Registration of 3-D aortic model onto X-ray images provides anatomical details for optimal valve deployment in Trans-catheter aortic valve implantation (TAVI) procedures. Fast and automatic contrast detection in the aortic root in fluoroscopic sequences facilitates a seamless workflow by triggering 2-D/3-D registration automatically when necessary. In this paper, we propose an integrated spatial and temporal analysis on fluoroscopic sequences to improve the performance of the contrast feature curve based method in our previous work [4]. For sequences with a high likelihood of containing contrasted aorta, a cascaded classifier is used to separate the contrasted aorta from the contrasted balloons. A local training procedure is then performed in sequences with contrasted aorta to identify the range of well-contrasted frames. Next, a multi-layer sparse Gabor feature based classifier is adopted to recognize faint contrast. Experiments on 69 sequences acquired during TAVI procedures achieved the correct classification rate of 12/12 for non-contrasted sequences and 56/57 for contrasted sequences.
['Wei You', 'Rui Liao', 'Michelle Yan', 'Matthias John']
Spatio-temporal analysis for automatic contrast injection detection on angiography during trans-catheter aortic valve implantation