AIMS: To propose and validate a novel approach to determine the optimal angiographic viewing angles for a selected coronary (target) segment from X-ray coronary angiography, without the need to reconstruct the entire coronary tree in three-dimensions (3D), such that subsequent interventions are carried out from the best view. METHODS AND RESULTS: The approach starts with standard quantitative coronary angiography (QCA) of the target vessel in two angiographic views. Next, the target vessel is reconstructed in 3D, and in a very simple and intuitive manner, the possible overlap of the target vessel and other vessel segments can be assessed, resulting in the best view with minimum foreshortening and overlap. A retrospective study including 67 patients was set up for the validation. The overlap prediction result was compared with the true overlap on the available angiographic views (TEST views). The foreshortening for the views proposed by the new approach software viewing angle (SVA) and the views used during the stent deployment software viewing angle (EVA) were compared. Two experienced interventional cardiologists visually evaluated the success of SVA with respect to EVA. The evaluation results were graded into five values ranging from -2 to 2. The overlap prediction algorithm successfully predicted the overlap condition for all 235 TEST views. EVA was associated with more foreshortening than SVA (8.9% ± 8.2% vs. 1.6% ± 1.5%, p<0.001). The average evaluated point for the success of SVA was 0.94 ± 0.80 (p <0.001), indicating that the evaluators were in favor of the optimal views determined by the proposed approach versus the views used during the actual intervention. CONCLUSIONS: The proposed approach is able to accurately and quickly determine the optimal viewing angles for the online support of coronary interventions.
AIMS: To propose and validate a novel approach to determine the optimal angiographic viewing angles for a selected coronary (target) segment from X-ray coronary angiography, without the need to reconstruct the entire coronary tree in three-dimensions (3D), such that subsequent interventions are carried out from the best view. METHODS AND RESULTS: The approach starts with standard quantitative coronary angiography (QCA) of the target vessel in two angiographic views. Next, the target vessel is reconstructed in 3D, and in a very simple and intuitive manner, the possible overlap of the target vessel and other vessel segments can be assessed, resulting in the best view with minimum foreshortening and overlap. A retrospective study including 67 patients was set up for the validation. The overlap prediction result was compared with the true overlap on the available angiographic views (TEST views). The foreshortening for the views proposed by the new approach software viewing angle (SVA) and the views used during the stent deployment software viewing angle (EVA) were compared. Two experienced interventional cardiologists visually evaluated the success of SVA with respect to EVA. The evaluation results were graded into five values ranging from -2 to 2. The overlap prediction algorithm successfully predicted the overlap condition for all 235 TEST views. EVA was associated with more foreshortening than SVA (8.9% ± 8.2% vs. 1.6% ± 1.5%, p<0.001). The average evaluated point for the success of SVA was 0.94 ± 0.80 (p <0.001), indicating that the evaluators were in favor of the optimal views determined by the proposed approach versus the views used during the actual intervention. CONCLUSIONS: The proposed approach is able to accurately and quickly determine the optimal viewing angles for the online support of coronary interventions.
Authors: Johan H C Reiber; Shengxian Tu; Joan C Tuinenburg; Gerhard Koning; Johannes P Janssen; Jouke Dijkstra Journal: Cardiovasc Diagn Ther Date: 2011-12
Authors: Shengxian Tu; Jing Jing; Niels R Holm; Kevin Onsea; Tao Zhang; Tom Adriaenssens; Christophe Dubois; Walter Desmet; Leif Thuesen; Yundai Chen; Johan H C Reiber Journal: Int J Cardiovasc Imaging Date: 2011-12-15 Impact factor: 2.357
Authors: A R van Rosendael; G Koning; A C Dimitriu-Leen; J M Smit; J M Montero-Cabezas; F van der Kley; J W Jukema; J H C Reiber; J J Bax; A J H A Scholte Journal: Int J Cardiovasc Imaging Date: 2017-06-22 Impact factor: 2.357
Authors: Áron Üveges; Csaba Jenei; Tibor Kiss; Zoltán Szegedi; Balázs Tar; Gábor Tamás Szabó; Dániel Czuriga; Zsolt Kőszegi Journal: Int J Cardiovasc Imaging Date: 2019-05-24 Impact factor: 2.357