An end‐to‐end deep learning method for reconstructing SMS‐PI accelerated musculoskeletal MRI

BACKGROUND: Deep Learning (DL) techniques have enabled up to 6-fold acceleration in musculoskeletal magnetic resonance imaging (MRI) while preserving diagnostic image quality. Further, improvements in acceleration and generalization require novel approaches. We propose a DL framework that integrates Simultaneous Multislice (SMS) imaging with Parallel Imaging (PI) to enhance current DL-based reconstruction. PURPOSE: To advance musculoskeletal Magnetic Resonance Imaging (MRI), by developing a DL reconstruction framework that combines SMS and PI, enabling acceleration of up to 8-fold and beyond, while maintaining image quality suitable for clinical interpretation. METHODS: End-to-End (E2E) DL framework for reconstructing Turbo Spin Echo (TSE) MRI data acquired with SMS and PI acceleration. The method unrolls a proximal gradient algorithm with Nesterov momentum and integrates a novel DL network for joint regularization across simultaneously acquired slices. Slice separation and k-space-to-image reconstruction are unified by embedding the full SMS forward model into the DL architecture. Data Consistency (DC) is modulated to enhance denoising, and a super-resolution module improves image sharpness. The robust DL model was trained on over 200 000 slices from 1.5T to 3T scans with diverse acquisition settings. RESULTS: The proposed E2E DL model outperforms prior methods at 8-fold and 12-fold acceleration, as measured by PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) metrics. Evaluation on prospectively acquired clinical scans by two radiologists confirms, that image quality and abnormality detection are comparable to standard acquisitions at lower acceleration. CONCLUSIONS: We extend state-of-the-art DL reconstruction frameworks by integrating slice separation directly into the model for SMS acquisitions. Our E2E DL approach achieves clinical-grade image quality at 8-fold acceleration across 20 subjects, reducing acquisition time by 27%. Preliminary results suggest potential for further acceleration up to 12-fold, demonstrating significant advancement beyond existing DL techniques.