PURPOSE: To test the ability of a model-based segmentation of the aortic root for consistent assessment of aortic valve structures in patients considered for transcatheter aortic valve implantation (TAVI) who underwent 256-slice cardiac computed tomography (CT). METHODS: Consecutive patients (n = 49) with symptomatic severe aortic stenosis considered for TAVI and patients without aortic stenosis (n = 17) underwent cardiac CT. Images were evaluated by two independent observers who measured the diameter of the aortic annulus and its distance to both coronary ostia (1) manually and (2) software-assisted. All acquired measures were compared with each other and to (3) fully automatic quantification. RESULTS: High correlations were observed for 3D measures of the aortic annulus conducted on multiple oblique planes (r = 0.87 and 0.84 between observers and model-based measures, and r = 0.81 between observers). Reproducibility was further improved by software-assisted versus manual assessment for all the acquired variables (r = 0.98 versus 0.81 for annulus diameter, r = 0.94 versus 0.85 for distance to the left coronary ostium, P < 0.01 for both). Thus, using software-assisted measurements very low limits of agreement were observed for the annulus diameter (95%CI of -1.2 to 0.6 mm) and within very low time-spent (0.6 ± 0.1 min for software-assisted versus 1.6 ± 0.3 min per patient for manual assessment, P < 0.001). Assessment of the aortic annulus using the 3D model-based instead of manual 2D-coronal measurements would have modified the implantation strategy in 12 of 49 patients (25%) with aortic stenosis. Four of 12 patients with potentially modified implantation strategy yielded postprocedural moderate paravalvular regurgitation, which may have been avoided by implantation of a larger prosthesis, as suggested by automatic 3D measures. CONCLUSION: Our study highlights the usefulness of software-assisted preprocedural assessment of the aortic annulus in patients considered for TAVI.
PURPOSE: To test the ability of a model-based segmentation of the aortic root for consistent assessment of aortic valve structures in patients considered for transcatheter aortic valve implantation (TAVI) who underwent 256-slice cardiac computed tomography (CT). METHODS: Consecutive patients (n = 49) with symptomatic severe aortic stenosis considered for TAVI and patients without aortic stenosis (n = 17) underwent cardiac CT. Images were evaluated by two independent observers who measured the diameter of the aortic annulus and its distance to both coronary ostia (1) manually and (2) software-assisted. All acquired measures were compared with each other and to (3) fully automatic quantification. RESULTS: High correlations were observed for 3D measures of the aortic annulus conducted on multiple oblique planes (r = 0.87 and 0.84 between observers and model-based measures, and r = 0.81 between observers). Reproducibility was further improved by software-assisted versus manual assessment for all the acquired variables (r = 0.98 versus 0.81 for annulus diameter, r = 0.94 versus 0.85 for distance to the left coronary ostium, P < 0.01 for both). Thus, using software-assisted measurements very low limits of agreement were observed for the annulus diameter (95%CI of -1.2 to 0.6 mm) and within very low time-spent (0.6 ± 0.1 min for software-assisted versus 1.6 ± 0.3 min per patient for manual assessment, P < 0.001). Assessment of the aortic annulus using the 3D model-based instead of manual 2D-coronal measurements would have modified the implantation strategy in 12 of 49 patients (25%) with aortic stenosis. Four of 12 patients with potentially modified implantation strategy yielded postprocedural moderate paravalvular regurgitation, which may have been avoided by implantation of a larger prosthesis, as suggested by automatic 3D measures. CONCLUSION: Our study highlights the usefulness of software-assisted preprocedural assessment of the aortic annulus in patients considered for TAVI.
Authors: E M A Wiegerinck; H A Marquering; N Y Oldenburger; M A Elattar; R N Planken; B A J M De Mol; J J Piek; J Baan Journal: Int J Cardiovasc Imaging Date: 2013-12-11 Impact factor: 2.357
Authors: Nina P Hofmann; Moritz Schuetz; Raffi Bekeredjian; Sven Pleger; Emanuel Chorianopoulos; Sorin Giusca; Florian André; Gitsios Gitsioudis; Christopher Schlett; Hans-Ulrich Kauczor; Hugo A Katus; Grigorios Korosoglou Journal: Eur J Radiol Open Date: 2017-06-26