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We propose a minimum cost path approach to track the centerlines of the internal and external carotid arteries in multispectral MR data. User interaction is limited to the annotation of three seed points. The cost image is based on both a measure of vessel medialness and lumen intensity similarity in two MRA image sequences: Black Blood MRA and Phase Contrast MRA. After intensity inhomogeneity correction and noise reduction, the two images are aligned using affine registration. The two parameters that control the contrast of the cost image were determined in an optimization experiment on 40 training datasets. Experiments on the training datasets also showed that a cost image composed of a combination of gradient-based medialness and lumen intensity similarity increases the tracking accuracy compared to using only one of the constituents. Furthermore, centerline tracking using both MRA sequences outperformed tracking using only one of these MRA images. An independent test set of 152 images from 38 patients served to validate the technique. The centerlines of 148 images were successfully extracted using the parameters optimized on the training sets. The average mean distance to the reference standard, manually annotated centerlines, was 0.98 mm, which is comparable to the in-plane resolution. This indicates that the proposed method has a high potential to replace the manual centerline annotation.

Next to aneurysm size, aneurysm growth over time is an important indicator for aneurysm rupture risk. Manual assessment of aneurysm growth is a cumbersome procedure, prone to inter-observer and intra-observer variability. In clinical practice, mainly qualitative assessment and/or diameter measurement are routinely performed. In this paper a semi-automated method for quantifying aneurysm volume growth over time in CTA data is presented. The method treats a series of longitudinal images as a 4D dataset. Using a 4D groupwise non-rigid registration method, deformations with respect to the baseline scan are determined. Combined with 3D aneurysm segmentation in the baseline scan, volume change is assessed using the deformation field at the aneurysm wall. For ten patients, the results of the method are compared with reports from expert clinicians, showing that the quantitative results of the method are in line with the assessment in the radiology reports. The method is also compared to an alternative method in which the volume is segmented in each 3D scan individually, showing that the 4D groupwise registration method agrees better with manual assessment.

°For electrophysiology intervention monitoring, we intend to reconstruct 4D ultrasound (US) of structures in the beating heart from 2D transesophageal US by scanplane rotation. The image acquisition is continuous but unsynchronized to the heart rate, which results in a sparsely and irregularly sampled dataset and a spatiotemporal interpolation method is desired. Previously, we showed the potential of normalized convolution (NC) for interpolating such datasets. We explored 4D interpolation by 3 different methods: NC, nearest neighbor (NN), and temporal binning followed by linear interpolation (LTB). The test datasets were derived by slicing three 4D echocardiography datasets at random rotation angles (, range: 0-180) and random normalized cardiac phase (, range: 0-1). Four different distributions of rotated 2D images with 600, 900, 1350, and 1800 2D input images were created from all TEE sets. A 2D Gaussian kernel was used for NC and optimal kernel sizes ( and ) were found by performing an exhaustive search. The RMS gray value error (RMSE) of the reconstructed images was computed for all interpolation methods. The estimated optimal kernels were in the range of = 3.24 - 3.69°/ = 0.045 - 0.048, = 2.79°/ = 0.031 - 0.038, = 2.34°/ = 0.023 - 0.026, and = 1.89°/ = 0.021 - 0.023 for 600, 900, 1350, and 1800 input images respectively. We showed that NC outperforms NN and LTB. For a small number of input images the advantage of NC is more pronounced.

Automatic quantification of carotid artery plaque composition is important in the development of methods that distinguish vulnerable from stable plaques. MRI has shown to be capable of imaging different components noninvasively. We present a new plaque classification method which uses 3D registration of histology data with ex vivo MRI data, using non-rigid registration, both for training and evaluation. This is more objective than previously presented methods, as it eliminates selection bias that is introduced when 2D MRI slices are manually matched to histological slices before evaluation. Histological slices of human atherosclerotic plaques were manually segmented into necrotic core, fibrous tissue and calcification. Classification of these three components was voxelwise evaluated. As features the intensity, gradient magnitude and Laplacian in four MRI sequences after different degrees of Gaussian smoothing, and the distances to the lumen and the outer vessel wall, were used. Performance of linear and quadratic discriminant classifiers for different combinations of features was evaluated. Best accuracy (72.5 ± 7.7%) was reached with the linear classifier when all features were used. Although this was only a minor improvement to the accuracy of a classifier that only included the intensities and distance features (71.6 ± 7.9%), the difference was statistically significant (paired t-test, p<0.05). Good sensitivity and specificity for calcification was reached (83% and 95% respectively), however, differentiation between fibrous (sensitivity 85%, specificity 60%) and necrotic tissue (sensitivity 49%, specificity 89%) was more difficult.