Projekte pro Jahr
Abstract
Purpose: To accelerate dynamic MR applications using infimal convolution of total generalized variation functionals (ICTGV) as spatio-temporal regularization for image reconstruction. Theory and Methods: ICTGV comprises a new image prior tailored to dynamic data that achieves regularization via optimal local balancing between spatial and temporal regularity. Here it is applied for the first time to the reconstruction of dynamic MRI data. CINE and perfusion scans were investigated to study the influence of time dependent morphology and temporal contrast changes. ICTGV regularized reconstruction from subsampled MR data is formulated as a convex optimization problem. Global solutions are obtained by employing a duality based non-smooth optimization algorithm. Results: The reconstruction error remains on a low level with acceleration factors up to 16 for both CINE and dynamic contrast-enhanced MRI data. The GPU implementation of the algorithm suites clinical demands by reducing reconstruction times of one dataset to less than 4 min. Conclusion: ICTGV based dynamic magnetic resonance imaging reconstruction allows for vast undersampling and therefore enables for very high spatial and temporal resolutions, spatial coverage and reduced scan time. With the proposed distinction of model and regularization parameters it offers a new and robust method of flexible decomposition into components with different degrees of temporal regularity.
Originalsprache | englisch |
---|---|
Seiten (von - bis) | 142–155 |
Fachzeitschrift | Magnetic Resonance in Medicine |
Jahrgang | 78 |
Ausgabenummer | 1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2017 |
ASJC Scopus subject areas
- Radiologie, Nuklearmedizin und Bildgebung
Fields of Expertise
- Human- & Biotechnology
Kooperationen
- BioTechMed-Graz
Fingerprint
Untersuchen Sie die Forschungsthemen von „Infimal convolution of total generalized variation functionals for dynamic MRI“. Zusammen bilden sie einen einzigartigen Fingerprint.Projekte
- 1 Abgeschlossen
-
Spezialforschungsbereich (SFB) MOBIS - F 32 Mathematische Optimierung und Anwendungen in der Biomedizinischen Wissenschaft
Santner, S. (Teilnehmer (Co-Investigator)), Scharfetter, H. (Teilnehmer (Co-Investigator)), Knoll, F. (Teilnehmer (Co-Investigator)), Steinbach, O. (Teilnehmer (Co-Investigator)), Of, G. (Teilnehmer (Co-Investigator)), Stollberger, R. (Projektleiter (Principal Investigator)) & Hofer, M. (Teilnehmer (Co-Investigator))
1/05/07 → 30/04/18
Projekt: Forschungsprojekt
Aktivitäten
- 1 Vortrag bei Workshop, Seminar oder Kurs
-
SFB Workshop: Imaging with Modulated/Incomplete Data 2016
Schlögl, M. (Redner/in)
2016Aktivität: Vortrag oder Präsentation › Vortrag bei Workshop, Seminar oder Kurs › Science to science
-
Accelerated Variational dynamic MRI reconstruction (AVIONIC)
Schlögl, M., Schwarzl, A., Holler, M., Bredies, K. & Stollberger, R., Jan. 2016.Publikation: Konferenzbeitrag › (Altdaten) Vortrag oder Präsentation › Begutachtung
-
Changing Temporal Resolution of DCE-MRI Radial VIBE Data by ICTGV Reconstruction
Schlögl, M., Holler, M., Bredies, K. & Stollberger, R., 2016.Publikation: Konferenzbeitrag › (Altdaten) Vortrag oder Präsentation › Begutachtung
-
ICTGV Reconstruction of DCE golden angle radial VIBE data
Schlögl, M., Holler, M., Bredies, K. & Stollberger, R., 2016.Publikation: Konferenzbeitrag › Abstract › Begutachtung