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Kang F, Xie Z, Ma W, Quan Z, Li G, Guo K, Li X, Ma T, Yang W, Zhao Y, Yi H, Zhao Y, Lu Y, Wang J. Validation and Evaluation of a Vendor-Provided Head Motion Correction Algorithm on the uMI Panorama PET/CT System. J Nucl Med 2024; 65:1313-1319. [PMID: 38991753 PMCID: PMC11294066 DOI: 10.2967/jnumed.124.267446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 05/13/2024] [Indexed: 07/13/2024] Open
Abstract
Brain PET imaging often faces challenges from head motion (HM), which can introduce artifacts and reduce image resolution, crucial in clinical settings for accurate treatment planning, diagnosis, and monitoring. United Imaging Healthcare has developed NeuroFocus, an HM correction (HMC) algorithm for the uMI Panorama PET/CT system, using a data-driven, statistics-based approach. The HMC algorithm automatically detects HM using a centroid-of-distribution technique, requiring no parameter adjustments. This study aimed to validate NeuroFocus and assess the prevalence of HM in clinical short-duration 18F-FDG scans. Methods: The study involved 317 patients undergoing brain PET scans, divided into 2 groups: 15 for HMC validation and 302 for evaluation. Validation involved patients undergoing 2 consecutive 3-min single-bed-position brain 18F-FDG scans-one with instructions to remain still and another with instructions to move substantially. The evaluation examined 302 clinical single-bed-position brain scans for patients with various neurologic diagnoses. Motion was categorized as small or large on the basis of a 5% SUV change in the frontal lobe after HMC. Percentage differences in SUVmean were reported across 11 brain regions. Results: The validation group displayed a large negative difference (-10.1%), with variation of 5.2% between no-HM and HM scans. After HMC, this difference decreased dramatically (-0.8%), with less variation (3.2%), indicating effective HMC application. In the evaluation group, 38 of 302 patients experienced large HM, showing a 10.9% ± 8.9% SUV increase after HMC, whereas most exhibited minimal uptake changes (0.1% ± 1.3%). The HMC algorithm not only enhanced the image resolution and contrast but also aided in disease identification and reduced the need for repeat scans, potentially optimizing clinical workflows. Conclusion: The study confirmed the effectiveness of NeuroFocus in managing HM in short clinical 18F-FDG studies on the uMI Panorama PET/CT system. It found that approximately 12% of scans required HMC, establishing HMC as a reliable tool for clinical brain 18F-FDG studies.
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Affiliation(s)
- Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Zhaojuan Xie
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Wenhui Ma
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Zhiyong Quan
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Guiyu Li
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Kun Guo
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Xiang Li
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Taoqi Ma
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | - Weidong Yang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
| | | | | | - Yumo Zhao
- United Imaging Healthcare, Shanghai, China
| | - Yihuan Lu
- United Imaging Healthcare, Shanghai, China
| | - Jing Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and
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Odagiri H, Watabe H, Takanami K, Akimoto K, Usui A, Kawakami H, Katsuki A, Uetake N, Dendo Y, Tanaka Y, Kodama H, Takase K, Kaneta T. Verification of the effect of data-driven brain motion correction on PET imaging. PLoS One 2024; 19:e0301919. [PMID: 38968191 PMCID: PMC11226115 DOI: 10.1371/journal.pone.0301919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/25/2024] [Indexed: 07/07/2024] Open
Abstract
INTRODUCTION Brain positron emission tomography/computed tomography (PET/CT) scans are useful for identifying the cause of dementia by evaluating glucose metabolism in the brain with F-18-fluorodeoxyglucose or Aβ deposition with F-18-florbetaben. However, since imaging time ranges from 10 to 30 minutes, movements during the examination might result in image artifacts, which interfere with diagnosis. To solve this problem, data-driven brain motion correction (DDBMC) techniques are capable of performing motion corrected reconstruction using highly accurate motion estimates with high temporal resolution. In this study, we investigated the effectiveness of DDBMC techniques on PET/CT images using a Hoffman phantom, involving continuous rotational and tilting motion, each expanded up to approximately 20 degrees. MATERIALS AND METHODS Listmode imaging was performed using a Hoffman phantom that reproduced rotational and tilting motions of the head. Brain motion correction processing was performed on the obtained data. Reconstructed images with and without brain motion correction processing were compared. Visual evaluations by a nuclear medicine specialist and quantitative parameters of images with correction and reference still images were compared. RESULTS Normalized Mean Squared Error (NMSE) results demonstrated the effectiveness of DDBMC in compensating for rotational and tilting motions during PET imaging. In Cases 1 and 2 involving rotational motion, NMSE decreased from 0.15-0.2 to approximately 0.01 with DDBMC, indicating a substantial reduction in differences from the reference image across various brain regions. In the Structural Similarity Index (SSIM), DDBMC improved it to above 0.96 Contrast assessment revealed notable improvements with DDBMC. In continuous rotational motion, % contrast increased from 42.4% to 73.5%, In tilting motion, % contrast increased from 52.3% to 64.5%, eliminating significant differences from the static reference image. These findings underscore the efficacy of DDBMC in enhancing image contrast and minimizing motion induced variations across different motion scenarios. CONCLUSIONS DDBMC processing can effectively compensate for continuous rotational and tilting motion of the head during PET, with motion angles of approximately 20 degrees. However, a significant limitation of this study is the exclusive validation of the proposed method using a Hoffman phantom; its applicability to the human brain has not been investigated. Further research involving human subjects is necessary to assess the generalizability and reliability of the presented motion correction technique in real clinical scenarios.
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Affiliation(s)
- Hayato Odagiri
- Department of Diagnostic Image Analysis, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Hiroshi Watabe
- Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center, Tohoku University, Sendai, Miyagi, Japan
| | - Kentaro Takanami
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Kazuma Akimoto
- Department of Diagnostic Image Analysis, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Akihito Usui
- Department of Diagnostic Image Analysis, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | | | | | | | - Yutaka Dendo
- Department of Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Yoshitaka Tanaka
- Department of Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Hiroyasu Kodama
- Department of Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Kei Takase
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Tomohiro Kaneta
- Department of Diagnostic Image Analysis, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan
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Virk T, Letendre T, Pathman T. The convergence of naturalistic paradigms and cognitive neuroscience methods to investigate memory and its development. Neuropsychologia 2024; 196:108779. [PMID: 38154592 DOI: 10.1016/j.neuropsychologia.2023.108779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 12/12/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
Studies that involve lab-based stimuli (e.g., words, pictures) are fundamental in the memory literature. At the same time, there is growing acknowledgment that memory processes assessed in the lab may not be analogous to how memory operates in the real world. Naturalistic paradigms can bridge this gap and over the decades a growing proportion of memory research has involved more naturalistic events. However, there is significant variation in the types of naturalistic studies used to study memory and its development, each with various advantages and limitations. Further, there are notable gaps in how often different types of naturalistic approaches have been combined with cognitive neuroscience methods (e.g., fMRI, EEG) to elucidate the neural processes and substrates involved in memory encoding and retrieval in the real world. Here we summarize and discuss what we identify as progressively more naturalistic methodologies used in the memory literature (movie, virtual reality, staged-events inside and outside of the lab, photo-taking, and naturally occurring event studies). Our goal is to describe each approach's benefits (e.g., naturalistic quality, feasibility), limitations (e.g., viability of neuroimaging method for event encoding versus event retrieval), and discuss possible future directions with each approach. We focus on child studies, when available, but also highlight past adult studies. Although there is a growing body of child memory research, naturalistic approaches combined with cognitive neuroscience methodologies in this domain remain sparse. Overall, this viewpoint article reviews how we can study memory through the lens of developmental cognitive neuroscience, while utilizing naturalistic and real-world events.
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Wang J, Bermudez D, Chen W, Durgavarjhula D, Randell C, Uyanik M, McMillan A. Motion-correction strategies for enhancing whole-body PET imaging. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2024; 4:1257880. [PMID: 39118964 PMCID: PMC11308502 DOI: 10.3389/fnume.2024.1257880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.
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Affiliation(s)
- James Wang
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Dalton Bermudez
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Weijie Chen
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Divya Durgavarjhula
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Computer Science, University of Wisconsin Madison, Madison, WI, United States
| | - Caitlin Randell
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Meltem Uyanik
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Alan McMillan
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
- Data Science Institute, University of Wisconsin Madison, Madison, WI, United States
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5
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Li X, Cheng Y, Han X, Cui B, Li J, Yang H, Xu G, Lin Q, Xiao X, Tang J, Lu J. Exploring the association of glioma tumor residuals from incongruent [ 18F]FET PET/MR imaging with tumor proliferation using a multiparametric MRI radiomics nomogram. Eur J Nucl Med Mol Imaging 2024; 51:779-796. [PMID: 37864593 DOI: 10.1007/s00259-023-06468-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/28/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE The study aimed to using multiparametric MRI radiomics to predict glioma tumor residuals (TRFET over MR) derived from incongruent [18F]fluoroethyl-L-tyrosine ([18F]FET) PET/MR imaging. METHODS One hundred ten patients with gliomas who underwent [18F]FET PET/MR scanning were retrospectively analyzed. The TRFET over MR was identified by the discrepancy-PET that the extent of resection (EOR) based on MRI subtracted the biological tumor volume on PET images. The MRI parameters and radiomics features were extracted based on EOR and selected by the least absolute shrinkage and selection operator to construct radiomics score (Rad-score). The correlation network analysis of all features was analyzed by Spearman's correlation tests. The methods for evaluating the clinical usefulness consisted of the receiver operating characteristic curve, the calibration curve, and decision curve analysis. RESULTS The Rad-score of the patients with the TRFET over MR was significantly higher than those with the non TRFET over MR (p < 0.001). The Rad-score was significantly correlated with the discrepancy-PET (r = 0.72, p < 0.001), Ki-67 level (r = 0.76, p < 0.001), and epidermal growth factor receptor (EGFR) of gliomas (r = 0.75, p < 0.001), respectively. Moreover, there was a difference of the correlation network analysis between the TRPET over MR group and non TRFET over MR group. The nomogram combing Rad-score and clinical features had the greatest performance in predicting TRFET over MR (AUC = 0.90/0.87, training/testing). There was a significant difference in prognosis (median OS, 17 m vs. 43 m) between patients with TRFET over MR and non TRFET over MR based on nomogram prediction (p < 0.001). CONCLUSION The nomogram based on MRI radiomics would predict gliomas tumor residuals caused by the absence of 18F-PET PET examination and adjust EOR to improve prognosis.
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Affiliation(s)
- Xiaoran Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Ye Cheng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Han
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Bixiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Jing Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Hongwei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Geng Xu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qingtang Lin
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xinru Xiao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Tang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China.
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Shiyam Sundar LK, Lassen ML, Gutschmayer S, Ferrara D, Calabrò A, Yu J, Kluge K, Wang Y, Nardo L, Hasbak P, Kjaer A, Abdelhafez YG, Wang G, Cherry SR, Spencer BA, Badawi RD, Beyer T, Muzik O. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies. J Nucl Med 2023; 64:1145-1153. [PMID: 37290795 DOI: 10.2967/jnumed.122.265362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/09/2023] [Indexed: 06/10/2023] Open
Abstract
We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Lyngby Lassen
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Gutschmayer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Daria Ferrara
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Anna Calabrò
- Department of Radiology, University of California-Davis, Davis, California
| | - Josef Yu
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kilian Kluge
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Yiran Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Lorenzo Nardo
- Department of Radiology, University of California-Davis, Davis, California
| | - Philip Hasbak
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | - Guobao Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Simon R Cherry
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Benjamin A Spencer
- Department of Radiology, University of California-Davis, Davis, California
| | - Ramsey D Badawi
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan
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7
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Chemli Y, Tétrault MA, Marin T, Normandin MD, Bloch I, El Fakhri G, Ouyang J, Petibon Y. Super-resolution in brain positron emission tomography using a real-time motion capture system. Neuroimage 2023; 272:120056. [PMID: 36977452 PMCID: PMC10122782 DOI: 10.1016/j.neuroimage.2023.120056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/28/2023] [Accepted: 03/25/2023] [Indexed: 03/29/2023] Open
Abstract
Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.
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Affiliation(s)
- Yanis Chemli
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; LTCI, Télécom Paris, Institut Polytechnique de Paris, France.
| | - Marc-André Tétrault
- Department of Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada.
| | - Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Marc D Normandin
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Isabelle Bloch
- Sorbonne Université, CNRS, LIP6, Paris, France; LTCI, Télécom Paris, Institut Polytechnique de Paris, France.
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Jinsong Ouyang
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Yoann Petibon
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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8
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Bhuiyan EH, Chowdhury MEH, Glover PM. Feasibility of tracking involuntary head movement for MRI using a coil as a magnetic dipole in a time-varying gradient. Magn Reson Imaging 2023; 101:76-89. [PMID: 37044168 DOI: 10.1016/j.mri.2023.03.021] [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: 02/01/2023] [Accepted: 03/29/2023] [Indexed: 04/14/2023]
Abstract
Accurate tracking involuntary head movements is fairly a challenging problem in MR imaging of the brain. Though there are few techniques available to monitor the head movement of the subject for a prospective motion correction, it is still an unsolved problem in MRI. In this theoretical study, we aim to describe an analytical investigation to track head movement inside an MR scanner by calculating the change in induced voltage in the head-mounted coils during the execution of time-varying gradients. We derive an expression to calculate the change in induced voltage in a coil placed in a time-varying gradient. We also derive a general equation to investigate the changes in the induced voltage in a set of coils mounted onto the head for the planar position and orientation of the coils. Each coil is considered as a magnetic dipole with location and sensitivity vectors. The changes of the vectors can track the head movement in the MR scanner by measuring the changes in the induced voltage in the coils. The dipole concept is valid for a wide range of coils. The changes in induced voltage in the coils are linear due to small changes in pose of the head. Movement parameters are estimated from the induced voltage changes. If the random noise voltage is less than 100 μV, it does not significantly affect movement parameters because the change in induced voltage in the coils dominates over the small noise voltage. This method and array of the coils may provide a real-life solution to the long-standing problem of head motion during MRI.
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Affiliation(s)
- E H Bhuiyan
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK; Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - M E H Chowdhury
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK; Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - P M Glover
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK
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9
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Liu J, Geng J. Recent progress on imaging technology and performance testing of PET/MR. RADIATION DETECTION TECHNOLOGY AND METHODS 2023. [DOI: 10.1007/s41605-022-00376-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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10
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Spangler-Bickell MG, Hurley SA, Pirasteh A, Perlman SB, Deller T, McMillan AB. Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging. J Nucl Med 2022; 63:1604-1610. [PMID: 35086896 PMCID: PMC9536704 DOI: 10.2967/jnumed.121.263309] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/25/2022] [Indexed: 11/16/2022] Open
Abstract
Head motion during brain PET imaging can significantly degrade the quality of the reconstructed image, leading to reduced diagnostic value and inaccurate quantitation. A fully data-driven motion correction approach was recently demonstrated to produce highly accurate motion estimates (<1 mm) with high temporal resolution (≥1 Hz), which can then be used for a motion-corrected reconstruction. This can be applied retrospectively with no impact on the clinical image acquisition protocol. We present a reader-based evaluation and an atlas-based quantitative analysis of this motion correction approach within a clinical cohort. Methods: Clinical patient data were collected over 2019-2020 and processed retrospectively. Motion was estimated using image-based registration on reconstructions of ultrashort frames (0.6-1.8 s), after which list-mode reconstructions that were fully motion-corrected were performed. Two readers graded the motion-corrected and uncorrected reconstructions. An atlas-based quantitative analysis was performed. Paired Wilcoxon tests were used to test for significant differences in reader scores and SUVs between reconstructions. The Levene test was used to determine whether motion correction had a greater impact on quantitation in the presence of motion than when motion was low. Results: Fifty standard clinical 18F-FDG brain PET datasets (age range, 13-83 y; mean ± SD, 59 ± 20 y; 27 women) from 3 scanners were collected. The reader study showed a significantly different, diagnostically relevant improvement by motion correction when motion was present (P = 0.02) and no impact in low-motion cases. Eight percent of all datasets improved from diagnostically unacceptable to acceptable. The atlas-based analysis demonstrated a significant difference between the motion-corrected and uncorrected reconstructions in cases of high motion for 7 of 8 regions of interest (P < 0.05). Conclusion: The proposed approach to data-driven motion estimation and correction demonstrated a clinically significant impact on brain PET image reconstruction.
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Affiliation(s)
- Matthew G Spangler-Bickell
- PET/MR Engineering, GE Healthcare, Waukesha, Wisconsin;
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin; and
| | - Samuel A Hurley
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin; and
| | - Ali Pirasteh
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin; and
- Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Scott B Perlman
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin; and
| | | | - Alan B McMillan
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin; and
- Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
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11
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Iwao Y, Akamatsu G, Tashima H, Takahashi M, Yamaya T. Brain PET motion correction using 3D face-shape model: the first clinical study. Ann Nucl Med 2022; 36:904-912. [PMID: 35854178 PMCID: PMC9515015 DOI: 10.1007/s12149-022-01774-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/10/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Head motions during brain PET scan cause degradation of brain images, but head fixation or external-maker attachment become burdensome on patients. Therefore, we have developed a motion correction method that uses a 3D face-shape model generated by a range-sensing camera (Kinect) and by CT images. We have successfully corrected the PET images of a moving mannequin-head phantom containing radioactivity. Here, we conducted a volunteer study to verify the effectiveness of our method for clinical data. METHODS Eight healthy men volunteers aged 22-45 years underwent a 10-min head-fixed PET scan as a standard of truth in this study, which was started 45 min after 18F-fluorodeoxyglucose (285 ± 23 MBq) injection, and followed by a 15-min head-moving PET scan with the developed Kinect based motion-tracking system. First, selecting a motion-less period of the head-moving PET scan provided a reference PET image. Second, CT images separately obtained on the same day were registered to the reference PET image, and create a 3D face-shape model, then, to which Kinect-based 3D face-shape model matched. This matching parameter was used for spatial calibration between the Kinect and the PET system. This calibration parameter and the motion-tracking of the 3D face shape by Kinect comprised our motion correction method. The head-moving PET with motion correction was compared with the head-fixed PET images visually and by standard uptake value ratios (SUVRs) in the seven volume-of-interest regions. To confirm the spatial calibration accuracy, a test-retest experiment was performed by repeating the head-moving PET with motion correction twice where the volunteer's pose and the sensor's position were different. RESULTS No difference was identified visually and statistically in SUVRs between the head-moving PET images with motion correction and the head-fixed PET images. One of the small nuclei, the inferior colliculus, was identified in the head-fixed PET images and in the head-moving PET images with motion correction, but not in those without motion correction. In the test-retest experiment, the SUVRs were well correlated (determinant coefficient, r2 = 0.995). CONCLUSION Our motion correction method provided good accuracy for the volunteer data which suggested it is useable in clinical settings.
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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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Henry D, Fulton R, Maclaren J, Aksoy M, Bammer R, Kyme A. Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3063260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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13
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Aide N, Lasnon C, Desmonts C, Armstrong IS, Walker MD, McGowan DR. Advances in PET-CT technology: An update. Semin Nucl Med 2021; 52:286-301. [PMID: 34823841 DOI: 10.1053/j.semnuclmed.2021.10.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 11/11/2022]
Abstract
This article reviews the current evolution and future directions in PET-CT technology focusing on three areas: time of flight, image reconstruction, and data-driven gating. Image reconstruction is considered with advances in point spread function modelling, Bayesian penalised likelihood reconstruction, and artificial intelligence approaches. Data-driven gating is examined with reference to respiratory motion, cardiac motion, and head motion. For each of these technological advancements, theory will be briefly discussed, benefits of their use in routine practice will be detailed and potential future developments will be discussed. Representative clinical cases will be presented, demonstrating the huge opportunities given to the PET community by hardware and software advances in PET technology when it comes to lesion detection, disease characterization, accurate quantitation and quicker scans. Through this review, hospitals are encouraged to embrace, evaluate and appropriately implement the wide range of new PET technologies that are available now or in the near future, for the improvement of patient care.
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Affiliation(s)
- Nicolas Aide
- Nuclear Medicine, Caen University Hospital, Caen, France; INSERM ANTICIPE, Normandie University, Caen, France.
| | - Charline Lasnon
- INSERM ANTICIPE, Normandie University, Caen, France; François Baclesse Cancer Center, Caen, France
| | - Cedric Desmonts
- Nuclear Medicine, Caen University Hospital, Caen, France; INSERM ANTICIPE, Normandie University, Caen, France
| | - Ian S Armstrong
- Nuclear Medicine, Manchester University NHS Foundation Trust, Manchester
| | - Matthew D Walker
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford
| | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford; Department of Oncology, University of Oxford, Oxford
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14
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Rezaei A, Spangler-Bickell M, Schramm G, Van Laere K, Nuyts J, Defrise M. Rigid motion tracking using moments of inertia in TOF-PET brain studies. Phys Med Biol 2021; 66. [PMID: 34464941 DOI: 10.1088/1361-6560/ac2268] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 08/31/2021] [Indexed: 11/11/2022]
Abstract
A data-driven method is proposed for rigid motion estimation directly from time-of-flight (TOF)-positron emission tomography (PET) emission data. Rigid motion parameters (translations and rotations) are estimated from the first and second moments of the emission data masked in a spherical volume. The accuracy of the method is analyzed on 3D analytical simulations of the PET-SORTEO brain phantom, and subsequently tested on18F-FDG as well as11C-PIB brain datasets acquired on a TOF-PET/CT scanner. The estimated inertia-based motion is later compared to rigid motion parameters obtained by directly registering the short frame backprojections. We find that the method provides sub mm/degree accuracies for the estimated rigid motion parameters for counts corresponding to typical 0.5 s, 1 s, and 2 s18F-FDG brain scans, with the current TOF resolutions clinically available. The method provides robust motion estimation for different types of patient motion, most notably for a continuous patient motion case where conventional frame-based approaches which rely on little to no intra-frame motion of short time intervals could fail. The method relies on the detection of stable eigenvectors for accurate motion estimation, and a monitoring of this condition can reveal time-frames where the motion estimation is less accurate, such as in dynamic PET studies.
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Affiliation(s)
- Ahmadreza Rezaei
- KU Leuven-University of Leuven, Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging; Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium
| | | | - Georg Schramm
- KU Leuven-University of Leuven, Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging; Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium
| | - Koen Van Laere
- KU Leuven-University of Leuven, Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging; Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium
| | - Johan Nuyts
- KU Leuven-University of Leuven, Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging; Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium
| | - Michel Defrise
- Department of Nuclear Medicine, Vrije Universiteit Brussel, B-1090, Brussels, Belgium
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15
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Yedavalli V, DiGiacomo P, Tong E, Zeineh M. High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping. Magn Reson Imaging Clin N Am 2021; 29:13-39. [PMID: 33237013 DOI: 10.1016/j.mric.2020.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.
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Affiliation(s)
- Vivek Yedavalli
- Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA 94305-5105, USA; Division of Neuroradiology, Johns Hopkins University, 600 N. Wolfe St. B-112 D, Baltimore, MD 21287, USA
| | - Phillip DiGiacomo
- Department of Bioengineering, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA
| | - Elizabeth Tong
- Department of Radiology, 300 Pasteur Drive, Room S031, Stanford, CA 94305-5105, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA.
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16
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Kyme AZ, Fulton RR. Motion estimation and correction in SPECT, PET and CT. Phys Med Biol 2021; 66. [PMID: 34102630 DOI: 10.1088/1361-6560/ac093b] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 06/08/2021] [Indexed: 11/11/2022]
Abstract
Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.
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Affiliation(s)
- Andre Z Kyme
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Sydney School of Health Sciences, The University of Sydney, Sydney, New South Wales, AUSTRALIA
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17
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Madore B, Preiswerk F, Bredfeldt JS, Zong S, Cheng CC. Ultrasound-based sensors to monitor physiological motion. Med Phys 2021; 48:3614-3622. [PMID: 33999423 DOI: 10.1002/mp.14949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 12/28/2020] [Accepted: 05/01/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. METHODS By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. RESULTS Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. CONCLUSION The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.
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Affiliation(s)
- Bruno Madore
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Frank Preiswerk
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Amazon Robotics, North Reading, MA, USA
| | - Jeremy S Bredfeldt
- Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenyan Zong
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cheng-Chieh Cheng
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
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18
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Bradley KM, Deller TW, Spangler-Bickell MG, Jansen FP, McGowan DR. A solution to PET brain motion artefact. J Neurol 2021; 268:3476-3477. [PMID: 34091746 DOI: 10.1007/s00415-021-10632-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Kevin M Bradley
- Oxford University Hospitals NHS FT, Oxford, UK.,Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK. .,Department of Oncology, University of Oxford, Oxford, UK.
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19
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Spangler-Bickell MG, Hurley SA, Deller TW, Jansen F, Bettinardi V, Carlson M, Zeineh M, Zaharchuk G, McMillan AB. Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging. Med Phys 2021; 48:3031-3041. [PMID: 33880778 PMCID: PMC9261293 DOI: 10.1002/mp.14889] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/07/2021] [Accepted: 04/02/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time. METHODS Data from 18 F-fluorodeoxyglucose (FDG) and 18 F-florbetaben (FBB) tracer studies with varying count rates are analyzed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations interframe motion is simulated and image-based registrations are performed to estimate that motion. RESULTS For FDG and FBB tracers using 4 × 105 true and scattered coincidence events per frame ensures that 95% of the registrations will be accurate to within 1 mm of the ground truth. This corresponds to a frame duration of 0.5-1 sec for typical clinical PET activity levels. Using four MLEM iterations with no subsets, a transaxial pixel size of 4 mm, a post-smoothing filter with 4-6 mm full width at half maximum, and averaging two or more frames to create the reference image provides an optimal set of parameters to produce accurate registrations while keeping the reconstruction and processing time low. CONCLUSIONS It is shown that very short frames (≤1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.
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Affiliation(s)
- Matthew G Spangler-Bickell
- Department of Radiology, University of Wisconsin, Madison, WI, USA
- PET/MR Engineering, GE Healthcare, Waukesha, WI, USA
| | - Samuel A Hurley
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | | | - Floris Jansen
- PET/MR Engineering, GE Healthcare, Waukesha, WI, USA
| | | | | | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Alan B McMillan
- Department of Radiology, University of Wisconsin, Madison, WI, USA
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20
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Abstract
Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.
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Affiliation(s)
- Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, Leuven, Belgium; University of Stellenbosch, Department of Nuclear Medicine, Cape Town, South Africa.
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21
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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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22
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Simultaneous feedback control for joint field and motion correction in brain MRI. Neuroimage 2020; 226:117286. [PMID: 32992003 DOI: 10.1016/j.neuroimage.2020.117286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/21/2020] [Accepted: 08/14/2020] [Indexed: 11/23/2022] Open
Abstract
T2*-weighted gradient-echo sequences count among the most widely used techniques in neuroimaging and offer rich magnitude and phase contrast. The susceptibility effects underlying this contrast scale with B0, making T2*-weighted imaging particularly interesting at high field. High field also benefits baseline sensitivity and thus facilitates high-resolution studies. However, enhanced susceptibility effects and high target resolution come with inherent challenges. Relying on long echo times, T2*-weighted imaging not only benefits from enhanced local susceptibility effects but also suffers from increased field fluctuations due to moving body parts and breathing. High resolution, in turn, renders neuroimaging particularly vulnerable to motion of the head. This work reports the implementation and characterization of a system that aims to jointly address these issues. It is based on the simultaneous operation of two control loops, one for field stabilization and one for motion correction. The key challenge with this approach is that the two loops both operate on the magnetic field in the imaging volume and are thus prone to mutual interference and potential instability. This issue is addressed at the levels of sensing, timing, and control parameters. Performance assessment shows the resulting system to be stable and exhibit adequate loop decoupling, precision, and bandwidth. Simultaneous field and motion control is then demonstrated in examples of T2*-weighted in vivo imaging at 7T.
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23
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Carlson ML, DiGiacomo PS, Fan AP, Goubran M, Khalighi MM, Chao SZ, Vasanawala M, Wintermark M, Mormino E, Zaharchuk G, James ML, Zeineh MM. Simultaneous FDG-PET/MRI detects hippocampal subfield metabolic differences in AD/MCI. Sci Rep 2020; 10:12064. [PMID: 32694602 PMCID: PMC7374580 DOI: 10.1038/s41598-020-69065-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/29/2020] [Indexed: 12/25/2022] Open
Abstract
The medial temporal lobe is one of the most well-studied brain regions affected by Alzheimer’s disease (AD). Although the spread of neurofibrillary pathology in the hippocampus throughout the progression of AD has been thoroughly characterized and staged using histology and other imaging techniques, it has not been precisely quantified in vivo at the subfield level using simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI). Here, we investigate alterations in metabolism and volume using [18F]fluoro-deoxyglucose (FDG) and simultaneous time-of-flight (TOF) PET/MRI with hippocampal subfield analysis of AD, mild cognitive impairment (MCI), and healthy subjects. We found significant structural and metabolic changes within the hippocampus that can be sensitively assessed at the subfield level in a small cohort. While no significant differences were found between groups for whole hippocampal SUVr values (p = 0.166), we found a clear delineation in SUVr between groups in the dentate gyrus (p = 0.009). Subfield analysis may be more sensitive for detecting pathological changes using PET-MRI in AD compared to global hippocampal assessment.
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Affiliation(s)
| | | | - Audrey P Fan
- Department of Radiology, Stanford University, Stanford, USA.,Department of Biomedical Engineering, University of California, Davis, Davis, USA.,Department of Neurology, University of California, Davis, Davis, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, USA
| | | | - Steven Z Chao
- Department of Neurology, Stanford University, Stanford, USA
| | - Minal Vasanawala
- Department of Radiology, Stanford University, Stanford, USA.,Nuclear Medicine Service, VA Palo Alto Health Care System, Palo Alto, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, USA
| | | | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, USA
| | - Michelle L James
- Department of Radiology, Stanford University, Stanford, USA.,Department of Neurology, Stanford University, Stanford, USA
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24
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Spangler-Bickell MG, Deller TW, Bettinardi V, Jansen F. Ultra-Fast List-Mode Reconstruction of Short PET Frames and Example Applications. J Nucl Med 2020; 62:287-292. [PMID: 32646873 DOI: 10.2967/jnumed.120.245597] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022] Open
Abstract
Standard clinical reconstructions usually require several minutes to complete, and this time is mostly independent of the duration of the data being reconstructed. Applications such as data-driven motion estimation, which require many short frames over the duration of the scan, become unfeasible with such long reconstruction times. In this work, we present an infrastructure whereby ultra-fast list-mode reconstructions of very short frames (≤1 s) are performed. With this infrastructure, it is possible to have a dynamic series of frames that can be used for various applications, such as data-driven motion estimation, whole-body surveys, quick reconstructions of gated data to select the optimal gate for a given attenuation map, and, if the infrastructure runs simultaneously with the scan, real-time display of the reconstructed data during the scan and automated alerts for patient motion. Methods: A fast ray-tracing time-of-flight projector was implemented and parallelized. The reconstruction parameters were optimized to allow for fast performance: only a few iterations are performed, without point-spread-function modeling, and scatter correction is not used. The resulting reconstructions are thus not quantitative but are acceptable for motion estimation and visualization purposes. Data-driven motion can be estimated using image registration, with the resultant motion data being used in a fully motion-corrected list-mode reconstruction. Results: The infrastructure provided images that can be used for visualization and gating purposes and for motion estimation using image registration. Several case studies are presented, including data-driven motion estimation and correction for brain studies, abdominal studies in which respiratory and cardiac motion is visible, and a whole-body survey. Conclusion: The presented infrastructure provides the capability to quickly create a series of very short frames for PET data that can be used in a variety of applications.
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Affiliation(s)
| | | | | | - Floris Jansen
- PET/MR Engineering, GE Healthcare, Waukesha, Wisconsin; and
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25
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Kyme AZ, Aksoy M, Henry DL, Bammer R, Maclaren J. Marker‐free optical stereo motion tracking for in‐bore MRI and PET‐MRI application. Med Phys 2020; 47:3321-3331. [DOI: 10.1002/mp.14199] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 04/12/2020] [Accepted: 04/15/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Andre Z. Kyme
- School of Biomedical Engineering Faculty of Engineering and Computer Science University of Sydney Sydney Australia
- The Brain & Mind Centre University of Sydney Sydney Australia
| | - Murat Aksoy
- Department of Radiology Stanford University USA
| | - David L. Henry
- The Brain & Mind Centre University of Sydney Sydney Australia
- Faculty of Health Sciences University of Sydney Sydney Australia
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DiGiacomo P, Maclaren J, Aksoy M, Tong E, Carlson M, Lanzman B, Hashmi S, Watkins R, Rosenberg J, Burns B, Skloss TW, Rettmann D, Rutt B, Bammer R, Zeineh M. A within-coil optical prospective motion-correction system for brain imaging at 7T. Magn Reson Med 2020; 84:1661-1671. [PMID: 32077521 DOI: 10.1002/mrm.28211] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 01/18/2020] [Accepted: 01/21/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE Motion artifact limits the clinical translation of high-field MR. We present an optical prospective motion correction system for 7 Tesla MRI using a custom-built, within-coil camera to track an optical marker mounted on a subject. METHODS The camera was constructed to fit between the transmit-receive coils with direct line of sight to a forehead-mounted marker, improving upon prior mouthpiece work at 7 Tesla MRI. We validated the system by acquiring a 3D-IR-FSPGR on a phantom with deliberate motion applied. The same 3D-IR-FSPGR and a 2D gradient echo were then acquired on 7 volunteers, with/without deliberate motion and with/without motion correction. Three neuroradiologists blindly assessed image quality. In 1 subject, an ultrahigh-resolution 2D gradient echo with 4 averages was acquired with motion correction. Four single-average acquisitions were then acquired serially, with the subject allowed to move between acquisitions. A fifth single-average 2D gradient echo was acquired following subject removal and reentry. RESULTS In both the phantom and human subjects, deliberate and involuntary motion were well corrected. Despite marked levels of motion, high-quality images were produced without spurious artifacts. The quantitative ratings confirmed significant improvements in image quality in the absence and presence of deliberate motion across both acquisitions (P < .001). The system enabled ultrahigh-resolution visualization of the hippocampus during a long scan and robust alignment of serially acquired scans with interspersed movement. CONCLUSION We demonstrate the use of a within-coil camera to perform optical prospective motion correction and ultrahigh-resolution imaging at 7 Tesla MRI. The setup does not require a mouthpiece, which could improve accessibility of motion correction during 7 Tesla MRI exams.
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Affiliation(s)
- Phillip DiGiacomo
- Department of Bioengineering, Stanford University, Stanford, California
| | - Julian Maclaren
- Department of Radiology, Stanford University, Stanford, California
| | - Murat Aksoy
- Department of Radiology, Stanford University, Stanford, California
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, California
| | - Mackenzie Carlson
- Department of Bioengineering, Stanford University, Stanford, California
| | - Bryan Lanzman
- Department of Radiology, Stanford University, Stanford, California
| | - Syed Hashmi
- Department of Radiology, Stanford University, Stanford, California
| | - Ronald Watkins
- Department of Radiology, Stanford University, Stanford, California
| | | | - Brian Burns
- Applied Sciences Lab West, GE Healthcare, Menlo Park, California
| | | | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, Minnesota
| | - Brian Rutt
- Department of Bioengineering, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
| | - Roland Bammer
- Department of Radiology, University of Melbourne, Melbourne, Australia
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California
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