Abstract #2838

Feasibility of ultra-fast, high-resolution, T2*-weighted brain imaging using 3D echo-planar imaging and CAIPIRINHA

Sreekanth Madhusoodhanan Nair¹, Jin Jin², Fei Han³, Brian Renner¹, Elaina Gombos¹, Ke Cheng Liu⁴, Sunil Patil⁵, John A Derbyshire⁶, Ken Sakaie⁷, Emmanuel Obusez⁷, Jonathan Lee⁷, Mark Elliott⁸, Russell T Shinohara⁹, Matthew K Schindler¹⁰, Jae W Song⁸, Michel Bilello⁸, Marwa Kaisey¹, Omar Al-Louzi¹, Nader Binesh¹¹, Marcel Maya¹¹, Javier Galvan¹¹, Hui Han¹², Debiao Li¹², Andrew Solomon¹³, Daniel S Reich¹⁴, Nancy L Sicotte¹, Mark Lowe⁷, Daniel Ontaneda¹⁵, and Pascal Sati^1,12

¹Department of Neurology, Cedars Sinai Medical Center, Los Angeles, CA, United States, ²Siemens Healthcare Pty Ltd, Brisbane, Australia, ³Siemens Medical Solutions, Los Angeles, CA, United States, ⁴Siemens Medical Solutions, Malvern, PA, United States, ⁵Siemens Medical Solutions, Baltimore, MD, United States, ⁶Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, United States, ⁷Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, ⁸Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁹Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States, ¹⁰Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States, ¹¹Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CO, United States, ¹²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ¹³Larner College of Medicine, The University of Vermont, Burlington, VT, United States, ¹⁴Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, CA, United States, ¹⁵Mellen Center, Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Multiple Sclerosis, Data Analysis, Parallel ImagingHigh-resolution T2*-weighted brain imaging using 3D echo planar imaging (3D-EPI) at 3T allows detection of new biomarkers of neurological disorders, such as the central vein sign in multiple sclerosis. However, current 3D-EPI scan times are suboptimal for a widespread implementation in hospitals and private imaging centers. In this study, we evaluated the feasibility of combining 3D-EPI acquisition with different CAIPIRINHA undersampling patterns. A significant reduction in scan time (up to 70% reduction) was achieved without any obvious aliasing artifacts, confirming the feasibility of ultra-fast, high-resolution T2* brain imaging for future clinical applications at 3T.

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