TY  - CHAP
Y1  - 2014///
N2  - In order to asses brain perfusion, one of the available methods is the estimation of parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) from Dynamic Susceptibility Contrast-MRI (DSC-MRI). This estimation requires both high temporal resolution to capture the rapid tracer kinetic, and high spatial resolution to detect small impairments and reliably discriminate boundaries.With this inmind, we propose a compressed sensing approach to decrease the acquisition time without sacrificing the reconstruction, especially in the region affected by tracer passage. To this end we propose the utilization of an available TVL1- L2 minimization scheme with a novel additional term that introduce the information on the volume at baseline (no tracer). We show on simulated data the benefit of such a scheme, that is able to achieve an accurate reconstruction even at high acceleration (x16), with a RMSE of 2.8, 10 times lower than the error obtained with the original reconstruction.
T2  - XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
SP  - 225
PB  - Springer International Publishing
UR  - http://dx.doi.org/10.1007/978-3-319-00846-2_56
TI  - Reconstruction of DSC-MRI Data from Sparse Data Exploiting Temporal Redundancy and Contrast Localization
AV  - none
M1  - 41
SN  - 978-3-319-00845-5
A1  - Boschetto, Davide
A1  - Castellaro, M.
A1  - Di Prima, P.
A1  - Bertoldo, A.
A1  - Grisan, Enrico
T3  - IFMBE Proceedings
KW  - Compressed sensing
KW  -  MRI
KW  -  Dynamic susceptibility
KW  -  DSC-MRI
KW  -  Contrast kinetics
EP  - 228
ID  - eprints3024
ER  -