Title:
Integrated spatiotemporal analysis for automatic contrast agent inflow detection on angiography and fluoroscopy during transcatheter aortic valve implantation.

Abstract:
In this paper, the authors propose an integrated spatial and temporal analysis for automatic detection of contrast agent inflow at the aortic root on fluoroscopic and angiographic sequences during transcatheter aortic valve implantation procedures as a means to automatically trigger registration of 3D aortic models.
The proposed contrast agent inflow detection method is based on a contrast feature curve, calculated using histogram analysis and a likelihood ratio test. Several image preprocessing steps are performed to enhance the properties of the contrast feature curve. For sequences with a dominant peak on its contrast feature curve, a cascaded classifier is then applied to differentiate the contrast-enhanced aorta from contrast-enhanced balloons. Finally, a multilayer classifier based on sparse Gabor features is used to recognize sequences containing a faint contrast-enhanced aorta.
The algorithm was evaluated using 105 sequences consisting of more than 12,000 frames, and achieved a detection accuracy of 99.1% (100% sensitivity and 98.5% specificity). The computation time for a typical sequence of 150 frames was ≈ 1 s on a single-core Dell PC with a 1 GHz Intel Xeon processor and 2 GB of RAM.
The authors developed a novel, automatic method for contrast agent inflow detection on x-ray sequences. With the achieved efficiency and accuracy, the proposed method is potentially feasible for clinical use as it facilitates a seamless workflow in utilizing patient-specific 3D models to provide anatomical details during transcatheter aortic valve implantation procedures.