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Samuel Patz^1,2, W. Scott Hoge^3,4, Bin Deng^2,5, Lauren O'Donnell^1,2, Fan Zhang^1,2, Yanmei Tie^2,6, Emma Tinney⁷, Ralph Sinkus⁸, and Katherine M. Breedlove^1,2
1Radiology, Brigham and Women's Hospital, Boston, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³SigProc Expert Solutions, Westwood, MA, United States, ⁴Imaginostics, Inc., Cambridge, MA, United States, ⁵Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁶Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States, ⁷Psychology, Northeastern University, Boston, MA, United States, ⁸INSERM, Paris, France

Keywords: White Matter, Elastography, superficial white matter heterogeneity, myelin water

Prior MRE work in the brain has utilized long TE pulse sequences (spiral and EPI) and therefore has necessarily not observed the signal from short T2 components. In particular, the signal from water compartmentalized in myelin membranes and that is a sensitive measure of white matter structural integrity, has been missed. Here we demonstrate high heterogeneity of the shear stiffness within the superficial white matter when utilizing MRE sequences with a short (10ms) TE.

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Chang-Le Chen¹, Jinghang Li¹, Linghai Wang¹, Noah Schweitzer¹, Dana Tudorascu^2,3, Howard Aizenstein^1,2, and Minjie Wu^1,2
1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States, ²Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States, ³Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States

Keywords: White Matter, Aging, brain age

The white matter (WM) network integrity is assumed to associate with Alzheimer's disease (AD)-related gray matter (GM) atrophy. To investigate this hypothesis, we estimated WM-specific brain age to quantify WM integrity and calculated twelve AD-related GM signatures in a longitudinal cognitively normal cohort. We identified that the change rate of WM brain age was significantly correlated with the left hippocampal and amygdala volumetric changes; that is, the accelerated aging in WM was associated with the more atrophic GM volumes. This result suggested that changes of structural network characterized by brain age metrics can reflect the alteration of AD-related GM signatures.

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Santiago Aja-Fernandez¹, Carmen Martin-Martin¹, Tomasz Pieciak¹, Alvaro Planchuelo-Gomez^1,2, Abrar Faiyaz³, Nasir Uddin³, Abhishek Tiwari⁴, Saurabh J. Shigwan⁴, Tianshu Zheng⁵, Zuozhen Cao⁵, Stefano B. Blumberg⁶, Snigdha Sen⁶, Mehmet Yigit Avci⁷, Zihan Li⁷, Xinyi Wang⁸, Zihao Tang⁸, Amelie Rauland⁹, Dorit Merhof¹⁰, Renata Manzano Maria¹¹, Vinicius P. Campos¹¹, SeyyedKazem HashemizadehKolowri¹², Edward DiBella¹², Chenxu Peng¹³, Zan Chen¹³, Irfan Ullah¹⁴, Merry Mani¹⁴, Samuel Eckstrom¹⁵, Steven H. Baete¹⁵, Scifitto Scifitto³, Rajeev Kumar Singh⁴, Dan Wu⁵, Tobias Goodwin-Allcock⁶, Paddy J. Slator⁶, Berkin Bilgic⁷, Qiyuan Tian⁷, Mariano Cabezas⁸, Tales Santini¹¹, Marcelo Andrade da Costa Vieira¹¹, Zhimin Shen¹³, Hesam Abdolmotalleby¹⁴, Patryk Filipiak¹⁵, Antonio Tristan-Vega¹, and Rodrigo de Luis-Garcia^1
1Universidad de Valladolid, Valladolid, Spain, ²CUBRIC, Cardiff University, Cardiff, United Kingdom, ³University of Rochester, Rochester, NY, United States, ⁴Shiv Nadar University, Delhi NCR, India, ⁵Zhejiang University, Hangzhou, China, ⁶Centre for Medical Image Computing, University College London, London, United Kingdom, ⁷Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ⁸University of Sydney, Sidney, Australia, ⁹RWTH Aachen University, Aachen, Germany, ¹⁰University of Regensburg, Regensburg, Germany, ¹¹University of São Paulo, São Paulo, Brazil, ¹²University of Utah, Salt Lake City, UT, United States, ¹³Zhejiang University of Technology, Hangzhou, China, ¹⁴University of Iowa, Iowa City, IA, United States, ¹⁵New York University, New York, NY, United States

Keywords: White Matter, Brain, Migraine

This work gathers the results of the QuadD22 challenge, held in MICCAI 2022. We evaluate whether Deep Learning (DL) Techniques are able to improve the quality of diffusion MRI data in clinical studies. To that end, we focused on a real study on migraine, where the differences between groups are drastically reduced when using 21 gradient directions instead of 61. Thus, we asked the participants to augment dMRI data acquired with only 21 directions to 61 via DL. The results were evaluated using a real clinical study with TBSS in which we statistically compared episodic migraine to chronic migraine.

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Ahmed Radwan¹, Robert Pretorius¹, and Stefan Sunaert^1
1Imaging and pathology, Translational MRI, KU Leuven, Leuven, Belgium

Keywords: White Matter, Machine Learning/Artificial Intelligence, Tractography & Fiber Modelling

Shape features of the arcuate fasciculi (AF) can be used for predicting language laterality by a machine-learning algorithm as determined by language task-based functional MRI (tb-fMRI) laterality index (LI) relatively accurately (AUC = 0.893, accuracy = 0.868) in a sample of 60 clinical preoperative patients with variable pathology. Constrained spherical deconvolution (CSD) tractograms seemed to give the best outcome of model training regardless of additional streamline filtering or anatomical constraint. The best-performing model appeared to prioritise bundle curl, irregularity and span over the more conventional measures of surface-area and volume.
 

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Junko Kikuta¹, Koji Kamagata¹, Masahiro Abe¹, Andica Christina¹, Yuya Saito¹, Kaito Takabayashi¹, Wataru Uchida¹, Hitoshi Naito², Hiroki Tabata³, Akihiko Wada¹, Yoshifumi Tamura^2,3, Ryuzo Kawamura^2,3, Hirotaka Watada^2,3, and Shigeki Aoki^1
1Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Japan, ²Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Japan, ³Sportology Center, Juntendo University Graduate School of Medicine, Bunkyo-ku, Japan

Keywords: White Matter, Brain

This study aimed to use advanced MRI techniques to investigate the effect of arterial stiffness on the white matter (WM) microstructure among older adults. Arterial stiffness was examined using the cardio-ankle vascular elasticity index (CAVI). The high-CAVI group (mean CAVI ≥ 9 points) and the low-CAVI group (mean CAVI < 9 points) were used. The neuronal fiber integrity of the WM was assessed by neurite orientation dispersion, density imaging, and magnetization transfer-saturation imaging. Our results suggest that arterial stiffness may be related to demyelination rather than axonal degeneration.

 

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Bradford A Moffat¹, Robert A Williams², Rebecca A Glarin³, Braden Thai³, Jennie Ponsford⁴, and Christopher C Rowe^5
1Radiology, University of Melbourne, Parkville, Australia, ²University of Melbourne, Parkville, Australia, ³Radiology, Royal Melbourne Hospital, Parkville, Australia, ⁴Psychology, Monash University, Clayton, Australia, ⁵Department of Nuclear Medicine and Centre for PET,, Austin Health, Heidelberg,, Australia

Keywords: White Matter, Diffusion Tensor Imaging, 7T DWI, CSD, tractography

This study presents a simple and robust correction of B1+ inhomogeneities in high resolution multi-shell DWI at 7T. 

Niraj Kumar Gupta¹, Neha Yadav¹, Aniket Aman¹, and Vivek Tiwari^1
1Department of Biological Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, India

Keywords: White Matter, Aging

Discriminating Cognitive Impairment and Alzheimer's Disease is challenging due to overlapping subtle structural changes and cognitive variations. Load and distribution of white matter hyperintensity across brain regions may elicit structural atrophy and cognitive disabilities via continuous vascular insult. Here, we have investigated the kinetics and regional distribution of  White matter hyperintensity with aging and its implications in brain structure and cognitive health using T2-FLAIR and T1w longitudinal MRI from the NACC cohort.

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Kengo Onda¹, Jill Chotiyanonta¹, Yuto Uchida¹, Xin Li¹, Susumu Mori^1,2, and Kenichi Oishi^1,2,3
1The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, The Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: White Matter, Neurodegeneration, White Matter Hyperintensity

White matter hyperintensity (WMH) in the brain is known to correlate with cognitive prognosis in many diseases; automated quantification tools for WMH have been developed, but most have been used to quantify study data from specific diseases imaged with a single scanning protocol. The low accuracy of these tools when used for clinical data with diverse scan protocols and diseases has been a problem in clinical applications. To overcome this limitation, we developed a deep-learning-based WMH quantification model for real-world clinical FLAIR images with high heterogeneity. The results show the potential of this method as a clinical tool.

Yanyao Du¹, Wenhan Yang¹, Sihong Huang¹, Fei Tang¹, Wei Zhao¹, Hu Guo², Huiting Zhang³, and Jun Liu^1
1The Second Xiangya Hospital, Central South University, Changsha, China, ²MR Application，Siemens Healthineers Ltd., Changsha, China, ³MR Scientific Marketing，Siemens Healthineers Ltd., Wuhan, China

Keywords: White Matter, Diffusion/other diffusion imaging techniques

This study aimed to explore the changes of brain white matter changes in short-term methamphetamine (MA) abstinence and to investigate potential imaging markers. Compared with healthy controls (HC), fractional anisotropy (FA), axial diffusivty (AD) and mean diffusivity (MD) values in short-term abstinence group were all increased, but there was no significant difference in the radial diffusivty value. The changes of FA, AD and MD value may be a new biomarker which is helpful to explore the potential mechanism of neurotoxicity damage.

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Jonathan Doucette^1,2,3, Christian Kames^1,2,3, and Alexander Rauscher^1,2,3,4
1UBC MRI Research Centre, Vancouver, BC, Canada, ²Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, ³Pediatrics, University of British Columbia, Vancouver, BC, Canada, ⁴Djavad Mowafaghian Centre for Brain Health, Vancouver, BC, Canada

Keywords: White Matter, Relaxometry, Brain, Myelin Water Fraction

$$$T_2$$$ distributions are typically computed using point estimates such as nonnegative least-squares (NNLS). This characterizes the most likely $$$T_2$$$-distribution arising from the data, but disregards other plausible solutions - of which there are many, due to the ill-posed nature of the inverse problem. Here, we instead propose to use Bayesian posterior sampling methods. To guide the difficult high-dimensional sampling problem, a data-driven domain transformation is learned alongside a deep generative prior. The resulting posterior samples produce more spatially consistent myelin water fraction (MWF) maps compared to NNLS, despite the purely voxelwise analysis, and additionally yields novel MWF uncertainty estimates.

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Jose Guerrero-Gonzalez¹, Olivia Surgent², Nagesh Adluru³, Gregory Kirk³, Steven Kecskemeti³, Douglas Dean III¹, Brittany Travers⁴, and Andrew Alexander^1
1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, ²Neuroscience Training Program, Waisman Center, University of Wisconsin - Madison, Madison, WI, United States, ³Waisman Center, University of Wisconsin - Madison, Madison, WI, United States, ⁴Kinesiology, University of Wisconsin - Madison, Madison, WI, United States

Keywords: White Matter, Diffusion/other diffusion imaging techniques, Autism

For neurological conditions that effect brain regions differently across individuals,     quantitative neuroimaging-based metrics of individual deviation may be more informative in characterizing neuroanatomical variation including normal from abnormal variations than commonly used group-wise analyses. This work uses a joint analysis of iTBSS (individual-specific Tract-Based Spatial Statistics) of multiple neuroimaging measures from diffusion and relaxometry to better characterize differences between autistic and non-autistic individuals.

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Andrew Crofts¹, Jessica Steventon², Joseph Whittaker³, Marcello Venzi³, Hannah Chandler⁴, Michael Germuska⁴, Mahfoudha Al Shezawi⁵, Eric Stohr⁵, Chris Pugh⁵, Barry McDonnell⁵, and Kevin Murphy^3
1National Institute for Quantum and Radiological Science and Technology, Chiba, Japan, ²CUBRIC, School of Medicine, Cardiff University, Cardiff, United Kingdom, ³CUBRIC, School of Physica and Astronomy, Cardiff University, Cardiff, United Kingdom, ⁴CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁵Cardiff Metropolitan University, Cardiff, United Kingdom

Keywords: White Matter, Aging

Damage to the deep white matter of the brain has been shown to correlate with hypertension and advanced age. However, the changes in the cerebral microvasculature that cause white matter lesions are unclear. Increased blood pressure causes morphological changes in cerebral vessels, and impaired vascular function and neurovascular coupling is a potential factor. Here, we demonstrate that age, central pulse pressure, and dual-echo MRI measures of CBF, CVR, CMRO2 and OEF influence the number and volume of white matter hyperintensities.

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Letizia Losa¹, Denis Peruzzo¹, Graeme Bydder², and Nivedita Agarwal^3
1CESNE, IRCCS E. Medea, Bosisio Parini, Italy, ²Department of Radiology, University of California, San Diego, San Diego, CA, United States, ³Neuroradiology, IRCCS E. Medea, Bosisio Parini, Italy

Keywords: White Matter, Quantitative Imaging, T1 mapping

Whole brain T1 mapping has been performed using several different sequences but remains mostly confined to research applications. A clinically reliable and fast method to evaluate the white matter (WM) is important for accurate diagnosis, evaluating the severity of disease and monitoring disease progression over time. In this study we assessed Multiplied, Added, Subtracted and/or Divided Inversion Recovery (MASDIR) technique in nine healthy subjects and found evidence that such technique greatly improves the visualisation of tissue of interest and allows calculation of T1 values in a reliable, fast and clinically usable manner.

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Geng Chen#^1,2, Hao Yang#¹, Lemroussi Wissal¹, Yong Xia*¹, and Pew-Thian Yap*^2
1Northwestern Polytechnical University, Xi'an, China, ²University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Keywords: White Matter, Diffusion/other diffusion imaging techniques

The anatomical resolution of MRI is typically limited by acquisition time constraints. While deep learning networks have shown great potential for post-acquisition MRI resolution enhancement, their training typically relies on low-high resolution image pairs, which are not always available in practice. Here, we propose using deep image prior (DIP) for unsupervised MRI resolution enhancement with network training relying only on low-resolution images. Experimental results indicate that our method super-resolve MR images effectively with realistic details.

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Won Beom Jung¹, Jaekyun Ryu¹, Chuluunbaatar Otgonbaatar², Jeonghak Song³, Juho Kim¹, and Hackjoon Shim^1,3
1Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Korea, Republic of, ²College of Medicine, Seoul National University, Seoul, Korea, Republic of, ³Magnetic Resonance Business Unit, Canon Medical Systems Korea, Seoul, Korea, Republic of

Keywords: White Matter, Diffusion/other diffusion imaging techniques

Diffusion tensor imaging (DTI) allows noninvasively to investigate the function of glymphatic system with ALPS index by utilizing the behavior of water molecules along the perivascular space. The effects of diffusion time with fixed TE for the evaluation of glymphatic system using diffusion tensor imaging have not been investigated. Here, we evaluated the effects of DTI with different diffusion time to ALPS index in the glymphatic system.

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Anna Ljusberg^1,2, Ida Blystad^2,3, Peter Lundberg^1,2, Emelie Adolfsson¹, and Anders Tisell^1,2
1Department of Medical Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden, ²Center for Medical Image science and Visualization (CMIV), Linköping University, Linköping, Sweden, ³Department of Radiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden

Keywords: White Matter, Radiotherapy

Adding quantitative MRI to existing examinations for patients with malignant glioma is valuable for observing changes in tissue specific parameters such as R₁, R₂, proton density and myelin concentration. Changes observed in this study, with a decrease of myelin concentration and increase of proton density, in normal appearing white matter can be interpreted as a demyelination caused by radiation therapy. For low doses (<30 Gy), but not for high (>30 Gy) the myelin concentration returns to baseline.

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Alexander Drobyshevsky¹, Daniil Aksenov¹, and Vasiliy Yarnykh^2
1NorthShore University HealthSystems, Evanston, IL, United States, ²Radiology, University of Washington, Seattle, WA, United States

Keywords: White Matter, Quantitative Imaging, myelin

Ultrastructural and immunochemical methods of myelin quantification were applied to validate a model-based MRI techniques, macromolecular proton fraction mapping (MPF), during early development in major white matter tracts and cerebral cortex in rabbits.  MPF trajectories were in general agreement with levels of myelination by histology and accurately reflected differential rate of development between white matter tracts and cortical regions.

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Suming Zhang¹, Xuan Bu², Lingxiao Cao¹, Hailong Li¹, Kaili Liang¹, Zilin Zhou¹, Yingxue Gao¹, Lianqing Zhang¹, Bin Li³, and Xiaoqi Huang^1
1Huaxi MR Research Center(HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, ²State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, ³Mental Health Center, West China Hospital of Sichuan University, Chengdu, China

Keywords: White Matter, Diffusion Tensor Imaging

In this study, we compared the classification performance of different machine learning models for discriminating OCD patients based on DTI tractography. Firstly, we extracted DTI metrics and tract volumes as features. Following feature selection, four machine learning models were performed for classification. Finally, a novel SHapley Additive exPlanations (SHAP) analysis was used to intepret the value of importance for each feature. We found that XGBoost exhibited the best classification performance among the four models. The model explanation by SHAP suggested that the volume of callosal orbital frontal tract was the most important factor in differentiating OCD from healthy controls.

Ronghua Mu¹, Wei Zheng^1,2, Xiaoyan Qin¹, Xin Li¹, Kan Deng³, Fuzhen Liu¹, Zeyu Zhuang^1,2, Peng Yang¹, Jian Lv¹, and Xiqi Zhu^1
1the Nanxishan Hospital, Guangxi Zhuang Autonomous Region, guilin, China, ²Graduate School of Guilin Medical University, guilin, China, ³Philips Healthcare, Guangzhou, China

Keywords: White Matter, White Matter

The objective of this study was to create a modified total load score suitable for community screening study by modifying mild WMH and EPVS. Our study found NCT-A (DST) variable abnormalities even in people with mild EPVS and WMH, suggesting that modified cerebral small vessel disease total burden is useful to screening study based on community people.

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Marta Gaviraghi¹, Eleonora Lupi¹, Simone Rancati², Fulvia Palesi¹, Francesco Grussu^3,4, Marco Battiston³, Carmen Tur^3,4, Alberto Calvi³, Sara Collorone³, Antonio Ricciardi³, Ferran Prados^3,5,6, Baris Kanber^3,7, Egidio D'Angelo^1,8, Rebecca S. Samson³, and Claudia A. M. Gandini Wheeler-Kingshott^1,3,8
1Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, ³NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom, ⁴Neurology-Neuroimmunology Department Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, ⁵Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom, ⁶E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain, ⁷Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, Italy, ⁸Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy

Keywords: White Matter, Multi-Contrast, Myelin

Quantifying myelin in vivo with MRI is useful for studying multiple sclerosis by calculating the g-ratio. To estimate the g-ratio, special sequences often must be set. We have developed a preliminary method for calculating g-ratio from clinical images i.e. T1w and T2w. We then compared three methods to obtain the g-ratio: from quantitative Magnetization Transfer (qMT) (gold standard), from Macromolecular Tissue Volume (MTV) and from T1w/T2w. The MTV (g_MTV) and T1w/T2w (g_T1wT2w) g-ratios were different from the qMT g-ratio (g_qMT). g_MTV flattens differences between healthy and pathological subjects whereas g_T1wT2w has the same median trend as g_qMT.