Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT

In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI.
Approach: Development of the proposed psSR network comprises two-stages: 1) a cohort-specific SR (csSR) network using clinical patient datasets, and 2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the Dice-similarity-coefficient (DSC).
Results. Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8 to 21.7) and 94.7% (0.38 to 0.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79 to 0.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively.
Significance. The proposed p...
Source: Physics in Medicine and Biology - Category: Physics Authors: Source Type: research