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Sousa JM, Appel L, Engström M, Nyholm D, Ahlström H, Lubberink M. Comparison of quantitative [ 11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system. Phys Med 2024; 117:103185. [PMID: 38042064 DOI: 10.1016/j.ejmp.2023.103185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/03/2023] [Accepted: 11/20/2023] [Indexed: 12/04/2023] Open
Abstract
PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.
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Affiliation(s)
- João M Sousa
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
| | - Lieuwe Appel
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden
| | | | - Dag Nyholm
- Department of Neurology, Uppsala University Hospital, Uppsala, Sweden; Department of Medical Sciences, Neurology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden; Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Mark Lubberink
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Physics, Uppsala University Hospital, Uppsala, Sweden
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2
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Prakken NHJ, Besson FL, Borra RJH, Büther F, Buechel RR, Catana C, Chiti A, Dierckx RAJO, Dweck MR, Erba PA, Glaudemans AWJM, Gormsen LC, Hristova I, Koole M, Kwee TC, Mottaghy FM, Polycarpou I, Prokop M, Stegger L, Tsoumpas C, Slart RHJA. PET/MRI in practice: a clinical centre survey endorsed by the European Association of Nuclear Medicine (EANM) and the EANM Forschungs GmbH (EARL). Eur J Nucl Med Mol Imaging 2023; 50:2927-2934. [PMID: 37378857 DOI: 10.1007/s00259-023-06308-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Affiliation(s)
- Niek H J Prakken
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Florent L Besson
- Commissariat À L'énergie Atomique Et Aux Énergies Alternatives (CEA), Centre National de La Recherche Scientifique (CNRS), InsermBioMaps, Orsay, France
- Department of Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Ronald J H Borra
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Florian Büther
- Department of Nuclear Medicine, University Hospital Münster, Munster, Germany
| | - Ronny R Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and , Harvard Medical School, Boston, MA, USA
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Rudi A J O Dierckx
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh Heart Centre, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, UK
| | - Paola A Erba
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Medicine and Surgery, University of Milan Bicocca, and Nuclear Medicine Unit ASST Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Aarhus N, Denmark
| | - Ivalina Hristova
- European Association of Nuclear Medicine Research Ltd. (EARL), Vienna, Austria
| | - Michel Koole
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Thomas C Kwee
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Felix M Mottaghy
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, MUMC+), Maastricht, The Netherlands
| | - Irene Polycarpou
- Department of Health Sciences, European University Cyprus, Nicosia, Cyprus
| | - Mathias Prokop
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lars Stegger
- Department of Nuclear Medicine, University Hospital Münster, Munster, Germany
| | - Charalampos Tsoumpas
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Riemer H J A Slart
- Medical Imaging Centre, Departments of Nuclear Medicine and Molecular Imaging, Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Biomedical Photonic Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands.
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3
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Hayat H, Wang R, Sun A, Mallett CL, Nigam S, Redman N, Bunn D, Gjelaj E, Talebloo N, Alessio A, Moore A, Zinn K, Wei GW, Fan J, Wang P. Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring. iScience 2023; 26:107083. [PMID: 37416468 PMCID: PMC10319838 DOI: 10.1016/j.isci.2023.107083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/10/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023] Open
Abstract
Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.
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Affiliation(s)
- Hasaan Hayat
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Rui Wang
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, MI, USA
| | - Aixia Sun
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Christiane L. Mallett
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
| | - Saumya Nigam
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Nathan Redman
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Demarcus Bunn
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Elvira Gjelaj
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Lyman Briggs College, Michigan State University, East Lansing, MI, USA
| | - Nazanin Talebloo
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA
| | - Adam Alessio
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Computational Mathematics, Science, and Engineering (CMSE), College of Natural Science, Michigan State University, East Lansing, MI, USA
| | - Anna Moore
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Kurt Zinn
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
| | - Guo-Wei Wei
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, MI, USA
- Departments of Computational Mathematics, Science, and Engineering (CMSE), College of Natural Science, Michigan State University, East Lansing, MI, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Jinda Fan
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
- Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA
| | - Ping Wang
- Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
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Rajagopal A, Natsuaki Y, Wangerin K, Hamdi M, An H, Sunderland JJ, Laforest R, Kinahan PE, Larson PEZ, Hope TA. Synthetic PET via Domain Translation of 3-D MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:333-343. [PMID: 37396797 PMCID: PMC10311993 DOI: 10.1109/trpms.2022.3223275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.
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Affiliation(s)
- Abhejit Rajagopal
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Yutaka Natsuaki
- Department of Radiation Oncology, University of New Mexico, Albuquerque, NM 87131 USA
| | | | - Mahdjoub Hamdi
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Hongyu An
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - John J Sunderland
- Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA
| | - Richard Laforest
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Paul E Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98195 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
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Nakajima S, Fushimi Y, Hinoda T, Sakata A, Okuchi S, Arakawa Y, Ishimori T, Nakamoto Y. Brain imaging of sequential acquisition using a flexible PET scanner and 3-T MRI: quantitative and qualitative assessment. Ann Nucl Med 2022; 37:209-218. [PMID: 36585566 DOI: 10.1007/s12149-022-01817-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/23/2022] [Indexed: 01/01/2023]
Abstract
OBJECTIVE A mobile PET scanner termed flexible PET (fxPET) has been designed to fit existing MRI systems. The purpose of this study was to assess brain imaging with fxPET combined with 3-T MRI in comparison with conventional PET (cPET)/CT. METHODS In this prospective study, 29 subjects with no visible lesions except for mild leukoaraiosis on whole brain imaging underwent 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) cPET/CT followed by fxPET and MRI. The registration differences between fxPET and MRI and between cPET and CT were compared by measuring spatial coordinates. Three-dimensional magnetization-prepared rapid acquisition gradient-echo T1-weighted imaging (T1WI) was acquired. We applied two methods of attenuation correction to the fxPET images: MR-based attenuation correction, which yielded fxPETMRAC; and CT-based attenuation correction, which yielded fxPETCTAC. The three PET datasets were co-registered to the T1WI. Following subcortical segmentation and cortical parcellation, volumes of interest were placed in each PET image to assess physiological accumulation in the brain. SUVmean was obtained and compared between the three datasets. We also visually evaluated image distortion and clarity of fxPETMRAC. RESULTS Mean misregistration of fxPET/MRI was < 3 mm for each margin. Mean registration differences were significantly larger for fxPET/MRI than for cPET/CT except for the superior margin. There were high correlations between the three PET datasets regarding SUVmean. On visual evaluation of image quality, the grade of distortion was comparable between fxPETMRAC and cPET, and the grade of clarity was acceptable but inferior for fxPETMRAC compared with cPET. CONCLUSIONS fxPET could successfully determine physiological [18F]FDG uptake; however, improved image clarity is desirable. In this study, fxPET/MRI at 3-T was feasible for brain imaging.
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Affiliation(s)
- Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Takuya Hinoda
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yoshiki Arakawa
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takayoshi Ishimori
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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Smeraldo A, Ponsiglione AM, Soricelli A, Netti PA, Torino E. Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review. Int J Nanomedicine 2022; 17:3343-3359. [PMID: 35937076 PMCID: PMC9346926 DOI: 10.2147/ijn.s362192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/29/2022] [Indexed: 11/23/2022] Open
Abstract
The recent advancements in hybrid positron emission tomography–magnetic resonance imaging systems (PET/MRI) have brought massive value in the investigation of disease processes, in the development of novel treatments, in the monitoring of both therapy response and disease progression, and, not least, in the introduction of new multidisciplinary molecular imaging approaches. While offering potential advantages over PET/CT, the hybrid PET/MRI proved to improve both the image quality and lesion detectability. In particular, it showed to be an effective tool for the study of metabolic information about lesions and pathological conditions affecting the brain, from a better tumor characterization to the analysis of metabolic brain networks. Based on the PRISMA guidelines, this work presents a systematic review on PET/MRI in basic research and clinical differential diagnosis on brain oncology and neurodegenerative disorders. The analysis includes literature works and clinical case studies, with a specific focus on the use of PET tracers and MRI contrast agents, which are usually employed to perform hybrid PET/MRI studies of brain tumors. A systematic literature search for original diagnostic studies is performed using PubMed/MEDLINE, Scopus and Web of Science. Patients, study, and imaging characteristics were extracted from the selected articles. The analysis included acquired data pooling, heterogeneity testing, sensitivity analyses, used tracers, and reported patient outcomes. Our analysis shows that, while PET/MRI for the brain is a promising diagnostic method for early diagnosis, staging and recurrence in patients with brain diseases, a better definition of the role of tracers and imaging agents in both clinical and preclinical hybrid PET/MRI applications is needed and further efforts should be devoted to the standardization of the contrast imaging protocols, also considering the emerging agents and multimodal probes.
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Affiliation(s)
- Alessio Smeraldo
- Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, 80125, Italy
- Interdisciplinary Research Center on Biomaterials, CRIB, Naples, 80125, Italy
- Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Naples, 80125, Italy
| | - Alfonso Maria Ponsiglione
- Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, 80125, Italy
| | - Andrea Soricelli
- Department of Motor Sciences and Healthiness, University of Naples “Parthenope”, Naples, 80133, Italy
| | - Paolo Antonio Netti
- Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, 80125, Italy
- Interdisciplinary Research Center on Biomaterials, CRIB, Naples, 80125, Italy
- Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Naples, 80125, Italy
| | - Enza Torino
- Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, 80125, Italy
- Interdisciplinary Research Center on Biomaterials, CRIB, Naples, 80125, Italy
- Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Naples, 80125, Italy
- Correspondence: Enza Torino, Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Piazzale Tecchio 80, Naples, 80125, Italy, Tel +39-328-955-8158, Email
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Juneau D, Pelletier-Galarneau M. Assessment of myocardial inflammation post-infarct with PET/MRI: Getting into the nitty-gritty. J Nucl Cardiol 2022; 29:1326-1328. [PMID: 33629249 DOI: 10.1007/s12350-021-02558-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 01/22/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Daniel Juneau
- Department of Medical Imaging, Centre Hospitalier de l'Université de Montréal, 1000 rue Saint-Denis, Montreal, Quebec, H2X 0C1, Canada.
- University of Ottawa Heart Institute, Ottawa, Canada.
| | - Matthieu Pelletier-Galarneau
- Department of Medical Imaging, Institut de Cardiologie de Montréal, Montreal, Canada
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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8
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El Ghannudi S, Ouvrard E, Mikail N, Leroy Freschini B, Schindler TH, Imperiale A. Cutting-Edge Imaging of Cardiac Metastases from Neuroendocrine Tumors: Lesson from a Case Series. Diagnostics (Basel) 2022; 12:diagnostics12051182. [PMID: 35626337 PMCID: PMC9139778 DOI: 10.3390/diagnostics12051182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 11/16/2022] Open
Abstract
With the increasing availability of high-performance medical imaging for the management of patients with neuroendocrine tumors (NETs), a progressive growth of asymptomatic and incidentally detected cardiac metastases (CMs) has been observed in the recent years. In clinical practice, CMs of NENs are often incidentally detected by whole-body 68Ga-labeled somatostatin analogs or 18F-fluorodihydroxyphenylalanine positron emission tomography/computed tomography, and afterwards accurately characterized by cardiac magnetic resonance (CMR) and/or gated cardiac computed tomography when CMR is contraindicated or not available. The interpreting physician should familiarize with the main imaging features of CM, a finding that may be encountered in NETs patients more than previously thought. Herein, we present a case series of four patients with CMs from small-intestine NETs highlighting strengths and weaknesses of a multimodality imaging approach in clinical practice.
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Affiliation(s)
- Soraya El Ghannudi
- Nuclear Medicine, Institut de Cancérologie de Strasbourg Europe (ICANS), University Hospitals of Strasbourg, 67093 Strasbourg, France; (E.O.); (B.L.F.)
- Department of Radiology, University Hospitals of Strasbourg, 67098 Strasbourg, France
- Correspondence: (S.E.G.); (A.I.)
| | - Eric Ouvrard
- Nuclear Medicine, Institut de Cancérologie de Strasbourg Europe (ICANS), University Hospitals of Strasbourg, 67093 Strasbourg, France; (E.O.); (B.L.F.)
| | - Nidaa Mikail
- Nuclear Medicine, ENETS Centre of Excellence, Beaujon Hospital (APHP), 92110 Clichy, France;
| | - Benjamin Leroy Freschini
- Nuclear Medicine, Institut de Cancérologie de Strasbourg Europe (ICANS), University Hospitals of Strasbourg, 67093 Strasbourg, France; (E.O.); (B.L.F.)
| | - Thomas H. Schindler
- Division of Nuclear Medicine, Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA;
| | - Alessio Imperiale
- Nuclear Medicine, Institut de Cancérologie de Strasbourg Europe (ICANS), University Hospitals of Strasbourg, 67093 Strasbourg, France; (E.O.); (B.L.F.)
- Molecular Imaging—DRHIM, IPHC, UMR 7178, CNRS/Unistra, 67093 Strasbourg, France
- Correspondence: (S.E.G.); (A.I.)
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9
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Chen K, Adeyeri O, Toueg T, Zeineh M, Mormino E, Khalighi M, Zaharchuk G. Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging. AJNR Am J Neuroradiol 2022; 43:354-360. [PMID: 35086799 PMCID: PMC8910791 DOI: 10.3174/ajnr.a7410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 11/15/2021] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions. MATERIALS AND METHODS We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads. RESULTS Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly. CONCLUSIONS This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.
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Affiliation(s)
- K.T. Chen
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California,Department of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan
| | - O. Adeyeri
- Department of Computer Science (O.A.), Salem State University, Salem, Massachusetts
| | - T.N. Toueg
- Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California
| | - M. Zeineh
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
| | - E. Mormino
- Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California
| | - M. Khalighi
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
| | - G. Zaharchuk
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
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10
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Kumar A, Shandal V, Juhász C, Chugani HT. PET imaging in epilepsy. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00049-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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11
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PET imaging of pancreatic cancer. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00207-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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12
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Lai X, Cai L, Tan JW, Zannoni EM, Odintsov B, Meng LJ. Design, Performance Evaluation, and Modeling of an Ultrahigh Resolution Detector Dedicated for Simultaneous SPECT/MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3053592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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13
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Bogdanovic B, Solari EL, Villagran Asiares A, McIntosh L, van Marwick S, Schachoff S, Nekolla SG. PET/MR Technology: Advancement and Challenges. Semin Nucl Med 2021; 52:340-355. [PMID: 34969520 DOI: 10.1053/j.semnuclmed.2021.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 01/07/2023]
Abstract
When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come.
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Affiliation(s)
- Borjana Bogdanovic
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Esteban Lucas Solari
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Alberto Villagran Asiares
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Sandra van Marwick
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Sylvia Schachoff
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Stephan G Nekolla
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
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14
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Currie GM, Leon JL, Nevo E, Kamvosoulis PV. PET/MR Part 4: Clinical Applications of PET/MRI. J Nucl Med Technol 2021; 50:jnmt.121.263288. [PMID: 34872917 DOI: 10.2967/jnmt.121.263288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Position emission tomography (PET) and magnetic resonance imaging (MRI) as a hybrid modality provides novel imaging opportunities. While there are a very broad array of pathologies that could benefit from PET/MRI, there is only a narrow range of applications where benefit over standard care justifies the higher resource utilization and, in particular, offers a net positive trade-off over PET/CT. This benefit is generally associated with the omission of CT and the associated radiation dose from the patient workup. This manuscript provides a summary of the generally accepted clinical applications of PET/MRI in both adult and pediatric populations. While there are a number of potential applications and certainly exciting research that may expand applications in the future, the purpose of this paper was to focus on current, mainstream applications. This is the final manuscript in a four-part integrated series sponsored by the SNMMI-TS PET/MR Task Force in conjunction with the SNMMI-TS Publication Committee.
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Affiliation(s)
| | | | - Elad Nevo
- Lucile Packard Children's Hospital, United States
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15
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Laudicella R, Bauckneht M, Cuppari L, Donegani MI, Arnone A, Baldari S, Burger IA, Quartuccio N. Emerging applications of imaging in glioma: focus on PET/MRI and radiomics. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00464-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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16
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Hamdi M, Natsuaki Y, Wangerin KA, An H, St James S, Kinahan PE, Sunderland JJ, Larson PEZ, Hope TA, Laforest R. Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion. J Med Imaging (Bellingham) 2021; 8:056001. [PMID: 34568511 DOI: 10.1117/1.jmi.8.5.056001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/02/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.
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Affiliation(s)
- Mahdjoub Hamdi
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Yutaka Natsuaki
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
| | | | - Hongyu An
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Sarah St James
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
| | - Paul E Kinahan
- University of Washington Seattle, Seattle, Washington, United States
| | - John J Sunderland
- University of Iowa, Carver College of Medicine, Department of Radiology, Iowa City, Iowa, United States
| | - Peder E Z Larson
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Thomas A Hope
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Richard Laforest
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
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17
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Abstract
PET/MR imaging is in routine clinical use and is at least as effective as PET/CT for oncologic and neurologic studies with advantages with certain PET radiopharmaceuticals and applications. In addition, whole body PET/MR imaging substantially reduces radiation dosages compared with PET/CT which is particularly relevant to pediatric and young adult population. For cancer imaging, assessment of hepatic, pelvic, and soft-tissue malignancies may benefit from PET/MR imaging. For neurologic imaging, volumetric brain MR imaging can detect regional volume loss relevant to cognitive impairment and epilepsy. In addition, the single-bed position acquisition enables dynamic brain PET imaging without extending the total study length which has the potential to enhance the diagnostic information from PET.
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Affiliation(s)
- Farshad Moradi
- Department of Radiology, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA.
| | - Andrei Iagaru
- Department of Radiology, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA
| | - Jonathan McConathy
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, JT 773, Birmingham, AL 35249, USA
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18
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Min LA, Castagnoli F, Vogel WV, Vellenga JP, van Griethuysen JJM, Lahaye MJ, Maas M, Beets Tan RGH, Lambregts DMJ. A decade of multi-modality PET and MR imaging in abdominal oncology. Br J Radiol 2021; 94:20201351. [PMID: 34387508 PMCID: PMC9328040 DOI: 10.1259/bjr.20201351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To investigate trends observed in a decade of published research on multimodality PET(/CT)+MR imaging in abdominal oncology, and to explore how these trends are reflected by the use of multimodality imaging performed at our institution. METHODS First, we performed a literature search (2009-2018) including all papers published on the multimodality combination of PET(/CT) and MRI in abdominal oncology. Retrieved papers were categorized according to a structured labelling system, including study design and outcome, cancer and lesion type under investigation and PET-tracer type. Results were analysed using descriptive statistics and evolutions over time were plotted graphically. Second, we performed a descriptive analysis of the numbers of MRI, PET/CT and multimodality PET/CT+MRI combinations (performed within a ≤14 days interval) performed during a similar time span at our institution. RESULTS Published research papers involving multimodality PET(/CT)+MRI combinations showed an impressive increase in numbers, both for retrospective combinations of PET/CT and MRI, as well as hybrid PET/MRI. Main areas of research included new PET-tracers, visual PET(/CT)+MRI assessment for staging, and (semi-)quantitative analysis of PET-parameters compared to or combined with MRI-parameters as predictive biomarkers. In line with literature, we also observed a vast increase in numbers of multimodality PET/CT+MRI imaging in our institutional data. CONCLUSIONS The tremendous increase in published literature on multimodality imaging, reflected by our institutional data, shows the continuously growing interest in comprehensive multivariable imaging evaluations to guide oncological practice. ADVANCES IN KNOWLEDGE The role of multimodality imaging in oncology is rapidly evolving. This paper summarizes the main applications and recent developments in multimodality imaging, with a specific focus on the combination of PET+MRI in abdominal oncology.
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Affiliation(s)
- Lisa A Min
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | | | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jisk P Vellenga
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands.,Faculty or Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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19
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Sanaat A, Mirsadeghi E, Razeghi B, Ginovart N, Zaidi H. Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation. Med Phys 2021; 48:5059-5071. [PMID: 34174787 PMCID: PMC8518550 DOI: 10.1002/mp.15063] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 05/30/2021] [Accepted: 06/08/2021] [Indexed: 12/03/2022] Open
Abstract
Purpose We assess the performance of a recurrent frame generation algorithm for prediction of late frames from initial frames in dynamic brain PET imaging. Methods Clinical dynamic 18F‐DOPA brain PET/CT studies of 46 subjects with ten folds cross‐validation were retrospectively employed. A novel stochastic adversarial video prediction model was implemented to predict the last 13 frames (25–90 minutes) from the initial 13 frames (0–25 minutes). The quantitative analysis of the predicted dynamic PET frames was performed for the test and validation dataset using established metrics. Results The predicted dynamic images demonstrated that the model is capable of predicting the trend of change in time‐varying tracer biodistribution. The Bland‐Altman plots reported the lowest tracer uptake bias (−0.04) for the putamen region and the smallest variance (95% CI: −0.38, +0.14) for the cerebellum. The region‐wise Patlak graphical analysis in the caudate and putamen regions for eight subjects from the test and validation dataset showed that the average bias for Ki and distribution volume was 4.3%, 5.1% and 4.4%, 4.2%, (P‐value <0.05), respectively. Conclusion We have developed a novel deep learning approach for fast dynamic brain PET imaging capable of generating the last 65 minutes time frames from the initial 25 minutes frames, thus enabling significant reduction in scanning time.
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Affiliation(s)
- Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ehsan Mirsadeghi
- Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Behrooz Razeghi
- Department of Computer Sciences, University of Geneva, Geneva, Switzerland.,School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Nathalie Ginovart
- Department of Psychiatry, Geneva University, Geneva, Switzerland.,Department of Basic Neurosciences, Geneva University, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands.,University Medical Center, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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20
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Zhao J, Xue Q, Chen X, You Z, Wang Z, Yuan J, Liu H, Hu L. Evaluation of SUVlean consistency in FDG and PSMA PET/MR with Dixon-, James-, and Janma-based lean body mass correction. EJNMMI Phys 2021; 8:17. [PMID: 33598849 PMCID: PMC7889776 DOI: 10.1186/s40658-021-00363-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/04/2021] [Indexed: 01/22/2023] Open
Abstract
PURPOSE To systematically evaluate the consistency of various standardized uptake value (SUV) lean body mass (LBM) normalization methods in a clinical positron emission tomography/magnetic resonance imaging (PET/MR) setting. METHODS SUV of brain, liver, prostate, parotid, blood, and muscle were measured in 90 18F-FDG and 28 18F-PSMA PET/MR scans and corrected for LBM using the James, Janma (short for Janmahasatian), and Dixon approaches. The prospective study was performed from December 2018 to August 2020 at Shanghai East Hospital. Forty dual energy X-ray absorptiometry (DXA) measurements of non-fat mass were used as the reference standard. Agreement between different LBM methods was assessed by linear regression and Bland-Altman statistics. SUV's dependency on BMI was evaluated by means of linear regression and Pearson correlation. RESULTS Compared to DXA, the Dixon approach presented the least bias in LBM/weight% than James and Janma models (bias 0.4±7.3%, - 8.0±9.4%, and - 3.3±8.3% respectively). SUV normalized by body weight (SUVbw) was positively correlated with body mass index (BMI) for both FDG (e.g., liver: r = 0.45, p < 0.001) and PSMA scans (r = 0.20, p = 0.31), while SUV normalized by lean body mass (SUVlean) revealed a decreased dependency on BMI (r = 0.22, 0.08, 0.14, p = 0.04, 0.46, 0.18 for Dixon, James, and Janma models, respectively). The liver SUVbw of obese/overweight patients was significantly larger (p < 0.001) than that of normal patients, whereas the bias was mostly eliminated in SUVlean. One-way ANOVA showed significant difference (p < 0.001) between SUVlean in major organs measured using Dixon method vs James and Janma models. CONCLUSION Significant systematic variation was found using different approaches to calculate SUVlean. A consistent correction method should be applied for serial PET/MR scans. The Dixon method provides the most accurate measure of LBM, yielding the least bias of all approaches when compared to DXA.
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Affiliation(s)
- Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Qiaoyi Xue
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Xing Chen
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhiwen You
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Hui Liu
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Lingzhi Hu
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
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21
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Chen S, Gu Y, Yu H, Chen X, Cao T, Hu L, Shi H. NEMA NU2-2012 performance measurements of the United Imaging uPMR790: an integrated PET/MR system. Eur J Nucl Med Mol Imaging 2021; 48:1726-1735. [PMID: 33388972 DOI: 10.1007/s00259-020-05135-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 11/24/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE In this paper, we aimed to evaluate the positron emission tomography (PET) performance of, to the best of our knowledge, the third commercially available whole-body integrated PET/magnetic resonance (MR) system. METHODS The PET system performance was measured following the NEMA standards with and without simultaneous MR operation. PET spatial resolution, sensitivity, scatter fraction, count-rate performance, accuracy of count losses and random corrections, image quality, and time-of-flight (TOF) resolution were quantitatively evaluated. Clinical scans were acquired at the PET/MR system and compared with images acquired at a PET/CT with the same digital detector technology. RESULTS Measurement results of essential PET performance were reported in the form of MR idle (MR pulsing). The axial, radial, and tangential spatial resolutions were measured as 2.72 mm (2.73 mm), 2.86 mm (2.85 mm), and 2.81 mm (2.82 mm) FWHM, respectively, at 1 cm radial offset. The NECR peak was measured as 129.2 kcps (129.5 kcps) at 14.7 kBq mL-1 (14.2 kBq mL-1). The scatter fraction at NECR peak was 37.9% (36.5%), and the maximum slice error below NECR was 4.1% (4.5%). Contrast recovery coefficients ranged from 51.8% (52.3%) for 10 mm hot sphere to 87.3% (87.2%) for 37 mm cold sphere. TOF resolution at 5.3 kBq mL-1 was measured at 535 ps (540 ps). With point source, TOF was measured to be 474 ps (485 ps). Clinical scans revealed similar image quality from the PET/MR and the comparative PET/CT system. CONCLUSION The PET performance of the newly introduced integrated PET/MR system is not significantly affected by the simultaneous operation of an MR sequence (2-point DIXON sequence). Measurement results demonstrate comparable performance with other state-of-the-art PET/MR systems. The clinical benefits of high spatial resolution and long axial coverage remain to be further evaluated in specific clinical imaging applications.
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Affiliation(s)
- Shuguang Chen
- Zhongshan Hospital, Fudan University, 1609 Xietu Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, China
| | - Yushen Gu
- Zhongshan Hospital, Fudan University, 1609 Xietu Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, China
| | - Haojun Yu
- Zhongshan Hospital, Fudan University, 1609 Xietu Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, China
| | - Xin Chen
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Tuoyu Cao
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Lingzhi Hu
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Hongcheng Shi
- Zhongshan Hospital, Fudan University, 1609 Xietu Road, Shanghai, 200032, China. .,Institute of Nuclear Medicine, Fudan University, Shanghai, China.
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22
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Moradi F, Brunsing RL, Sheth VR, Iagaru A. Positron Emission Tomography–Magnetic Resonance Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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23
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Ando T, Kemp B, Warnock G, Sekine T, Kaushik S, Wiesinger F, Delso G. Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data. Front Neurosci 2020; 14:569706. [PMID: 33324141 PMCID: PMC7725704 DOI: 10.3389/fnins.2020.569706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/03/2020] [Indexed: 11/13/2022] Open
Abstract
Aim Attenuation correction using zero-echo time (ZTE) - magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer's disease (AD) by performing simulation study. Methods We recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient's specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference. Results Positron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (-0.0796 ± 0.0938 vs. -0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R 2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8-84.5%), respectively). Conclusion For FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.
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Affiliation(s)
- Takahiro Ando
- Department of Radiology, Nippon Medical School, Tokyo, Japan
| | - Bradley Kemp
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Geoffrey Warnock
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.,PMOD Technologies Ltd., Zurich, Switzerland
| | - Tetsuro Sekine
- Department of Radiology, Nippon Medical School, Tokyo, Japan.,Department of Radiology, Nippon Medical School Musashi-Kosugi Hospital, Kawasaki, Japan.,Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
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Murthy V, Smith RL, Tao DH, Lawhn-Heath CA, Korenchan DE, Larson PEZ, Flavell RR, Hope TA. 68Ga-PSMA-11 PET/MRI: determining ideal acquisition times to reduce noise and increase image quality. EJNMMI Phys 2020; 7:54. [PMID: 32844310 PMCID: PMC7447708 DOI: 10.1186/s40658-020-00322-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/12/2020] [Indexed: 12/21/2022] Open
Abstract
Background In this study, we investigate the impact of increased PET acquisition time per bed position on lesion detectability, standard uptake value, and image noise in 68Ga-PSMA-11 PET/MRI scans. Methods Scans of twenty patients were analyzed in this study. Patients were injected with 68Ga-PSMA-11 (mean, 5.50 ± 1.49 mCi) and imaged on a 3.0 T time-of-flight PET/MRI. PET images were retrospectively reconstructed using 0.5, 1, 2, 4, 7, and 10 min of PET data. Lesion detectability was evaluated on a 5-point Likert Scale for each lesion in each reconstruction. Quantitative analysis was performed measuring image noise and lesion uptake. Results A total of 55 lesions were identified, and lesion detectability increased from 2.07 ± 1.14 for 0.5 min to 4.93 ± 0.26 for 10 min (p < 0.001), with no significant difference detected between 7 and 10 min of scan time. Average SUVmax decreased from 9.89 ± 6.62 for 0.5 min to 8.64 ± 6.81 for 10 min. Noise decreased from 0.72 ± 0.22 for 0.5 min to 0.31 ± 0.12 for 10 min (p < 0.001) and were nearly equivalent between 7 and 10 min. Pairwise interaction terms between size, SUVmax, and scan time were all found to be significant, although the interaction term between SUVmax and scan time was found to be the most significant. Conclusions Increased acquisition duration improves image quality by increasing detectability and reducing noise. In patients with biochemical recurrence, increased acquisition time up to 7 min improves lesion detection.
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Affiliation(s)
- Vishnu Murthy
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Raven L Smith
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Dora H Tao
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Courtney A Lawhn-Heath
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Dave E Korenchan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Robert R Flavell
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. .,UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. .,Department of Radiology, San Francisco VA Medical Center, San Francisco, CA, USA.
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25
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PET/MRI in breast cancer patients: Added value, barriers to implementation, and solutions. Clin Imaging 2020; 68:24-28. [PMID: 32562923 DOI: 10.1016/j.clinimag.2020.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/18/2020] [Accepted: 06/01/2020] [Indexed: 11/21/2022]
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Beyer T, Bidaut L, Dickson J, Kachelriess M, Kiessling F, Leitgeb R, Ma J, Shiyam Sundar LK, Theek B, Mawlawi O. What scans we will read: imaging instrumentation trends in clinical oncology. Cancer Imaging 2020; 20:38. [PMID: 32517801 PMCID: PMC7285725 DOI: 10.1186/s40644-020-00312-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/17/2020] [Indexed: 12/16/2022] Open
Abstract
Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.
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Affiliation(s)
- Thomas Beyer
- QIMP Team, Centre for Medical Physics and Biomedical Engineering, Medical University Vienna, Währinger Gürtel 18-20/4L, 1090, Vienna, Austria.
| | - Luc Bidaut
- College of Science, University of Lincoln, Lincoln, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospital, London, UK
| | - Marc Kachelriess
- Division of X-ray imaging and CT, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, DE, Germany
| | - Fabian Kiessling
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstrasse 20, 52074, Aachen, DE, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359, Bremen, DE, Germany
| | - Rainer Leitgeb
- Centre for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, AT, Austria
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lalith Kumar Shiyam Sundar
- QIMP Team, Centre for Medical Physics and Biomedical Engineering, Medical University Vienna, Währinger Gürtel 18-20/4L, 1090, Vienna, Austria
| | - Benjamin Theek
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstrasse 20, 52074, Aachen, DE, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359, Bremen, DE, Germany
| | - Osama Mawlawi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Sander CY, Hansen HD, Wey HY. Advances in simultaneous PET/MR for imaging neuroreceptor function. J Cereb Blood Flow Metab 2020; 40:1148-1166. [PMID: 32169011 PMCID: PMC7238372 DOI: 10.1177/0271678x20910038] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Hybrid imaging using PET/MRI has emerged as a platform for elucidating novel neurobiology, molecular and functional changes in disease, and responses to physiological or pharmacological interventions. For the central nervous system, PET/MRI has provided insights into biochemical processes, linking selective molecular targets and distributed brain function. This review highlights several examples that leverage the strengths of simultaneous PET/MRI, which includes measuring the perturbation of multi-modal imaging signals on dynamic timescales during pharmacological challenges, physiological interventions or behavioral tasks. We discuss important considerations for the experimental design of dynamic PET/MRI studies and data analysis approaches for comparing and quantifying simultaneous PET/MRI data. The primary focus of this review is on functional PET/MRI studies of neurotransmitter and receptor systems, with an emphasis on the dopamine, opioid, serotonin and glutamate systems as molecular neuromodulators. In this context, we provide an overview of studies that employ interventions to alter the activity of neuroreceptors or the release of neurotransmitters. Overall, we emphasize how the synergistic use of simultaneous PET/MRI with appropriate study design and interventions has the potential to expand our knowledge about the molecular and functional dynamics of the living human brain. Finally, we give an outlook on the future opportunities for simultaneous PET/MRI.
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Affiliation(s)
- Christin Y Sander
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA
| | - Hanne D Hansen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA.,Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA
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Créhange G, Soussan M, Gensanne D, Decazes P, Thariat J, Thureau S. Interest of positron-emission tomography and magnetic resonance imaging for radiotherapy planning and control. Cancer Radiother 2020; 24:398-402. [PMID: 32247688 DOI: 10.1016/j.canrad.2020.02.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 02/07/2020] [Indexed: 12/24/2022]
Abstract
Computed tomography (CT) in the treatment position is currently indispensable for planning radiation therapy. Other imaging modalities, such as magnetic resonance imaging (MRI) and positron emission-tomography (PET), can be used to improve the definition of the tumour and/or healthy tissue but also to provide functional data of the target volume. Accurate image registration is essential for treatment planning, so MRI and PET scans should be registered at the planning CT scan. Hybrid PET/MRI scans with a hard plane can be used but pose the problem of the absence of CT scans. Finally, techniques for moving the patient on a rigid air-cushioned table allow PET/CT/MRI scans to be performed in the treatment position while limiting the patient's movements exist. At the same time, the advent of MRI-linear accelerator systems allows to redefine image-guided radiotherapy and to propose treatments with daily recalculation of the dose. The place of PET during treatment remains more confidential and currently only in research and prototype status. The same development of imaging during radiotherapy is underway in proton therapy.
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Affiliation(s)
- G Créhange
- Département de radiothérapie oncologique, institut Curie, 26, rue d'Ulm, 75005 Paris, France
| | - M Soussan
- Service de médecine nucléaire, hôpital Avicenne, AP-HP, hôpitaux universitaires, 125, rue de Stalingrad, 93000 Bobigny, France
| | - D Gensanne
- Département de radiothérapie et de physique médicale, centre Henri-Becquerel, 1, rue d'Amiens, 76038 Rouen, France; Quantif-Litis EA4108, université de Rouen Normandie, rue d'Amiens, 76000 Rouen, France
| | - P Decazes
- Quantif-Litis EA4108, université de Rouen Normandie, rue d'Amiens, 76000 Rouen, France; Département d'imagerie-médecine nucléaire, centre Henri-Becquerel, 1, rue d'Amiens, 76038 Rouen, France
| | - J Thariat
- Département d'onco-radiothérapie, centre François-Baclesse, 3, avenue General-Harris, 14000 Caen, France; Association Advance Resource Centre for Hadrontherapy in Europe (Archade), 3, avenue General-Harris, 14000 Caen, France; Université de Caen Normandie (Unicaen), 3, avenue General-Harris, 14000 Caen, France; Laboratoire de physique corpusculaire, Institut national de physique nucléaire et de physique des particules (IN2P3), 6, boulevard Maréchal-Juin, 14000 Caen, France
| | - S Thureau
- Département de radiothérapie et de physique médicale, centre Henri-Becquerel, 1, rue d'Amiens, 76038 Rouen, France; Quantif-Litis EA4108, université de Rouen Normandie, rue d'Amiens, 76000 Rouen, France; Département d'imagerie-médecine nucléaire, centre Henri-Becquerel, 1, rue d'Amiens, 76038 Rouen, France; Laboratoire de physique corpusculaire, Institut national de physique nucléaire et de physique des particules (IN2P3), 6, boulevard Maréchal-Juin, 14000 Caen, France.
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