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Opportunities and challenges of diffusion spectrum magnetic resonance imaging: Achievements and prospects over the past decade in China
MAO Chunping  MAO Jiaji  ZHANG Xiang  WANG Mengzhu  YAN Xu  SHEN Jun 

Cite this article as: Mao CP, Mao JJ, Zhang X, et al. Opportunities and challenges of diffusion spectrum magnetic resonance imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 37-45. DOI:10.12015/issn.1674-8034.2022.10.005.


[Abstract] Diffusion spectrum imaging (DSI) is an emerging advanced diffusion magnetic resonance imaging (dMRI) technology in recent years. Technologically, DSI is reconstructed model-freely, which applies the probability density function (PDF) to acquire diffusion signals in the entire q-space of water molecules within the voxels of human tissues, and uses high angular resolution to accurately detect the information of intricate crossing fibers within the tissues in vivo. DSI fiber tracking is currently the most reliable technique for tracking brain white matter fiber bundles. Conventional dMRI technology can only reflect part of the pathophysiological information of a certain disease. DSI technology can integrate multiple diffusion models into one model and ultimately obtain more comprehensive pathophysiological information of a certain disease. To date, the clinical application of DSI technology has been extended from brain diseases to body diseases initially and show good promise in the diagnosis and evaluation of diseases. However, the DSI technology has certain requirement of hardware of MRI unit. There are still some challenges on the genuinity and quantification of tractography derived from DSI. The optimized selection and combination of advanced diffusion models in certain disease remain to be determine through extended clinical application of DIS in the future. The postprocess technique of DSI is still necessary to be automatized and produced for promoting its wide clinical application in the diagnosis and therapy of different diseases. Herein, the achievements of Chinese scholars in the research of central nervous system diseases and body diseases by using DSI technology in the past decade were reviewed and the current challenges and future direction of DSI were summarized. The purpose is aimed to provide reference for better development of DSI technology and promote its extensive application in clinic.
[Keywords] central nervous system;cancer;Alzheimer's disease;Parkinson's disease;epilepsy;glioma;corticospinal tract injury;breast cancer;magnetic resonance imaging;diffusion spectrum imaging;q-space

MAO Chunping1   MAO Jiaji1   ZHANG Xiang1   WANG Mengzhu2   YAN Xu2   SHEN Jun1*  

1 Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China

2 MR Scientific Marketing, Siemens Medical Systems Co., Ltd, Shanghai 201318, China

Shen J, E-mail: shenjun@mail.sysu.edu.cn

Conflicts of interest   None.

Received  2022-09-06
Accepted  2022-10-14
DOI: 10.12015/issn.1674-8034.2022.10.005
Cite this article as: Mao CP, Mao JJ, Zhang X, et al. Opportunities and challenges of diffusion spectrum magnetic resonance imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 37-45.DOI:10.12015/issn.1674-8034.2022.10.005

[1]
Wedeen VJ, Hagmann P, Tseng WY, et al. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging[J]. Magn Reson Med, 2005, 54(6): 1377-1386. DOI: 10.1002/mrm.20642.
[2]
Glenn GR, Kuo LW, Chao YP, et al. Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging[J]. AJNR Am J Neuroradiol, 2016, 37(7): 1216-1222. DOI: 10.3174/ajnr.A4714.
[3]
Edwards LJ, Pine KJ, Ellerbrock I, et al. NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter[J/OL]. Front Neurosci, 2017, 11 [2022-09-05]. http://www.ajnr.org/content/37/7/1216.long. DOI: 10.3389/fnins.2017.00720.
[4]
Sosnovik DE, Wang R, Dai G, et al. Diffusion MR tractography of the heart[J/OL]. J Cardiovasc Magn Reson, 2009, 11(1) [2022-09-05]. https://jcmr-online.biomedcentral.com/articles/10.1186/1532-429X-11-47. DOI: 10.1186/1532-429X-11-47.
[5]
Kodiweera C, Alexander AL, Harezlak J, et al. Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study[J]. Neuroimage, 2016, 128: 180-192. DOI: 10.1016/j.neuroimage.2015.12.033.
[6]
Özarslan E, Koay CG, Shepherd TM, et al. Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure[J]. Neuroimage, 2013, 78: 16-32. DOI: 10.1016/j.neuroimage.2013.04.016.
[7]
Kuo LW, Chen JH, Wedeen VJ, et al. Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system[J]. Neuroimage, 2008, 41(1): 7-18. DOI: 10.1016/j.neuroimage.2008.02.016.
[8]
Yeh CH, Cho KH, Lin HC, et al. Reduced encoding diffusion spectrum imaging implemented with a bi-Gaussian model[J]. IEEE Trans Med Imaging, 2008, 27(10): 1415-1424. DOI: 10.1109/TMI.2008.922189.
[9]
Reese TG, Benner T, Wang R, et al. Halving imaging time of whole brain diffusion spectrum imaging and diffusion tractography using simultaneous image refocusing in EPI[J]. J Magn Reson Imaging, 2009, 29(3): 517-522. DOI: 10.1002/jmri.21497.
[10]
Canales-Rodríguez EJ, Iturria-Medina Y, Alemán-Gómez Y, et al. Deconvolution in diffusion spectrum imaging[J]. Neuroimage, 2010, 50(1): 136-149. DOI: 10.1016/j.neuroimage.2009.11.066.
[11]
Setsompop K, Cohen-Adad J, Gagoski BA, et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging[J]. Neuroimage, 2012, 63(1): 569-580. DOI: 10.1016/j.neuroimage.2012.06.033.
[12]
Menzel MI, Tan ET, Khare K, et al. Accelerated diffusion spectrum imaging in the human brain using compressed sensing[J]. Magn Reson Med, 2011, 66(5): 1226-1233. DOI: 10.1002/mrm.23064.
[13]
Bilgic B, Setsompop K, Cohen-Adad J, et al. Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries[J]. Magn Reson Med, 2012, 68(6): 1747-1754. DOI: 10.1002/mrm.24505.
[14]
Zhao ZY, Liu XZ, Fan MX, et al. Research progress of diffusion spectrum imaging[J]. Chin J Magn Reson Imaging, 2016, 7(7): 535-540. DOI: 10.12015/issn.1674-8034.2016.07.011.
[15]
Gong ZB, Chen HH, Liu SF, et al. Research progress of magnetic resonance diffusion spectrum imaging in the nervous system[J]. Chin J Magn Reson Imaging, 2020, 11(9): 809-812, 816. DOI: 10.12015/issn.1674-8034.2020.09.020.
[16]
Yang WJ, Zhao SH, Lu MJ. Progress of cardiovascular magnetic resonance diffusion tensor imaging and diffusion spectrum magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2021, 12(10): 93-97. DOI: 10.12015/issn.1674-8034.2021.10.024.
[17]
Leng B, Han S, Bao Y, et al. The uncinate fasciculus as observed using diffusion spectrum imaging in the human brain[J]. Neuroradiology, 2016, 58(6): 595-606. DOI: 10.1007/s00234-016-1650-9.
[18]
Wu Y, Sun D, Wang Y, et al. Tracing short connections of the temporo-parieto-occipital region in the human brain using diffusion spectrum imaging and fiber dissection[J]. Brain Res, 2016, 1646: 152-159. DOI: 10.1016/j.brainres.2016.05.046.
[19]
Wei PH, Mao ZQ, Cong F, et al. In vivo visualization of connections among revised Papez circuit hubs using full q-space diffusion spectrum imaging tractography[J]. Neuroscience, 2017, 357: 400-410. DOI: 10.1016/j.neuroscience.2017.04.003.
[20]
Bao Y, Wang Y, Wang W, et al. The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?[J/OL]. Front Neuroanat, 2017, 11 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fnana.2017.00119/full. DOI: 10.3389/fnana.2017.00119.
[21]
Sun C, Wang Y, Cui R, et al. Human Thalamic-Prefrontal Peduncle Connectivity Revealed by Diffusion Spectrum Imaging Fiber Tracking[J/OL]. Front Neuroanat, 2018, 12 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fnana.2018.00024/full. DOI: 10.3389/fnana.2018.00024.
[22]
Ou SQ, Wei PH, Fan XT, et al. Delineating the Decussating Dentato-rubro-thalamic Tract and Its Connections in Humans Using Diffusion Spectrum Imaging Techniques[J]. Cerebellum, 2022, 21(1): 101-115. DOI: 10.1007/s12311-021-01283-2.
[23]
Lin HY, Gau SS, Huang-Gu SL, et al. Neural substrates of behavioral variability in attention deficit hyperactivity disorder: based on ex-Gaussian reaction time distribution and diffusion spectrum imaging tractography[J]. Psychol Med, 2014, 44(8): 1751-1764. DOI: 10.1017/S0033291713001955.
[24]
Chiang HL, Chen YJ, Shang CY, et al. Different neural substrates for executive functions in youths with ADHD: a diffusion spectrum imaging tractography study[J]. Psychol Med, 2016, 46(6): 1225-1238. DOI: 10.1017/S0033291715002767.
[25]
Chiang HL, Hsu YC, Shang CY, et al. White matter endophenotype candidates for ADHD: a diffusion imaging tractography study with sibling design[J]. Psychol Med, 2020, 50(7): 1203-1213. DOI: 10.1017/S0033291719001120.
[26]
Tsai CJ, Lin HY, Tseng IW, et al. White matter microstructural integrity correlates of emotion dysregulation in children with ADHD: A diffusion imaging tractography study[J/OL]. Prog Neuropsychopharmacol Biol Psychiatry, 2021, 110 [2022-09-05]. https://www.sciencedirect.com/science/article/abs/pii/S0278584621000841. DOI: 10.1016/j.pnpbp.2021.110325.
[27]
Wu CH, Hwang TJ, Chen PJ, et al. Reduced structural integrity and functional lateralization of the dorsal language pathway correlate with hallucinations in schizophrenia: a combined diffusion spectrum imaging and functional magnetic resonance imaging study[J]. Psychiatry Res, 2014, 224(3): 303-310. DOI: 10.1016/j.pscychresns.2014.08.010.
[28]
Lin YC, Shih YC, Tseng WY, et al. Cingulum correlates of cognitive functions in patients with mild cognitive impairment and early Alzheimer's disease: a diffusion spectrum imaging study[J]. Brain Topogr, 2014, 27(3): 393-402. DOI: 10.1007/s10548-013-0346-2.
[29]
Chang YL, Chao RY, Hsu YC, et al. White matter network disruption and cognitive correlates underlying impaired memory awareness in mild cognitive impairment[J/OL]. Neuroimage Clin, 2021, 30 [2022-09-05]. https://www.sciencedirect.com/science/article/pii/S221315822100070X. DOI: 10.1016/j.nicl.2021.102626.
[30]
Zhang Q, Xiao Y, Lin L, et al. Diffusion spectrum imaging in white matter microstructure in subjects with type 2 diabetes[J/OL]. PLoS One, 2018, 13(11) [2022-09-05]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203271. DOI: 10.1371/journal.pone.0203271.
[31]
Tsai TH, Su HT, Hsu YC, et al. White matter microstructural alterations in amblyopic adults revealed by diffusion spectrum imaging with systematic tract-based automatic analysis[J]. Br J Ophthalmol, 2019, 103(4): 511-516. DOI: 10.1136/bjophthalmol-2017-311733.
[32]
Liang L, Lin H, Lin F, et al. Quantitative visual pathway abnormalities predict visual field defects in patients with pituitary adenomas: a diffusion spectrum imaging study[J]. Eur Radiol, 2021, 31(11): 8187-8196. DOI: 10.1007/s00330-021-07878-x.
[33]
Le H, Zeng W, Zhang H, et al. Mean Apparent Propagator MRI Is Better Than Conventional Diffusion Tensor Imaging for the Evaluation of Parkinson's Disease: A Prospective Pilot Study[J/OL]. Front Aging Neurosci, 2020, 12 [2022-09-05]. https://doi.org/10.3389/fnagi.2020.563595. DOI: 10.3389/fnagi.2020.563595.
[34]
Chen HJ, Zhan C, Cai LM, et al. White matter microstructural impairments in amyotrophic lateral sclerosis: A mean apparent propagator MRI study[J/OL]. Neuroimage Clin, 2021, 32 [2022-09-05]. https://www.sciencedirect.com/science/article/pii/S2213158221003077. DOI: 10.1016/j.nicl.2021.102863.
[35]
Wang NY, Wang HS, Liu YC, et al. Investigating the white matter correlates of reading performance: Evidence from Chinese children with reading difficulties[J/OL]. PLoS One, 2021, 16(3) [2022-09-05]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248434. DOI: 10.1371/journal.pone.0248434.
[36]
Fan LY, Lo YC, Hsu YC, et al. Developmental Differences of Structural Connectivity and Effective Connectivity in Semantic Judgments of Chinese Characters[J/OL]. Front Hum Neurosci, 2020, 14 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00233/full. DOI: 10.3389/fnhum.2020.00233.
[37]
Zhang Z, Jia X, Guan X, et al. White Matter Abnormalities of Auditory Neural Pathway in Sudden Sensorineural Hearing Loss Using Diffusion Spectrum Imaging: Different Findings From Tinnitus[J/OL]. Front Neurosci, 2020, 14 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fnins.2020.00200/full. DOI: 10.3389/fnins.2020.00200.
[38]
Zhang Y, Zhang Z, Jia X, et al. Imaging Parameters of the Ipsilateral Medial Geniculate Body May Predict Prognosis of Patients with Idiopathic Unilateral Sudden Sensorineural Hearing Loss on the Basis of Diffusion Spectrum Imaging[J]. AJNR Am J Neuroradiol, 2021, 42(1): 152-159. DOI: 10.3174/ajnr.A6874.
[39]
Wang YH, Wang ZM, Wei PH, et al. Lateralizing the affected side of hippocampal sclerosis with quantitative high angular resolution diffusion scalars: a preliminary approach validated by diffusion spectrum imaging[J/OL]. Ann Transl Med, 2021, 9(4) [2022-09-05]. https://atm.amegroups.com/article/view/62314/html. DOI: 10.21037/atm-20-5719.
[40]
Ma K, Zhang X, Zhang H, et al. Mean apparent propagator-MRI: A new diffusion model which improves temporal lobe epilepsy lateralization[J/OL]. Eur J Radiol, 2020, 126 [2022-09-05]. https://www.ejradiology.com/article/S0720-048X(20)30103-0/fulltext. DOI: 10.1016/j.ejrad.2020.108914.
[41]
Wang ZM, Wei PH, Zhang M, et al. Diffusion spectrum imaging predicts hippocampal sclerosis in mesial temporal lobe epilepsy patients[J]. Ann Clin Transl Neurol, 2022, 9(3): 242-252. DOI: 10.1002/acn3.51503.
[42]
Wei PH, Mao ZQ, Cong F, et al. Connection between bilateral temporal regions: Tractography using human connectome data and diffusion spectrum imaging[J]. J Clin Neurosci, 2017, 39: 103-108. DOI: 10.1016/j.jocn.2017.01.012.
[43]
Zhang H, He WJ, Liang LH, et al. Diffusion Spectrum Imaging of Corticospinal Tracts in Idiopathic Normal Pressure Hydrocephalus[J/OL]. Front Neurol, 2021, 12 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fneur.2021.636518/full. DOI: 10.3389/fneur.2021.636518.
[44]
Yang X, Li H, He W, et al. Quantification of changes in white matter tract fibers in idiopathic normal pressure hydrocephalus based on diffusion spectrum imaging[J/OL]. Eur J Radiol, 2022, 149 [2022-09-05]. https://www.ejradiology.com/article/S0720-048X(22)00044-4/fulltext. DOI: 10.1016/j.ejrad.2022.110194.
[45]
Luo SP, Chen FF, Zhang HW, et al. Trigeminal Nerve White Matter Fiber Abnormalities in Primary Trigeminal Neuralgia: A Diffusion Spectrum Imaging Study[J/OL]. Front Neurol, 2022, 12 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fneur.2021.798969/full. DOI: 10.3389/fneur.2021.798969.
[46]
Mao J, Zeng W, Zhang Q, et al. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models[J/OL]. BMC Med Imaging, 2020, 20(1) [2022-09-05]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-020-00524-w. DOI: 10.1186/s12880-020-00524-w.
[47]
Wang P, Weng L, Xie S, et al. Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma[J/OL]. Eur J Radiol, 2021, 138 [2022-09-05]. https://www.ejradiology.com/article/S0720-048X(21)00102-9/fulltext. DOI: 10.1016/j.ejrad.2021.109622.
[48]
Jiang R, Jiang S, Song S, et al. Laplacian-Regularized Mean Apparent Propagator-MRI in Evaluating Corticospinal Tract Injury in Patients with Brain Glioma[J]. Korean J Radiol, 2021, 22(5): 759-769. DOI: 10.3348/kjr.2020.0949.
[49]
Gao A, Zhang H, Yan X, et al. Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping[J]. Radiology, 2022, 302(3): 652-661. DOI: 10.1148/radiol.210820.
[50]
Mao C, Jiang W, Huang J, et al. Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer[J/OL]. Front Oncol, 2022, 12 [2022-09-05]. https://www.frontiersin.org/articles/10.3389/fonc.2022.817070/full. DOI: 10.3389/fonc.2022.817070.
[51]
Wedeen VJ, Rosene DL, Wang R, et al. The geometric structure of the brain fiber pathways[J]. Science, 2012, 335(6076): 1628-1634. DOI: 10.1126/science.1215280.
[52]
Maier-Hein KH, Neher PF, Houde JC, et al. The challenge of mapping the human connectome based on diffusion tractography[J/OL]. Nat Commun, 2017, 8(1) [2022-09-05]. https://www.nature.com/articles/s41467-017-01285-x. DOI: 10.1038/s41467-017-01285-x.
[53]
Sarwar T, Ramamohanarao K, Zalesky A. A critical review of connectome validation studies[J/OL]. NMR Biomed, 2021, 34(12) [2022-09-05]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4605. DOI: 10.1002/nbm.4605.

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