Data-Driven Regularization Parameter Selection in Dynamic MRI

Journal of Imaging

Mikko Kettunen¹  Olli Gröhn¹  Ville Kolehmainen²  Matti Hanhela²  Marko Vauhkonen²  Kati Niinimäki³ 

[1] A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland;Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland;Xray Division, Planmeca Oy, Asentajankatu 6, 00880 Helsinki, Finland;

关键词: compressed sensing;    dynamic MRI;    parameter selection;    regularization parameter;    S-curve;   

DOI  :  10.3390/jimaging7020038

来源: DOAJ

【 摘 要 】

In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. Two formulations are proposed. Simultaneous search for a parameter pair yielding expected sparsity in both domains (S-surface), and a sequential parameter selection using the S-curve method (Sequential S-curve). The approaches are evaluated using simulated and experimental DCE-MRI. In the simulated test case, both methods produce a parameter pair and reconstruction that is close to the root mean square error (RMSE) optimal pair and reconstruction. In the experimental test case, the methods produce almost equal parameter selection, and the reconstructions are of high perceived quality. Both methods lead to a highly feasible selection of the regularization parameters in both test cases while the sequential method is computationally more efficient.

【 授权许可】

Unknown