Abstract #4638

Accelerated MRI using intelligent protocolling and subject-specific denoising

Keerthi Sravan Ravi^1,2, Gautham Nandakumar³, Nikita Thomas³, Mason Lim³, Enlin Qian^2,4, Marina Manso Jimeno^2,4, Pavan Poojar⁵, Zhezhen Jin⁶, Patrick Quarterman⁷, Maggie Fung⁷, Girish Srinivasan³, John Thomas Vaughan Jr.², and Sairam Geethanath⁵

¹Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States, ³PhenoMX, Chicago, IL, United States, ⁴Department of Biomedical Engineering, Columbia University, New York, NY, United States, ⁵Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology,, Mt. Sinai, New York, NY, United States, ⁶Department of Biostatistics, Columbia University Irving Medical Center, New York, NY, United States, ⁷MR Clinical Solutions, GE, New York, NY, United States

Synopsis

Keywords: Data Acquisition, Data AcquisitionThe routine brain screen protocol employed at our institution was accelerated using Look Up Tables to achieve a 1.94x gain in imaging throughput. Image-denoising was performed on accelerated data by leveraging deep learning models trained on contrast-specific publicly available datasets. These were corrupted by native noise during forward modeling. In addition, subject-specific denoising was demonstrated. The superior performance of denoised data on automated volumetry of Alzheimer’s Disease (AD) relevant brain anatomies on T₁w data demonstrated potential for accelerated AD imaging.

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