Abstract #1136

gNET: gSlider Self-Supervised Neural Network for Accelerated Reconstruction of Super-resolution Diffusion MRI

Caique de Oliveira Kobayashi^1,2,3, Yohan Jun^1,4,5, Jaejin Cho^1,4,5, Xiaoqing Wang^4,5,6, Zihan Li⁷, Qiyuan Tian⁷, and Berkin Bilgic^1,4,5

¹Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, ²Mechanical Engineering, Escola Politécnica da USP, São Paulo, Brazil, ³Technical University of Munich, Munich, Germany, ⁴Radiology, Harvard Medical School, Boston, MA, United States, ⁵Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States, ⁶Boston Children’s Hospital, Boston, MA, United States, ⁷Department of Biomedical Engineering, Tsinghua University, Beijin, China

Synopsis

Keywords: Diffusion Reconstruction, Diffusion/other diffusion imaging techniques, gSlider, deep-learning, self-supervised, AI/ML Image Reconstruction

Motivation: gSlider utilizes radio-frequency encoding to acquire high and isotropic resolution brain diffusion-MRI with high SNR. However, this comes at the cost of prolonged acquisition time, which also increases the sensitivity to motion.

Goal(s): This work proposes gSlider Network (gNET) to accelerate gSlider from acquisitions with jointly subsampled RF- and q-space.

Approach: The self-supervised model was trained and tested on a 1mm³ resolution BUDA-gSlider dataset (T_acq = 32 min). FSL and the DIMOND self-supervised were used to estimate the diffusion parameters.

Results: gNET achieved an acceleration factor of R=2 and, when combined with DIMOND, reached a total R=4-fold (T_acq = 8 min).

Impact: gNET facilitates super-resolution dMRI by reducing the acquisition time by 4-fold with high fidelity. Its application may propel new discoveries in the neuroscientific field and the clinical translation of the gSlider framework.

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