The determination of hemodynamic significance of coronary artery lesions from cardiac computed tomography angiography (CCTA) based on blood flow simulations has the potential to improve CCTA’s specificity, thus resulting in improved clinical decision making. Accurate coronary lumen segmentation required for flow simulation is challenging due to several factors. Specifically, the partial-volume effect (PVE) in small-diameter lumina may result in overestimation of the lumen diameter that can lead to an erroneous hemodynamic significance assessment. In this work, we present a coronary artery segmentation algorithm tailored specifically for flow simulations by accounting for the PVE. Our algorithm detects lumen regions that may be subject to the PVE by analyzing the intensity values along the coronary centerline and integrates this information into a machine-learning based graph min-cut segmentation framework to obtain accurate coronary lumen segmentations. We demonstrate the improvement in hemodynamic significance assessment achieved by accounting for the PVE in the automatic segmentation of 91 coronary artery lesions from 85 patients. We compare hemodynamic significance assessments by means of fractional flow reserve (FFR) resulting from simulations on 3D models generated by our segmentation algorithm with and without accounting for the PVE. By accounting for the PVE we improved the area under the ROC curve for detecting hemodynamically significant CAD by 29% (N=91, 0.85 vs. 0.66, p<0.05, Delong’s test) with invasive FFR threshold of 0.8 as the reference standard. Our algorithm has the potential to facilitate non-invasive hemodynamic significance assessment of coronary lesions.
We present a spectral-based method for the 2D/3D rigid registration of X-ray images to a CT scan. The method
uses a Fourier-based representation to decompose the six rigid transformation parameters problem into a twoparameter
out-of-plane rotation and a four-parameter in-plane transformation problems. Preoperatively, a set
of Digitally Reconstructed Radiographs (DRRs) are generated offline from the CT in the expected in-plane
location ranges of the fluoroscopic X-ray imaging devices. Each DRR is transformed into a imaging device
in-plane invariant features space. Intraoperatively, a few 2D projections of the patient anatomy are acquired
with an X-ray imaging device. Each projection is transformed into its in-plane invariant representation. The
out-of-plane parameters are first computed by maximization of the Normalized Cross-Correlation between the
invariant representations of the DRRs and the X-ray images. Then, the in-plane parameters are computed with
the phase correlation method based on the Fourier-Mellin transform. Experimental results on publicly available
data sets show that our method can robustly estimate the out-of-plane parameters with accuracy of 1.5° in less
than 1sec for out-of-plane rotations of 10° or more, and perform the entire registration in less than 10secs.
We present an affinity-based optimization method for nearly-automatic vessels segmentation in CTA scans.
The desired segmentation is modeled as a function that minimizes a quadratic affinity-based functional. The
functional incorporates intensity and geometrical vessel shape information and a smoothing constraint. Given a
few user-defined seeds, the minimum of the functional is obtained by solving a single set of linear equations. The
binary segmentation is then obtained by applying a user-selected threshold. The advantages of our method are
that it requires fewer initialization seeds, is robust, and yields better results than existing graph-based interactive
segmentation methods. Experimental results on 20 vessel segments including the carotid arteries bifurcation and
noisy parts of the carotid yield a mean symmetric surface error of 0.54mm (std=0.28).
We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI
maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis
grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an
anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing
of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans
using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the
resulting classification model with the leave-one out technique and compared it to both full multi-class SVM
and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and
yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy
of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only
discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may
be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of
more invasive approaches, such as biopsy or contrast-enhanced imaging.
We present a new non-uniform adaptive sampling method for the estimation of mutual information in multi-modal
image registration. The method uses the Fast Discrete Curvelet Transform to identify regions along anatomical
curves on which the mutual information is computed. Its main advantages of over other non-uniform sampling
schemes are that it captures the most informative regions, that it is invariant to feature shapes, orientations,
and sizes, that it is efficient, and that it yields accurate results. Extensive evaluation on 20 validated clinical
brain CT images to Proton Density (PD) and T1 and T2-weighted MRI images from the public RIRE database
show the effectiveness of our method. Rigid registration accuracy measured at 10 clinical targets and compared
to ground truth measurements yield a mean target registration error of 0.68mm(std=0.4mm) for CT-PD and
0.82mm(std=0.43mm) for CT-T2. This is 0.3mm (1mm) more accurate in the average (worst) case than five
existing sampling methods. Our method has the lowest registration errors recorded to date for the registration
of CT-PD and CT-T2 images in the RIRE website when compared to methods that were tested on at least three
We present a new variational-based method for automatic liver vessels segmentation from abdominal CTA images.
The segmentation task is formulated as a functional minimization problem within a variational framework. We
introduce a new functional that incorporates both geometrical vesselness measure and vessels surface properties.
The functional describes the distance between the desired segmentation and the original image. To minimize the
functional, we derive the Euler-Lagrange equation from it and solve it using the conjugate gradients algorithm.
Our approach is automatic and improves upon other Hessian-based methods in the detection of bifurcations
and complex vessels structures by incorporating a surface term into the functional. To assess our method, we
conducted with an expert radiologist two comparative studies on 8 abdominal CTA clinical datasets. In the first
study, the radiologist assessed the presence of 11 vascular bifurcations on each dataset, totaling of 73 bifurcations.
The radiologist qualitatively compared the bifurcations segmentation of our method and that of a Hessian-based
threshold method. Our method correctly segmented 88% of the bifurcations with a higher visibility score of
82%, as compared to only 55% in the Hessian-based method with a visibility score of 33%. In the second study,
the radiologist assessed the individual vessels visibility on the 3D segmentation images and on the original CTA
slices. Ten main liver vessels were examined in each dataset The overall visibility score was 93%. These results
indicate that our method is suitable for the automatic segmentation and visualization of the liver vessels.