The determination of myocardial volume at risk distal to coronary stenosis provides important information for prognosis
and treatment of coronary artery disease. In this paper, we present a novel computational framework for estimating the
myocardial volume at risk in computed tomography angiography (CTA) imagery. Initially, epicardial and endocardial
surfaces, and coronary arteries are extracted using an active contour method. Then, the extracted coronary arteries are
projected onto the epicardial surface, and each point on this surface is associated with its closest coronary artery using
the geodesic distance measurement. The likely myocardial region at risk on the epicardial surface caused by a stenosis is
approximated by the region in which all its inner points are associated with the sub-branches distal to the stenosis on the
coronary artery tree. Finally, the likely myocardial volume at risk is approximated by the volume in between the region
at risk on the epicardial surface and its projection on the endocardial surface, which is expected to yield computational
savings over risk volume estimation using the entire image volume. Furthermore, we expect increased accuracy since, as
compared to prior work using the Euclidean distance, we employ the geodesic distance in this work. The experimental
results demonstrate the effectiveness of the proposed approach on pig heart CTA datasets.
We propose a novel framework for population analysis of DW-MRI data using the Tubular Surface Model. We
focus on the Cingulum Bundle (CB) - a major tract for the Limbic System and the main connection of the Cingulate
Gyrus, which has been associated with several aspects of Schizophrenia symptomatology. The Tubular Surface
Model represents a tubular surface as a center-line with an associated radius function. It provides a natural way
to sample statistics along the length of the fiber bundle and reduces the registration of fiber bundle surfaces to that
of 4D curves. We apply our framework to a population of 20 subjects (10 normal, 10 schizophrenic) and obtain
excellent results with neural network based classification (90% sensitivity, 95% specificity) as well as unsupervised
clustering (k-means). Further, we apply statistical analysis to the feature data and characterize the discrimination
ability of local regions of the CB, as a step towards localizing CB regions most relevant to Schizophrenia.
We propose a new model for pharmacokinetic analysis based on the one proposed by Tofts. Our model
both eliminates the need for estimating the Arterial Input Function (AIF) and normalizes analysis so
that comparisons across patients can be performed. Previous methods have attempted to circumvent
the AIF estimation by using the pharmacokinetic parameters of multiple reference regions (RR). Viewing
anatomical structures as filters, pharmacokinetic analysis tells us that 'similar' structures will be similar
filters. By cascading the inverse filter at a RR with the filter at the voxel being analyzed, we obtain a
transfer function relating the concentration of a voxel to that of the RR. We show that this transfer function
simplifies into a five-parameter nonlinear model with no reference to the AIF. These five parameters are
combinations of the three parameters of the original model at the RR and the region of interest. Contrary
to existing methods, ours does not require explicit estimation of the pharmacokinetic parameters of the
RR. Also, cascading filters in the frequency domain allows us to manipulate more complex models, such as
accounting for the vascular tracer component. We believe that our model can improve analysis across MR
parameters because the analyzed and reference enhancement series are from the same image. Initial results
are promising with the proposed model parameters exhibiting values that are more consistent across lesions
in multiple patients. Additionally, our model can be applied to multiple voxels to estimate the original
pharmacokinetic parameters as well as the AIF.
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