Quantification of multicontrast vascular MR images with NLSnake, an active contour model: in vitro validation and in vivo evaluation.

Vessel-wall measurements from multicontrast MRI provide information on plaque structure and evolution. This requires the extraction of numerous contours. In this work a contour-extraction method is proposed that uses an active contour model (NLSnake) adapted for a wide range of MR vascular images. This new method employs length normalization for the purpose of deformation computation and offers the advantages of simplified parameter tuning, fast convergence, and minimal user interaction. The model can be initialized far from the boundaries of the region to be segmented, even by only one pixel. The accuracy and reproducibility of NLSnake endoluminal contours were assessed on vascular phantom MR angiography (MRA) and high-resolution in vitro MR images of rabbit aorta. An in vivo evaluation was performed on rabbit and clinical data for both internal and external vessel-wall contours. In phantoms with 95% stenoses, NLSnake measured 94.3% +/- 3.8%, and the accuracy was even better for milder stenoses. In the images of rabbit aorta, variability between NLSnake and experts was less than interobserver variability, while the maximum intravariability of NLSnake was equal to 1.25%. In conclusion, the NLSnake technique successfully quantified the vessel lumen in multicontrast MR images using constant parameters.

Mapping myocardial perfusion with an intravascular MR contrast agent: robustness of deconvolution methods at various blood flows.

Evaluation of quantitative parameters such as regional myocardial blood flow (rMBF), blood volume (rMBV), and mean transit time (rMTT) by MRI is gaining acceptance for clinical applications, but still lacks robust postprocessing methods for map generation. Moreover, robustness should be preserved over the full range of myocardial flows and volumes. Using experimental data from an isolated pig heart preparation, synthetic MR kinetics were generated and four deconvolution approaches were evaluated. These methods were then applied to the first-pass T(1) images of the isolated pig heart using an intravascular contrast agent and rMBF, rMBV and rMTT maps were generated. In both synthetic and experimental data, the fit between calculated and original data reached equally good results with the four techniques. rMBV was the only parameter estimated correctly in numerical experiments. Moreover, using the algebraic method ARMA, abnormal regions were well delineated on rMBV maps. At high flows, rMBF was underestimated at the experimental noise level. Finally, rMTT maps appeared noisy and highly unreliable, especially at high flows. In conclusion, over the myocardial flow range, i.e., 0-400 ml/min/100g, rMBF identification was biased in presence of noise, whereas rMBV was correctly identified. Thus, rMBV mapping could be a fast and robust way to detect abnormal myocardial regions.