We have constructed a fourth generation anthropomorphic phantom which, in addition to the realistic description of the
human anatomy, includes a coronary artery disease model. A <i>watertight</i> version of the NURBS-based Cardiac-Torso
(NCAT) phantom was generated by converting the individual NURBS surfaces of each organ into closed, manifold and
non-self-intersecting tessellated surfaces. The resulting 330 surfaces of the phantom organs and tissues are now comprised
of ~5×10<sup>6</sup> triangles whose size depends on the individual organ surface normals. A database of the elemental composition of each organ was generated, and material properties such as density and scattering cross-sections were defined using
PENELOPE. A 300 μm resolution model of a heart with 55 coronary vessel segments was constructed by fitting smooth
triangular meshes to a high resolution cardiac CT scan we have segmented, and was consequently registered inside the torso
model. A coronary artery disease model that uses hemodynamic properties such as blood viscosity and resistivity was used
to randomly place plaque within the artery tree. To generate x-ray images of the aforementioned phantom, our group has
developed an efficient Monte Carlo radiation transport code based on the subroutine package PENELOPE, which employs
an octree spatial data-structure that stores and traverses the phantom triangles. X-ray angiography images were generated
under realistic imaging conditions (90 kVp, 10° Wanode spectra with 3 mm Al filtration, ~5×10<sup>11</sup> x-ray source photons, and 10% per volume iodine contrast in the coronaries). The images will be used in an optimization algorithm to select the
optimal technique parameters for a variety of imaging tasks.
We developed an algorithm based on a rule-based threshold framework to segment the coronary arteries from
angiographic computed tomography (CTA) data. Computerized segmentation of the coronary arteries is a
challenging procedure due to the presence of diverse anatomical structures surrounding the heart on cardiac
CTA data. The proposed algorithm incorporates various levels of image processing and organ information
including region, connectivity and morphology operations. It consists of three successive stages. The first stage
involves the extraction of the three-dimensional scaffold of the heart envelope. This stage is semiautomatic
requiring a reader to review the CTA scans and manually select points along the heart envelope in slices. These
points are further processed using a surface spline-fitting technique to automatically generate the heart envelope.
The second stage consists of segmenting the left heart chambers and coronary arteries using grayscale threshold,
size and connectivity criteria. This is followed by applying morphology operations to further detach the left and
right coronary arteries from the aorta. In the final stage, the 3D vessel tree is reconstructed and labeled using
an Isolated Connected Threshold technique. The algorithm was developed and tested on a patient coronary
artery CTA that was graciously shared by the Department of Radiology of the Massachusetts General Hospital.
The test showed that our method constantly segmented the vessels above 79% of the maximum gray-level and
automatically extracted 55 of the 58 coronary segments that can be seen on the CTA scan by a reader. These
results are an encouraging step toward our objective of generating high resolution models of the male and female
heart that will be subsequently used as phantoms for medical imaging system optimization studies.
Cardiovascular disease is considered the leading cause of death in the US, accounting for 38% of all deaths. There are gender differences in the size of coronary arteries and in the character and location of atherosclerotic lesions that affect the detection of coronary artery disease with the medical imaging modalities currently used (e.g. angiography, computed tomography). These differences also affect the safety and effectiveness of image-guided interventions using therapeutic devices. For the optimization of the medical imaging modalities used for this specific task we require the generation of clinically-realistic, gender-specific images of healthy and pathological coronary angiograms. For this purpose we have created a gender-specific statistical model of a pathological coronary artery tree. Starting from "healthy" heart-phantoms created from high resolution CT scans of cadaver hearts of both genders, the model uses prevalence data obtained from clinical studies of patients with significant (>50% stenosis) coronary artery disease (CAD). The model determines the plaque deposit locations and character (length, percent stenosis) for each case, based on a flow model. These data are then used to generate artificially diseased artery trees, embedded in a gender-specific torso model. Using an x-ray and optical photon Monte-Carlo simulation program, we then generate simulated angiograms exhibiting realistic disease patterns. The severity of each angiogram is determined from a set of rules that combines the geometrically increasing severity of lesions, the cumulative effects of multiple obstructions, the significance of their locations, the modifying influence of the collaterals, and the size and quality of the distal vessels. The simulated angiograms will consequently be read by model and human observers. The probability of detection derived in combination with the severity score will be used as a figure of merit for the patient- and gender-specific optimization of the imaging modality under investigation.