Title :
Cerebral perfusion maps from dynamic contrast MRI data utilizing Rician statistics
Author :
Fitzgerald, Niall ; Sullivan, Finbarr O. ; Newman, George
Author_Institution :
Dept of Stat., UCC, Cork, Ireland
fDate :
Oct. 24 2009-Nov. 1 2009
Abstract :
Bolus tracking of contrast agent with MRI is a well established technique for measurement of local cerebral hemodynamic parameters flow, volume and mean transit time. When performed on a voxel-by-voxel basis, it allows development of hemodynamic parameter maps useful for assessment of ischemic damage following stroke and tumor characterization in cancer. The analysis of the acquired dynamic data requires the use of deconvolution to reconstruct the residue function (R) of the contrast agent. Measurement of the tissue time course and the arterial input function are obtained by T2 or T2* weighted sequences. Reconstruction of R provides estimates of flow, volume and mean transit time. The raw MRI scan signal intensity is well approximated by Rician statistics. The standard approach to estimation involves logarithmic transformation and least squares deconvolution. At low signal to noise ratio this approach is not efficient and as an alternative this work adopts an iterative re-weighted non-linear least squares (IRWNLLS) algorithm to incorporate Rician statistics, impose constraints on the residue function and optimize for tracer arrival delay. The algorithm is implemented on a voxel-by-voxel basis and cerebral maps for the hemodynamic parameters flow, volume and mean transit time are presented. In addition, an automatic segmentation technique which takes into account both spatial and temporal variation is presented. This segmentation technique is shape driven, choosing only voxels that correlate highly with a well-known arterial input function template.
Keywords :
biological tissues; biomedical MRI; blood flow measurement; brain; cancer; deconvolution; haemorheology; image reconstruction; image sequences; iterative methods; least squares approximations; neurophysiology; optimisation; statistical analysis; tumours; Rician statistics; T2 weighted sequences; T2* weighted sequences; arterial input function; arterial input function template; automatic segmentation technique; bolus tracking; cancer; cerebral perfusion maps; dynamic contrast MRI data; hemodynamic parameter flow; image reconstruction; ischemic damage; iterative re-weighted nonlinear least square algorithm; least squares deconvolution; logarithmic transformation; optimization; residue function; scan signal intensity; signal-to-noise ratio; spatial variation; stroke; temporal variation; tissue time course; tracer arrival delay; tumor characterization; voxel-by-voxel basis; Deconvolution; Fluid flow measurement; Hemodynamics; Iterative algorithms; Least squares approximation; Magnetic resonance imaging; Rician channels; Statistics; Time measurement; Volume measurement; MRI; Rice distribution; iterative re-weighted non-linear least squares; maximum likelihood; perfusion;
Conference_Titel :
Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-3961-4
Electronic_ISBN :
1095-7863
DOI :
10.1109/NSSMIC.2009.5401910