Project detail

Magnetic resonance perfusion imaging using compressed sensing

Duration: 01.01.2016 — 31.12.2018

Funding resources

Czech Science Foundation - Standardní projekty

- whole funder (2016-01-01 - 2018-12-31)

On the project

Perfuzní analýza je významná experimentální zobrazovací technika používaná pro diagnostiku a hodnocení odezvy terapie. Analýza založená na magnetické rezonanci (MR) napomáhá identifikovat stav a dynamické chování onkologických a kardiovaskulárních onemocnění a umožňuje efektivnější léčbu. MR perfuzní analýza se provádí ze získaných signálů pomocí odhadu parametrů, které popisují vlastnosti tkání. Přesná kvantifikace parametrů komplexních modelů prokrvení tkání vyžaduje vysoký odstup signálu od šumu a souběžné vysoké prostorové a časové rozlišení. Současné studie ukazují, že metody kvantifikace založené na pokročilých farmakokinetických modelech a slepé dekonvoluci dosáhly svých limitů. Cílem projektu je využít poznatky z oblasti komprimovaného snímání a vyvinout nové metody získávání a rekonstrukce v MR, které berou v úvahu specifika perfuzní analýzy. To povede k výraznému zlepšení v přesnosti odhadu perfuzních parametrů, a tím přispěje k odstranění současných technických bariér a k lepšímu porozumění povahy onemocnění.

Description in English
Perfusion analysis is an important experimental technique used for diagnostics and evaluation of therapy response. Analysis based on magnetic resonance (MR) helps in identifying the state and dynamic behaviour of oncological and cardiovascular diseases and enables bettertreatment. MR perfusion analysis is done by means of estimating parameters describing tissue properties from the acquired signal. Accurate quantification of the parameters driving complex perfusion models requires low signal to noise ratio and high spatial and temporal resolution. Recent studies show that the quantification methods based on advanced pharmacokinetic models and blind deconvolution have reached their limits. The goal of the project is to utilize knowledge from the field of compressed sensing to develop novel methods of MR acquisition and reconstruction taking into account the specifics of perfusion analysis. This will lead to significant improvements in the accuracy of perfusion parameter estimation, thus contributing to lifting today’s technical barriers and understanding better the nature of diseases.

Keywords
Perfuzní zobrazování; magnetická rezonance; MRI; DCE-MRI; komprimované snímání; komprimační vzorkování; dekonvoluce

Key words in English
Perfusion imaging; magnetic resonance; MRI; DCE-MRI; compressed sensing; compressive sampling; deconvolution

Mark

GA16-13830S

Default language

Czech

People responsible

Mangová Marie, Ing., Ph.D. - fellow researcher
Novosadová Michaela, Ing., Ph.D. - fellow researcher
Rajmic Pavel, prof. Mgr., Ph.D. - principal person responsible

Units

Department of Telecommunications
- responsible department (2016-01-01 - 2018-12-31)
Faculty of Electrical Engineering and Communication
- beneficiary (2016-01-01 - 2018-12-31)

Results

RAJMIC, P.; NOVOSADOVÁ, M. On the Limitation of Convex Optimization for Sparse Signal Segmentation. In Proceedings of the 39th International Conference on Telecommunications and Signal Processing (TSP) 2016. 2016. p. 550-554. ISBN: 978-1-5090-1288-6.
Detail

JIŘÍK, R.; MANGOVÁ, M.; RAJMIC, P.; MACÍČEK, O.; SOUČEK, K.; STARČUK, Z. Advanced Pharmacokinetic Modeling in Small-Animal Compressed-Sensing DCE-MRI. ESMRMB 2019 - 36th Annual Scientific Meeting. Rotterdam, Netherlands: 2019. p. 1 (1 s.).
Detail

DAŇKOVÁ, M.; RAJMIC, P. Content-aware low-rank plus sparse model for perfusion MRI reconstruction. Strobl, Rakousko: 2016. p. 1 (1 s.).
Detail

DAŇKOVÁ, M.; RAJMIC, P. Debiasing incorporated into reconstruction of low-rank modelled dynamic MRI data. Aalborg, Dánsko: 2016. p. 53-55.
Detail

DAŇKOVÁ, M.; RAJMIC, P. Low-rank model for dynamic MRI: joint solving and debiasing. In ESMRMB 2016, 33rd Annual Scientific Meeting, Vienna, AT, September 29--October 1: Abstracts, Friday. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE. Berlin: Springer, 2016. p. 200-201. ISSN: 1352-8661.
Detail

MANGOVÁ, M.; RAJMIC, P.; JIŘÍK, R. Dynamic Magnetic Resonance Imaging using Compressed Sensing with Multi-scale Low Rank Penalty. In Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP) 2017. Barcelona: 2017. p. 780-783. ISBN: 978-1-5090-3981-4.
Detail

NOVOSADOVÁ, M.; RAJMIC, P. Piecewise-polynomial Signal Segmentation Using Reweighted Convex Optimization. In Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP) 2017. Barcelona: 2017. p. 769-774. ISBN: 978-1-5090-3981-4.
Detail

RAJMIC, P.; NOVOSADOVÁ, M.; DAŇKOVÁ, M. Piecewise-polynomial Signal Segmentation Using Convex Optimization. Kybernetika, 2017, vol. 53, no. 6, p. 1131-1149. ISSN: 0023-5954.
Detail

JIŘÍK, R.; DAŇKOVÁ, M.; RAJMIC, P.; KRÁTKÁ, L.; DVOŘÁKOVÁ, L.; DRAŽANOVÁ, E.; STARČUK, Z. Absolute Quantification of Brain Perfusion using Golden Angle Compressed Sensing DCE-MRI. ISMRM 2016, 25th Annual Meeting & Exhibition, Honolulu, HI, USA, April 22--27. 2017. p. 1 (1 s.).
Detail

BARTOŠ, M.; ŠOREL, M.; MANGOVÁ, M.; RAJMIC, P.; STANDARA, M.; JIŘÍK, R. DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization. Joint Annual Meeting ISMRM-ESMRMB 2018, Paris Expo Porte de Versailles, Paris, France, June 16--21. 2018. p. 1-3.
Detail

WALNER, H.; BARTOŠ, M.; MANGOVÁ, M.; KEUNEN, O.; BJERKVIG, R.; JIŘÍK, R.; ŠOREL, M. Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data. In World Congress on Medical Physics and Biomedical Engineering 2018. 2019. p. 267-271. ISBN: 978-981-10-9035-6.
Detail

NOVOSADOVÁ, M.; RAJMIC, P. Image edges resolved well when using an overcomplete piecewise-polynomial model. In Proceedings of the 12th International Conference on Signal Processing and Communication Systems (ICSPCS). Cairns: IEEE, 2018. p. 1-10. ISBN: 978-1-5386-5601-3.
Detail

MACÍČEK, O.; JIŘÍK, R.; MIKULKA, J.; BARTOŠ, M.; ŠPRLÁKOVÁ, A.; KEŘKOVSKÝ, M.; STARČUK, Z.; BARTUŠEK, K.; TAXT, T. Time-Efficient Perfusion Imaging Using DCE- and DSC-MRI. Measurement Science Review, 2018, vol. 18, no. 6, p. 262-271. ISSN: 1335-8871.
Detail

NOVOSADOVÁ, M.; RAJMIC, P. Piecewise-polynomial curve fitting using group sparsity. In Proceedings of the 8th International Congress on Ultra Modern Telecommunications and Control Systems. Lisbon: EDAS Conference Services, 2016. p. 320-325. ISBN: 978-1-4673-8817-7.
Detail

NOVOSADOVÁ, M.; RAJMIC, P.; ŠOREL, M. Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising. EURASIP Journal on Advances in Signal Processing, 2019, vol. 2019, no. 6, p. 1-15. ISSN: 1687-6172.
Detail

BARTOŠ, M.; RAJMIC, P.; ŠOREL, M.; MANGOVÁ, M.; KEUNEN, O.; JIŘÍK, R. Spatially regularized estimation of the tissue homogeneity model parameters in DCE-MRI using proximal minimization. Magnetic Resonance in Medicine, 2019, vol. 82, no. 6, p. 2257-2272. ISSN: 1522-2594.
Detail

NOVOSADOVÁ, M.: S - edge_detector; Piecewise Polynomial Edge Image Detector. pracovna SD5.66, Technická 12, 61600 Brno. URL: http://www.utko.feec.vutbr.cz/~rajmic/software/edge_detect.zip. (software)
Detail

Link

http://www.utko.feec.vutbr.cz/~rajmic/projects/gacr-2017-19/

Responsibility: Rajmic Pavel, prof. Mgr., Ph.D.