Background: Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. Methods: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. Results: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm<sup>2</sup> for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. Conclusions: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.
We propose a novel method for nonrigid registration of coronary arteries within frames of a fluoroscopic X-ray angiogram sequence with propagated deformation field. The aim is to remove the motion of coronary arteries in order to simplify further registration of the 3D vessel structure obtained from computed tomography angiography, with the x-ray sequence. The Proposed methodology comprises two stages: propagated adjacent pairwise nonrigid registration, and, sequence-wise fixed frame nonrigid registration. In the first stage, a propagated nonrigid transformation reduces the disparity search range for each frame sequentially. In the second stage, nonrigid registration is applied for all frames with a fixed target frame, thus generating a motion-aligned sequence. Experimental evaluation conducted on a set of 7 fluoroscopic angiograms resulted in reduced target registration error, compared to previous methods, showing the effectiveness of the proposed methodology.