Biomedical subjects

S Sourbron

Publications and source records attributed to S Sourbron.

4 recordsLinked to original sources

PubMed2006

Pixel-by-pixel deconvolution of bolus-tracking data: optimization and implementation.

Quantification of haemodynamic parameters with a deconvolution analysis of bolus-tracking data is an ill-posed problem which requires regularization. In a previous study, simulated data without structural errors were used to validate two methods for a pixel-by-pixel analysis: standard-form Tikhonov regularization with either the L-curve criterion (LCC) or generalized cross validation (GCV) for selecting the regularization parameter. However, problems of image artefacts were reported when the methods were applied to patient data. The aim of this study was to investigate the nature of these problems in more detail and evaluate strategies of optimization for routine application in the clinic. In addition we investigated to which extent the calculation time of the algorithm can be minimized. In order to ensure that the conclusions are relevant for a larger range of clinical applications, we relied on patient data for evaluation of the algorithms. Simulated data were used to validate the conclusions in a more quantitative manner. We conclude that the reported problems with image quality can be removed by appropriate optimization of either LCC or GCV. In all examples this could be achieved with LCC without significant perturbation of the values in pixels where the regularization parameter was originally selected accurately. GCV could not be optimized for the renal data, and in the CT data only at the cost of image resolution. Using the implementations given, calculation times were sufficiently short for routine application in the clinic.

Algorithms↗

PubMed2005

Quantification of renal perfusion and function on a voxel-by-voxel basis: a feasibility study.

The feasibility of a voxel-by-voxel deconvolution analysis of T(1)-weighted DCE data in the human kidney and its potential for obtaining quantification of perfusion and filtration was investigated. Measurements were performed on 14 normal humans and 1 transplant at 1.5 T using a Turboflash sequence. Signal time-courses were converted to tracer concentrations and deconvolved with an aorta AIF. Parametric maps of relative renal blood flow (rRBF), relative renal volume of distribution (rRVD), relative mean transit time (rMTT), and whole cortex extraction fraction (E) were obtained from the impulse response functions. For the normals average cortical rRBF, rRVD, rMTT, and E were 1.6 mL/min/mL (SD 0.8), 0.4 mL/mL (SD 0.1), 17s (SD 7), and 22.6% (SD 6.1), respectively. A gradual voxelwise rRBF increase is found from the center of two infarction zones toward the edges. Voxel IRFs showed more detail on the nefron substructure than ROI IRFs. In conclusion, quantitative voxelwise perfusion mapping based on deconvolved T(1)-DCE renal data is feasible, but absolute quantification requires inflow correction. rRBF maps and quantitative values are sufficiently sensitive to detect perfusion abnormality in pathologic areas, but further research is necessary to separate perfusion from extraction and to characterize the different compartments of the nephron on the (sub)voxel level.

Algorithms↗

PubMed2004

Choice of the regularization parameter for perfusion quantification with MRI.

Truncated singular value decomposition (TSVD) is an effective method for the deconvolution of dynamic contrast enhanced (DCE) MRI. Two robust methods for the selection of the truncation threshold on a pixel-by-pixel basis--generalized cross validation (GCV) and the L-curve criterion (LCC)--were optimized and compared to paradigms in the literature. GCV and LCC were found to perform optimally when applied with a smooth version of TSVD, known as standard form Tikhonov regularization (SFTR). The methods lead to improvements in the estimate of the residue function and of its maximum, and converge properly with SNR. The oscillations typically observed in the solution vanish entirely, and perfusion is more accurately estimated at small mean transit times. This results in improved image contrast and increased sensitivity to perfusion abnormalities, at the cost of 1-2 min in calculation time and hyperintense clusters in the image. Preliminary experience with clinical data suggests that the latter problem can be resolved using spatial continuity and/or hybrid thresholding methods. In the simulations GCV and LCC are equivalent in terms of performance, but GCV thresholding is faster.

Arteries↗

PubMed2001

Diffusion and perfusion MRI: basic physics.

Diffusion and perfusion MR imaging are now being used increasingly in neuro-vascular clinical applications. While diffusion weighted magnetic resonance imaging exploits the translational mobility of water molecules to obtain information on the microscopic behaviour of the tissues (presence of macromolecules, presence and permeability of membranes, equilibrium intracellular-extracellular water, ellipsis), perfusion weighted imaging makes use of endogenous and exogenous tracers for monitoring their hemodynamic status. The combination of both techniques is extremely promising for the early detection and assessment of stroke, for tumor characterisation and for the evaluation of neurodegenerative diseases. This article provides a brief review of the basic physics principles underlying the methodologies followed.

Cerebrovascular Disorders↗