Year: 2025
Type: article
Abstract: Purpose To determine how various compressed sensing (CS) models can accelerate alternating Look‐Locker mapping. Methods An alternating Look‐Locker acquisition was retrospectively accelerated by factors of 1–12. The data was reconstructed into 12 images with multiple CS models, which utilized combinations of spatial total variation, locally low‐rank regularization, and subspace constraints. Complex non‐linear least squares signal fitting was performed to obtain the maps. The accelerated maps were compared against the map of a full data reference reconstruction. Results A subspace‐constrained reconstruction model with spatial total variation and locally low‐rank regularization outperformed all other models as measured by map normalized root mean squared error, structural similarity index, and normalized mean absolute deviation. The subspace constraint benefited models utilizing spatial total variation but, conversely, did not benefit models utilizing only locally low‐rank regularization. Conclusion The radial 3‐D alternating Look‐Locker mapping acquisition was successfully accelerated by up to a factor of 12 with various CS models. The best‐performing model was a subspace‐constrained reconstruction, which utilized spatial total variation and locally low‐rank regularization.
Source: Magnetic Resonance in Medicine
DOI: 10.1002/mrm.30610
Authors: Antti Aarnio, Olli Nykänen, Ville Kolehmainen, Mikko J. Nissi
Institution: University of Eastern Finland
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,680
OpenAlex ID: W4411336414