[2604.08781] PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.08781 (eess) [Submitted on 9 Apr 2026] Title:PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging Authors:Arda Atalik, Hui Xue, Rhodri H. Davies, Thomas A. Treibel, Daniel K. Sodickson, Michael S. Hansen, Peter Kellman View a PDF of the paper titled PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging, by Arda Atalik and 6 other authors View PDF Abstract:Purpose: To develop and evaluate a deep learning (DL) method for free-breathing phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) cardiac MRI that produces diagnostic-quality images from a single acquisition over two heartbeats, eliminating the need for 8 to 24 motion-corrected (MOCO) signal averages. Materials and Methods: Raw data comprising 800,653 slices from 55,917 patients, acquired on 1.5T and 3T scanners across multiple sites from 2016 to 2024, were used in this retrospective study. Data were split by patient: 640,000 slices (42,822 patients) for training and the remainder for validation and testing, without overlap. The training and testing data were from different institutions. PSIRNet, a physics-guided DL network with 845 million parameters, was trained end-to-end to reconstruct PSIR images with surface coil correction from a single interleaved IR/PD acquisition over two heartbeats. Reconstruction quality was evaluated using SSIM, PSNR, a...