Abstract #2925

Deep Unfolding MR reconstruction - weighting the k-space sampling Density in training Loss (wkDeLo)

Parna Eshraghi Boroojeni¹, Wejie Gan², Jiaming Liu², Yuyang Hu², Yasheng Chen³, Paul Commean⁴, Cihat Eldeniz⁵, Tongyao Wang⁶, Gary Skolnick⁷, Corinne Merrill⁷, Kamlesh Patel⁷, Hongyu An⁶, and Ulugbek Kamilov⁸

¹Washington University in Saint Louis, Saint Louis, MO, United States, ²Engineering - Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO, United States, ³Neurology, Washington University in Saint Louis, Saint Louis, MO, United States, ⁴Radiology - Main - Research - Radiological Sciences, Washington University in Saint Louis, Saint Louis, MO, United States, ⁵Radiology - Research Imaging Facilities - MR Facility, Washington University in Saint Louis, Saint Louis, MO, United States, ⁶Radiology - Main - Research - Radiological Sciences - Biomedica, Washington University in Saint Louis, Saint Louis, MO, United States, ⁷Surgery - Plastics, Washington University in Saint Louis, Saint Louis, MO, United States, ⁸Engineering - Electrical & Systems Engineering, Washington University in Saint Louis, Saint Louis, MO, United States

Synopsis

Keywords: Image Reconstruction, Image ReconstructionWe develop a self-supervised and physics-guided deep unfolding (DU) network for MR image reconstruction by weighting the k-space sampling Density in network training Loss (wDeLo). We have demonstrated that high-quality MR images at a spatial resolution of 0.6x0.6x0.8 mm3 could be achieved using an acquisition time of 1 minute (x6.25 acceleration) or 45 seconds (8x acceleration). Compared to SSDU and its uniform weighted counterpart (un-wDeLo), the wDeLo method significantly improves PSNR and SSIM. It had fewer artifacts, lower noise, and preserved image sharpness.

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