Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization.
Med Image Comput Comput Assist Interv
; 12906: 350-360, 2021.
Article
en En
| MEDLINE
| ID: mdl-35059693
ABSTRACT
Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning. However, most of these methods rely on estimates of the coil sensitivity profiles, or on calibration data for estimating model parameters. Prior work has shown that these methods degrade in performance when the quality of these estimators are poor or when the scan parameters differ from the training conditions. Here we introduce Deep J-Sense as a deep learning approach that builds on unrolled alternating minimization and increases robustness our algorithm refines both the magnetization (image) kernel and the coil sensitivity maps. Experimental results on a subset of the knee fastMRI dataset show that this increases reconstruction performance and provides a significant degree of robustness to varying acceleration factors and calibration region sizes.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos