Abstract #4188

Denoising highly accelerated T2*-weighted brain MRI using a deep learning convolutional neural network

Bryan Quah¹, Sreekanth Madhusoodhanan Nair¹, Jin Jin², Fei Han³, Brian Renner¹, Elaina Gombos¹, Ke Cheng Liu³, Sunil Patil³, John A. Derbyshire⁴, Ken Sakaie⁵, Emmanuel Obusez⁵, Jonathan Lee⁵, Mark Elliot⁶, Russell T. Shinohara⁷, Matthew K. Schindler⁸, Jae W. Song⁶, Michel Bilello⁶, Marwa Kaisey¹, Nader Binesh⁹, Marcel Maya⁹, Javier Galvan⁹, Hui Han¹⁰, Debiao Li¹⁰, Andrew Solomon¹¹, Daniel S. Reich¹², Nancy L. Sicotte¹, Mark Lowe⁵, Daniel Ontaneda¹³, Omar Al-Louzi¹, and Pascal Sati^1,10

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

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Processing, Denoising, NeuroimagingThe scan time of high-resolution T2*-weighted brain imaging using 3D echo-planar imaging (3D-EPI) can be significantly reduced by applying Controlled Aliasing In Parallel Imaging Results In Higher Acceleration (CAIPIRINHA). However, this comes at the expense of a significant reduction in image quality. In this study, we evaluated the feasibility of using a deep learning-based approach (DnCNN) to denoise highly accelerated 3D-EPI scans acquired at 3T. Our results show that DnCNN was able to efficiently denoise highly accelerated T2*-weighted brain scans while preserving anatomical and pathological details.

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