Title:  Development of a Multimodal Framework for Cardiac Computed Tomography Gating

Committee:

Dr. Pamela Bhatti, ECE, Chair , Advisor

Dr. Srini Tridandapani, Emory, Co-Advisor

Dr. James McClellan, ECE

Dr. Xiangyang Tang, BME

Dr. David Anderson, ECE

Dr. William Auffermann, University of Utah

Abstract:

Cardiovascular disease (CVD) is the leading cause of death globally. The gold standard for evaluating CVDs, catheter coronary angiography, is invasive and expensive. An alternative technique, computed tomography angiography (CTA), is less invasive, relatively inexpensive and faster. However, CTA suffers from limited temporal resolution resulting in cardiac motion artifacts. Therefore, it is crucial to obtain CTA data when the cardiac motion is minimal within the cardiac cycle, known as the cardiac quiescence. The objective of this work is to improve the diagnostic quality and reduce radiation exposure of cardiac CTA. Cardiac quiescence predicted from electrocardiography (ECG), an indication of the electrical activity, is suboptimal. As an alternative, seismocardiography (SCG) that directly measures the mechanical motion of the heart has demonstrated its effectiveness in detecting cardiac quiescence. In this work, an SCG-based quiescence prediction method was developed, and reported improved accuracy in quiescence prediction. To further improve the performance, a multimodal framework that fuses quiescence predictions derived from ECG and SCG signals on a beat-by-beat basis was developed using an artificial neural network. The diagnostic quality of reconstructed CTA images at predicted timings was interpreted by a radiologist and the assessment indicated improvement with the inclusion of SCG signals. Lastly, a proof-of-concept prototype that implements the multimodal framework in a near real-time manner via integration of existing hardware and software was developed and tested. Testing of this prototype using both the pre-recorded and real-time data demonstrated the feasibility of the real-time ECG-SCG multimodal framework.