Coronary artery disease (CAD) remains the leading cause of death in both the United States and the world at large. This is primarily due to the extreme difficulty associated with preemptive diagnosis of CAD. Currently, only about 20% of all patients are diagnosed with CAD prior to the occurrence of a heart attack. This is the result of limitations in current techniques, which are either noninvasive, extremely expensive, or have very poor correlation with the actual disease state of the patient. Phonoangiography is an alternative approach to the diagnosis of CAD that relies upon detection of the sound generated by turbulent flow downstream from occlusions. Although the technique is commonly used for the carotid arteries, in the case of the coronary arteries the technique is hampered by signal-to-noise problems as well as uncertainty regarding the spectral characteristics associated with CAD. To date, these signal processing difficulties have prevented the use of the technique clinically. This research introduces an alternative approach to the processing of phonoangiographic data based upon knowledge of the acoustic transfer within the chest. The validity of the proposed approach was examined using transfer functions which were calculated for 14 physiologically relevant locations within the chest using a 2-D Finite Element Model (FEM) generated from physiologic data. These transfer functions were then used to demonstrate the technique using test cases generated with the FEM. Finally, the vulnerability of the technique to noise was quantified through calculation of matrix condition numbers for the chest acoustic transfer at each frequency. These results show that while in general the technique is susceptible to noise; noise tolerance is greatly improved within the frequency range most likely to correspond to an occlusion. Taken together, these results suggest that the proposed technique has the potential to make phonoangiography viable as a screening technique for CAD. Such a technique would greatly reduce the cost of CAD, measured in terms of both financial cost as well as lives. / Master of Science