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Xie T, Cui ZX, Luo C, Wang H, Liu C, Zhang Y, Wang X, Zhu Y, Chen G, Liang D, Jin Q, Zhou Y, Wang H. Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction. Phys Med Biol 2024; 69:155019. [PMID: 38981592 DOI: 10.1088/1361-6560/ad6117] [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: 03/14/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.
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Affiliation(s)
- Taofeng Xie
- School of Mathematical Sciences, Inner Mongolia University, Hohhot, People's Republic of China
- College of Computer and Information, Inner Mongolia Medical University, Hohhot, People's Republic of China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, People's Republic of China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Chen Luo
- School of Mathematical Sciences, Inner Mongolia University, Hohhot, People's Republic of China
| | - Huayu Wang
- School of Mathematical Sciences, Inner Mongolia University, Hohhot, People's Republic of China
| | - Congcong Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, People's Republic of China
| | - Yuanzhi Zhang
- Inner Mongolia Medical University Affiliated Hospital, Hohhot, People's Republic of China
| | - Xuemei Wang
- Inner Mongolia Medical University Affiliated Hospital, Hohhot, People's Republic of China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, People's Republic of China
| | - Guoqing Chen
- School of Mathematical Sciences, Inner Mongolia University, Hohhot, People's Republic of China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, People's Republic of China
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Qiyu Jin
- School of Mathematical Sciences, Inner Mongolia University, Hohhot, People's Republic of China
| | - Yihang Zhou
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, People's Republic of China
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Li W, Zhang M, Huang R, Hu J, Wang L, Ye G, Meng H, Lin X, Liu J, Li B, Zhang Y, Li Y. Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study. Hum Brain Mapp 2024; 45:e26604. [PMID: 38339890 DOI: 10.1002/hbm.26604] [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: 09/24/2023] [Revised: 12/20/2023] [Accepted: 01/10/2024] [Indexed: 02/12/2024] Open
Abstract
Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.
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Affiliation(s)
- Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruodong Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jialin Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lijun Wang
- Department of Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guanyu Ye
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 2024; 92:103046. [PMID: 38052145 DOI: 10.1016/j.media.2023.103046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Affiliation(s)
- Sanuwani Dayarathna
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
| | | | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom
| | - Munawar Hayat
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Zhaolin Chen
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia
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4
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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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5
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Strobel J, Müller HP, Ludolph AC, Beer AJ, Sollmann N, Kassubek J. New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review. Cells 2023; 12:2776. [PMID: 38132096 PMCID: PMC10742083 DOI: 10.3390/cells12242776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.
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Affiliation(s)
- Joachim Strobel
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Hans-Peter Müller
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
| | - Albert C. Ludolph
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
| | - Ambros J. Beer
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany;
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan Kassubek
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
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6
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Islam KT, Zhong S, Zakavi P, Chen Z, Kavnoudias H, Farquharson S, Durbridge G, Barth M, McMahon KL, Parizel PM, Dwyer A, Egan GF, Law M, Chen Z. Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images. Sci Rep 2023; 13:21183. [PMID: 38040835 PMCID: PMC10692211 DOI: 10.1038/s41598-023-48438-1] [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: 08/22/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023] Open
Abstract
Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.
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Affiliation(s)
- Kh Tohidul Islam
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Australian National Imaging Facility, Brisbane, QLD, Australia
| | - Parisa Zakavi
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Zhifeng Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Helen Kavnoudias
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia
| | | | - Gail Durbridge
- Herston Imaging Research Facility, University of Queensland, Brisbane, QLD, Australia
| | - Markus Barth
- School of Information Technology and Electrical Engineering and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Katie L McMahon
- School of Clinical Science, Herston Imaging Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
| | - Paul M Parizel
- David Hartley Chair of Radiology, Department of Radiology, Royal Perth Hospital, Perth, WA, Australia
- Medical School, University of Western Australia, Perth, WA, Australia
| | - Andrew Dwyer
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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7
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Kas A, Rozenblum L, Pyatigorskaya N. Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging. Magn Reson Imaging Clin N Am 2023; 31:591-604. [PMID: 37741643 DOI: 10.1016/j.mric.2023.06.004] [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] [Indexed: 09/25/2023]
Abstract
Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.
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Affiliation(s)
- Aurélie Kas
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France.
| | - Laura Rozenblum
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France
| | - Nadya Pyatigorskaya
- Neuroradiology Department, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, UMR S 1127, CNRS UMR 722, Institut du Cerveau, Paris, France
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8
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Shan Y, Yan SZ, Wang Z, Cui BX, Yang HW, Yuan JM, Yin YY, Shi F, Lu J. Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis. EJNMMI Res 2023; 13:79. [PMID: 37668814 PMCID: PMC10480127 DOI: 10.1186/s13550-023-01028-8] [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: 06/14/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis. RESULTS Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject. CONCLUSIONS Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.
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Affiliation(s)
- Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Shao-Zhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Bi-Xiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Hong-Wei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Jian-Min Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Ya-Yan Yin
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China.
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9
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Adams NE, Jafarian A, Perry A, Rouse MA, Shaw AD, Murley AG, Cope TE, Bevan-Jones WR, Passamonti L, Street D, Holland N, Nesbitt D, Hughes LE, Friston KJ, Rowe JB. Neurophysiological consequences of synapse loss in progressive supranuclear palsy. Brain 2023; 146:2584-2594. [PMID: 36514918 PMCID: PMC10232290 DOI: 10.1093/brain/awac471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.
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Affiliation(s)
- Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Alistair Perry
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Alexander D Shaw
- Washington Singer Laboratories, University of Exeter, Exeter EX4 4QG, UK
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Negin Holland
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - David Nesbitt
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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10
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Cruz N, Herculano-Carvalho M, Roque D, Faria CC, Cascão R, Ferreira HA, Reis CP, Matela N. Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma. Pharmaceutics 2023; 15:pharmaceutics15030928. [PMID: 36986790 PMCID: PMC10054750 DOI: 10.3390/pharmaceutics15030928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/25/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023] Open
Abstract
Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease. Standard of care in newly diagnosed glioblastoma is maximal tumour resection followed by initial concomitant radiotherapy and temozolomide (TMZ) and then further chemotherapy with TMZ. Imaging techniques are key not only to diagnose the extent of the affected tissue but also for surgery planning and even for intraoperative use. Eligible patients may combine TMZ with tumour treating fields (TTF) therapy, which delivers low-intensity and intermediate-frequency electric fields to arrest tumour growth. Nonetheless, the blood–brain barrier (BBB) and systemic side effects are obstacles to successful chemotherapy in GBM; thus, more targeted, custom therapies such as immunotherapy and nanotechnological drug delivery systems have been undergoing research with varying degrees of success. This review proposes an overview of the pathophysiology, possible treatments, and the most (not all) representative examples of the latest advancements.
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Affiliation(s)
- Nuno Cruz
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- iMED.ULisboa, Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
| | - Manuel Herculano-Carvalho
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Diogo Roque
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Cláudia C. Faria
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Rita Cascão
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
| | - Hugo Alexandre Ferreira
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Catarina Pinto Reis
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- iMED.ULisboa, Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
- Correspondence: (C.P.R.); (N.M.); Tel.: +351-217-946-400 (ext. 14244) (C.P.R.); Fax: +351-217-946-470 (C.P.R.)
| | - Nuno Matela
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Correspondence: (C.P.R.); (N.M.); Tel.: +351-217-946-400 (ext. 14244) (C.P.R.); Fax: +351-217-946-470 (C.P.R.)
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11
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Fang L, Huang H, Quirk JD, Zheng J, Shen D, Manion B, Mixdorf M, Karmakar P, Sudlow GP, Tang R, Achilefu S. Analysis of Stable Chelate-free Gadolinium Loaded Titanium Dioxide Nanoparticles for MRI-Guided Radionuclide Stimulated Cancer Treatment. CURR ANAL CHEM 2022; 18:826-835. [PMID: 36561765 PMCID: PMC9770661 DOI: 10.2174/1573411018666220321102736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Recent studies demonstrate that titanium dioxide nanoparticles (TiO2 NPs) are an effective source of reactive oxygen species (ROS) for photodynamic therapy and radionuclide stimulated dynamic therapy (RaST). Unfortunately tracking the in vivo distribution of TiO2 NPs noninvasively remains elusive. Objective Given the use of gadolinium (Gd) chelates as effective contrast agents for magnetic resonance imaging (MRI), this study aims to (1) develop hybrid TiO2-Gd NPs that exhibit high relaxivity for tracking the NPs without loss of ROS generating capacity; and (2) establish a simple colorimetric assay for quantifying Gd loading and stability. Methods A chelate-free, heat-induced method was used to load Gd onto TiO2 NPs, which was coated with transferrin (Tf). A sensitive colorimetric assay and inductively coupled plasma mass spectrometry (ICP-MS) were used to determine Gd loading and stability of the TiO2-Gd-Tf NPs. Measurement of the relaxivity was performed on a 1.4 T relaxometer and a 4.7 T small animal magnetic resonance scanner to estimate the effects of magnetic field strength. ROS was quantified by activated dichlorodihydrofluorescein diacetate fluorescence. Cell uptake of the NPs and RaST were monitored by fluorescence microscopy. Both 3 T and 4.7 T scanners were used to image the in vivo distribution of intravenously injected NPs in tumor-bearing mice. Results A simple colorimetric assay accurately determined both the loading and stability of the NPs compared with the expensive and complex ICP-MS method. Coating of the TiO2-Gd NPs with Tf stabilized the nanoconstruct and minimized aggregation. The TiO2-Gd-Tf maintained ROS-generating capability without inducing cell death at a wide range of concentrations but induced significant cell death under RaST conditions in the presence of F-18 radiolabeled 2-fluorodeoxyglucose. The longitudinal (r1 = 10.43 mM-1s-1) and transverse (r2 = 13.43 mM-1s-1) relaxivity of TiO2-Gd-Tf NPs were about twice and thrice, respectively, those of clinically used Gd contrast agent (Gd-DTPA; r1 = 3.77 mM-1s-1 and r2 = 5.51 mM-1s-1) at 1.4 T. While the r1 (8.13 mM-1s-1) reduced to about twice that of Gd-DTPA (4.89 mM-1s-1) at 4.7 T, the corresponding r2 (87.15 mM-1s-1) increased by a factor 22.6 compared to Gd-DTPA (r2 = 3.85). MRI of tumor-bearing mice injected with TiO2-Gd-Tf NPs tracked the NPs distribution and accumulation in tumors. Conclusion This work demonstrates that Arsenazo III colorimetric assay can substitute ICP-MS for determining the loading and stability of Gd-doped TiO2 NPs. The new nanoconstruct enabled RaST effect in cells, exhibited high relaxivity, and enhanced MRI contrast in tumors in vivo, paving the way for in vivo MRI-guided RaST.
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Affiliation(s)
- Lei Fang
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States,Department of Biomedical Engineering, School of Engineering, Washington University in St. Louis, Saint Louis, United States
| | - Hengbo Huang
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States,Department of Biomedical Engineering, School of Engineering, Washington University in St. Louis, Saint Louis, United States
| | - James D. Quirk
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Jie Zheng
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Duanwen Shen
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Brad Manion
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Matthew Mixdorf
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Partha Karmakar
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Gail P. Sudlow
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Rui Tang
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States
| | - Samuel Achilefu
- School of Medicine, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, United States,Department of Biomedical Engineering, School of Engineering, Washington University in St. Louis, Saint Louis, United States,Department of Biochemistry and Molecular Biophysics, School of Medicine, Washington University in St. Louis, Saint Louis, United States,Department of Biomedical Engineering, University of Texas Southwestern, Dallas, United States,Address correspondence to this author at the Department of Biomedical Engineering, School of Engineering, Washington University in St. Louis, Saint Louis, United States;
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12
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Sun Z, Meikle S, Calamante F. CONN-NLM: A Novel CONNectome-Based Non-local Means Filter for PET-MRI Denoising. Front Neurosci 2022; 16:824431. [PMID: 35712456 PMCID: PMC9197079 DOI: 10.3389/fnins.2022.824431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background Advancements in hybrid positron emission tomography-magnetic resonance (PET-MR) systems allow for combining the advantages of each modality. Integrating information from MRI and PET can be valuable for diagnosing and treating neurological disorders. However, combining diffusion MRI (dMRI) and PET data, which provide highly complementary information, has rarely been exploited in image post-processing. dMRI has the ability to investigate the white matter pathways of the brain through fibre tractography, which enables comprehensive mapping of the brain connection networks (the "connectome"). Novel methods are required to combine information present in the connectome and PET to increase the full potential of PET-MRI. Methods We developed a CONNectome-based Non-Local Means (CONN-NLM) filter to exploit synergies between dMRI-derived structural connectivity and PET intensity information to denoise PET images. PET-MR data are parcelled into a number of regions based on a brain atlas, and the inter-regional structural connectivity is calculated based on dMRI fibre-tracking. The CONN-NLM filter is then implemented as a post-reconstruction filter by combining the nonlocal means filter and a connectivity-based cortical smoothing. The effect of this approach is to weight voxels with similar PET intensity and highly connected voxels higher when computing the weighted-average to perform more informative denoising. The proposed method was first evaluated using a novel computer phantom framework to simulate realistic hybrid PET-MR images with different lesion scenarios. CONN-NLM was further assessed with clinical dMRI and tau PET examples. Results The results showed that CONN-NLM has the capacity to improve the overall PET image quality by reducing noise while preserving lesion contrasts, and it outperformed a range of filters that did not use dMRI information. The simulations demonstrate that CONN-NLM can handle various lesion contrasts consistently, as well as lesions with different levels of inter-connectivity. Conclusion CONN-NLM has unique advantages of providing more informative and accurate PET smoothing by adding complementary structural connectivity information from dMRI, representing a new avenue to exploit synergies between MRI and PET.
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Affiliation(s)
- Zhuopin Sun
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Steven Meikle
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.,Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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13
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Zhang M, Guan Z, Zhang Y, Sun W, Li W, Hu J, Li B, Ye G, Meng H, Huang X, Lin X, Wang J, Liu J, Li B, Li Y. Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study. Neuroimage Clin 2022; 34:102977. [PMID: 35259618 PMCID: PMC8904621 DOI: 10.1016/j.nicl.2022.102977] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 12/21/2022]
Abstract
Hybrid PET/MRI was used to explore network segregation and glucose metabolism in AD. DMN, CEN, and SN showed reduced segregation in AD. In salience network, segregation coupled with glucose metabolism in CN group. The coupled segregation and glucose metabolism in CN disappeared in MCI and AD. Reduced segregation and hypometabolism were associated with cognitive impairments.
The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.
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Affiliation(s)
- Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ziyun Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wanqing Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jialin Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Binyin Li
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guanyu Ye
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jin Wang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai 200025, China.
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
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14
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Jamadar SD, Liang EX, Zhong S, Ward PGD, Carey A, McIntyre R, Chen Z, Egan GF. Monash DaCRA fPET-fMRI: A dataset for comparison of radiotracer administration for high temporal resolution functional FDG-PET. Gigascience 2022; 11:6576243. [PMID: 35488859 PMCID: PMC9055854 DOI: 10.1093/gigascience/giac031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/31/2022] [Accepted: 03/06/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND "Functional" [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level-dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the 2 primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. FINDINGS In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts 3 radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n = 5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The bolus group received the full FDG dose in a standard bolus administration, the infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the bolus-infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. CONCLUSIONS The Monash DaCRA fPET-fMRI dataset provides significant reuse value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
| | - Emma X Liang
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,National Imaging Facility, 4072, Australia
| | - Phillip G D Ward
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
| | - Alexandra Carey
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Department of Medical Imaging, Monash Health, VIC 3800, Australia
| | - Richard McIntyre
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Department of Medical Imaging, Monash Health, VIC 3800, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Monash Data Futures Institute, Monash University , Melbourne, VIC 3800, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
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15
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Voigt K, Liang EX, Misic B, Ward PGD, Egan GF, Jamadar SD. Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships. Cereb Cortex 2022; 33:1476-1488. [PMID: 35441214 PMCID: PMC9930619 DOI: 10.1093/cercor/bhac150] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterization of the human brain. How these 2 modalities differ in their contribution to cognition and behavior is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that although for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, whereas for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.
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Affiliation(s)
- Katharina Voigt
- Corresponding author: Turner Institute for Brain and Mental Health, Monash Biomedical Imaging, 770 Blackburn Road, Clayton, VIC 3800, Australia.
| | - Emma X Liang
- Monash Biomedical Imaging, Monash University, 770 Blackburn Road, 3800 Clayton VIC, Australia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, 3801 University Street Montréal, Quebec H3A 2B4, Canada
| | - Phillip G D Ward
- Monash Biomedical Imaging, Monash University, 770 Blackburn Road, 3800 Clayton VIC, Australia
| | - Gary F Egan
- School of Psychological Sciences Turner and Turner Institute for Brain and Mental Health, Monash University, 18 Innovation Walk, 3800 Clayton VIC, Australia,Monash Biomedical Imaging, Monash University, 770 Blackburn Road, 3800 Clayton VIC, Australia
| | - Sharna D Jamadar
- School of Psychological Sciences Turner and Turner Institute for Brain and Mental Health, Monash University, 18 Innovation Walk, 3800 Clayton VIC, Australia,Monash Biomedical Imaging, Monash University, 770 Blackburn Road, 3800 Clayton VIC, Australia
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16
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Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR. Phys Med 2022; 97:36-43. [PMID: 35339864 DOI: 10.1016/j.ejmp.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 02/18/2022] [Accepted: 03/14/2022] [Indexed: 01/06/2023] Open
Abstract
In positron emission tomography (PET), 68Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple 68Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered 68Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [11C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every 68Ge-AC map, and averaging the resulting 68Ge-AC maps. [11C]PE2I PET images were reconstructed using CTR-AC and a patient-specific 68Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R1) and non-displaceable binding potential (BPND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R1 (-0.1 ± 3.2%), and putamen BPND (3.7 ± 8.1%). SUV and R1 precision (SD of %bias) were modest and lowest in the anterior cortex, with an R1 %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.
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17
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Moran C, Than S, Callisaya M, Beare R, Srikanth V. New Horizons-Cognitive Dysfunction Associated With Type 2 Diabetes. J Clin Endocrinol Metab 2022; 107:929-942. [PMID: 34788847 DOI: 10.1210/clinem/dgab797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Indexed: 11/19/2022]
Abstract
The prevalence of type 2 diabetes (T2D) and cognitive dysfunction increases with age. As society ages, clinicians will be increasingly tasked with managing older people who have both T2D and cognitive dysfunction. T2D is associated with an increased risk of cognitive dysfunction and hence there is increasing interest in whether T2D is a causal factor in the pathogenesis of cognitive decline and dementia. Recent advances in the use of sensitive measures of in vivo brain dysfunction in life-course studies can help understand potential mechanistic pathways and also help guide recommendations for clinical practice. In this article we will describe new horizons in the understanding of cognitive dysfunction associated with T2D. Coming from a clinical perspective, we discuss potential mechanisms and pathways linking the 2 conditions and the contribution of multimodal neuroimaging and study designs to advancing understanding in the field. We also highlight the important issues on the horizon that will need addressing in clinical identification, management, and risk reduction for people with coexistent T2D and cognitive dysfunction.
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Affiliation(s)
- Chris Moran
- Academic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, 3199 Victoria, Australia
- Department of Geriatric Medicine, Peninsula Health, Melbourne, 3199 Victoria, Australia
- Department of Geriatric Medicine, Alfred Health, Melbourne, 3004 Victoria, Australia
| | - Stephanie Than
- Academic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, 3199 Victoria, Australia
- Department of Geriatric Medicine, Peninsula Health, Melbourne, 3199 Victoria, Australia
| | - Michele Callisaya
- Academic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, 3199 Victoria, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, 7000 Tasmania, Australia
| | - Richard Beare
- Academic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, 3199 Victoria, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, 3052 Victoria, Australia
| | - Velandai Srikanth
- Academic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, 3199 Victoria, Australia
- Department of Geriatric Medicine, Peninsula Health, Melbourne, 3199 Victoria, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, 7000 Tasmania, Australia
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18
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Pain CD, Egan GF, Chen Z. Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement. Eur J Nucl Med Mol Imaging 2022; 49:3098-3118. [PMID: 35312031 PMCID: PMC9250483 DOI: 10.1007/s00259-022-05746-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/25/2022] [Indexed: 12/21/2022]
Abstract
Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.
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Affiliation(s)
- Cameron Dennis Pain
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia.
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Department of Data Science and AI, Monash University, Melbourne, Australia
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19
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Shan Y, Wang Z, Song S, Xue Q, Ge Q, Yang H, Cui B, Zhang M, Zhou Y, Lu J. Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted. Front Neurosci 2022; 16:824152. [PMID: 35310105 PMCID: PMC8926297 DOI: 10.3389/fnins.2022.824152] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/24/2022] [Indexed: 12/04/2022] Open
Abstract
Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and 18F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.
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Affiliation(s)
- Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Shuangshuang Song
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Qiaoyi Xue
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Qi Ge
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 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, 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, Beijing, China
| | - Miao Zhang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 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, Beijing, China
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20
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Jamadar SD, Zhong S, Carey A, McIntyre R, Ward PGD, Fornito A, Premaratne M, Jon Shah N, O'Brien K, Stäb D, Chen Z, Egan GF. Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex. Sci Data 2021; 8:267. [PMID: 34654823 PMCID: PMC8520012 DOI: 10.1038/s41597-021-01042-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/12/2021] [Indexed: 01/21/2023] Open
Abstract
Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18-49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia. .,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Australia. .,Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia.
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,National Imaging Facility, Clayton, Australia
| | - Alexandra Carey
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,Department of Medical Imaging, Monash Health, Clayton, VIC, Australia
| | - Richard McIntyre
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,Department of Medical Imaging, Monash Health, Clayton, VIC, Australia
| | - Phillip G D Ward
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Australia.,Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
| | - Alex Fornito
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Australia.,Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
| | - Malin Premaratne
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, Australia
| | - N Jon Shah
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich, Jülich, Germany
| | - Kieran O'Brien
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Clayton, Australia
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Clayton, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,Monash Data Futures Institute, Monash University, Clayton, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Australia.,Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
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21
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Tournier N, Comtat C, Lebon V, Gennisson JL. Challenges and Perspectives of the Hybridization of PET with Functional MRI or Ultrasound for Neuroimaging. Neuroscience 2021; 474:80-93. [DOI: 10.1016/j.neuroscience.2020.10.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/08/2023]
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22
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Brown R, Kolbitsch C, Delplancke C, Papoutsellis E, Mayer J, Ovtchinnikov E, Pasca E, Neji R, da Costa-Luis C, Gillman AG, Ehrhardt MJ, McClelland JR, Eiben B, Thielemans K. Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200208. [PMID: 34218674 DOI: 10.1098/rsta.2020.0208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 05/10/2023]
Abstract
SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Richard Brown
- Institute of Nuclear Medicine, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Christoph Kolbitsch
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | | | - Evangelos Papoutsellis
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - Johannes Mayer
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Evgueni Ovtchinnikov
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Edoardo Pasca
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Casper da Costa-Luis
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Bjoern Eiben
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
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23
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Sudarshan VP, Upadhyay U, Egan GF, Chen Z, Awate SP. Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data. Med Image Anal 2021; 73:102187. [PMID: 34348196 DOI: 10.1016/j.media.2021.102187] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 07/12/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively.
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Affiliation(s)
- Viswanath P Sudarshan
- Computer Science and Engineering (CSE) Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India; IITB-Monash Research Academy, Indian Institute of Technology (IIT) Bombay, Mumbai, India
| | - Uddeshya Upadhyay
- Computer Science and Engineering (CSE) Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India
| | - Gary F Egan
- Monash Biomedical Imaging (MBI), Monash University, Melbourne, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging (MBI), Monash University, Melbourne, Australia
| | - Suyash P Awate
- Computer Science and Engineering (CSE) Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India.
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24
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Application of PET Imaging in the Brain Regions of the Emotional Control Loop in Patients with Generalized Anxiety Disorder. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4505227. [PMID: 34336151 PMCID: PMC8321712 DOI: 10.1155/2021/4505227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/03/2021] [Accepted: 07/14/2021] [Indexed: 11/19/2022]
Abstract
Objective This study uses PET imaging to observe the uptake and metabolism of 18F-fluorodeoxyglucose (18F-FDG) in the multibrain areas of the emotional control loop in patients with generalized anxiety disorder (GAD) and investigate the brain of GAD patient's functional abnormality mechanism. Methods The thesis clinically collected 20 GAD patients and 20 healthy subjects. Dynamic PET-CT scans were used. At the same time, 18F-FDG whole-brain uptake and metabolism data were collected. Image fusion and semiquantitative analysis were used to measure emotional control loops. The maximum standard uptake value (SUVmax) and dynamic uptake and metabolic changes of 11 time points in the brain area at 150 min were measured. Results Compared with the healthy control group, the peak uptake of the bilateral prefrontal cortex and the average uptake rate before the peak in GAD patients were significantly reduced (P < 0.05), and the average metabolic rate after the peak was significantly increased (P < 0.05). The peak uptake of the left striatum and the left hippocampus, the average uptake rate before the peak, and the average metabolic rate after the peak were all significantly reduced (P < 0.05); There were no obvious changes in the three indexes of the right striatum and the right hippocampus. Conclusion There are 18F-FDG uptake and metabolic disorders in multiple brain areas of the affective control loop of GAD patients. The abnormal peak and rate of uptake may be related to the pathogenesis of GAD.
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25
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Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study. Cereb Cortex 2021; 31:2855-2867. [PMID: 33529320 DOI: 10.1093/cercor/bhaa393] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/26/2020] [Accepted: 11/30/2020] [Indexed: 12/12/2022] Open
Abstract
Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Phillip G D Ward
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Emma X Liang
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia
| | - Edwina R Orchard
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Vic, 3800 Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Melbourne, Vic, 3800 Australia.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800 Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
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26
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Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET. Neuroimage 2021; 233:117928. [PMID: 33716154 DOI: 10.1016/j.neuroimage.2021.117928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 02/08/2021] [Accepted: 02/28/2021] [Indexed: 02/07/2023] Open
Abstract
Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.
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27
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Bhattarai A, Egan GF, Talman P, Chua P, Chen Z. Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis. J Magn Reson Imaging 2021; 55:1283-1300. [PMID: 33586315 DOI: 10.1002/jmri.27530] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 01/18/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Anjan Bhattarai
- Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia.,Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Paul Talman
- Department of Neuroscience, Barwon Health, Geelong, Victoria, Australia
| | - Phyllis Chua
- Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia.,Statewide Progressive Neurological Services, Calvary Health Care Bethlehem, Melbourne, Victoria, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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28
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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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29
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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30
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Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method. Neuroimage 2020; 226:117603. [PMID: 33271271 DOI: 10.1016/j.neuroimage.2020.117603] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/11/2020] [Accepted: 11/24/2020] [Indexed: 12/14/2022] Open
Abstract
Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.
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31
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Clinical utility of deep learning motion correction for T1 weighted MPRAGE MR images. Eur J Radiol 2020; 133:109384. [PMID: 33186856 DOI: 10.1016/j.ejrad.2020.109384] [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: 08/13/2020] [Revised: 10/12/2020] [Accepted: 10/23/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE To evaluate the clinical utility of the application of a deep learning motion correction technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice. METHODS An encoder-decoder deep learning network inspired by InceptionResnet was trained on public datasets. The clinical utility of the trained network was evaluated retrospectively on 27 3D MPRAGE T1 weighted motion degraded MR images identified by radiologists during reporting. The assessment of image quality was performed by one board-certified radiologist and one senior radiology trainee for nine neuroanatomical regions of the brain using a five-point visual grading scale. RESULTS The deep learning motion correction technique resulted in reduced ghosting, ringing and blurring for all the brain regions investigated. The larger regions of interest such as ventricles improved the least (1.81 to 1.16, p-value: < 0.0001) while the smaller but complex regions such as the hippocampus improved most (3.0 to 1.67, p-value: < 0.0001). The Wilcox rank tests of image quality differences for the nine neuroanatomical regions were all statistically significant (p < 0.001). Overall, 60 % of the neuroanatomical regions were improved, 39 % were unchanged and 1 % were degraded. Out of the unchanged cases, 28 % were already scored at the highest image quality before motion correction. It was found that approximately 13 % of repeated scans could be avoided using the DL motion correction approach. CONCLUSION The deep learning motion correction technique improved the overall visual perception of the 3D T1 weighted MPRAGE brain images. This would improve the clinical utility of otherwise motion degraded images and allow visualisation of normal anatomy and even subtle pathology.
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32
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Jamadar SD, Ward PGD, Close TG, Fornito A, Premaratne M, O'Brien K, Stäb D, Chen Z, Shah NJ, Egan GF. Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. Sci Data 2020; 7:363. [PMID: 33087725 PMCID: PMC7578808 DOI: 10.1038/s41597-020-00699-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/11/2020] [Indexed: 12/13/2022] Open
Abstract
Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
- Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia.
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.
| | - Phillip G D Ward
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia
| | - Thomas G Close
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Australian National Imaging Facility, Brisbane, QLD, Australia
| | - Alex Fornito
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Malin Premaratne
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Kieran O'Brien
- Siemens Healthineers, Siemens Healthcare Pty Ltd, Bayswater, VIC, 3153, Australia
| | - Daniel Stäb
- Siemens Healthineers, Siemens Healthcare Pty Ltd, Bayswater, VIC, 3153, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - N Jon Shah
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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33
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Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation. J Digit Imaging 2020; 33:1224-1241. [PMID: 32607906 PMCID: PMC7573060 DOI: 10.1007/s10278-020-00361-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
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Affiliation(s)
- Imene Mecheter
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar.
| | - Lejla Alic
- Magnetic Detection and Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
| | - Maysam Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX, USA
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34
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Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-55190-2_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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35
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Abstract
PURPOSE OF REVIEW Hybrid PET- MRI is a technique that has the ability to improve diagnostic accuracy in many applications, whereas PET and MRI performed separately often fail to provide accurate responses to clinical questions. Here, we review recent studies and current developments in PET-MRI, focusing on clinical applications. RECENT FINDINGS The combination of PET and MRI imaging methods aims at increasing the potential of each individual modality. Combined methods of image reconstruction and correction of PET-MRI attenuation are being developed, and a number of applications are being introduced into clinical practice. To date, the value of PET-MRI has been demonstrated for the evaluation of brain tumours in epilepsy and neurodegenerative diseases. Continued advances in data analysis regularly improve the efficiency and the potential application of multimodal biomarkers. SUMMARY PET-MRI provides simultaneous of anatomical, functional, biochemical and metabolic information for the personalized characterization and monitoring of neurological diseases. In this review, we show the advantage of the complementarity of different biomarkers obtained using PET-MRI data. We also present the recent advances made in this hybrid imaging modality and its advantages in clinical practice compared with MRI and PET separately.
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36
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Li S, Jamadar SD, Ward PG, Premaratne M, Egan GF, Chen Z. Analysis of continuous infusion functional PET (fPET) in the human brain. Neuroimage 2020; 213:116720. [DOI: 10.1016/j.neuroimage.2020.116720] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/03/2020] [Accepted: 03/06/2020] [Indexed: 12/16/2022] Open
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37
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Pozaruk A, Pawar K, Li S, Carey A, Cheng J, Sudarshan VP, Cholewa M, Grummet J, Chen Z, Egan G. Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging. Eur J Nucl Med Mol Imaging 2020; 48:9-20. [DOI: 10.1007/s00259-020-04816-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 04/06/2020] [Indexed: 12/13/2022]
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38
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Sudarshan VP, Egan GF, Chen Z, Awate SP. Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior. Med Image Anal 2020; 62:101669. [DOI: 10.1016/j.media.2020.101669] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 02/20/2020] [Accepted: 02/21/2020] [Indexed: 12/18/2022]
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39
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Harisinghani MG, O’Shea A, Weissleder R. Advances in clinical MRI technology. Sci Transl Med 2019; 11:11/523/eaba2591. [DOI: 10.1126/scitranslmed.aba2591] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/03/2019] [Indexed: 11/02/2022]
Abstract
Advances in MRI technologies have the potential to detect, characterize, and monitor a wide variety of diseases.
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Affiliation(s)
- Mukesh G. Harisinghani
- Department of Radiology, Massachusetts General Hospital, Fruit Street, Boston, MA 02114, USA
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Aileen O’Shea
- Department of Radiology, Massachusetts General Hospital, Fruit Street, Boston, MA 02114, USA
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Ralph Weissleder
- Department of Radiology, Massachusetts General Hospital, Fruit Street, Boston, MA 02114, USA
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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40
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Jamadar SD, Ward PGD, Li S, Sforazzini F, Baran J, Chen Z, Egan GF. Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex. Neuroimage 2019; 189:258-266. [DOI: 10.1016/j.neuroimage.2019.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/01/2018] [Accepted: 01/03/2019] [Indexed: 01/24/2023] Open
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41
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Chen Z, Sforazzini F, Baran J, Close T, Shah NJ, Egan GF. MR-PET head motion correction based on co-registration of multicontrast MR images. Hum Brain Mapp 2019; 42:4081-4091. [PMID: 30604898 DOI: 10.1002/hbm.24497] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 10/22/2018] [Accepted: 12/05/2018] [Indexed: 01/01/2023] Open
Abstract
Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia
| | | | - Jakub Baran
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzesow, Rzesow, Poland
| | - Thomas Close
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Australian National Imaging Facility, St Lucia, Australia
| | - Nadim Jon Shah
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, Australia
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42
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Brooks A, Drake LR, Shao X, Zhao A, Scott PJH, Kilbourn MR. Evaluation of Enzyme Substrate Radiotracers as PET/MRS Hybrid Imaging Agents. ACS Med Chem Lett 2018; 9:1140-1143. [PMID: 30429959 PMCID: PMC6231179 DOI: 10.1021/acsmedchemlett.8b00402] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/03/2018] [Indexed: 01/23/2023] Open
Abstract
The development of a positron emission tomography (PET)/magnetic resonance spectroscopy (MRS) hybrid imaging agent allows for functional imaging by both methods with a single imaging agent. Enzyme substrates that are cleaved to form two metabolites present an interesting opportunity, as the unique metabolites generated might each be detected by a different modality. To be successful, such enzyme substrates would require administration of doses that (a) reach the in vivo target tissue at concentrations necessary for MRS imaging, (b) do not show substrate inhibition of tissue uptake or enzymatic activity, and (c) provide PET images that still reflect the action of the enzyme. We report in vitro and in vivo proof-of-concept studies of a carbon-11 small molecule substrate for brain monoamine oxidases that, upon enzyme-mediated cleavage, produces two metabolites, one detectable by PET and the other by MRS.
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Affiliation(s)
- Allen
F. Brooks
- Department
of Radiology, University of Michigan Medical
School, 1301 Catherine
Street, Ann Arbor, Michigan 48109, United States
| | - Lindsey R. Drake
- Department
of Medicinal Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Xia Shao
- Department
of Radiology, University of Michigan Medical
School, 1301 Catherine
Street, Ann Arbor, Michigan 48109, United States
| | - Austin Zhao
- Department
of Radiology, University of Michigan Medical
School, 1301 Catherine
Street, Ann Arbor, Michigan 48109, United States
| | - Peter J. H. Scott
- Department
of Radiology, University of Michigan Medical
School, 1301 Catherine
Street, Ann Arbor, Michigan 48109, United States
- Department
of Medicinal Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Michael R. Kilbourn
- Department
of Radiology, University of Michigan Medical
School, 1301 Catherine
Street, Ann Arbor, Michigan 48109, United States
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43
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Baran J, Chen Z, Sforazzini F, Ferris N, Jamadar S, Schmitt B, Faul D, Shah NJ, Cholewa M, Egan GF. Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications. BMC Med Imaging 2018; 18:41. [PMID: 30400875 PMCID: PMC6220492 DOI: 10.1186/s12880-018-0283-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/24/2018] [Indexed: 12/29/2022] Open
Abstract
Background Attenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images. Methods MR UTE images from simultaneous MR and PET imaging of five healthy volunteers was used to generate a whole head, bone and air template image for inclusion into an improved MR derived attenuation correction map, and applied to PET image data for quantitative analysis. Bone, air and soft tissue were segmented based on Gaussian Mixture Models with probabilistic tissue maps as a priori information. We present results for two approaches for bone attenuation coefficient assignments: one using a constant attenuation correction value; and another using an estimated continuous attenuation value based on a calibration fit. Quantitative comparisons were performed to evaluate the accuracy of the reconstructed PET images, with respect to a reference image reconstructed with manually segmented attenuation maps. Results The DICE coefficient analysis for the air and bone regions in the images demonstrated improvements compared to the UTE approach, and other state-of-the-art techniques. The most accurate whole brain and regional brain analyses were obtained using constant bone attenuation coefficient values. Conclusions A novel attenuation correction method for PET data reconstruction is proposed. Analyses show improvements in the quantitative accuracy of the reconstructed PET images compared to other state-of-the-art AC methods for simultaneous PET/MR scanners. Further evaluation is needed with radiopharmaceuticals other than FDG, and in larger cohorts of participants.
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Affiliation(s)
- Jakub Baran
- Monash Biomedical Imaging, Monash University, Melbourne, Australia. .,Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Rzeszow, Poland. .,Institute of Nuclear Physics Polish Academy of Science, Krakow, Poland.
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia
| | | | - Nicholas Ferris
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Monash Imaging, Monash Health, Clayton, Australia
| | - Sharna Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Melbourne, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Melbourne, Australia
| | - Ben Schmitt
- Siemens Healthcare Pty Ltd, Sydney, Australia
| | - David Faul
- Siemens Healthcare Pty Ltd, New York, USA
| | - Nadim Jon Shah
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Marian Cholewa
- Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Rzeszow, Poland
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Melbourne, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Melbourne, Australia
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44
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The renaissance of functional 18F-FDG PET brain activation imaging. Eur J Nucl Med Mol Imaging 2018; 45:2338-2341. [DOI: 10.1007/s00259-018-4165-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 09/10/2018] [Indexed: 10/28/2022]
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45
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Abstract
The time is ripe for a renewed and interdisciplinary approach to organizational research that incorporates neuroscientific techniques. Like all methods, they have methodological, analytical, and interpretational limitations; however, the potential gains from using these techniques are far more considerable. We have therefore assembled a succinct yet authoritative collection of articles on the topic of neuroscience in organizational research, to serve as a solid introduction to the methods of neuroscience and what they can accomplish. The special topic is organized into two parts. The first includes a set of accessible reviews of the palette of brain imaging, mapping, and stimulation techniques (fMRI, fNIRS, EEG, MEG, and NIBS) as well as examples of the application of neuroscience methods to various disciplines including economics, marketing, finance, organizational behavior, neuroethology, as well an integrative translational critique on a variety of applications. The second is a collection of articles resulting from a competitive call for submissions that cover various neuroscience topics but also address important methodological and philosophical issues. The articles lay out a roadmap for the effective integration of neuroscientific methods into organizational research.
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46
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Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, Shah NJ, Egan GF. From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies. Hum Brain Mapp 2018; 39:5126-5144. [PMID: 30076750 DOI: 10.1002/hbm.24314] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 06/25/2018] [Accepted: 07/02/2018] [Indexed: 12/17/2022] Open
Abstract
Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
| | - Shenpeng Li
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | | | - Jakub Baran
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszów, Rzeszów, Poland
| | - Nicholas Ferris
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - Nadim Jon Shah
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
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