Year: 2025
Type: article
Abstract: ABSTRACT Parallel magnetic resonance imaging (pMRI) reduces MRI acquisition time, with Sensitivity Encoding (SENSE) being a widely used method that exploits coil sensitivity maps for efficient image reconstruction. However, SENSE can introduce aliasing and noise artifacts, especially at high acceleration factors. To address this limitation, we propose a deep learning‐based postprocessing framework that enhances SENSE‐reconstructed images using a Convolutional Autoencoder (CAE). The CAE is applied after the SENSE reconstruction to reduce artifacts and improve image quality, without modifying the original reconstruction pipeline. A dataset of 842 fully sampled anatomical images is used to simulate 8‐channel coil data, with both uniform and variable‐density (VD) undersampling applied at different acceleration factors. The CAE is trained on paired inputs (SENSE‐reconstructed images) and targets (fully sampled references) to learn the mapping from degraded to high‐quality images. Quantitative evaluation using Peak Signal‐to‐Noise Ratio (PSNR) and Normalized Mean Squared Error (NMSE), along with qualitative visual assessment, shows that the proposed CAE‐SENSE framework significantly improves image fidelity, particularly with variable‐density undersampling. These results demonstrate the potential of deep learning as a complementary tool to enhance conventional parallel imaging methods in accelerated MRI.
DOI: 10.1002/ima.70254
Authors: Amel Korti, Amène Bekki, Alain Lalande
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4415948416