| Zugriffsnummer | 45212 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Adaptive sparsity level and dictionary size estimation for image reconstruction in accelerated 2D radial cine MRI |
| Autor(in); Institution |
Pali, Marie-Christine; Department of Mathematics, University of Innsbruck, Innsbruck, AUSTRIA
Schäffter, Tobias; 8, Medizinphysik und metrologische Informationstechnik, PTB-Berlin
Kolbitsch, Christoph; 8.1, Biomedizinische Magnetresonanz, PTB-Berlin
Kofler, Andreas; 8.1, Biomedizinische Magnetresonanz, PTB-Berlin
|
| Quelle/Jahr | Medical Physics: 48 (2021), 1, 178 - 192 |
| ISSN | 0094-2405 (PRINT) ; 2473-4209 (ONLINE) |
| DOI | |
| Verlag | Hoboken, NJ: Wiley |
| Zusammenfassung | Purpose: In the past, Dictionary Learning (DL) and Sparse Coding (SC) have been proposed for the regularization of image reconstruction problems. The regularization is given by a sparse approximation of all image‐patches using a learned dictionary, i.e. an overcomplete set of basis functions learned from data. Despite its competitiveness, DL and SC require the tuning of two essential hyper‐parameters: the sparsity level S ‐ the number of basis functions of the dictionary, called atoms, which are used to approximate each patch, and K ‐ the overall number of such atoms in the dictionary. These two hyper‐parameters usually have to be chosen a‐priori and are determined by repetitive and computationally expensive experiments. Further, the final reported values vary depending on the specific situation. As a result, the clinical application of the method is limited, as standardized reconstruction protocols have to be used. Methods: In this work, we use adaptive DL and propose a novel adaptive sparse coding algorithm for 2D radial cine MR image reconstruction. Using adaptive DL and adaptive SC, the optimal dictionary size K as well as the optimal sparsity level S are chosen dependent on the considered data. Results: Our three main results are the following: First, adaptive DL and adaptive SC deliver results which are comparable or better than the most widely used non‐adaptive version of DL and SC. Second, the time needed for the regularization is accelerated due to the fact that the sparsity level S is never overestimated. Finally, the a‐priori choice of S and K is no longer needed but is optimally chosen dependent on the data under consideration. Conclusions: Adaptive DL and adaptive SC can highly facilitate the application of DL‐ and SC‐based regularization methods. While in this work we focussed on 2D radial cine MR image reconstruction, we expect the method to be applicable to different imaging modalities as well. |
| Themenbereich der Metrologie | Metrologie in der Medizin |