Accelerated MR imaging using compressive sensing with no free parameters.
Magn Reson Med
; 68(5): 1450-7, 2012 Nov.
Article
en En
| MEDLINE
| ID: mdl-22266597
ABSTRACT
We describe and evaluate a robust method for compressive sensing MRI reconstruction using an iterative soft thresholding framework that is data-driven, so that no tuning of free parameters is required. The approach described here combines a Nesterov type optimal gradient scheme for iterative update along with standard wavelet-based adaptive denoising methods, resulting in a leaner implementation compared with the nonlinear conjugate gradient method. Tests with T2 weighted brain data and vascular 3D phase contrast data show that the image quality of reconstructions is comparable with those from an empirically tuned nonlinear conjugate gradient approach. Statistical analysis of image quality scores for multiple datasets indicates that the iterative soft thresholding approach as presented here may improve the robustness of the reconstruction and the image quality, when compared with nonlinear conjugate gradient that requires manual tuning for each dataset. A data-driven approach as illustrated in this article should improve future clinical applicability of compressive sensing image reconstruction.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Encéfalo
/
Imagen por Resonancia Magnética
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Interpretación de Imagen Asistida por Computador
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Aumento de la Imagen
/
Compresión de Datos
Tipo de estudio:
Diagnostic_studies
/
Guideline
Límite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2012
Tipo del documento:
Article
País de afiliación:
Estados Unidos