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Abstract #1278

Performance of data driven learned sampling patterns for accelerating brain 3D-T1Ρ MRI

Rajiv G Menon1, Marcelo V.W. Zibetti1, and Ravinder R. Regatte1
1New York University Langone Health, New York, NY, United States

3D-T has many useful biomedical applications but requires long data acquisition times. The goal of the study was to apply a fast data-driven optimization approach, bias- accelerated subset selection (BASS), to generate optimal sampling patterns (SPs) for compressed sensing (CS) reconstruction for brain 3D-T1ρ MRI. Five healthy volunteers were recruited and fully sampled (FS) Cartesian, 3D-T1ρ MRI was obtained. The performance of Poisson disc (PD) and optimized SP were compared using normalized root mean square error (NRMSE). The data-driven optimized SP provides upto 2 times (NRMSE=0.09 optimized SP vs 0.18 PD-SP) improvement in images at the highest AFs tested.

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