nuclear Medicine, Tehran, Iran
| | - Mehrshad Abbasi
- Tehran University of Medical Sciences, Department of Nuclear Medicine, Vali-Asr Hospital, Tehran, Iran
| | - Eyachew Misganew Tsegaw
- Debre Tabor University Faculty of Natural and Computational Sciences, Department of Physics, Debre Tabor, Ethiopia
| | - Mohammad Amin
- Shahed University Faculty of Science, Department of Computer Science, Tehran, Iran
| | - Ali Salimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Mohammadi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mohammed Reza Ay
- Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran,Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran,* Address for Correspondence: Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS); Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran Phone: +989125789765 E-mail:
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Lyu Y, Chen G, Lu Z, Chen Y, Mok GSP. The effects of mismatch between SPECT and CT images on quantitative activity estimation - A simulation study. Z Med Phys 2023; 33:54-69. [PMID: 35644776 PMCID: PMC10082378 DOI: 10.1016/j.zemedi.2022.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Quantitative activity estimation is essential in nuclear medicine imaging. Mismatch between SPECT and CT images at the same imaging time point due to patient movement degrades accuracy in both diagnostic studies and target radionuclide therapy dosimetry. This work aims to study the mismatch effects between CT and SPECT data on attenuation correction (AC), volume-of-interest (VOI) delineation, and registration for activity estimation. METHODS Nine 4D XCAT phantoms were generated at 1, 24, and 144 h post In-111 Zevalin injection, varying in activity distributions, body sizes, and organ sizes. Realistic noisy SPECT projections were generated by an analytical projector and reconstructed with a quantitative OS-EM method. CT images were shifted, corresponding to SPECT images at each imaging time point, from -5 to 5 voxels and also according to a clinical reference. The effect of mismatched AC maps was evaluated using mismatched CT images for AC in SPECT reconstruction while VOIs were mapped out from matched CTs. The effect of mismatched VOI drawings was evaluated using mismatched CTs to map out target organs while using matched CTs for AC. The effect of mismatched CT images for registration was evaluated by registering sequential mismatched CTs to align corresponding SPECT images, with no AC and VOI mismatch. Bi-exponential curve fitting was performed to obtain time-integrated activity (TIA). Organ activity errors (%OAE) and TIA errors (%TIAE) were calculated. RESULTS According to the clinical reference, %OAE was larger for organs near ribs for AC effect. For VOI effect, %OAE was larger for small and low uptake organs. For registration effect, %TIAE were larger when mismatch existed in more numbers of SPECT/CT images, while no substantial difference was observed when using mismatched CT at different imaging time points as registration reference. %TIAE was highest for VOI, followed by registration and AC, e.g., 20.62%±8.61%, 9.33%±4.66% and 1.13%±0.90% respectively for kidneys. CONCLUSIONS The mismatch between CT and SPECT images poses a significant impact on the accuracy of quantitative activity estimation, attributed particularly from VOI delineation errors. It is recommended to perform registration between emission and transmission images at the same time point to ensure diagnostic and dosimetric accuracy.
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Affiliation(s)
- Yingqing Lyu
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Gefei Chen
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Zhonglin Lu
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Yue Chen
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, No. 25, Taiping St., Luzhou, Sichuan, China.
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China; Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China; Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Science, University of Macau, Taipa, Macau SAR, China.
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Hashizume K, Ichikawa Y, Tomita Y, Sakuma H. Impact of CT tube-voltage and bone density on the quantitative assessment of tracer uptake in Tc-99m bone SPECT/CT: A phantom study. Phys Med 2022; 104:18-22. [PMID: 36356500 DOI: 10.1016/j.ejmp.2022.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 10/05/2022] [Accepted: 10/23/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To evaluate the effect of computed tomography (CT) tube voltage and CT density for CT-based attenuation correction (CTAC) on quantification of tracer uptake in single photon emission computed tomography (SPECT)/CT. METHODS A cylindrical phantom contained 7 cylinders with diameter of 30 mm. The central cylinder and background part were filled with 17 kBq/ml of 99mTc-pertechnetate solution. Of the remaining 6 cylinders, one cylinder was filled with water and 5 cylinders were filled with each own different concentration of K2HPO4 solution (120, 275, 450, 666, and 960 mg/cm3) to simulate different bone densities. The 6 cylinders also contained 99mTc-pertechnetate solution with the same radioactivity concentration (207 kBq/ml). CT scans were performed with 4 different tube voltages of 80, 100, 120, and 140 kVp for CTAC. The radioactivity concentration in the 6 cylinders were measured on the SPECT images processed with 4 different attenuation coefficient maps derived from each tube voltage of CT images. RESULTS Compared with the water cylinder without K2HPO4 solution, the measured radioactivity of the highest density cylinder (K2HPO4 solution concentration: 960 mg/cm3) was found to be overestimated by 3.3 % and 4.3 %, respectively, when the tube voltage was 120 kVp and 140 kVp (p = 0.022). The use of low-tube voltage, such as 80 kVp, has improved the quantitative accuracy of bone SPECT/CT. CONCLUSIONS SPECT quantitative evaluation of tracers in high-density objects tends to overestimate as tube voltage for CTAC increases. However, the overestimation in quantitative SPECT/CT evaluation in simulated bone area is less than 5% at most.
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Mizuta T, Yamakawa Y, Minagawa S, Kobayashi T, Ohtani A, Takenouchi S, Hanaoka K, Watanabe S, Morimoto-Ishikawa D, Yamada T, Kaida H, Ishii K. Attenuation correction for phantom tests: an alternative to maximum-likelihood attenuation correction factor-based correction for clinical studies in time-of-flight PET. Ann Nucl Med 2022; 36:998-1006. [PMID: 36167889 DOI: 10.1007/s12149-022-01788-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/14/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES This study evaluates the phantom attenuation correction (PAC) method as an alternative to maximum-likelihood attenuation correction factor (ML-ACF) correction in time-of-flight (TOF) brain positron emission tomography (PET) studies. METHODS In the PAC algorithm, a template emission image [Formula: see text] and a template attenuation coefficient image [Formula: see text] are prepared as a data set based on phantom geometry. Position-aligned attenuation coefficient image [Formula: see text] is derived by aligning [Formula: see text] using parameters that match the template emission image [Formula: see text] to measured emission image [Formula: see text]. Then, attenuation coefficient image [Formula: see text] combined with a headrest image is used for scatter and attenuation correction in the image reconstruction. To evaluate the PAC algorithm as an alternative to ML-ACF, Hoffman 3D brain and cylindrical phantoms were measured to obtain the image quality indexes of contrast and uniformity. These phantoms were also wrapped with a radioactive sheet to obtain attenuation coefficient images using ML-ACF. Emission images were reconstructed with attenuation correction by PAC and ML-ACF, and the results were compared using contrast and uniformity as well as visual assessment. CT attenuation correction (CT-AC) was also applied as a reference. RESULTS The contrast obtained by ML-ACF was slightly overestimated due to its unique experimental condition for applying ML-ACF in Hoffman 3D brain phantom but the uniformity was almost equivalent among ML-ACF, CT-AC, and PAC. PAC showed reasonable result without overestimation compared to ML-ACF and CT-AC. CONCLUSIONS PAC is an attenuation correction method that can ensure the performance in phantom test, and is considered to be a reasonable alternative to clinically used ML-ACF-based attenuation correction.
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Affiliation(s)
- Tetsuro Mizuta
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan.
| | - Yoshiyuki Yamakawa
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Suzuka Minagawa
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | | | - Atsushi Ohtani
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Shiho Takenouchi
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Daisuke Morimoto-Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Hayato Kaida
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan.,Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama, Japan
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan.,Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama, Japan
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Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y. Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study. IEEE Trans Radiat Plasma Med Sci 2022; 6:755-765. [PMID: 36059429 PMCID: PMC9438341 DOI: 10.1109/trpms.2021.3138372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Attenuation correction (AC) is important for accurate interpretation of SPECT myocardial perfusion imaging (MPI). However, it is challenging to perform AC in dedicated cardiac systems not equipped with a transmission imaging capability. Previously, we demonstrated the feasibility of generating attenuation-corrected SPECT images using a deep learning technique (SPECTDL) directly from non-corrected images (SPECTNC). However, we observed performance variability across patients which is an important factor for clinical translation of the technique. In this study, we investigate the feasibility of overcoming the performance variability across patients for the direct AC in SPECT MPI by proposing to develop an advanced network and a data management strategy. To investigate, we compared the accuracy of the SPECTDL for the conventional U-Net and Wasserstein cycle GAN (WCycleGAN) networks. To manage the training data, clustering was applied to a representation of data in the lower-dimensional space, and the training data were chosen based on the similarity of data in this space. Quantitative analysis demonstrated that DL model with an advanced network improves the global performance for the AC task with the limited data. However, the regional results were not improved. The proposed data management strategy demonstrated that the clustered training has potential benefit for effective training.
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Affiliation(s)
- Mahsa Torkaman
- Radiology and Biomedical Imaging Department, University of California, San Francisco, CA, USA
| | - Jaewon Yang
- Radiology and Biomedical Imaging Department, University of California, San Francisco, CA, USA
| | - Luyao Shi
- Biomedical Engineering Department, Yale University, New Haven, CT, USA
| | - Rui Wang
- Radiology and Biomedical Imaging Department, Yale University, New Haven, CT, USA
| | - Edward J Miller
- Radiology and Biomedical Imaging Department, Yale University, New Haven, CT, USA
| | - Albert J Sinusas
- Biomedical Engineering Department, Yale University, New Haven, CT, USA; Radiology and Biomedical Imaging Department, Yale University, New Haven, CT, USA
| | - Chi Liu
- Biomedical Engineering Department, Yale University, New Haven, CT, USA; Radiology and Biomedical Imaging Department, Yale University, New Haven, CT, USA
| | - Grant T Gullberg
- Radiology and Biomedical Imaging Department, University of California, San Francisco, CA, USA
| | - Youngho Seo
- Radiology and Biomedical Imaging Department, University of California, San Francisco, CA, USA
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Ahangari S, Beck Olin A, Kinggård Federspiel M, Jakoby B, Andersen TL, Hansen AE, Fischer BM, Littrup Andersen F. A deep learning-based whole-body solution for PET/MRI attenuation correction. EJNMMI Phys 2022; 9:55. [PMID: 35978211 PMCID: PMC9385907 DOI: 10.1186/s40658-022-00486-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.
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Affiliation(s)
- Sahar Ahangari
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.
| | - Anders Beck Olin
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Thomas Lund Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Diagnostic Radiology, Rigshospitalet, Copenhagen, Denmark
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Toyonaga T, Shao D, Shi L, Zhang J, Revilla EM, Menard D, Ankrah J, Hirata K, Chen MK, Onofrey JA, Lu Y. Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. Eur J Nucl Med Mol Imaging 2022; 49:3086-3097. [PMID: 35277742 PMCID: PMC10725742 DOI: 10.1007/s00259-022-05748-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/25/2022] [Indexed: 11/04/2022]
Abstract
A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). METHODS Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. RESULTS µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine. CONCLUSIONS The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.
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Affiliation(s)
- Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | | | | | - Kenji Hirata
- Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Yale New Haven Hospital, New Haven, CT, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Urology, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
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Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. Eur J Nucl Med Mol Imaging 2022; 49:3046-3060. [PMID: 35169887 PMCID: PMC9253078 DOI: 10.1007/s00259-022-05718-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/06/2022] [Indexed: 12/22/2022]
Abstract
PURPOSE Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. RESULTS For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Huidong Xie
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China
| | | | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Visage Imaging, Inc, San Diego, CA, USA
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Imaging and Radiological Sciences, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Edward J Miller
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA.
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Pieszko K, Shanbhag AD, Lemley M, Hyun M, Van Kriekinge S, Otaki Y, Liang JX, Berman DS, Dey D, Slomka PJ. Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans. Eur J Nucl Med Mol Imaging 2022. [PMID: 35751666 DOI: 10.1007/s00259-022-05866-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/04/2022] [Indexed: 12/19/2022]
Abstract
PURPOSE We sought to evaluate inter-scan and inter-reader agreement of coronary calcium (CAC) scores obtained from dedicated, ECG-gated CAC scans (standard CAC scan) and ultra-low-dose, ungated computed tomography attenuation correction (CTAC) scans obtained routinely during cardiac PET/CT imaging. METHODS From 2928 consecutive patients who underwent same-day 82Rb cardiac PET/CT and gated CAC scan in the same hybrid PET/CT scanning session, we have randomly selected 200 cases with no history of revascularization. Standard CAC scans and ungated CTAC scans were scored by two readers using quantitative clinical software. We assessed the agreement between readers and between two scan protocols in 5 CAC categories (0, 1-10, 11-100, 101-400, and > 400) using Cohen's Kappa and concordance. RESULTS Median age of patients was 70 (inter-quartile range: 63-77), and 46% were male. The inter-scan concordance index and Cohen's Kappa for readers 1 and 2 were 0.69; 0.75 (0.69, 0.81) and 0.72; 0.8 (0.75, 0.85) respectively. The inter-reader concordance index and Cohen's Kappa (95% confidence interval [CI]) was higher for standard CAC scans: 0.9 and 0.92 (0.89, 0.96), respectively, vs. for CTAC scans: 0.83 and 0.85 (0.79, 0.9) for CTAC scans (p = 0.02 for difference in Kappa). Most discordant readings between two protocols occurred for scans with low extent of calcification (CAC score < 100). CONCLUSION CAC can be quantitatively assessed on PET CTAC maps with good agreement with standard scans, however with limited sensitivity for small lesions. CAC scoring of CTAC can be performed routinely without modification of PET protocol and added radiation dose.
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Sanaat A, Shiri I, Ferdowsi S, Arabi H, Zaidi H. Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models' Performance and Robustness. J Digit Imaging 2022; 35:469-481. [PMID: 35137305 PMCID: PMC9156620 DOI: 10.1007/s10278-021-00536-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/29/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022] Open
Abstract
A small dataset commonly affects generalization, robustness, and overall performance of deep neural networks (DNNs) in medical imaging research. Since gathering large clinical databases is always difficult, we proposed an analytical method for producing a large realistic/diverse dataset. Clinical brain PET/CT/MR images including full-dose (FD), low-dose (LD) corresponding to only 5 % of events acquired in the FD scan, non-attenuated correction (NAC) and CT-based measured attenuation correction (MAC) PET images, CT images and T1 and T2 MR sequences of 35 patients were included. All images were registered to the Montreal Neurological Institute (MNI) template. Laplacian blending was used to make a natural presentation using information in the frequency domain of images from two separate patients, as well as the blending mask. This classical technique from the computer vision and image processing communities is still widely used and unlike modern DNNs, does not require the availability of training data. A modified ResNet DNN was implemented to evaluate four image-to-image translation tasks, including LD to FD, LD+MR to FD, NAC to MAC, and MRI to CT, with and without using the synthesized images. Quantitative analysis using established metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and joint histogram analysis was performed for quantitative evaluation. The quantitative comparison between the registered small dataset containing 35 patients and the large dataset containing 350 synthesized plus 35 real dataset demonstrated improvement of the RMSE and SSIM by 29% and 8% for LD to FD, 40% and 7% for LD+MRI to FD, 16% and 8% for NAC to MAC, and 24% and 11% for MRI to CT mapping task, respectively. The qualitative/quantitative analysis demonstrated that the proposed model improved the performance of all four DNN models through producing images of higher quality and lower quantitative bias and variance compared to reference images.
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Affiliation(s)
- Amirhossein Sanaat
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Isaac Shiri
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Sohrab Ferdowsi
- University of Applied Sciences and Arts of Western, Geneva, Switzerland
| | - Hossein Arabi
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Habib Zaidi
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Geneva University Neurocenter, Geneva University, 1205 Geneva, Switzerland ,grid.4494.d0000 0000 9558 4598Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands ,grid.10825.3e0000 0001 0728 0170Department of Nuclear Medicine, University of Southern Denmark, DK-500 Odense, Denmark
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Allen TJ, Bancroft LCH, Kumar M, Bradshaw TJ, Strigel RM, McMillan AB, Fowler AM. Gadolinium-Based Contrast Agent Attenuation Does Not Impact PET Quantification in Simultaneous Dynamic Contrast Enhanced Breast PET/MR. Med Phys 2022; 49:5206-5215. [PMID: 35621727 DOI: 10.1002/mp.15781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 04/18/2022] [Accepted: 05/17/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Simultaneous PET/MR imaging involves injection of a radiopharmaceutical and often also includes administration of a gadolinium-based contrast agent (GBCA). Phantom model studies indicate that attenuation of annihilation photons by GBCAs does not bias quantification metrics of PET radiopharmaceutical uptake. However, a direct comparison of attenuation corrected PET values before and after administration of GBCA has not been performed in patients imaged with simultaneous dynamic PET/MR. The purpose of this study was to investigate the attenuating effect of GBCAs on standardized uptake value (SUV) quantification of 18 F-fluorodeoxyglucose (FDG) uptake in invasive breast cancer and normal tissues using simultaneous PET/MR. METHODS The study included 13 women with newly diagnosed invasive breast cancer imaged using simultaneous dedicated prone breast PET/MR with FDG. PET data collection and two-point Dixon based MR attenuation correction sequences began simultaneously before the administration of GBCA to avoid a potential impact of GBCA on the attenuation correction map. A standard clinical dose of GBCA was intravenously administered for the dynamic contrast enhanced MR sequences obtained during the simultaneous PET data acquisition. PET data were dynamically reconstructed into 60 frames of 30 seconds each. Three timing windows were chosen consisting of a single frame (30 seconds), two frames (60 seconds), or four frames (120 seconds) immediately before and after contrast administration. SUVmax and SUVmean of the biopsy-proven breast malignancy, fibroglandular tissue of the contralateral normal breast, descending aorta, and liver were calculated prior to and following GBCA administration. Percent change in the SUV metrics were calculated to test for a statistically significant, non-zero percent change using Wilcoxon signed-rank tests. RESULTS No statistical change in SUVmax or SUVmean was found for the breast malignancies or normal anatomical regions during the timing windows before and after GBCA administration. CONCLUSIONS GBCAs do not significantly impact the results of PET quantification by means of additional attenuation. However, GBCAs may still affect quantification by affecting MR acquisitions used for MR-based attenuation correction which this study did not address. Corrections to account for attenuation due to clinical concentrations of GBCAs are not necessary in simultaneous PET/MR examinations when MR-based attenuation correction sequences are performed prior to GBCA administration. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Timothy J Allen
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Manoj Kumar
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Tyler J Bradshaw
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA.,University of Wisconsin Carbone Cancer Center, 600 Highland Ave., Madison, WI, 53792, USA
| | - Alan B McMillan
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Amy M Fowler
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA.,University of Wisconsin Carbone Cancer Center, 600 Highland Ave., Madison, WI, 53792, USA
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36
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Sousa JM, Appel L, Engström M, Papadimitriou S, Nyholm D, Ahlström H, Lubberink M. Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR. Phys Med 2022; 97:36-43. [PMID: 35339864 DOI: 10.1016/j.ejmp.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 02/18/2022] [Accepted: 03/14/2022] [Indexed: 01/06/2023] Open
Abstract
In positron emission tomography (PET), 68Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple 68Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered 68Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [11C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every 68Ge-AC map, and averaging the resulting 68Ge-AC maps. [11C]PE2I PET images were reconstructed using CTR-AC and a patient-specific 68Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R1) and non-displaceable binding potential (BPND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R1 (-0.1 ± 3.2%), and putamen BPND (3.7 ± 8.1%). SUV and R1 precision (SD of %bias) were modest and lowest in the anterior cortex, with an R1 %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.
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Hagio T, Poitrasson-Rivière A, Moody JB, Renaud JM, Arida-Moody L, Shah RV, Ficaro EP, Murthy VL. "Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning. Eur J Nucl Med Mol Imaging 2022; 49:3140-3149. [PMID: 35312837 DOI: 10.1007/s00259-022-05735-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 02/13/2022] [Indexed: 12/26/2022]
Abstract
PURPOSE Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve diagnostic accuracy, approximately 3/4ths of clinical MPI worldwide remains non-attenuation-corrected (NAC). In this work, we propose a novel deep learning (DL) algorithm to provide "virtual" DL attenuation-corrected (DLAC) perfusion polar maps solely from NAC data without concurrent computed tomography (CT) imaging or additional scans. METHODS SPECT MPI studies (N = 11,532) with paired NAC and CTAC images were retrospectively identified. A convolutional neural network-based DL algorithm was developed and trained on half of the population to predict DLAC polar maps from NAC polar maps. Total perfusion deficit (TPD) was evaluated for all polar maps. TPDs from NAC and DLAC polar maps were compared to CTAC TPDs in linear regression analysis. Moreover, receiver-operating characteristic analysis was performed on NAC, CTAC, and DLAC TPDs to predict obstructive CAD as diagnosed from invasive coronary angiography. RESULTS DLAC TPDs exhibited significantly improved linear correlation (p < 0.001) with CTAC (R2 = 0.85) compared to NAC vs. CTAC (R2 = 0.68). The diagnostic performance of TPD was also improved with DLAC compared to NAC with an area under the curve (AUC) of 0.827 vs. 0.780 (p = 0.012) with no statistically significant difference between AUC for CTAC and DLAC. At 88% sensitivity, specificity was improved by 18.9% for DLAC and 25.6% for CTAC. CONCLUSIONS The proposed DL algorithm provided attenuation correction comparable to CTAC without the need for additional scans. Compared to conventional NAC perfusion imaging, DLAC significantly improved diagnostic accuracy.
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Affiliation(s)
- Tomoe Hagio
- INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
| | | | - Jonathan B Moody
- INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA
| | - Jennifer M Renaud
- INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA
| | - Liliana Arida-Moody
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ravi V Shah
- Department of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Edward P Ficaro
- INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.,Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
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Lindén J, Teuho J, Teräs M, Klén R. Evaluation of three methods for delineation and attenuation estimation of the sinus region in MR-based attenuation correction for brain PET-MR imaging. BMC Med Imaging 2022; 22:48. [PMID: 35300592 PMCID: PMC8928695 DOI: 10.1186/s12880-022-00770-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 03/03/2022] [Indexed: 11/12/2022] Open
Abstract
Background Attenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method). Methods The study population consisted of data of 10 subjects which had undergone PET-CT and PET-MRI. PET images were reconstructed with and without time-of-flight (TOF) using CT-based attenuation correction (CTAC) as reference. Comparison was done visually, using DICE coefficients, correlation, analyzing attenuation coefficients, and quantitative analysis of PET and bias atlas images. Results The median DICE coefficients were 0.824, 0.853, 0.849 for the bulk, cuboid and template method, respectively. The median attenuation coefficients were 0.0841 cm−1, 0.0876 cm−1, 0.0861 cm−1 and 0.0852 cm−1, for CTAC, bulk, cuboid and template method, respectively. The cuboid and template methods showed error of less than 2.5% in attenuation coefficients. An increased correlation to CTAC was shown with the cuboid and template methods. In the regional analysis, improvement in at least 49% and 80% of VOI was seen with non-TOF and TOF imaging. All methods showed errors less than 2.5% in non-TOF and less than 2% in TOF reconstructions. Conclusions We evaluated two proof-of-concept methods for improving quantitative accuracy in PET/MRI imaging and showed that bias can be further reduced by inclusion of TOF. Largest improvements were seen in the regions of olfactory bulb, Heschl's gyri, lingual gyrus and cerebellar vermis. However, the overall effect of inclusion of the sinus region as separate class in MRAC to PET quantification in the brain was considered modest. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00770-0.
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Affiliation(s)
- Jani Lindén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland. .,Department of Mathematics and Statistics, University of Turku, Vesilinnantie 5, 20014, Turku, Finland.
| | - Jarmo Teuho
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland.,Department of Medical Physics, Turku University Hospital, Hämeentie 11, 20521, Turku, Finland
| | - Mika Teräs
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, 20521, Turku, Finland.,Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20014, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland
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Mizuta T, Kobayashi T, Yamakawa Y, Hanaoka K, Watanabe S, Morimoto-Ishikawa D, Yamada T, Kaida H, Ishii K. Initial evaluation of a new maximum-likelihood attenuation correction factor-based attenuation correction for time-of-flight brain PET. Ann Nucl Med 2022; 36:420-426. [PMID: 35138565 DOI: 10.1007/s12149-022-01721-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/17/2022] [Indexed: 11/01/2022]
Abstract
AIM The aim of this study was to evaluate an image reconstruction algorithm, including a new maximum-likelihood attenuation correction factor (ML-ACF) for time of flight (TOF) brain positron emission tomography (PET). METHODS The implemented algorithm combines an ML-ACF method that simultaneously estimates both the emission image and attenuation sinogram from TOF emission data, and a scaling method based on anatomical features. To evaluate the algorithm's quantitative accuracy, three-dimensional brain phantom images were acquired and soft-tissue attenuation coefficients and emission values were analyzed. RESULTS The heterogeneous distributions of attenuation coefficients in soft tissue, skull, and nasal cavity were sufficiently visualized. The attenuation coefficient of soft tissue remained within 5% of theoretical value. Attenuation-corrected emission showed no lateral differences, and significant differences among soft tissue were within the error range. CONCLUSION The ML-ACF-based attenuation correction implemented for TOF brain PET worked well and obtained practical levels of accuracy.
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Affiliation(s)
- Tetsuro Mizuta
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan.
| | | | - Yoshiyuki Yamakawa
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Daisuke Morimoto-Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan
| | - Hayato Kaida
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan.,Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Japan.,Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan
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Hwang D, Kang SK, Kim KY, Choi H, Lee JS. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur J Nucl Med Mol Imaging 2021; 49:1833-1842. [PMID: 34882262 DOI: 10.1007/s00259-021-05637-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/24/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET. METHODS One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNNNAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. RESULTS The error of the attenuation correction factors estimated using μ-CT and μ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). CONCLUSION The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.
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Affiliation(s)
- Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Seung Kwan Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Kyeong Yun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
- Brightonix Imaging Inc., Seoul, South Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea.
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Puig O, Henriksen OM, Andersen FL, Lindberg U, Højgaard L, Law I, Ladefoged CN. Deep-learning-based attenuation correction in dynamic [ 15O]H 2O studies using PET/MRI in healthy volunteers. J Cereb Blood Flow Metab 2021; 41:3314-3323. [PMID: 34250821 PMCID: PMC8669198 DOI: 10.1177/0271678x211029178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Quantitative [15O]H2O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [15O]H2O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [15O]H2O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [15O]H2O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range -2.4%- -1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R2 over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [15O]H2O-PET but RESOLUTE is associated with a small but systematic underestimation.
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Affiliation(s)
- Oriol Puig
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Otto M Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ulrich Lindberg
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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McMillan AB, Bradshaw TJ. Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography. PET Clin 2021; 16:543-552. [PMID: 34364816 PMCID: PMC10562009 DOI: 10.1016/j.cpet.2021.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Recent developments in artificial intelligence (AI) technology have enabled new developments that can improve attenuation and scatter correction in PET and single-photon emission computed tomography (SPECT). These technologies will enable the use of accurate and quantitative imaging without the need to acquire a computed tomography image, greatly expanding the capability of PET/MR imaging, PET-only, and SPECT-only scanners. The use of AI to aid in scatter correction will lead to improvements in image reconstruction speed, and improve patient throughput. This article outlines the use of these new tools, surveys contemporary implementation, and discusses their limitations.
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Affiliation(s)
- Alan B McMillan
- Department of Radiology, University of Wisconsin, 3252 Clinical Science Center, 600 Highland Avenue, Madison, WI 53792, USA.
| | - Tyler J Bradshaw
- Department of Radiology, University of Wisconsin, 3252 Clinical Science Center, 600 Highland Avenue, Madison, WI 53792, USA. https://twitter.com/tybradshaw11
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Zhang R, Wang M, Zhou Y, Wang S, Shen Y, Li N, Wang P, Tan J, Meng Z, Jia Q. Impacts of acquisition and reconstruction parameters on the absolute technetium quantification of the cadmium-zinc-telluride-based SPECT/CT system: a phantom study. EJNMMI Phys 2021; 8:66. [PMID: 34568990 PMCID: PMC8473509 DOI: 10.1186/s40658-021-00412-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 09/10/2021] [Indexed: 02/06/2023] Open
Abstract
Background The digital cadmium–zinc–telluride (CZT)-based SPECT system has many advantages, including better spatial and energy resolution. However, the impacts of different acquisition and reconstruction parameters on CZT SPECT quantification might still need to be validated. This study aimed to evaluate the impacts of acquisition parameters (the main energy window and acquisition time per frame) and reconstruction parameters (the number of iterations, subsets in iterative reconstruction, post-filter, and image correction methods) on the technetium quantification of CZT SPECT/CT. Methods A phantom (PET NEMA/IEC image quality, USA) was filled with four target-to-background (T/B) ratios (32:1, 16:1, 8:1, and 4:1) of technetium. Mean uptake values (the calculated mean concentrations for spheres) were measured to evaluate the recovery coefficient (RC) changes under different acquisition and reconstruction parameters. The corresponding standard deviations of mean uptake values were also measured to evaluate the quantification error. Image quality was evaluated using the National Electrical Manufacturers Association (NEMA) NU 2–2012 standard. Results For all T/B ratios, significant correlations were found between iterations and RCs (r = 0.62–0.96 for 1–35 iterations, r = 0.94–0.99 for 35–90 iterations) as well as between the full width at half maximum (FWHM) of the Gaussian filter and RCs (r = − 0.86 to − 1.00, all P values < 0.05). The regression coefficients of 1–35 iterations were higher than those of 35–90 iterations (0.51–1.60 vs. 0.02–0.19). RCs calculated with AC (attenuation correction) + SC (scatter correction) + RR (resolution recovery correction) combination were more accurate (53.82–106.70%) than those calculated with other combinations (all P values < 0.05). No significant statistical differences (all P values > 0.05) were found between the 15% and 20% energy windows except for the 32:1 T/B ratio (P value = 0.023) or between the 10 s/frame and 120 s/frame acquisition times except for the 4:1 T/B ratio (P value = 0.015) in terms of RCs. Conclusions CZT-SPECT/CT of technetium resulted in good quantification accuracy. The favourable acquisition parameters might be a 15% energy window and 40 s/frame of acquisition time. The favourable reconstruction parameters might be 35 iterations, 20 subsets, the AC + SC + RR correction combination, and no filter. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00412-4.
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Affiliation(s)
- Ruyi Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Miao Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Yaqian Zhou
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Shen Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Yiming Shen
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Ning Li
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Peng Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Jian Tan
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China
| | - Zhaowei Meng
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China.
| | - Qiang Jia
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, 300052, People's Republic of China.
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Cuccurullo V, Manti F, De Risi M, Cascini GL. FDG-CT/PET false positive case in hip prosthesis: a clue to avoid error. Radiol Case Rep 2021; 16:2601-4. [PMID: 34285728 DOI: 10.1016/j.radcr.2021.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 11/23/2022] Open
Abstract
A 62 years old woman 6 months after left total hip prosthesis referred to our institution for persistent pain and warm, stiff, and swollen joint. 18F-FDG CT/PET Images showed an intense focal uptake corresponding to the external margin of inter-trochanteric region of prosthesis and inside the stem inferiorly, but common decision was to reconstruct PET images without attenuation correction and now showed a complete and unexpected disappearance of focal and pathological FDG uptake. This case shows the potential propagation of CT artifacts into PET emission data close to metal implants and should be taken in account together to SUV values.
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Sari H, Reaungamornrat J, Catalano O, Vera-Olmos J, Izquierdo-Garcia D, Morales MA, Torrado-Carvajal A, Ng SCT, Malpica N, Kamen A, Catana C. Evaluation of Deep Learning-based Approaches to Segment Bowel Air Pockets and Generate Pelvis Attenuation Maps from CAIPIRINHA-accelerated Dixon MR Images. J Nucl Med 2021; 63:468-475. [PMID: 34301782 DOI: 10.2967/jnumed.120.261032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 06/06/2021] [Indexed: 11/16/2022] Open
Abstract
Attenuation correction (AC) remains a challenge in pelvis PET/MR imaging. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvis attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, which can introduce bias in the reconstructed PET images. The aims of this work were to develop deep learning-based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3D CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semi-automated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning-, model- and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semi-automated segmentations, with a mean Dice similarity coefficient of 0.75. Volumetric similarity score between two segmentations was 0.85 ± 0.14. The mean absolute relative change (RCs) with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning and model-based μ-maps, respectively. The average RC between PET images reconstructed with deep learning and CT-based μ-maps was 2.6%. Conclusion: We presented a deep learning-based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images with comparable accuracy to semi-automatic segmentations. We also showed that the μ-maps synthesized using a deep learning-based method from CAIPIRINHA-accelerated Dixon images are more accurate than those generated with the model-based approach available on integrated PET/MRI scanner.
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Affiliation(s)
- Hasan Sari
- Athinoula A. Martinos Center for Biomedical Imaging, United States
| | | | - Onofrio Catalano
- Athinoula A. Martinos Center for Biomedical Imaging, United States
| | | | | | | | | | | | | | - Ali Kamen
- Siemens Corporate Research, United States
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, United States
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Schaefferkoetter J, Yan J, Moon S, Chan R, Ortega C, Metser U, Berlin A, Veit-Haibach P. Deep learning for whole-body medical image generation. Eur J Nucl Med Mol Imaging 2021; 48:3817-3826. [PMID: 34021779 DOI: 10.1007/s00259-021-05413-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation. PURPOSE This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types. MATERIALS AND METHODS This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation. RESULTS A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions. CONCLUSION The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data.
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Affiliation(s)
- Joshua Schaefferkoetter
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA.
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada.
| | - Jianhua Yan
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Sangkyu Moon
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Rosanna Chan
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Alejandro Berlin
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
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Bruckmann NM, Lindemann ME, Grueneisen J, Grafe H, Li Y, Sawicki LM, Rischpler C, Herrmann K, Umutlu L, Quick HH, Schaarschmidt BM. Comparison of pre- and post-contrast-enhanced attenuation correction using a CAIPI-accelerated T1-weighted Dixon 3D-VIBE sequence in 68Ga-DOTATOC PET/MRI. Eur J Radiol 2021; 139:109691. [PMID: 33892276 DOI: 10.1016/j.ejrad.2021.109691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/10/2021] [Accepted: 03/29/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To investigate the influence of contrast agent administration on attenuation correction (AC) based on a CAIPIRINHA (CAIPI)-accelerated T1-weighted Dixon 3D-VIBE sequence in 68Ga-DOTATOC PET/MRI. MATERIAL AND METHODS Fifty-one patients with neuroendocrine tumors underwent whole-body 68Ga-DOTATOC PET/MRI for tumor staging. Two PET reconstructions were performed using AC-maps that were created using a high-resolution CAIPI-accelerated Dixon-VIBE sequence with an additional bone atlas and truncation correction using the HUGE (B0 homogenization using gradient enhancement) method before and after application of Gadolinium (Gd)-based contrast agent. Standardized uptake values (SUVs) of 21 volumes of interest (VOIs) were compared between in both PET data sets per patient. A student's t-test for paired samples was performed to test for potential differences between both AC-maps and both reconstructed PET data sets. Bonferroni correction was performed to prevent α-error accumulation, p < 0.0024 was considered to indicate statistical significance. RESULTS Significant quantitative differences between SUVmax were found in the perirenal fat (19.65 ± 48.03 %, p < 0.0001), in the axillary fat (17.46 ± 63.67 %, p < 0.0001) and in the dorsal subcutaneous fat on level of lumbar vertebral body L4 (10.26 ± 25.29 %, p < 0.0001). Significant differences were also evident in the lungs apical (5.80 ± 10.53 %, p < 0.0001), dorsal at the level of the pulmonary trunk (15.04 ± 19.09 %, p < 0.0001) and dorsal in the basal lung (51.27 ± 147.61 %, p < 0.0001). CONCLUSION The administration of (Gd)-contrast agents in this study has shown a considerable influence on the AC-maps in PET/MRI and, consequently impacted quantification in the reconstructed PET data. Therefore, dedicated PET/MRI staging protocols have to be adjusted so that AC-map acquisition is performed prior to contrast agent administration.
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Affiliation(s)
- Nils Martin Bruckmann
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, D-40225, Germany.
| | - Maike E Lindemann
- High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Johannes Grueneisen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Hong Grafe
- High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany; Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Yan Li
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Lino M Sawicki
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, D-40225, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
| | - Benedikt Michael Schaarschmidt
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, D-45147, Germany
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Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med 2021; 35:691-701. [PMID: 33811600 DOI: 10.1007/s12149-021-01611-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/17/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVES Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomography (TCT) images from non-AC (NAC) PET images using a convolutional neural network (CNN) with a huge dataset of various radiotracers for brain PET imaging. METHODS The proposed framework is comprised of three steps: (1) NAC PET image generation, (2) synthetic TCT generation using CNN, and (3) PET image reconstruction. We trained the CNN by combining the mixed image dataset of six radiotracers to avoid overfitting, including [18F]FDG, [18F]BCPP-EF, [11C]Racropride, [11C]PIB, [11C]DPA-713, and [11C]PBB3. We used 1261 brain NAC PET and TCT images (1091 for training and 70 for testing). We did not include [11C]Methionine subjects in the training dataset, but included them in the testing dataset. RESULTS The image quality of the synthetic TCT images obtained using the CNN trained on the mixed dataset of six radiotracers was superior to those obtained using the CNN trained on the split dataset generated from each radiotracer. In the [18F]FDG study, the mean relative PET biases of the emission-segmented AC (ESAC) and deep AC were 8.46 ± 5.24 and - 5.69 ± 4.97, respectively. The deep AC PET and TCT AC PET images exhibited excellent correlation for all seven radiotracers (R2 = 0.912-0.982). CONCLUSION These results indicate that our proposed deep AC framework can be leveraged to provide quantitatively superior PET images when using the CNN trained on the mixed dataset of PET tracers than when using the CNN trained on the split dataset which means specific for each tracer.
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Affiliation(s)
- Fumio Hashimoto
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan.
| | - Masanori Ito
- Global Strategic Challenge Center, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan.
| | - Kibo Ote
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan
| | - Takashi Isobe
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan
| | - Hiroyuki Okada
- Global Strategic Challenge Center, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan.,Hamamatsu Medical Imaging Center, Hamamatsu Medical Photonics Foundation, Hamamatsu, 434-8601, Japan
| | - Yasuomi Ouchi
- Department of Biofunctional Imaging, Preeminent Medical Photonics Education and Research Center, Hamamatsu University School of Medicine, Hamamatsu, 431-3192, Japan
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Alnabelsi T, Thakkar A, Ahmed AI, Han Y, Al-Mallah MH. PET/CT Myocardial Perfusion Imaging Acquisition and Processing: Ten Tips and Tricks to Help You Succeed. Curr Cardiol Rep 2021; 23:39. [PMID: 33694057 DOI: 10.1007/s11886-021-01476-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/17/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE OF REVIEW Positron emission tomography (PET) is a leading non-invasive modality for the diagnosis of coronary artery disease due to its diagnostic accuracy and high image quality. With the latest advances in PET systems, clinicians are able to assess for myocardial ischemia and myocardial blood flow while exposing patients to extremely low radiation doses. This review will focus on the basics of acquisition and processing of hybrid PET/CT systems from appropriate patient selection to common artifacts and pitfalls. RECENT FINDINGS The continued development of hybrid PET/CT technology is producing scanners with exquisite sensitivity capable of generating high-quality images while exposing patients to low radiation doses. List mode acquisition is an essential component in all modern PET/CT scanners allowing simultaneous dynamic and ECG-gated imaging without lengthening scan duration. Various PET radiotracers are currently being developed but rubidium-82 and 13N-ammonia remain the most commonly used perfusion radiotracers. The development of mini 13N-ammonia cyclotrons is a promising tool that should increase access to this radiotracer. Misregistration, attenuation from extra-cardiac activity, and patient motion are the most common causes of artifacts during perfusion imaging. Techniques to automatically realign images and correct respiratory or patient motion artifacts continue to evolve. Despite the continuous evolution of PET imaging techniques, basic knowledge of scan parameters, acquisition techniques, and post processing tools remains essential to ensure high-quality images are produced and artifacts are recognized and corrected. Future research should focus on optimizing scanners to allow for shorter scan protocols and lower radiation exposure as well as continue developing techniques to minimize and correct for motion and misregistration artifacts.
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Affiliation(s)
- Talal Alnabelsi
- Houston Methodist Academic Institute, Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA
| | - Akanksha Thakkar
- Houston Methodist Academic Institute, Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA
| | - Ahmed Ibrahim Ahmed
- Houston Methodist Academic Institute, Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA
| | - Yushui Han
- Houston Methodist Academic Institute, Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA
| | - Mouaz H Al-Mallah
- Houston Methodist Academic Institute, Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA.
- Weill Cornell Medicine, New York, NY, USA.
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Sakaguchi K, Kaida H, Yoshida S, Ishii K. Attenuation correction using deep learning for brain perfusion SPECT images. Ann Nucl Med 2021; 35:589-99. [PMID: 33751364 DOI: 10.1007/s12149-021-01600-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/15/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. This study constructs a convolutional neural network (CNN) to generate attenuation-corrected SPECT images directly from non-attenuation-corrected SPECT images. METHODS We constructed an auto-encoder (AE) using a CNN to correct the attenuation in brain perfusion SPECT images. SPECT image datasets of 270 (44,528 slices including augmentation), 60 (5002 slices), and 30 (2558 slices) cases were used for training, validation, and testing, respectively. The acquired projection data were reconstructed in three patterns: uniform attenuation correction using Chang's method (Chang-AC), non-uniform attenuation correction using CT (CT-AC), and no attenuation correction (No-AC). The AE learned an end-to-end mapping between the No-AC and CT-AC images. The No-AC images in the test dataset were loaded into the trained AE, which generated images simulating the CT-AC images as output. The generated SPECT images were employed as attenuation-corrected images using the AE (AE-AC). The accuracy of the AE-AC images was evaluated in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity metric (SSIM). The intensities of the AE-AC and CT-AC images were compared by voxel-by-voxel and region-by-region analysis. RESULTS The PSNRs of the AE-AC and Chang-AC images, compared using CT-AC images, were 62.2, and 57.9, and their SSIM values were 0.9995 and 0.9985, respectively. The AE-AC and CT-AC images were visually and statistically in good agreement. CONCLUSIONS The proposed AE-AC method yields highly accurate attenuation-corrected brain perfusion SPECT images.
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