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Abstract #2893

Deep Learning-Based Inversion for Diffusion-Relaxation Correlation Spectrum and its Application in Prostate Cancer Diagnosis

Yinqiao Yi1, Guangyu Wu2, Chenglong Wang1, Yang Song3, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Siemens Healthineers Ltd., Shanghai, China

Synopsis

Keywords: Prostate, Prostate

Motivation: Diffusion-relaxation correlation spectrum imaging (DR-CSI) allows for sub-voxel study of prostate microstructures. However, traditional inversion methods for DR-CSI are both unstable and computationally intensive.

Goal(s): To improve the speed and robustness of DR-CSI inversion with deep learning, and apply it to prostate cancer (PCa) detection.

Approach: Models for image denoising and inversion were trained with synthesized DR-CSI data.

Results: The peak distribution regions of the T2-ADC spectra within prostate cancer (PCa) regions exhibit substantial differences compared to those in non-PCa regions. Component analysis of T2-ADC spectra has potential in the diagnosis of prostate cancer.

Impact: Deep learning can accelerates the inversion substantially and increase the robustness of results, which can facilitate the application of sub-voxel analysis based on DR-CSI.

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