2017/01/01 by Peter Zaspel, Zaspel, Peter
Engineering · Medicine · #Advanced MRI Techniques and Applications #FOS: Mathematics #MRI in cancer diagnosis #Medical Imaging Techniques and Applications #Numerical Analysis (math.NA) #Optical Imaging and Spectroscopy Techniques #Photoacoustic and Ultrasonic Imaging
paper · pdf · doi:10.48550/arxiv.1810.09290
openalex publication_date 2018/10/22 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
We consider the solution of inverse problems in dynamic contrast-enhanced\nimaging by means of Ensemble Kalman Filters. Our quantity of interest is blood\nperfusion, i.e. blood flow rates in tissue. While existing approaches to\ncompute blood perfusion parameters for given time series of radiological\nmeasurements mainly rely on deterministic, deconvolution-based methods, we aim\nat recovering probabilistic solution information for given noisy measurements.\nTo this end, we model radiological image capturing as sequential data\nassimilation process and solve it by an Ensemble Kalman Filter. Thereby, we\nrecover deterministic results as ensemble-based mean and are able to compute\nreliability information such as probabilities for the perfusion to be in a\ngiven range. Our target application is the inference of blood perfusion\nparameters in the human brain. A numerical study shows promising results for\nartificial measurements generated by a Digital Perfusion Phantom.\n