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Gao Y, Xiong Z, Shan S, Liu Y, Rong P, Li M, Wilman AH, Pike GB, Liu F, Sun H. Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks. Med Image Anal 2024; 94:103160. [PMID: 38552528 DOI: 10.1016/j.media.2024.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 03/09/2024] [Accepted: 03/23/2024] [Indexed: 04/16/2024]
Abstract
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a simulated-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms. The PnP OA-LFE module's versatility was further demonstrated by its successful application to xQSM, a distinct DL-QSM network for dipole inversion. In conclusion, this work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.
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Affiliation(s)
- Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Zhuang Xiong
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia
| | - Shanshan Shan
- State Key Laboratory of Radiation, Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China
| | - Yin Liu
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Alan H Wilman
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Feng Liu
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia; School of Engineering, University of Newcastle, Newcastle, Australia
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Lee J, Ji S, Oh SH. So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging. Magn Reson Med Sci 2024:rev.2024-0001. [PMID: 38644201 DOI: 10.2463/mrms.rev.2024-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.
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Affiliation(s)
- Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Sooyeon Ji
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Se-Hong Oh
- Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea
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Varga Z, Keller J, Robinson SD, Serranova T, Nepozitek J, Zogala D, Trnka J, Ruzicka E, Sonka K, Dusek P. Whole brain pattern of iron accumulation in REM sleep behavior disorder. Hum Brain Mapp 2024; 45:e26675. [PMID: 38590155 PMCID: PMC11002348 DOI: 10.1002/hbm.26675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/10/2024] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Isolated REM sleep behavior disorder (iRBD) is an early stage of synucleinopathy with most patients progressing to Parkinson's disease (PD) or related conditions. Quantitative susceptibility mapping (QSM) in PD has identified pathological iron accumulation in the substantia nigra (SN) and variably also in basal ganglia and cortex. Analyzing whole-brain QSM across iRBD, PD, and healthy controls (HC) may help to ascertain the extent of neurodegeneration in prodromal synucleinopathy. 70 de novo PD patients, 70 iRBD patients, and 60 HCs underwent 3 T MRI. T1 and susceptibility-weighted images were acquired and processed to space standardized QSM. Voxel-based analyses of grey matter magnetic susceptibility differences comparing all groups were performed on the whole brain and upper brainstem levels with the statistical threshold set at family-wise error-corrected p-values <.05. Whole-brain analysis showed increased susceptibility in the bilateral fronto-parietal cortex of iRBD patients compared to both PD and HC. This was not associated with cortical thinning according to the cortical thickness analysis. Compared to iRBD, PD patients had increased susceptibility in the left amygdala and hippocampal region. Upper brainstem analysis revealed increased susceptibility within the bilateral SN for both PD and iRBD compared to HC; changes were located predominantly in nigrosome 1 in the former and nigrosome 2 in the latter group. In the iRBD group, abnormal dopamine transporter SPECT was associated with increased susceptibility in nigrosome 1. iRBD patients display greater fronto-parietal cortex involvement than incidental early-stage PD cohort indicating more widespread subclinical neuropathology. Dopaminergic degeneration in the substantia nigra is paralleled by susceptibility increase, mainly in nigrosome 1.
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Affiliation(s)
- Zsoka Varga
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Keller
- Radiodiagnostic DepartmentNa Homolce HospitalPragueCzech Republic
| | - Simon Daniel Robinson
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaAustria
| | - Tereza Serranova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Nepozitek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - David Zogala
- Department of Nuclear Medicine, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Trnka
- Department of Nuclear Medicine, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Evzen Ruzicka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Karel Sonka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
- Department of Radiology, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
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Liu M, Zhao S, Chen Z. Interscanner reproducibility of volumetric quantitative susceptibility mapping about cerebral subcortical gray nuclei at different MR vendors with the same magnetic strength. Brain Behav 2024; 14:e3473. [PMID: 38594225 PMCID: PMC11004039 DOI: 10.1002/brb3.3473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/05/2024] [Accepted: 03/16/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND AND PURPOSE Quantitative susceptibility mapping (QSM) technique was a new quantitative magnetic resonance imaging technique to evaluate the cerebral iron deposition in clinical practice. The current study was aimed to investigate the reproducibility of the volumetric susceptibility value of the subcortical gray nuclei at two different MR vendor with the same magnetic strength. METHODS Cerebral magnitude and phase images of 21 normal subjects were acquired from a 3D multiecho enhanced gradient recalled echo sequence at two different 3.0T MR scanner, and then the magnetic susceptibility images were generated by STI software. The brain structural images were coregistered with magnitude images and generated the normalized parameters, and then generated the normalized susceptibility images. The subcortical gray nuclei template was applied to extract the volumetric susceptibility value of the target nuclei. RESULTS ICC value (95% CI) of the caudate, putamen and GP were 0.847 (0.660-0.935), 0.848 (0.663-0.935) and 0.838 (0.643-0.931), respectively. The ICC value of the thalamus was 0.474 (0.064-0.747). Ninety-five point two percent (20/21) of the difference points of the susceptibility located between the 95% LA for the caudate at the two different 3.0T MR scanner, while the less than 95% of the difference points of the susceptibility value located between the 95% LA for the putamen, globus pallidus and thalamus. CONCLUSION The current study identified that the caudate had the stable reproducibility of the magnetic susceptibility value, and the other basal ganglion nuclei should be cautious for the quantitative evaluation of the magnetic susceptibility value at different 3.0T MR scanner.
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Affiliation(s)
- Mengqi Liu
- Department of RadiologyHainan Hospital of PLA General HospitalSanyaChina
- Department of RadiologyFirst Medical Center of PLA General HospitalBeijingChina
| | - Shuqiang Zhao
- Department of RadiologyHainan Hospital of PLA General HospitalSanyaChina
| | - Zhiye Chen
- Department of RadiologyHainan Hospital of PLA General HospitalSanyaChina
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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Thomas GEC, Hannaway N, Zarkali A, Shmueli K, Weil RS. Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease. Mov Disord 2024; 39:546-559. [PMID: 38173297 DOI: 10.1002/mds.29702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/29/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Dementia is common in Parkinson's disease (PD), but there is wide variation in its timing. A critical gap in PD research is the lack of quantifiable markers of progression, and methods to identify early stages of dementia. Atrophy-based magnetic resonance imaging (MRI) has limited sensitivity in detecting or tracking changes relating to PD dementia, but quantitative susceptibility mapping (QSM), sensitive to brain tissue iron, shows potential for these purposes. OBJECTIVE The objective of the paper is to study, for the first time, the longitudinal relationship between cognition and QSM in PD in detail. METHODS We present a longitudinal study of clinical severity in PD using QSM, including 59 PD patients (without dementia at study onset), and 22 controls over 3 years. RESULTS In PD, increased baseline susceptibility in the right temporal cortex, nucleus basalis of Meynert, and putamen was associated with greater cognitive severity after 3 years; and increased baseline susceptibility in basal ganglia, substantia nigra, red nucleus, insular cortex, and dentate nucleus was associated with greater motor severity after 3 years. Increased follow-up susceptibility in these regions was associated with increased follow-up cognitive and motor severity, with further involvement of hippocampus relating to cognitive severity. However, there were no consistent increases in susceptibility over 3 years. CONCLUSIONS Our study suggests that QSM may predict changes in cognitive severity many months prior to overt cognitive involvement in PD. However, we did not find robust longitudinal changes in QSM over the course of the study. Additional tissue metrics may be required together with QSM for it to monitor progression in clinical practice and therapeutic trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
| | - Naomi Hannaway
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Angelika Zarkali
- Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Rimona S Weil
- Dementia Research Centre, UCL Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Movement Disorders Consortium, University College London, London, UK
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Jung S, Jeon S, Gho SM, Lee HJ, Jung KJ, Kim DH. Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network. Neuroimage 2024; 288:120528. [PMID: 38311125 DOI: 10.1016/j.neuroimage.2024.120528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/14/2023] [Accepted: 01/27/2024] [Indexed: 02/06/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.
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Affiliation(s)
- Siyun Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | - Soohyun Jeon
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | | | - Ho-Joon Lee
- Department of Radiology, Inje University Haeundae Paik Hospital, South Korea
| | - Kyu-Jin Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea.
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Alushaj E, Handfield-Jones N, Kuurstra A, Morava A, Menon RS, Owen AM, Sharma M, Khan AR, MacDonald PA. Increased iron in the substantia nigra pars compacta identifies patients with early Parkinson'sdisease: A 3T and 7T MRI study. Neuroimage Clin 2024; 41:103577. [PMID: 38377722 PMCID: PMC10944193 DOI: 10.1016/j.nicl.2024.103577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/19/2023] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Degeneration in the substantia nigra (SN) pars compacta (SNc) underlies motor symptoms in Parkinson's disease (PD). Currently, there are no neuroimaging biomarkers that are sufficiently sensitive, specific, reproducible, and accessible for routine diagnosis or staging of PD. Although iron is essential for cellular processes, it also mediates neurodegeneration. MRI can localize and quantify brain iron using magnetic susceptibility, which could potentially provide biomarkers of PD. We measured iron in the SNc, SN pars reticulata (SNr), total SN, and ventral tegmental area (VTA), using quantitative susceptibility mapping (QSM) and R2* relaxometry, in PD patients and age-matched healthy controls (HCs). PD patients, diagnosed within five years of participation and HCs were scanned at 3T (22 PD and 23 HCs) and 7T (17 PD and 21 HCs) MRI. Midbrain nuclei were segmented using a probabilistic subcortical atlas. QSM and R2* values were measured in midbrain subregions. For each measure, groups were contrasted, with Age and Sex as covariates, and receiver operating characteristic (ROC) curve analyses were performed with repeated k-fold cross-validation to test the potential of our measures to classify PD patients and HCs. Statistical differences of area under the curves (AUCs) were compared using the Hanley-MacNeil method (QSM versus R2*; 3T versus 7T MRI). PD patients had higher QSM values in the SNc at both 3T (padj = 0.001) and 7T (padj = 0.01), but not in SNr, total SN, or VTA, at either field strength. No significant group differences were revealed using R2* in any midbrain region at 3T, though increased R2* values in SNc at 7T MRI were marginally significant in PDs compared to HCs (padj = 0.052). ROC curve analyses showed that SNc iron measured with QSM, distinguished early PD patients from HCs at the single-subject level with good diagnostic accuracy, using 3T (mean AUC = 0.83, 95 % CI = 0.82-0.84) and 7T (mean AUC = 0.80, 95 % CI = 0.79-0.81) MRI. Mean AUCs reported here are from averages of tests in the hold-out fold of cross-validated samples. The Hanley-MacNeil method demonstrated that QSM outperforms R2* in discriminating PD patients from HCs at 3T, but not 7T. There were no significant differences between 3T and 7T in diagnostic accuracy of QSM values in SNc. This study highlights the importance of segmenting midbrain subregions, performed here using a standardized atlas, and demonstrates high accuracy of SNc iron measured with QSM at 3T MRI in identifying early PD patients. QSM measures of SNc show potential for inclusion in neuroimaging diagnostic biomarkers of early PD. An MRI diagnostic biomarker of PD would represent a significant clinical advance.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 3K7, Canada; Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada
| | - Nicholas Handfield-Jones
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 3K7, Canada; Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Anisa Morava
- School of Kinesiology, Faculty of Health Sciences, Western University, London, Ontario N6A 3K7, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Adrian M Owen
- Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada; Department of Physiology and Pharmacology, Western University, London, Ontario N6A 3K7, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario N6A 3K7, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario N6A 3K7, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario N6A 3K7, Canada.
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Mathew RS, Paluru N, Yalavarthy PK. Model resolution-based deconvolution for improved quantitative susceptibility mapping. NMR Biomed 2024; 37:e5055. [PMID: 37803940 DOI: 10.1002/nbm.5055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/22/2023] [Accepted: 09/02/2023] [Indexed: 10/08/2023]
Abstract
Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.
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Affiliation(s)
- Raji Susan Mathew
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India
| | - Phaneendra K Yalavarthy
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India
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Krishnan S, George SS, Radhakrishnan V, Raghavan S, Thomas B, Thulaseedharan JV, Puthenveedu DK. Quantitative susceptibility mapping from basal ganglia and related structures: correlation with disease severity in progressive supranuclear palsy. Acta Neurol Belg 2024; 124:151-160. [PMID: 37580639 DOI: 10.1007/s13760-023-02352-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVE We examined whether mean magnetic susceptibility values from deep gray matter structures in patients with progressive supranuclear palsy (PSP) differed from those in patients with Parkinson's disease (PD) and healthy volunteers, and correlated with the PSP rating scale. METHODS Head of caudate nucleus, putamen, globus pallidus, substantia nigra and red nucleus were the regions of interest. Mean susceptibility values from these regions in PSP patients were estimated using quantitative susceptibility mapping. Correlations with clinical severity of disease as measured by the PSP rating scale were examined. The mean susceptibility values were also compared with those from healthy volunteers and age- and disease duration-matched patients with PD. RESULTS Data from 26 healthy volunteers, 26 patients with PD and 27 patients with PSP, were analysed. Patients with PSP had higher mean susceptibility values from all regions of interest when compared to both the other groups. The PSP rating scale scores correlated strongly with mean susceptibility values from the red nucleus and moderately with those from the putamen and substantia nigra. The scores did not correlate with mean susceptibility values from the caudate nucleus or globus pallidus. In patients with PD, the motor deficits correlated moderately with mean susceptibility values from substantia nigra. CONCLUSIONS In patients with PSP, mean susceptibility values indicating the severity of mineralization of basal ganglia and related structures correlate with disease severity, the correlation of red nucleus being the strongest. Further studies are warranted to explore whether mean susceptibility values could serve as biomarkers for PSP.
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Affiliation(s)
- Syam Krishnan
- Comprehensive Care Centre for Movement Disorders, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
| | - Sneha Susan George
- Comprehensive Care Centre for Movement Disorders, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Vineeth Radhakrishnan
- Comprehensive Care Centre for Movement Disorders, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Sheelakumari Raghavan
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Jissa Vinoda Thulaseedharan
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Divya Kalikavil Puthenveedu
- Comprehensive Care Centre for Movement Disorders, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
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Zhang J, Nguyen TD, Solomon E, Li C, Zhang Q, Li J, Zhang H, Spincemaille P, Wang Y. mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping. Magn Reson Med 2024; 91:344-356. [PMID: 37655444 DOI: 10.1002/mrm.29854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO). METHODS A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard. RESULTS The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal. CONCLUSION mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.
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Affiliation(s)
- Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Eddy Solomon
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Chao Li
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Department of Applied Physics, Cornell University, Ithaca, New York, USA
| | - Qihao Zhang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jiahao Li
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
| | | | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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12
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Yan Y, Wang Z, Wei W, Yang Z, Guo L, Wang Z, Wei X. Correlation of brain iron deposition and freezing of gait in Parkinson's disease: a cross-sectional study. Quant Imaging Med Surg 2023; 13:7961-7972. [PMID: 38106290 PMCID: PMC10721991 DOI: 10.21037/qims-23-267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/07/2023] [Indexed: 12/19/2023]
Abstract
Background Quantitative susceptibility mapping (QSM) is a novel imaging method for detecting iron content in the brain. The study aimed determine whether the iron deposition in the brains of people with Parkinson's disease (PD) is correlated with freezing of gait (FOG). Methods We retrospectively collected the data of 24 patients with PD from the Movement Disorders Program and 36 healthy controls (HCs) from January 2021 to December 2021. Clinical assessments included mental intelligence scales, Parkinson rating scales, motor-related scales, and clinical gait assessments. All exercise scales and gait assessments were performed in the "ON" and "OFF" states. Magnetic resonance imaging (MRI) data were collected using 3-dimensional fast low-angle shot sequences. We chose the bilateral red nucleus, substantia nigra, thalamus, putamen, caudate nucleus, and globus pallidus as regions of interest for QSM analysis. Results The iron deposition in the substantia nigra of the PD group was significantly higher than that of the HC group (P<0.01). In the PD group, the iron deposition in the substantia nigra of patients with FOG was significantly higher than that in patients without FOG (P=0.04). The iron deposition in the substantia nigra was positively correlated with the New Freezing of Gait Questionnaire (P=0.03). The scores for depression and anxiety of the PD group were significantly higher than those of the HC group, while the Berg balance scale score was significantly lower (P<0.01). Conclusions The iron deposition in the substantia nigra of patients with PD is increased compared with that of controls and is associated with FOG. QSM can be used to detect brain iron deposition in patients with PD, which would help to explore the mechanism of abnormal neurobiological activity in FOG.
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Affiliation(s)
- Ying Yan
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zheng Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wei Wei
- Division of Science and Technology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Lingfei Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xuan Wei
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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13
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Zhu X, Gao Y, Liu F, Crozier S, Sun H. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources. Z Med Phys 2023; 33:578-590. [PMID: 36064695 PMCID: PMC10751722 DOI: 10.1016/j.zemedi.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the field induced by these pathological susceptibility sources. METHOD This study proposes a new deep learning-based method, BFRnet, to remove the background field in healthy and hemorrhagic subjects. The network is built with the dual-frequency octave convolutions on the U-net architecture, trained with synthetic field maps containing significant susceptibility sources. The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects. Robustness against acquisition field-of-view (FOV) orientation and brain masking are also investigated. RESULTS For both simulation and in vivo experiments, BFRnet led to the best visually appealing results in the local field and QSM results with the minimum contrast loss and the most accurate hemorrhage susceptibility measurements among all five methods. In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and further brain edge erosions. It is also observed that BFRnet performed the best among all BFR methods for acquisition FOVs oblique to the main magnetic field. CONCLUSION The proposed BFRnet improved the accuracy of local field reconstruction in the hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet method was effective for acquisitions of tilted orientations and retained whole brains without edge erosion as often required by traditional BFR methods.
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Affiliation(s)
- Xuanyu Zhu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Yang Gao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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14
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López-Aguirre M, Matarazzo M, Blesa J, Monje MHG, Rodríguez-Rojas R, Sánchez-Ferro A, Obeso JA, Pineda-Pardo JA. Dopaminergic denervation and associated MRI microstructural changes in the nigrostriatal projection in early Parkinson's disease patients. NPJ Parkinsons Dis 2023; 9:144. [PMID: 37852988 PMCID: PMC10584921 DOI: 10.1038/s41531-023-00586-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023] Open
Abstract
Loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and a profound reduction of striatal dopamine are two hallmarks of Parkinson's disease (PD). However, it's unclear whether degeneration starts at the neuronal soma or the striatal presynaptic terminals, and how microstructural degeneration is linked to dopaminergic loss is also uncertain. In this study, thirty de novo PD patients and twenty healthy subjects (HS) underwent 6-[18F]-fluoro-L-dopa (FDOPA) PET and MRI studies no later than 12 months from clinical diagnosis. FDOPA uptake rate (Ki), fractional volume of free-water (FW), and iron-sensitive R2* relaxometry were quantified within nigrostriatal regions. Inter-group differences (PD vs HS) were studied using non-parametric statistics and complemented with Cohen's d effect sizes and Bayesian statistics. Correlation analyses were performed exploring biomarker dependencies and their association with bradykinesia scores. PD patients exhibited a significant decline in nigrostriatal dopaminergic activity, being post-commissural putamen (-67%) and posterolateral SNc (-11.7%) the most affected subregions within striatum and SNc respectively. Microstructural alterations (FW) were restricted to the hemisphere corresponding to the most affected side and followed similar spatial gradients as FDOPA Ki (+20% in posterior putamen and +11% in posterolateral SNc). R2* revealed no relevant significant changes. FDOPA and FW were correlated within the posterolateral SNc, and clinical severity was associated with FDOPA Ki loss. The asymmetry between striatal and SNc changes for both dopaminergic depletion and microstructural degeneration biomarkers is consistent with a neurodegenerative process that begins in the striatal terminals before progressing toward the cell bodies in the SNc.
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Affiliation(s)
- M López-Aguirre
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- PhD Program in Physics, Complutense University of Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - M Matarazzo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - J Blesa
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - M H G Monje
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Ken and Ruth Davee Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - R Rodríguez-Rojas
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - A Sánchez-Ferro
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain
- Department of Medicine, Complutense University of Madrid, Madrid, Spain
| | - J A Obeso
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- University CEU-San Pablo, Madrid, Spain
| | - J A Pineda-Pardo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal). Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.
- University CEU-San Pablo, Madrid, Spain.
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15
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Alushaj E, Hemachandra D, Kuurstra A, Menon RS, Ganjavi H, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder. Neuroimage Clin 2023; 40:103519. [PMID: 37797434 PMCID: PMC10568416 DOI: 10.1016/j.nicl.2023.103519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
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16
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Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
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Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
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17
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García Saborit M, Jara A, Muñoz N, Milovic C, Tepper A, Alliende LM, Mena C, Iruretagoyena B, Ramirez-Mahaluf JP, Diaz C, Nachar R, Castañeda CP, González A, Undurraga J, Crossley N, Tejos C. Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis. Schizophr Bull 2023; 49:1355-1363. [PMID: 37030007 PMCID: PMC10483330 DOI: 10.1093/schbul/sbad041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
BACKGROUND Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes. METHODS We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM. RESULTS Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM. CONCLUSIONS Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.
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Affiliation(s)
- Marisleydis García Saborit
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Alejandro Jara
- Department of Statistics, Mathematics Faculty, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Néstor Muñoz
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Carlos Milovic
- School of Electrical Engineering, Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Angeles Tepper
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Luz María Alliende
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Carlos Mena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Bárbara Iruretagoyena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | | | - Camila Diaz
- Pharmacovigilance, Instituto Psiquiátrico Dr. J. Horwitz Barak, Santiago, Chile
| | - Ruben Nachar
- Pharmacovigilance, Instituto Psiquiátrico Dr. J. Horwitz Barak, Santiago, Chile
| | | | - Alfonso González
- Early Intervention Program, Instituto Psiquiátrico Dr J. Horwitz Barak, Santiago, Chile
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Early Intervention Program, Instituto Psiquiátrico Dr J. Horwitz Barak, Santiago, Chile
- Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Nicolas Crossley
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Cristian Tejos
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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18
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Bordin V, Pirastru A, Bergsland N, Cazzoli M, Baselli G, Baglio F. Optimal echo times for quantitative susceptibility mapping: A test-retest study on basal ganglia and subcortical brain nuclei. Neuroimage 2023; 278:120272. [PMID: 37437701 DOI: 10.1016/j.neuroimage.2023.120272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/16/2023] [Accepted: 07/09/2023] [Indexed: 07/14/2023] Open
Abstract
Quantitative Susceptibility Mapping (QSM) is a recent MRI-technique able to quantify the bulk magnetic susceptibility of myelin, iron, and calcium in the brain. Its variability across different acquisition parameters has prompted the need for standardisation across multiple centres and MRI vendors. However, a high level of agreement between repeated imaging acquisitions is equally important. With this study we aimed to assess the inter-scan repeatability of an optimised multi-echo GRE sequence in 28 healthy volunteers. We extracted and compared the susceptibility measures from the scan and rescan acquisitions across 7 bilateral brain regions (i.e., 14 regions of interest (ROIs)) relevant for neurodegeneration. Repeatability was first assessed while reconstructing QSM with a fixed number of echo times (i.e., 8). Excellent inter-scan repeatability was found for putamen, globus pallidus and caudate nucleus, while good performance characterised the remaining structures. An increased variability was instead noted for small ROIs like red nucleus and substantia nigra. Secondly, we assessed the impact exerted on repeatability by the number of echoes used to derive QSM maps. Results were impacted by this parameter, especially in smaller regions. Larger brain structures, on the other hand, showed more consistent performance. Nevertheless, with either 8 or 7 echoes we managed to obtain good inter-scan repeatability on almost all ROIs. These findings indicate that the designed acquisition/reconstruction protocol has wide applicability, particularly in clinical or research settings involving longitudinal acquisitions (e.g. rehabilitation studies).
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Affiliation(s)
- Valentina Bordin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Alice Pirastru
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Niels Bergsland
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy; Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Marta Cazzoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Coray RC, Berberat J, Zimmermann J, Seifritz E, Stock AK, Beste C, Cole DM, Unschuld PG, Quednow BB. Striatal Iron Deposition in Recreational MDMA (Ecstasy) Users. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:956-966. [PMID: 36848948 DOI: 10.1016/j.bpsc.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/30/2022] [Accepted: 02/17/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND The common club drug MDMA (also known as ecstasy) enhances mood, sensory perception, energy, sociability, and euphoria. While MDMA has been shown to produce neurotoxicity in animal models, research on its potential neurotoxic effects in humans is inconclusive and has focused primarily on the serotonin system. METHODS We investigated 34 regular, largely pure MDMA users for signs of premature neurodegenerative processes in the form of increased iron load in comparison to a group of 36 age-, sex-, and education-matched MDMA-naïve control subjects. We used quantitative susceptibility mapping, a novel tool able to detect even small tissue (nonheme) iron accumulations. Cortical and relevant subcortical gray matter structures were grouped into 8 regions of interest and analyzed. RESULTS Significantly increased iron deposition in the striatum was evident in the MDMA user group. The effect survived correction for multiple comparisons and remained after controlling for relevant confounding factors, including age, smoking, and stimulant co-use. Although no significant linear relationship between measurements of the amounts of MDMA intake (hair analysis and self-reports) and quantitative susceptibility mapping values was observed, increased striatal iron deposition might nevertheless point to MDMA-induced neurotoxic processes. Additional factors (hyperthermia and simultaneous co-use of other substances) that possibly amplify neurotoxic effects of MDMA during the state of acute intoxication are discussed. CONCLUSIONS The demonstrated increased striatal iron accumulation may indicate that regular MDMA users have an increased risk potential for neurodegenerative diseases with progressing age.
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Affiliation(s)
- Rebecca C Coray
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland.
| | - Jatta Berberat
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland; Institute of Neuroradiology, Kantonsspital Aarau, Aarau, Switzerland
| | - Josua Zimmermann
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - David M Cole
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland; Translational Psychiatry Lab, University Psychiatric Clinics Basel, University of Basel, Basel, Switzerland
| | - Paul G Unschuld
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland
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20
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Yu JJ, Li C, Qian ZM, Liu Y. Brain iron deposition is positively correlated with cognitive impairment in patients with chronic cerebral hypoperfusion: a MRI susceptibility mapping study. Clin Radiol 2023; 78:601-607. [PMID: 37003892 DOI: 10.1016/j.crad.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/14/2023] [Accepted: 02/27/2023] [Indexed: 04/03/2023]
Abstract
AIM To investigate the relationship of brain iron deposition with cognitive impairment in patients with chronic cerebral hypoperfusion (CHP). MATERIALS AND METHODS Brain iron deposition was detected using quantitative susceptibility mapping (QSM), and cognitive function by neuropsychological tests including the Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADLs), and verbal fluency tests in a total of 40 participants, 23 with CHP and 17 age- and sex-matched healthy participants without CHP (controls). RESULTS The neuropsychological tests revealed that cognitive impairment (p<0.05) and susceptibility values (p<0.05) of the bilateral hippocampus (HP) and substantia nigra (SN) in CHP patients were significantly higher than those of the controls. The susceptibility values of bilateral HP and left putamen correlated closely with the scores of neuropsychological tests in the CHP patients (p<0.05, r2>0.1). The susceptibility values in the left putamen and bilateral HP were significantly higher in CHP patients with mild cognitive impairment (MCI; n=8) than those of CHP patients without MCI (n=15; p<0.05). CONCLUSIONS The present findings indicated that brain iron deposition in specific areas may be responsible for the cognitive impairment in CHP patients, and that QSM is a useful tool to determine brain iron, predicting cognitive impairment in CHP patients.
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Affiliation(s)
- J-J Yu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China
| | - C Li
- Department of Medical Imaging, Chongqing University Central Hospital, Chongqing, China
| | - Z-M Qian
- Institute of Translational & Precision Medicine, Nantong University, 19 Qi Xiu Road, Nantong, JS 226019, China.
| | - Y Liu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China.
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21
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Nepozitek J, Varga Z, Dostalova S, Perinova P, Keller J, Robinson S, Ibarburu V, Prihodova I, Bezdicek O, Ruzicka E, Sonka K, Dusek P. Magnetic susceptibility changes in the brainstem reflect REM sleep without atonia severity in isolated REM sleep behavior disorder. NPJ Parkinsons Dis 2023; 9:112. [PMID: 37452075 PMCID: PMC10349141 DOI: 10.1038/s41531-023-00557-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023] Open
Abstract
REM sleep without atonia (RWA) is the hallmark of isolated REM sleep behavior disorder (iRBD) and is caused by neurodegeneration of brainstem structures. Previously, quantitative susceptibility mapping (QSM) was shown to detect microstructural tissue changes in neurodegenerative diseases. The goal of the study was to compare brainstem magnetic susceptibility (MS) in iRBD and controls using the voxel-based QSM approach and to examine the association between brainstem MS and severity of RWA in iRBD. Sixty iRBD patients and 41 healthy controls were included in the study. Phasic, tonic, mixed RWA and SINBAR score was quantified. QSM maps were reconstructed with QSMbox software from a multi-gradient-echo sequence acquired at 3T MRI system and normalized using a custom T1 template. Voxel-based analysis with age and gender as covariates was performed using a two-sample t-test model for between-group comparison and using a linear regression model for association with the RWA parameters. Statistical maps were generated using threshold free cluster enhancement with p-value p < 0.05, corrected for family wise error. Compared to controls, the iRBD group had higher MS in bilateral substantia nigra (SN), red nucleus and the ventral tegmental area. MS positively correlated with iRBD duration in the right pedunculotegmental nucleus and white matter of caudal mesencephalic and pontine tegmentum and with phasic RWA in bilateral SN. QSM was able to detect MS abnormalities in several brainstem structures in iRBD. Association of MS levels in the brainstem with the intensity of RWA suggests that increased iron content in SN is related to RWA severity.
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Affiliation(s)
- Jiri Nepozitek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
| | - Zsoka Varga
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Simona Dostalova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Pavla Perinova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jiri Keller
- Radiodiagnostic Department, Na Homolce Hospital, Prague, Czech Republic
| | - Simon Robinson
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Veronika Ibarburu
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Iva Prihodova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Ondrej Bezdicek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Evzen Ruzicka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Karel Sonka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
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22
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Carlos AF, Josephs KA. The Role of Clinical Assessment in the Era of Biomarkers. Neurotherapeutics 2023; 20:1001-1018. [PMID: 37594658 PMCID: PMC10457273 DOI: 10.1007/s13311-023-01410-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
Hippocratic Medicine revolved around the three main principles of patient, disease, and physician and promoted the systematic observation of patients, rational reasoning, and interpretation of collected information. Although these remain the cardinal features of clinical assessment today, Medicine has evolved from a more physician-centered to a more patient-centered approach. Clinical assessment allows physicians to encounter, observe, evaluate, and connect with patients. This establishes the patient-physician relationship and facilitates a better understanding of the patient-disease relationship, as the ultimate goal is to diagnose, prognosticate, and treat. Biomarkers are at the core of the more disease-centered approach that is currently revolutionizing Medicine as they provide insight into the underlying disease pathomechanisms and biological changes. Genetic, biochemical, radiographic, and clinical biomarkers are currently used. Here, we define a seven-level theoretical construct for the utility of biomarkers in neurodegenerative diseases. Level 1-3 biomarkers are considered supportive of clinical assessment, capable of detecting susceptibility or risk factors, non-specific neurodegeneration or dysfunction, and/or changes at the individual level which help increase clinical diagnostic accuracy and confidence. Level 4-7 biomarkers have the potential to surpass the utility of clinical assessment through detection of early disease stages and prediction of underlying pathology. In neurodegenerative diseases, biomarkers can potentiate, but cannot substitute, clinical assessment. In this current era, aside from adding to the discovery, evaluation/validation, and implementation of more biomarkers, clinical assessment remains crucial to maintaining the personal, humanistic, and sociocultural aspects of patient care. We would argue that clinical assessment is a custom that should never go obsolete.
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Affiliation(s)
- Arenn F Carlos
- Department of Neurology, Mayo Clinic, 200 1st St. S.W., Rochester, MN, 55905, USA.
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic, 200 1st St. S.W., Rochester, MN, 55905, USA
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23
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Silva J, Milovic C, Lambert M, Montalba C, Arrieta C, Irarrazaval P, Uribe S, Tejos C. Toward a realistic in silico abdominal phantom for QSM. Magn Reson Med 2023; 89:2402-2418. [PMID: 36695213 PMCID: PMC10952412 DOI: 10.1002/mrm.29597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/18/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms. METHODS Synthetic susceptibility andR 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility andR 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM andR 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms. RESULTS QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms. CONCLUSION The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility,R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.
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Affiliation(s)
- Javier Silva
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Carlos Milovic
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- School of Electrical EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile
| | - Mathias Lambert
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Cristian Montalba
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristóbal Arrieta
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Pablo Irarrazaval
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de ChileSantiagoChile
| | - Sergio Uribe
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristian Tejos
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
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24
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Qamar MA, Rota S, Batzu L, Subramanian I, Falup-Pecurariu C, Titova N, Metta V, Murasan L, Odin P, Padmakumar C, Kukkle PL, Borgohain R, Kandadai RM, Goyal V, Chaudhuri KR. Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests. Front Neurol 2023; 14:1174698. [PMID: 37305739 PMCID: PMC10248458 DOI: 10.3389/fneur.2023.1174698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/02/2023] [Indexed: 06/13/2023] Open
Abstract
We have recently published the notion of the "vitals" of Parkinson's, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This "dashboard," termed the Chaudhuri's vitals of Parkinson's, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson's. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson's syndrome to describe Parkinson's disease, as the term "disease" is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson's, which is now considered by many as a syndrome.
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Affiliation(s)
- Mubasher A. Qamar
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Silvia Rota
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Lucia Batzu
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Indu Subramanian
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Parkinson’s Disease Research, Education and Clinical Centers, Greater Los Angeles Veterans Affairs Medical Center, Los Angeles, CA, United States
| | - Cristian Falup-Pecurariu
- Faculty of Medicine, Transilvania University of Braşov, Brașov, Romania
- Department of Neurology, County Clinic Hospital, Brașov, Romania
| | - Nataliya Titova
- Department of Neurology, Neurosurgery and Medical Genetics, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russia
- Department of Neurodegenerative Diseases, Federal State Budgetary Institution “Federal Center of Brain Research and Neurotechnologies” of the Federal Medical Biological Agency, Moscow, Russia
| | - Vinod Metta
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Lulia Murasan
- Faculty of Medicine, Transilvania University of Braşov, Brașov, Romania
- Department of Neurology, County Clinic Hospital, Brașov, Romania
| | - Per Odin
- Department of Neurology, University Hospital, Lund, Sweden
| | | | - Prashanth L. Kukkle
- Center for Parkinson’s Disease and Movement Disorders, Manipal Hospital, Karnataka, India, Bangalore
- Parkinson’s Disease and Movement Disorders Clinic, Bangalore, Karnataka, India
| | - Rupam Borgohain
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rukmini Mridula Kandadai
- Department of Neurology, Nizam’s Institute of Medical Sciences, Autonomous University, Hyderabad, India
| | - Vinay Goyal
- Neurology Department, Medanta, Gurugram, India
| | - Kallo Ray Chaudhuri
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
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25
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Wu T, Liu C, Thamizhchelvan AM, Fleischer C, Peng X, Liu G, Mao H. Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging. Chem Biomed Imaging 2023; 1:121-139. [PMID: 37235188 PMCID: PMC10207347 DOI: 10.1021/cbmi.3c00019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/20/2023] [Accepted: 04/01/2023] [Indexed: 05/28/2023]
Abstract
Biomedical imaging, especially molecular imaging, has been a driving force in scientific discovery, technological innovation, and precision medicine in the past two decades. While substantial advances and discoveries in chemical biology have been made to develop molecular imaging probes and tracers, translating these exogenous agents to clinical application in precision medicine is a major challenge. Among the clinically accepted imaging modalities, magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) exemplify the most effective and robust biomedical imaging tools. Both MRI and MRS enable a broad range of chemical, biological and clinical applications from determining molecular structures in biochemical analysis to imaging diagnosis and characterization of many diseases and image-guided interventions. Using chemical, biological, and nuclear magnetic resonance properties of specific endogenous metabolites and native MRI contrast-enhancing biomolecules, label-free molecular and cellular imaging with MRI can be achieved in biomedical research and clinical management of patients with various diseases. This review article outlines the chemical and biological bases of several label-free chemically and molecularly selective MRI and MRS methods that have been applied in imaging biomarker discovery, preclinical investigation, and image-guided clinical management. Examples are provided to demonstrate strategies for using endogenous probes to report the molecular, metabolic, physiological, and functional events and processes in living systems, including patients. Future perspectives on label-free molecular MRI and its challenges as well as potential solutions, including the use of rational design and engineered approaches to develop chemical and biological imaging probes to facilitate or combine with label-free molecular MRI, are discussed.
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Affiliation(s)
- Tianhe Wu
- Department
of Radiology and Imaging Sciences, Emory
University School of Medicine, Atlanta, Georgia 30322, United States
| | - Claire Liu
- F.M.
Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland 21205, United States
| | - Anbu Mozhi Thamizhchelvan
- Department
of Radiology and Imaging Sciences, Emory
University School of Medicine, Atlanta, Georgia 30322, United States
| | - Candace Fleischer
- Department
of Radiology and Imaging Sciences, Emory
University School of Medicine, Atlanta, Georgia 30322, United States
| | - Xingui Peng
- Jiangsu
Key Laboratory of Molecular and Functional Imaging, Department of
Radiology, Zhongda Hospital, Medical School
of Southeast University, Nanjing, Jiangsu 210009, China
| | - Guanshu Liu
- F.M.
Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland 21205, United States
- Russell
H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Hui Mao
- Department
of Radiology and Imaging Sciences, Emory
University School of Medicine, Atlanta, Georgia 30322, United States
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26
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Sasikumar S, Strafella AP. Structural and Molecular Imaging for Clinically Uncertain Parkinsonism. Semin Neurol 2023; 43:95-105. [PMID: 36878467 DOI: 10.1055/s-0043-1764228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
Neuroimaging is an important adjunct to the clinical assessment of Parkinson disease (PD). Parkinsonism can be challenging to differentiate, especially in early disease stages, when it mimics other movement disorders or when there is a poor response to dopaminergic therapies. There is also a discrepancy between the phenotypic presentation of degenerative parkinsonism and the pathological outcome. The emergence of more sophisticated and accessible neuroimaging can identify molecular mechanisms of PD, the variation between clinical phenotypes, and the compensatory mechanisms that occur with disease progression. Ultra-high-field imaging techniques have improved spatial resolution and contrast that can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. We highlight the imaging modalities that can be accessed in clinical practice and recommend an approach to the diagnosis of clinically uncertain parkinsonism.
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Affiliation(s)
- Sanskriti Sasikumar
- Morton and Gloria Shulman Movement Disorder Unit and Edmond J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, University of Toronto, Toronto Western Hospital, UHN, Ontario, Canada
| | - Antonio P Strafella
- Morton and Gloria Shulman Movement Disorder Unit and Edmond J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, University of Toronto, Toronto Western Hospital, UHN, Ontario, Canada.,Krembil Brain Institute, University Health Network and Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
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27
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Cognolato F, O'Brien K, Jin J, Robinson S, Laun FB, Barth M, Bollmann S. NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data. Med Image Anal 2023; 84:102700. [PMID: 36529002 DOI: 10.1016/j.media.2022.102700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/16/2022] [Accepted: 11/21/2022] [Indexed: 11/24/2022]
Abstract
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.
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Affiliation(s)
- Francesco Cognolato
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Simon Robinson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurology, Medical University of Graz, Graz, Austria; Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Markus Barth
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Steffen Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
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He N, Chen Y, LeWitt PA, Yan F, Haacke EM. Application of Neuromelanin MR Imaging in Parkinson Disease. J Magn Reson Imaging 2023; 57:337-352. [PMID: 36017746 PMCID: PMC10086789 DOI: 10.1002/jmri.28414] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 01/20/2023] Open
Abstract
MRI has been used to develop biomarkers for movement disorders such as Parkinson disease (PD) and other neurodegenerative disorders with parkinsonism such as progressive supranuclear palsy and multiple system atrophy. One of these imaging biomarkers is neuromelanin (NM), whose integrity can be assessed from its contrast and volume. NM is found mainly in certain brain stem structures, namely, the substantia nigra pars compacta (SNpc), the ventral tegmental area, and the locus coeruleus. Another major biomarker is brain iron, which often increases in concert with NM degeneration. These biomarkers have the potential to improve diagnostic certainty in differentiating between PD and other neurodegenerative disorders similar to PD, as well as provide a better understanding of pathophysiology. Mapping NM in vivo has clinical importance for gauging the premotor phase of PD when there is a greater than 50% loss of dopaminergic SNpc melanized neurons. As a metal ion chelator, NM can absorb iron. When NM is released from neurons, it deposits iron into the intracellular tissues of the SNpc; the result is iron that can be imaged and measured using quantitative susceptibility mapping. An increase of iron also leads to the disappearance of the nigrosome-1 sign, another neuroimage biomarker for PD. Therefore, mapping NM and iron changes in the SNpc are a practical means for improving early diagnosis of PD and in monitoring disease progression. In this review, we discuss the functions and location of NM, how NM-MRI is performed, the automatic mapping of NM and iron content, how NM-related imaging biomarkers can be used to enhance PD diagnosis and differentiate it from other neurodegenerative disorders, and potential advances in NM imaging methods. With major advances currently evolving for rapid imaging and artificial intelligence, NM-related biomarkers are likely to have increasingly important roles for enhancing diagnostic capabilities in PD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Peter A LeWitt
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA.,Department of Neurology, Henry Ford Hospital, Parkinson's Disease and Movement Disorders Program, Detroit, Michigan, USA
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.,Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA.,Department of Radiology, Wayne State University School of Medicine, Detroit, Michigan, USA.,SpinTech, Inc, Bingham Farms, Michigan, USA
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Verma S, Goel T, Tanveer M. Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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30
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Guan X, Guo T, Zhou C, Wu J, Zeng Q, Li K, Luo X, Bai X, Wu H, Gao T, Gu L, Liu X, Cao Z, Wen J, Chen J, Wei H, Zhang Y, Liu C, Song Z, Yan Y, Pu J, Zhang B, Xu X, Zhang M. Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study. Neuroimage 2022; 264:119683. [PMID: 36243270 DOI: 10.1016/j.neuroimage.2022.119683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Qingze Zeng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Kaicheng Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xiao Luo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Zhengye Cao
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaping Yan
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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31
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Dadarwal R, Ortiz-Rios M, Boretius S. Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates. Neuroimage 2022; 264:119730. [PMID: 36332851 DOI: 10.1016/j.neuroimage.2022.119730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/12/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.
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Affiliation(s)
- Rakshit Dadarwal
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Georg-August University of Göttingen, Göttingen, Germany.
| | - Michael Ortiz-Rios
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Georg-August University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
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32
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Lancione M, Bosco P, Costagli M, Nigri A, Aquino D, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Pirastru A, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CA, Tosetti M, Biagi L. Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T. Phys Med 2022; 103:37-45. [DOI: 10.1016/j.ejmp.2022.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
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Ji Y, Zheng K, Li S, Ren C, Shen Y, Tian L, Zhu H, Zhou Z, Jiang Y. Insight into the potential role of ferroptosis in neurodegenerative diseases. Front Cell Neurosci 2022; 16:1005182. [PMID: 36385946 PMCID: PMC9647641 DOI: 10.3389/fncel.2022.1005182] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/14/2022] [Indexed: 11/30/2022] Open
Abstract
Ferroptosis is a newly discovered way of programmed cell death, mainly caused by the accumulation of iron-dependent lipid peroxides in cells, which is morphologically, biochemically and genetically different from the previously reported apoptosis, necrosis and autophagy. Studies have found that ferroptosis plays a key role in the occurrence and development of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and vascular dementia, which suggest that ferroptosis may be involved in regulating the progression of neurodegenerative diseases. At present, on the underlying mechanism of ferroptosis in neurodegenerative diseases is still unclear, and relevant research is urgently needed to clarify the regulatory mechanism and provide the possibility for the development of agents targeting ferroptosis. This review focused on the regulatory mechanism of ferroptosis and its various effects in neurodegenerative diseases, in order to provide reference for the research on ferroptosis in neurodegenerative diseases.
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Affiliation(s)
- Yingying Ji
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
| | - Kai Zheng
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
| | - Shiming Li
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
| | - Caili Ren
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
| | - Ying Shen
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lin Tian
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
| | - Haohao Zhu
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
- *Correspondence: Haohao Zhu
| | - Zhenhe Zhou
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
- Zhenhe Zhou
| | - Ying Jiang
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, China
- Ying Jiang
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34
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Murdoch R, Stotesbury H, Kawadler JM, Saunders DE, Kirkham FJ, Shmueli K. Quantitative susceptibility mapping (QSM) and R2 * of silent cerebral infarcts in sickle cell anemia. Front Neurol 2022; 13:1000889. [PMID: 36341122 PMCID: PMC9632444 DOI: 10.3389/fneur.2022.1000889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Silent cerebral infarction (SCI) is the most commonly reported radiological abnormality in patients with sickle cell anemia (SCA) and is associated with future clinical stroke risk. To date, there have been few histological and quantitative MRI studies of SCI and multiple radiological definitions exist. As a result, the tissue characteristics and composition of SCI remain elusive. The objective of this work was therefore to investigate the composition of segmented SCI lesions using quantitative MRI for R2 * and quantitative magnetic susceptibility mapping (QSM). 211 SCI lesions were segmented from 32 participants with SCA and 6 controls. SCI were segmented according to two definitions (FLAIR+/-T1w-based threshold) using a semi-automated pipeline. Magnetic susceptibility (χ) and R2 * maps were calculated from a multi-echo gradient echo sequence and mean SCI values were compared to an equivalent region of interest in normal appearing white matter (NAWM). SCI χ and R2 * were investigated as a function of SCI definition, patient demographics, anatomical location, and cognition. Compared to NAWM, SCI were significantly less diamagnetic (χ = -0.0067 ppm vs. -0.0153 ppm, p < 0.001) and had significantly lower R2 * (16.7 s-1 vs. 19.2 s-1, p < 0.001). SCI definition had a significant effect on the mean SCI χ and R2 * , with lesions becoming significantly less diamagnetic and having significantly lower R2 * after the application of a more stringent T1w-based threshold. SCI-NAWM R2 * decrease was significantly greater in patients with SCA compared with controls (-2.84 s-1 vs. -0.64 s-1, p < 0.0001). No significant association was observed between mean SCI-NAWM χ or R2* differences and subject age, lesion anatomical location, or cognition. The increased χ and decreased R2 * in SCI relative to NAWM observed in both patients and controls is indicative of lower myelin or increased water content within the segmented lesions. The significant SCI-NAWM R2 * differences observed between SCI in patients with SCA and controls suggests there may be differences in tissue composition relative to NAWM in SCI in the two populations. Quantitative MRI techniques such as QSM and R2 * mapping can be used to enhance our understanding of the pathophysiology and composition of SCI in patients with SCA as well as controls.
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Affiliation(s)
- Russell Murdoch
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hanne Stotesbury
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Jamie M. Kawadler
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Dawn E. Saunders
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Fenella J. Kirkham
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- University Hospital Southampton NHS Foundation Trust, and Clinical and Experimental Sciences, University of Southampton, Southampton, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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Khedher L, Bonny JM, Marques A, Durand E, Pereira B, Chupin M, Vidal T, Chassain C, Defebvre L, Carriere N, Fraix V, Moro E, Thobois S, Metereau E, Mangone G, Vidailhet M, Corvol JC, Lehéricy S, Menjot de Champfleur N, Geny C, Spampinato U, Meissner W, Frismand S, Schmitt E, Doé de Maindreville A, Portefaix C, Remy P, Fénelon G, Luc Houeto J, Colin O, Rascol O, Peran P, Durif F. Intrasubject subcortical quantitative referencing to boost MRI sensitivity to Parkinson's disease. Neuroimage Clin 2022; 36:103231. [PMID: 36279753 PMCID: PMC9668635 DOI: 10.1016/j.nicl.2022.103231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Several postmortem studies have shown iron accumulation in the substantia nigra of Parkinson's disease patients. Iron concentration can be estimated via MRI-R2∗ mapping. To assess the changes in R2∗ occurring in Parkinson's disease patients compared to controls, a multicentre transversal study was carried out on a large cohort of Parkinson's disease patients (n = 163) with matched controls (n = 82). In this study, 44 patients and 11 controls were removed due to motion artefacts, 21 patient and 6 controls to preserve matching. Thus, 98 patients and 65 age and sex-matched healthy subjects were selected with enough image quality. The study was conducted on patients with early to late stage Parkinson's disease. The images were acquired at 3Tesla in 12 clinical centres. R2∗ values were measured in subcortical regions of interest (substantia nigra, red nucleus, striatum, globus pallidus externus and globus pallidus internus) contralateral (dominant side) and ipsilateral (non dominant side) to the most clinically affected hemibody. As the observed inter-subject R2∗ variability was significantly higher than the disease effect, an original strategy (intrasubject subcortical quantitative referencing, ISQR) was developed using the measurement of R2∗ in the red nucleus as an intra-subject reference. R2∗ values significantly increased in Parkinson's disease patients when compared with controls; in the substantia nigra (SN) in the dominant side (D) and in the non dominant side (ND), respectively (PSN_D and PSN_ND < 0.0001). After stratification into four subgroups according to the disease duration, no significant R2∗ difference was found in all regions of interest when comparing Parkinson's disease subgroups. By applying our ISQR strategy, R2(ISQR)∗ values significantly increased in the substantia nigra (PSN_D and PSN_ND < 0.0001) when comparing all Parkinson's disease patients to controls. R2(ISQR)∗ values in the substantia nigra significantly increased with the disease duration (PSN_D = 0.01; PSN_ND = 0.03) as well as the severity of the disease (Hoehn & Yahr scale <2 and ≥ 2, PSN_D = 0.02). Additionally, correlations between R2(ISQR)∗ and clinical features, mainly related to the severity of the disease, were found. Our results support the use of ISQR to reduce variations not directly related to Parkinson's disease, supporting the concept that ISQR strategy is useful for the evaluation of Parkinson's disease.
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Affiliation(s)
- Laila Khedher
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,AgroResonance, INRAE, 2018. Nuclear Magnetic Resonance Facility for Agronomy, Food and Health, doi: 10.15454/1.5572398324758228E12, France,Corresponding author at: AgroResonance, INRAE, UR370 QuaPA, Saint-Genès-Champanelle F-63122, France.
| | - Jean-Marie Bonny
- AgroResonance, INRAE, 2018. Nuclear Magnetic Resonance Facility for Agronomy, Food and Health, doi: 10.15454/1.5572398324758228E12, France,AgroResonance UR370 QuaPA - INRAE, Saint-Genès-Champanelle 63122, France
| | - Ana Marques
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Elodie Durand
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Bruno Pereira
- Clermont-Ferrand University Hospital, Biostatistics Unit (DRCI), Clermont-Ferrand, France
| | - Marie Chupin
- Sorbonne Université, Institut du Cerveau - ICM, CATI, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Tiphaine Vidal
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Carine Chassain
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Luc Defebvre
- Department of Movement Disorder and NS-PARK/FCRIN Network, Inserm 1172 University of Lille, Lille, France
| | - Nicolas Carriere
- Department of Movement Disorder and NS-PARK/FCRIN Network, Inserm 1172 University of Lille, Lille, France
| | - Valerie Fraix
- Service de Neurologie, CHU de Grenoble and NS-PARK/FCRIN Network, Université Grenoble Alpes, Grenoble Institute of Neuroscience, Grenoble, France
| | - Elena Moro
- Service de Neurologie, CHU de Grenoble and NS-PARK/FCRIN Network, Université Grenoble Alpes, Grenoble Institute of Neuroscience, Grenoble, France
| | - Stéphane Thobois
- CNRS, Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Lyon, France,Université Claude Bernard, Lyon I, Lyon, France,Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C and NS-PARK/FCRIN Network, Lyon, France
| | - Elise Metereau
- CNRS, Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Lyon, France,Université Claude Bernard, Lyon I, Lyon, France,Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C and NS-PARK/FCRIN Network, Lyon, France
| | - Graziella Mangone
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Marie Vidailhet
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Nicolas Menjot de Champfleur
- Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France,I2FH, Institut d'Imagerie Fonctionnelle Humaine, Hôpital Gui de Chauliac, CHRU de Montpellier, Montpellier, France
| | - Christian Geny
- Department of Geriatrics and NS-PARK/FCRIN Network, Montpellier University Hospital, Montpellier University, Montpellier, France,EuroMov Laboratory, University of Montpellier, 700 Avenue du Pic Saint Loup, Montpellier, Montpellier 34090, France
| | - Umberto Spampinato
- Service de Neurologie - Maladies Neurodégénératives and NS-PARK/FCRIN Network, CHU Bordeaux, Bordeaux F-33000, France
| | - Wassilios Meissner
- Service de Neurologie - Maladies Neurodégénératives and NS-PARK/FCRIN Network, CHU Bordeaux, Bordeaux F-33000, France,Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, Bordeaux F-33000, France,Dept. Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Solène Frismand
- Service de Neurologie and NS-PARK/FCRIN Network, CHRU-Nancy, Nancy, France
| | - Emmanuelle Schmitt
- Service de Neurologie and NS-PARK/FCRIN Network, CHRU-Nancy, Nancy, France
| | | | - Christophe Portefaix
- Department of Radiology, Hôpital Maison blanche, Reims, France,CReSTIC Laboratory (EA 3804), University of Reims Champagne-Ardenne, Reims, France
| | - Philippe Remy
- Centre Expert Parkinson and NS-PARK/FCRIN Network, CHU Henri Mondor, AP-HP et Equipe Neuropsychologie Interventionnelle, INSERM-IMRB, Faculté de Santé, Université Paris-Est Créteil et Ecole Normale Supérieure Paris Sorbonne Université, Créteil, France
| | - Gilles Fénelon
- Centre Expert Parkinson and NS-PARK/FCRIN Network, CHU Henri Mondor, AP-HP et Equipe Neuropsychologie Interventionnelle, INSERM-IMRB, Faculté de Santé, Université Paris-Est Créteil et Ecole Normale Supérieure Paris Sorbonne Université, Créteil, France
| | - Jean Luc Houeto
- INSERM, CHU de Poitiers, Université de Poitiers, Centre d’Investigation Clinique CIC1402, Service de Neurologie and NS-PARK/FCRIN Network, Poitiers, France – CHU - Centre Expert Parkinson de Limoges, Limoges, France
| | - Olivier Colin
- INSERM, CHU de Poitiers, Université de Poitiers, Centre d’Investigation Clinique CIC1402, Service de Neurologie and NS-PARK/FCRIN Network, Poitiers, France– CH Brive la Gaillarde, France
| | - Olivier Rascol
- Centre d'Investigation Clinique CIC 1436, UMR 1214 TONIC and NS-PARK/FCRIN Network, INSERM, CHU de Toulouse et Université de Toulouse3, Toulouse, France
| | - Patrice Peran
- Centre d'Investigation Clinique CIC 1436, UMR 1214 TONIC and NS-PARK/FCRIN Network, INSERM, CHU de Toulouse et Université de Toulouse3, Toulouse, France
| | - Franck Durif
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
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Arribarat G, Cartiaux B, Boucher S, Montel C, Gros-Dagnac H, Fave Y, Péran P, Mogicato G, Deviers A. Ex vivo susceptibility-weighted imaging anatomy of canine brain–comparison of imaging and histological sections. Front Neuroanat 2022; 16:948159. [PMID: 36124091 PMCID: PMC9481421 DOI: 10.3389/fnana.2022.948159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Now that access of large domestic mammals to high-field MRI becomes more common, techniques initially implemented for human patients can be used for the structural and functional study of the brain of these animals. Among them, susceptibility-weighted imaging (SWI) is a recent technique obtained from gradient echo (GE) imaging that allow for an excellent anatomical tissue contrast and a non-invasive assessment of brain iron content. The goal of this study was to design an optimal GE SWI imaging protocol to be used in dogs undergoing an MRI examination of the brain in a 3-Tesla scanner. This imaging protocol was applied to ex vivo brains from four dogs. The imaging protocol was validated by visual inspection of the SWI images that provided a high anatomical detail, as demonstrated by their comparison with corresponding microscopic sections. As resolvable brain structures were labeled, this study is the first to provide an anatomic description of SWI images of the canine brain. Once validated in living animals, this GE SWI imaging protocol could be easily included in routine neuroimaging protocols to improve the diagnosis of various intracranial diseases of dogs, or be used in future comparative studies aiming at evaluating brain iron content in animals.
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Affiliation(s)
- Germain Arribarat
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Benjamin Cartiaux
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France
| | - Samuel Boucher
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Charles Montel
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France
| | - Hélène Gros-Dagnac
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Yoann Fave
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Giovanni Mogicato
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France
- *Correspondence: Giovanni Mogicato
| | - Alexandra Deviers
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France
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Uchida Y, Kan H, Sakurai K, Oishi K, Matsukawa N. Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations. Front Neurosci 2022; 16:938092. [PMID: 35992906 PMCID: PMC9389285 DOI: 10.3389/fnins.2022.938092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/14/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and a distressing diagnosis for individuals and caregivers. Researchers and clinical trials have mainly focused on β-amyloid plaques, which are hypothesized to be one of the most important factors for neurodegeneration in AD. Meanwhile, recent clinicopathological and radiological studies have shown closer associations of tau pathology rather than β-amyloid pathology with the onset and progression of Alzheimer’s symptoms. Toward a biological definition of biomarker-based research framework for AD, the 2018 National Institute on Aging–Alzheimer’s Association working group has updated the ATN classification system for stratifying disease status in accordance with relevant pathological biomarker profiles, such as cerebral β-amyloid deposition, hyperphosphorylated tau, and neurodegeneration. In addition, altered iron metabolism has been considered to interact with abnormal proteins related to AD pathology thorough generating oxidative stress, as some prior histochemical and histopathological studies supported this iron-mediated pathomechanism. Quantitative susceptibility mapping (QSM) has recently become more popular as a non-invasive magnetic resonance technique to quantify local tissue susceptibility with high spatial resolution, which is sensitive to the presence of iron. The association of cerebral susceptibility values with other pathological biomarkers for AD has been investigated using various QSM techniques; however, direct evidence of these associations remains elusive. In this review, we first briefly describe the principles of QSM. Second, we focus on a large variety of QSM applications, ranging from common applications, such as cerebral iron deposition, to more recent applications, such as the assessment of impaired myelination, quantification of venous oxygen saturation, and measurement of blood– brain barrier function in clinical settings for AD. Third, we mention the relationships among QSM, established biomarkers, and cognitive performance in AD. Finally, we discuss the role of QSM as an imaging biomarker as well as the expectations and limitations of clinically useful diagnostic and therapeutic implications for AD.
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Affiliation(s)
- Yuto Uchida
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Yuto Uchida,
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, Ōbu, Japan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Noriyuki Matsukawa,
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Du G, Wang E, Sica C, Chen H, De Jesus S, Lewis MM, Kong L, Connor J, Mailman RB, Huang X. Dynamics of Nigral Iron Accumulation in Parkinson's Disease: From Diagnosis to Late Stage. Mov Disord 2022; 37:1654-1662. [PMID: 35614551 PMCID: PMC9810258 DOI: 10.1002/mds.29062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/08/2022] [Accepted: 05/02/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Higher nigral iron has been reported in Parkinson's disease (PD). OBJECTIVE The aim is to understand the dynamics of nigral iron accumulation in PD and its association with drug treatment. METHODS Susceptibility magnetic resonance imaging data were obtained from 79 controls and 18 drug-naive (PDDN ) and 87 drug-treated (PDDT ) PD patients. Regional brain iron in basal ganglia and cerebellar structures was estimated using quantitative susceptibility mapping. Nigral iron was compared between PDDN and PDDT subgroups defined by disease duration (early [PDE, <2 years], middle [PDM, 2-6 years], and later [PDL, >6 years]). Associations with both disease duration and types of antiparkinson drugs were explored using regression analysis. RESULTS Compared to controls, PDDN had lower iron in the substantia nigra (P = 0.018), caudate nucleus (P = 0.038), and globus pallidus (P = 0.01) but not in the putamen or red nucleus. In contrast, PDDT had higher iron in the nigra (P < 0.001) but not in other regions, compared to either controls or PDDN . Iron in the nigra increased with disease duration (PDE > PDDN [P = 0.001], PDM > PDE [P = 0.045]) except for PDM versus PDL (P = 0.226). Levodopa usage was associated with higher (P = 0.013) nigral iron, whereas lower nigral iron was correlated with selegiline usage (P = 0.030). CONCLUSION Nigral iron is lower before the start of dopaminergic medication and then increases throughout the disease until it plateaus at late stages, suggesting increased iron may not be an etiological factor. Interestingly, PD medications may have differential associations with iron accumulation that need further investigation. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Guangwei Du
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033,Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Ernest Wang
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Christopher Sica
- Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Hairong Chen
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Sol De Jesus
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Mechelle M. Lewis
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033,Department of Pharmacology, Pennsylvania State College of Medicine, Hershey, PA 17033
| | - Lan Kong
- School of Public Health Sciences, Pennsylvania State College of Medicine, Hershey, PA 17033
| | - James Connor
- Department of Neurosurgery, Penn State Milton S. Hershey Medical Center, Hershey PA 17033
| | - Richard B. Mailman
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033,Department of Pharmacology, Pennsylvania State College of Medicine, Hershey, PA 17033
| | - Xuemei Huang
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey PA 17033,Department of Pharmacology, Pennsylvania State College of Medicine, Hershey, PA 17033,Department of Kinesiology, Pennsylvania State University, University Park PA 16802
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Lancione M, Donatelli G, Del Prete E, Campese N, Frosini D, Cencini M, Costagli M, Biagi L, Lucchi G, Tosetti M, Godani M, Arnaldi D, Terzaghi M, Provini F, Pacchetti C, Cortelli P, Bonanni E, Ceravolo R, Cosottini M. Evaluation of iron overload in nigrosome 1 via quantitative susceptibility mapping as a progression biomarker in prodromal stages of synucleinopathies. Neuroimage 2022; 260:119454. [PMID: 35810938 DOI: 10.1016/j.neuroimage.2022.119454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/17/2022] [Accepted: 07/05/2022] [Indexed: 10/17/2022] Open
Abstract
Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies, such as Parkinson's disease (PD), which are characterized by the loss of dopaminergic neurons in substantia nigra, associated with abnormal iron load. The assessment of presymptomatic biomarkers predicting the onset of neurodegenerative disorders is critical for monitoring early signs, screening patients for neuroprotective clinical trials and understanding the causal relationship between iron accumulation processes and disease development. Here, we used Quantitative Susceptibility Mapping (QSM) and 7T MRI to quantify iron deposition in Nigrosome 1 (N1) in early PD (ePD) patients, iRBD patients and healthy controls and investigated group differences and correlation with disease progression. We evaluated the radiological appearance of N1 and analyzed its iron content in 35 ePD, 30 iRBD patients and 14 healthy controls via T2*-weighted sequences and susceptibility (χ) maps. N1 regions of interest (ROIs) were manually drawn on control subjects and warped onto a study-specific template to obtain probabilistic N1 ROIs. For each subject the N1 with the highest mean χ was considered for statistical analysis. The appearance of N1 was rated pathological in 45% of iRBD patients. ePD patients showed increased N1 χ compared to iRBD patients and HC but no correlation with disease duration, indicating that iron load remains stable during the early stages of disease progression. Although no difference was reported in iron content between iRBD and HC, N1 χ in the iRBD group increases as the disease evolves. QSM can reveal temporal changes in N1 iron content and its quantification may represent a valuable presymptomatic biomarker to assess neurodegeneration in the prodromal stages of PD.
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Affiliation(s)
- Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy; Imago7 Research Foundation, Pisa, Italy
| | - Graziella Donatelli
- Imago7 Research Foundation, Pisa, Italy; Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.
| | - Eleonora Del Prete
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Nicole Campese
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Daniela Frosini
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Matteo Cencini
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy; Imago7 Research Foundation, Pisa, Italy
| | - Mauro Costagli
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy; Imago7 Research Foundation, Pisa, Italy
| | - Giacomo Lucchi
- Neuroradiology Unit, Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy; Imago7 Research Foundation, Pisa, Italy
| | | | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Michele Terzaghi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; IRCCS Mondino Foundation, Pavia, Italy
| | - Federica Provini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Clinica Neurologica Rete Metropolitana, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Claudio Pacchetti
- Parkinson's Disease and Movement Disorders Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Pietro Cortelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Clinica Neurologica Rete Metropolitana, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Enrica Bonanni
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Roberto Ceravolo
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Mirco Cosottini
- Neuroradiology Unit, Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
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Gao Y, Xiong Z, Fazlollahi A, Nestor PJ, Vegh V, Nasrallah F, Winter C, Pike GB, Crozier S, Liu F, Sun H. Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks. Neuroimage 2022; 259:119410. [PMID: 35753595 DOI: 10.1016/j.neuroimage.2022.119410] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/12/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 seconds using the trained iQFM and iQSM neural networks.
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Affiliation(s)
- Yang Gao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Zhuang Xiong
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Amir Fazlollahi
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Peter J Nestor
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, Brisbane, Australia
| | - Fatima Nasrallah
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Craig Winter
- Kenneth G Jamieson Department of Neurosurgery, Royal Brisbane and Women's Hospital, Brisbane, Australia; Centre for Clinical Research, University of Queensland, Brisbane, Australia; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Australia
| | - G Bruce Pike
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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Mazzucchi S, Del Prete E, Costagli M, Frosini D, Paoli D, Migaleddu G, Cecchi P, Donatelli G, Morganti R, Siciliano G, Cosottini M, Ceravolo R. Morphometric imaging and quantitative susceptibility mapping as complementary tools in the diagnosis of parkinsonisms. Eur J Neurol 2022; 29:2944-2955. [PMID: 35700041 PMCID: PMC9545010 DOI: 10.1111/ene.15447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 11/26/2022]
Abstract
Background and purpose In the quest for in vivo diagnostic biomarkers to discriminate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA, mainly p phenotype), many advanced magnetic resonance imaging (MRI) techniques have been studied. Morphometric indices, such as the Magnetic Resonance Parkinsonism Index (MRPI), demonstrated high diagnostic value in the comparison between PD and PSP. The potential of quantitative susceptibility mapping (QSM) was hypothesized, as increased magnetic susceptibility (Δχ) was reported in the red nucleus (RN) and medial part of the substantia nigra (SNImed) of PSP patients and in the putamen of MSA patients. However, disease‐specific susceptibility values for relevant regions of interest are yet to be identified. The aims of the study were to evaluate the diagnostic potential of a multimodal MRI protocol combining morphometric and QSM imaging in patients with determined parkinsonisms and to explore its value in a population of undetermined cases. Method Patients with suspected degenerative parkinsonism underwent clinical evaluation, 3 T brain MRI and clinical follow‐up. The MRPI was manually calculated on T1‐weighted images. QSM maps were generated from 3D multi‐echo T2*‐weighted sequences. Results In determined cases the morphometric evaluation confirmed optimal diagnostic accuracy in the comparison between PD and PSP but failed to discriminate PD from MSA‐p. Significant nigral and extranigral differences were found with QSM. RN Δχ showed excellent diagnostic accuracy in the comparison between PD and PSP and good accuracy in the comparison of PD and MSA‐p. Optimal susceptibility cut‐off values of RN and SNImed were tested in undetermined cases in addition to MRPI. Conclusions A combined use of morphometric imaging and QSM could improve the diagnostic phase of degenerative parkinsonisms.
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Affiliation(s)
- Sonia Mazzucchi
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eleonora Del Prete
- Neurology Unit, Department of Medical Specialties, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy.,Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Daniela Frosini
- Neurology Unit, Department of Medical Specialties, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Davide Paoli
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Paolo Cecchi
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Graziella Donatelli
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.,Imago7 Research Foundation, Pisa, Italy
| | | | - Gabriele Siciliano
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Mirco Cosottini
- Imago7 Research Foundation, Pisa, Italy.,Neuroradiology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberto Ceravolo
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Centre for Neurodegenerative Diseases, Parkinson's Disease and Movement Disorders, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
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Lancione M, Cencini M, Costagli M, Donatelli G, Tosetti M, Giannini G, Zangaglia R, Calandra-Buonaura G, Pacchetti C, Cortelli P, Cosottini M. Diagnostic accuracy of quantitative susceptibility mapping in multiple system atrophy: The impact of echo time and the potential of histogram analysis. Neuroimage Clin 2022; 34:102989. [PMID: 35303599 PMCID: PMC8927993 DOI: 10.1016/j.nicl.2022.102989] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/25/2022] [Accepted: 03/10/2022] [Indexed: 11/07/2022]
Abstract
We performed histogram analysis on χ maps at different TEs on MSA patients and HC. We found altered χ distribution in Pu, SN, GP, CN for MSAp and in SN, DN for MSAc. QSM diagnostic accuracy is TE-dependent and is enhanced at short TEs. Short TEs capture rapidly-decaying contributions of high χ sources. Histogram features detect χ spatial heterogeneities improving diagnostic accuracy.
The non-invasive quantification of iron stores via Quantitative Susceptibility Mapping (QSM) could play an important role in the diagnosis and the differential diagnosis of atypical Parkinsonisms. However, the susceptibility (χ) values measured via QSM depend on echo time (TE). This effect relates to the microstructural organization within the voxel, whose composition can be altered by the disease. Moreover, pathological iron deposition in a brain area may not be spatially uniform, and conventional Region of Interest (ROI)-based analysis may fail in detecting alterations. Therefore, in this work we evaluated the impact of echo time on the diagnostic accuracy of QSM on a population of patients with Multiple System Atrophy (MSA) of either Parkinsonian (MSAp) or cerebellar (MSAc) phenotypes. In addition, we tested the potential of histogram analysis to improve QSM classification accuracy. We enrolled 32 patients (19 MSAp and 13 MSAc) and 16 healthy controls, who underwent a 7T MRI session including a gradient-recalled multi-echo sequence for χ mapping. Nine histogram features were extracted from the χ maps computed for each TE in atlas-based ROIs covering deep brain nuclei, and compared among groups. Alterations of susceptibility distribution were found in the Putamen, Substantia Nigra, Globus Pallidus and Caudate Nucleus for MSAp and in the Substantia Nigra and Dentate Nucleus for MSAc. Increased iron deposition was observed in a larger number of ROIs for the two shortest TEs and the standard deviation, the 75th and the 90th percentile were the most informative features yielding excellent diagnostic accuracy with area under the ROC curve > 0.9. In conclusion, short TEs may enhance QSM diagnostic performances, as they can capture variations in rapidly-decaying contributions of high χ sources. The analysis of histogram features allowed to reveal fine heterogeneities in the spatial distribution of susceptibility alteration, otherwise undetected by a simple evaluation of ROI χ mean values.
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Affiliation(s)
- Marta Lancione
- IRCCS Stella Maris, Pisa, Italy; IMAGO7 Foundation, Pisa, Italy
| | - Matteo Cencini
- IRCCS Stella Maris, Pisa, Italy; IMAGO7 Foundation, Pisa, Italy
| | - Mauro Costagli
- IRCCS Stella Maris, Pisa, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genova, Genova, Italy.
| | - Graziella Donatelli
- IMAGO7 Foundation, Pisa, Italy; Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Michela Tosetti
- IRCCS Stella Maris, Pisa, Italy; IMAGO7 Foundation, Pisa, Italy
| | - Giulia Giannini
- Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Roberta Zangaglia
- Parkinson and Movement Disorder Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanna Calandra-Buonaura
- Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudio Pacchetti
- Parkinson and Movement Disorder Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Pietro Cortelli
- Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
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Oh G, Bae H, Ahn HS, Park SH, Moon WJ, Ye JC. Unsupervised Resolution-Agnostic Quantitative Susceptibility Mapping using Adaptive Instance Normalization. Med Image Anal 2022; 79:102477. [DOI: 10.1016/j.media.2022.102477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022]
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Rao IY, Hanson LR, Johnson JC, Rosenbloom MH, Frey WH. Brain Glucose Hypometabolism and Iron Accumulation in Different Brain Regions in Alzheimer's and Parkinson's Diseases. Pharmaceuticals (Basel) 2022; 15:551. [PMID: 35631378 PMCID: PMC9143620 DOI: 10.3390/ph15050551] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/17/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023] Open
Abstract
The aim of this study was to examine the relationship between the presence of glucose hypometabolism (GHM) and brain iron accumulation (BIA), two potential pathological mechanisms in neurodegenerative disease, in different regions of the brain in people with late-onset Alzheimer's disease (AD) or Parkinson's disease (PD). Studies that conducted fluorodeoxyglucose positron emission tomography (FDG-PET) to map GHM or quantitative susceptibility mapping-magnetic resonance imaging (QSM-MRI) to map BIA in the brains of patients with AD or PD were reviewed. Regions of the brain where GHM or BIA were reported in each disease were compared. In AD, both GHM and BIA were reported in the hippocampus, temporal, and parietal lobes. GHM alone was reported in the cingulate gyrus, precuneus and occipital lobe. BIA alone was reported in the caudate nucleus, putamen and globus pallidus. In PD, both GHM and BIA were reported in thalamus, globus pallidus, putamen, hippocampus, and temporal and frontal lobes. GHM alone was reported in cingulate gyrus, caudate nucleus, cerebellum, and parietal and occipital lobes. BIA alone was reported in the substantia nigra and red nucleus. GHM and BIA are observed independent of one another in various brain regions in both AD and PD. This suggests that GHM is not always necessary or sufficient to cause BIA and vice versa. Hypothesis-driven FDG-PET and QSM-MRI imaging studies, where both are conducted on individuals with AD or PD, are needed to confirm or disprove the observations presented here about the potential relationship or lack thereof between GHM and BIA in AD and PD.
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Affiliation(s)
- Indira Y. Rao
- HealthPartners Center for Memory and Aging, 295 Phalen Boulevard, St. Paul, MN 55130, USA; (I.Y.R.); (L.R.H.); (M.H.R.)
| | - Leah R. Hanson
- HealthPartners Center for Memory and Aging, 295 Phalen Boulevard, St. Paul, MN 55130, USA; (I.Y.R.); (L.R.H.); (M.H.R.)
- HealthPartners Institute, Bloomington, MN 55425, USA
| | - Julia C. Johnson
- HealthPartners Struthers Parkinson’s Center, Minneapolis, MN 55427, USA;
| | - Michael H. Rosenbloom
- HealthPartners Center for Memory and Aging, 295 Phalen Boulevard, St. Paul, MN 55130, USA; (I.Y.R.); (L.R.H.); (M.H.R.)
| | - William H. Frey
- HealthPartners Center for Memory and Aging, 295 Phalen Boulevard, St. Paul, MN 55130, USA; (I.Y.R.); (L.R.H.); (M.H.R.)
- HealthPartners Institute, Bloomington, MN 55425, USA
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Lambert M, Tejos C, Langkammer C, Milovic C. Hybrid data fidelity term approach for quantitative susceptibility mapping. Magn Reson Med 2022; 88:962-972. [PMID: 35435267 PMCID: PMC9324845 DOI: 10.1002/mrm.29218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 11/06/2022]
Abstract
Purpose Methods Results Conclusions
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Affiliation(s)
- Mathias Lambert
- Department of Electrical Engineering Pontificia Universidad Catolica de Chile Santiago Chile
- Biomedical Imaging Center Pontificia Universidad Catolica de Chile Santiago Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Santiago Chile
| | - Cristian Tejos
- Department of Electrical Engineering Pontificia Universidad Catolica de Chile Santiago Chile
- Biomedical Imaging Center Pontificia Universidad Catolica de Chile Santiago Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Santiago Chile
| | - Christian Langkammer
- Department of Neurology Medical University of Graz Graz Austria
- BioTechMed Graz Graz Austria
| | - Carlos Milovic
- Department of Electrical Engineering Pontificia Universidad Catolica de Chile Santiago Chile
- Biomedical Imaging Center Pontificia Universidad Catolica de Chile Santiago Chile
- Department of Medical Physics and Biomedical Engineering University College London London UK
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Nikparast F, Ganji Z, Danesh Doust M, Faraji R, Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process? Insights Imaging 2022; 13:74. [PMID: 35416533 PMCID: PMC9008086 DOI: 10.1186/s13244-022-01207-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/13/2022] [Indexed: 12/14/2022] Open
Abstract
The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.
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Affiliation(s)
- Farzaneh Nikparast
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Danesh Doust
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. .,Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR Biomed 2022; 35:e4292. [PMID: 32207195 DOI: 10.1002/nbm.4292] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
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Affiliation(s)
- Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
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Yang L, Cheng Y, Sun Y, Xuan Y, Niu J, Guan J, Rong Y, Jia Y, Zhuang Z, Yan G, Wu R. Combined Application of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Techniques to Investigate the Effect of Iron Deposition on Microstructural Changes in the Brain in Parkinson's Disease. Front Aging Neurosci 2022; 14:792778. [PMID: 35370619 PMCID: PMC8965454 DOI: 10.3389/fnagi.2022.792778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 02/23/2022] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES Brain iron deposition and microstructural changes in brain tissue are associated with Parkinson's disease (PD). However, the correlation between these factors in Parkinson's disease has been little studied. This study aimed to use quantitative susceptibility mapping combined with diffusion kurtosis imaging to investigate the effects of iron deposition on microstructural tissue alterations in the brain. METHODS Quantitative susceptibility mapping and diffusion kurtosis imaging were performed on 24 patients with early PD, 13 patients with advanced PD, and 25 healthy controls. The mean values of magnetic susceptibility and diffusion kurtosis were calculated for the bilateral substantia nigra, red nucleus, putamen, globus pallidus, and caudate nucleus, and compared between the groups. Correlation analyses between the diffusion kurtosis of each nucleus and its magnetic susceptibility parameters in PD patients and healthy controls were performed. RESULTS The study found a significant increase in iron deposition in the substantia nigra, red nucleus, putamen and globus pallidus, bilaterally, in patients with PD. Mean kurtosis values were increased in the substantia nigra but decreased in the globus pallidus; axial kurtosis values were decreased in both the substantia nigra and red nucleus; radial kurtosis values were increased in the substantia nigra but showed an opposite trend in the globus pallidus and caudate nucleus. In the substantia nigra of patients with PD, magnetic susceptibility was positively correlated with mean and radial kurtosis values, and negatively correlated with axial kurtosis. None of these correlations were significantly different in the control group. In the putamen, magnetic susceptibility was positively correlated with mean, axial, and radial kurtosis only in patients with advanced-stage PD. CONCLUSION Our study provides new evidence for brain iron content and microstructural alterations in patients with PD. Iron deposition may be a common mechanism for microstructural alterations in the substantia nigra and putamen of patients with PD. Tracking the dynamic changes in iron content and microstructure throughout the course of PD will help us to better understand the dynamics of iron metabolism and microstructural alterations in the pathogenesis of PD and to develop new approaches to monitor and treat PD.
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Affiliation(s)
- Lin Yang
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Yan Cheng
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Yongyan Sun
- Department of Pharmacy, Guangdong Second Provincial General Hospital, Zhuhai Hospital, Zhuhai, China
| | - Yinghua Xuan
- Department of Basic Medicine, Xiamen Medical College, Xiamen, China
| | - Jianping Niu
- Department of Neurology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Jitian Guan
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Yunjie Rong
- Department of Ultrasound, Foshan Women and Children’s Hospital Affiliated to Southern Medical University, Foshan, China
| | - Yanlong Jia
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Zerui Zhuang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Gen Yan
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Renhua Wu
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
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Reith TP, Prah MA, Choi EJ, Lee J, Wujek R, Al-Gizawiy M, Chitambar CR, Connelly JM, Schmainda KM. Basal Ganglia Iron Content Increases with Glioma Severity Using Quantitative Susceptibility Mapping: A Potential Biomarker of Tumor Severity. Tomography 2022; 8:789-797. [PMID: 35314642 PMCID: PMC8938779 DOI: 10.3390/tomography8020065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/25/2022] [Accepted: 03/08/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND AND PURPOSE Gliomas have been found to alter iron metabolism and transport in ways that result in an expansion of their intracellular iron compartments to support aggressive tumor growth. This study used deep neural network trained quantitative susceptibility mapping to assess basal ganglia iron concentrations in glioma patients. MATERIALS AND METHODS Ninety-two patients with brain lesions were initially enrolled in this study and fifty-nine met the inclusion criteria. Susceptibility-weighted images were collected at 3.0 T and used to construct quantitative susceptibility maps via a deep neural network-based method. The regions of interest were manually drawn within basal ganglia structures and the mean voxel intensities were extracted and averaged across multiple slices. One-way ANCOVA tests were conducted to compare the susceptibility values of groups of patients based on tumor grade while controlling for age, sex, and tumor type. RESULTS The mean basal ganglia susceptibility for patients with grade IV tumors was higher than that for patients with grade II tumors (p = 0.00153) and was also higher for patients with grade III tumors compared to patients with grade II tumors (p = 0.020), after controlling for age, sex, and tumor type. Patient age influenced susceptibility values (p = 0.00356), while sex (p = 0.69) and tumor type (p = 0.11) did not. CONCLUSIONS The basal ganglia iron content increased with glioma severity. Basal ganglia iron levels may thus be a useful biomarker in glioma prognosis and treatment, especially with regard to iron-based cancer therapies.
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Affiliation(s)
- Thomas P. Reith
- Medical College of Wisconsin, Biophysics, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (T.P.R.); (M.A.P.); (M.A.-G.); (C.R.C.)
| | - Melissa A. Prah
- Medical College of Wisconsin, Biophysics, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (T.P.R.); (M.A.P.); (M.A.-G.); (C.R.C.)
| | - Eun-Jung Choi
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea; (E.-J.C.); (J.L.)
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea; (E.-J.C.); (J.L.)
| | - Robert Wujek
- Medical College of Wisconsin, Biomedical Engineering, Marquette University, 1515 W. Wisconsin Ave., Milwaukee, WI 53233, USA;
| | - Mona Al-Gizawiy
- Medical College of Wisconsin, Biophysics, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (T.P.R.); (M.A.P.); (M.A.-G.); (C.R.C.)
| | - Christopher R. Chitambar
- Medical College of Wisconsin, Biophysics, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (T.P.R.); (M.A.P.); (M.A.-G.); (C.R.C.)
- Medical College of Wisconsin, Hematology & Oncology, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Jennifer M. Connelly
- Medical College of Wisconsin, Neurology & Neurosurgery, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kathleen M. Schmainda
- Medical College of Wisconsin, Biophysics, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (T.P.R.); (M.A.P.); (M.A.-G.); (C.R.C.)
- Medical College of Wisconsin, Radiology, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Correspondence:
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Zhang Y, Yang M, Wang F, Chen Y, Liu R, Zhang Z, Jiang Z. Histogram Analysis of Quantitative Susceptibility Mapping for the Diagnosis of Parkinson's Disease. Acad Radiol 2022; 29 Suppl 3:S71-S79. [PMID: 33189552 DOI: 10.1016/j.acra.2020.10.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/14/2020] [Accepted: 10/26/2020] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the diagnostic performance of histogram analysis combined with quantitative susceptibility mapping (QSM) for differentiating Parkinson's disease (PD) patients from healthy controls. METHODS We included 35 patients with PD diagnosed by two neurologists from August 2019 to January 2020 in our hospital in this prospective study. The clinical diagnosis was based on the Movement Disorder Society Clinical Diagnostic Criteria for PD. At the same time, 23 healthy volunteers matched for age and sex were recruited as controls. The Mini Mental State Examination, the third part of the Parkinson's Disease Rating Scale, the Hoehn & Yahr stages, and disease duration (year) were used to assess the PD patients. QSM was performed using a 3T MR scanner. The regions of interest were depicted according to the head of the caudate nucleus(CN), globus pallidus(GP), putamina (PUT), thalmus(TH), substantia nigra (SN), red nucleus(RN), and dentate nucleus. Then the corresponding histogram features were extracted. The Mann-Whitney U test was used to identify significant histogram features for differentiating PD patients from healthy controls. Area under the receiver operating characteristics curve (AUC) analysis was conducted to evaluate the diagnostic performance of all significant histogram features. Multivariate logistic regression analysis was performed to identify the best combined model for all seven nuclei. Differences among the AUCs were compared pairwise. RESULTS Histogram features in all nuclei except TH showed significant differences between the groups. Among the single features, the 10th percentile of SN (SNP10) yielded the highest AUC of 0.894, with the highest specificity of 86.86% for differentiating PD patients from healthy controls. The 75th percentile of PUT (PUTP75) yielded the highest sensitivity of 97.14%. In the multivariate logistic regression analysis, SNP10 combined with PUTP75 yielded the highest diagnostic performance with the highest AUC of 0.911, the highest specificity of 91.30% and an excellent sensitivity of 92.40%. CONCLUSION QSM combined with histogram analysis successfully distinguished PD patients from healthy controls, and the result was notably superior to the mean value.
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