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Accelerating Neuroradiology Protocols with Deep Learning MR Image Reconstruction. Which Methods Result in the Highest Perceived Image Quality?

Gregory Avey¹, Laura Eisenmenger¹, Nathan Kim¹, Alexey Samsonov¹, James Holmes¹, Lloyd Estkowski², and Tabassum Kennedy¹
¹University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, ²GE Healthcare, Madison, WI, United States

Using Deep Learning (DL) reconstruction to accelerate MRI exam acquisition. DL allows for up to 78% reduction in scan time while matching overall baseline subjective image quality. However, not all acceleration methods are equally favorable to DL acceleration.

Figure 1. Demonstration of Deep Learning Reconstruction Technique

A. Parallel imaging acceleration factor of 4, conventional Fourier transform reconstruction, 78% reduction in acquisition time from the baseline sequence with increased noise.

B. Same acquisition as in figure A, deep learning reconstruction demonstrating reduction of apparent noise to less than baseline exam with DL reconstruction technique

C. Magnified detail view of A.

D. Magnified detail view of B.

Figure 2. T2 Weighted Comparisons

A. Baseline T2w FSE – time to acquire 2:24

B. R=2 T2w FSE – time to acquire 1:20

C. R=3 T2w FSE – time to acquire 1:20

D. R=4 T2w FSE – time to acquire 0:48

E. NEX=1 T2w FSE – time to acquire 1:20

F: Reduced Matrix T2w FSE – time to acquire 1:52