Physics‐informed deep learning for T2‐deblurred superresolution turbo spin echo MRI

Abstract

PurposeDeep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low‐resolution training images by simple k‐space truncation, but this does not properly model in‐plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k‐space regions. To fill this gap, we developed a T2‐deblurred deep learning SR method for the SR of 3D‐TSE images.MethodsA SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high‐resolution k‐space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase‐encoding directions) of genetically engineered mouse embryo model TSE‐MR images.ResultsThe proposed method can produce high‐quality 3 × 3 SR images for a typical 500‐slice volume with 6–7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging‐quality expert scores for prospective evaluation.ConclusionThe proposed T2‐deblurring method improved accuracy and image quality of deep learning–based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.

Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction *

“…Zapf et al (2022) applied PINNs and the finite element method to estimate diffusion coefficients by modeling the long-term spread of molecules in the human brain from noisy MRI data. Chen et al (2023) proposed the T2-deblurred MR super-resolution method by exploring the physics-informed deep learning for its potential to enhance MR resolution and simulate degradation effects. This integration offers a comprehensive approach combining data-driven and physics-based methodologies, enhancing the potential for more robust and clinically relevant predictions in GBM recurrence monitoring.…”