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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.
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