David K.B. Li¹, Frederik Barkhof², Scott Newsome³, June Halper⁴, Lori Saslow⁴, Brenda Banwell⁵, Laura Barlow⁶, Kathleen Costello⁷, Peter Damiri⁸, Marilyn Maes⁹, Sarah Morrow¹⁰, Jiwon Oh¹¹, Friedemann Paul¹², Patrick Quarterman¹³, Daniel Reich¹⁴, Jason Shewchuk¹⁵, Russell Shinohara¹⁶, Wim Van Hecke¹⁷, Kim van de Ven¹⁸, Amy Verrinder⁹, Mitchell Wallin¹⁹, Jerry Wolinsky²⁰, and Anthony Traboulsee^21
1Radiology, University of British Columbia, Vancouver, BC, Canada, ²VU University Medical Center, Amsterdam, Netherlands, ³Johns Hopkins University, Baltimore, MD, United States, ⁴Consortium of MS Centers, Hackensack, NJ, United States, ⁵Children's Hospital of Philadelphia, Philadelphia, PA, United States, ⁶UBC MRI Research Center, University of British Columbia, Vancouver, BC, Canada, ⁷National MS Society, New York, NY, United States, ⁸Multiple Sclerosis Association of America, Cherry Hill, NJ, United States, ⁹Cortechs Labs, San Diego, CA, United States, ¹⁰London MS Clinic, Western University, London, ON, Canada, ¹¹University of Toronto, Toronto, ON, Canada, ¹²NeuroCure Clinical Research Center, Charité Universitätsmedizin, Berlin, Germany, ¹³General Electric Healthcare, Milwaukee, WI, United States, ¹⁴Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, United States, ¹⁵University of British Columbia, Vancouver, BC, Canada, ¹⁶University of Pennsylvania, Philadelphia, PA, United States, ¹⁷icometrix, Leuven, Belgium, ¹⁸BIU MR, Philips Healthcare, Eindhoven, Netherlands, ¹⁹VA MS Medical Center of Excellence-East, Washington, DC, United States, ²⁰McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States, ²¹Neurology, University of British Columbia, Vancouver, BC, Canada

Standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis (MS) have been available for over a decade.  These guidelines are useful and useable, but not yet widely used. An expert panel with representatives from CMSC, NAIMS, NMSS, MSAA and MRI vendors updated the MRI protocol with the vision of creating international guidelines to be universally adopted as the standard of care for use of MRI in MS. Novel methods of disseminating information to payers, patient groups, MRI Centers and MRI vendors so that the MRI protocol will be used were discussed and will be explored.

Mereze Visagie¹, Atlee Witt¹, Sanjana Satish¹, Shekinah Malone², Anna Combes¹, Kristin P O'Grady^1,3, Dylan Lawless^1,4, Francesca Bagnato⁵, Colin McKnight³, and Seth A Smith^1,3
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, ²Meharry Medical College, Nashville, TN, United States, ³Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ⁵Clinical Neurology, Vanderbilt University Medical Center, Nashville, TN, United States

In multiple sclerosis (MS), detection of lesions in the spinal cord with MRI is important for diagnosis and monitoring of disease progression.  Despite improved clinical MRI sequences, motion and pulsation artifact remain a challenge for small lesion identification.  We sought to compare sensitivity for lesion detection between multi-echo gradient echo (mFFE) and T2-weighted fast-spin-echo (T2-FSE) sequences at 3T in 16 relapsing-remitting MS patients with low disability. By comparing lesion fraction and average lesion burden, we demonstrated that mFFE has greater sensitivity for spinal cord lesions than T2-FSE.  

Xianfu Luo^1,2, Ulrike W. Kaunzner³, Thanh D. Nguyen¹, Yeona Kang⁴, Elizabeth Sweeney⁵, Weiyuan Huang¹, Yi Wang¹, and Susan Gauthier^3
1Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States, ²Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, China, ³Department of Neurology, Weill Medical College of Cornell University, New York, NY, United States, ⁴Department of Radiology/Nuclear Medicine, Weill Medical College of Cornell University, New York, NY, United States, ⁵Department of Healthcare Policy and Research, Weill Medical College of Cornell University, New York, NY, United States

Both MR phase imaging and quantitative susceptibility mapping (QSM) are used to assess the presence of chronic active multiple sclerosis lesions.  It is important to evaluate which measure can detect ongoing inflammation in chronic active lesions most accurately. This study combined PK11195-PET with QSM versus phase imaging, and demonstrated that QSM can detected higher uptake of PK11195, as compared to phase imaging.

Elizabeth Margaret Sweeney¹, Thanh Nygen¹, Amy Kuceyeski¹, Sarah Ryan ², Shun Zhang¹, Yi Wang¹, and Susan Gauthier^1
1Weill Cornell, New York, NY, United States, ²University of Colorado Denver, Denver, CO, United States

We propose a method to estimate multiple sclerosis (MS) lesion age (less than or greater than a year old) using non-gadolinium magnetic resonance imaging. The method utilizes the less invasive Quantitative Susceptibility Map. Radiomic features are calculated over a lesion and a random forest classification model is used.  In a validation set, the model has an AUC of 0.79 (95% CI: [0.63, 0.86]) and an accuracy of 0.73 (95% CI: [0.60, 0.80]). This method can be used to aid in the diagnosis of MS, as part of the diagnostic criteria is to show lesion dissemination in time. 

Zezhong Ye¹, Ajit George¹, Anthony T. Wu², Xuan Niu¹, Joshua Lin³, Gautam Adusumilli⁴, Robert T. Naismith⁴, Anne H. Cross⁴, Peng Sun¹, and Sheng-Kwei Song^1
1Radiology, Washington University School of Medicine, Saint Louis, MO, United States, ²Biomedical Engineering, Washington University, Saint Louis, MO, United States, ³Keck School of Medicine, The University of Southern California, Los Angeles, CA, United States, ⁴Neurology, Washington University School of Medicine, Saint Louis, MO, United States

MS lesions have heterogeneous pathology, including inflammation, demyelination, axonal injury, and neuronal loss. Our laboratory has developed a diffusion basis spectrum imaging (DBSI) technique to address the shortcomings of MRI-based MS. Primary DBSI metrics have been demonstrated to be associated with MS pathologies in animal models and human tissue. We propose that profiles of multiple DBSI metrics can identify important patterns within MS lesions and normal appearing white matter. Here we report that Diffusion Histology Imaging (DHI), an improved approach that combines a deep neural network (DNN) algorithm with DBSI-derived diffusion metrics, accurately detected and classified various MS lesion subtypes.

Arnaud Attyé^1,2, Stenzel Cackowski³, Alan Tucholka⁴, Pauline Roca⁴, Pascal Rubini⁴, Sebastien Verclytte⁵, Lucie Colas⁵, Juliette Ding⁵, Jean-François Budzik⁵, Felix Renard⁶, Emmanuel L Barbier³, Romain Casey^7,8,9,10, Sandra Vukusic^7,8, and François Cotton^7,11
1Grenoble alpes university, Grenoble, France, ²Sydney Imaging Lab, Sydney university, Sydney, Australia, ³Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France, ⁴Pixyl Medical, Grenoble, France, ⁵Lille Catholic University, Lille, France, ⁶Laboratoire d'informatique de Grenoble, Grenoble, France, ⁷Claude Bernard Lyon 1 University, Lyon, France, ⁸Lyon University Hospital, Lyon, France, ⁹Observatoire Français de la Sclérose en Plaques, INSERM 1028 et CNRS UMR 5292, Lyon, France, ¹⁰EUGENE DEVIC EDMUS Foundation against multiple sclerosis, Lyon, France, ¹¹CREATIS, CNRS UMR 5220 - INSERM U1206, Lyon, France

MRI is central to the study of white matter lesions in multiple sclerosis (MS). To date, the distribution of MS lesions, as evaluated on FLAIR imaging, has not been linked to patients’ disability prediction. Based on an international data challenge with 1500 MS patients and ground truth 2-year Expanded Disability Status Scale (EDSS), we have proposed an adaptive machine learning framework to predict the clinical disability. Here, we report the encouraging finding that our algorithm predicts the 2-year EDSS score with an accuracy estimated to 81%, only based on a single initial FLAIR sequence, added to sex and gender information.

Govind Nair¹, Shila Azodi¹, Tsemacha Dubuche¹, Yair Mina¹, Ikesinachi Osuorah¹, Joan Ohayon¹, Tianxia Wu¹, Daniel S Reich¹, and Steve Jacobson^1
1National Institutes of Health, Bethesda, MD, United States

We sought to better understand the relationship between atrophy along the entire spinal cord and disease burden in multiple sclerosis using MRI. Towards this, we analyzed spinal cord cross-sectional area in 48 healthy control and 250+ subjects clinically diagnosed with various phenotypes of multiple sclerosis. Our results show cervical cord atrophy early in the onset of the disease, which correlated with clinical measures of disease severity. However, these correlations were reduced as the disease progressed. Such studies may help in better understanding of disease progression and can play a role as an imaging marker in clinical trials.

Burcu Zeydan^1,2, Selen Ucem^2,3, Tsemacha Dubuche⁴, Shila Azodi⁴, Govind Bhagavatheeshwaran^4,5, Jan-Mendelt Tillema ², John Port¹, Daniel Reich⁵, Steven Jacobson⁴, Kejal Kantarci¹, and Orhun H. Kantarci^2
1Radiology, Mayo Clinic, Rochester, MN, United States, ²Neurology, Mayo Clinic, Rochester, MN, United States, ³Marmara University School of Medicine, Istanbul, Turkey, ⁴Viral Immunology Section, Neuroimmunology and Neurovirology Division, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States, ⁵Translational Neuroradiology Section, Neuroimmunology and Neurovirology Division, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States

Subclinical progression reflecting neurodegeneration can be measured and followed through spinal cord volume monitoring in multiple sclerosis (MS). The increased atrophy is reflected more prominently in the caudal cervical spinal cord segment. In this study, we identified the C5 level area measurement which can reflect whole cervical spinal cord area in patients with MS using both semi-automated and manual measurements. We propose that the C5 level area measurement can replace whole cervical spinal cord area measurement in MS as a more practical biomarker of progression.

Kamyar Taheri¹, Irene M. Vavasour¹, Shawna Abel¹, Lisa Eunyoung Lee¹, Poljanka Johnson¹, Stephen Ristow¹, Roger Tam¹, Cornelia Laule¹, Nathalie Ackermans¹, Alice J. Schabas¹, Jillian Chan¹, Ana-Luiza Sayao¹, Virginia Devonshire¹, Robert Carruthers¹, Anthony Traboulsee¹, Shannon H. Kolind¹, and Adam V. Dvorak^1
1University of British Columbia, Vancouver, BC, Canada

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system, with MRI routinely performed for brain but often neglected in spinal cord. When cord imaging IS performed, atrophy is usually assessed at the C2/3 segment. We aimed to validate cord cross-sectional-area (CSA) measurements using T1-weighted whole-brain images. In controls, strong correlations were seen between C1 CSA from cord and brain images, and between C1 and C2/3 CSA from cord images.

In MS, C1 CSA from brain images and C2/3 CSA from cord images correlated. We showed that metrics obtained from brain images could provide relevant cord atrophy measures.