Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view

International audienceThe short-axis view defined such that a series of slices are perpendicular to the long-axis of the left ventricle (LV) is one of the most important views in cardiovascular imaging. Raw trans-axial Computed Tomography (CT) images must be often reformatted prior to diagnostic interpretation in short-axis view. The clinical importance of this refor-matting requires the process to be accurate and reproducible. It is often performed after manual localization of landmarks on the image (e.g. LV apex, centre of the mitral valve, etc.) being slower and not fully reproducible as compared to automatic approaches. We propose a fast, automatic and reproducible method to reformat CT images from original trans-axial orientation to short-axis view. A deep learning based seg-mentation method is used to automatically segment the LV endocardium and wall, and the right ventricle epicardium. Surface meshes are then obtained from the corresponding masks and used to automatically detect the shape features needed to find the transformation that locates the cardiac chambers on their standard, mathematically defined, short-axis position. 25 datasets with available manual reformatting performed by experienced cardiac radiologists are used to show that our reformatted images are of equivalent quality

Surface matching for high-accuracy registration of the lateral skull base

PURPOSE The accuracy achievable when utilizing image guidance depends to a large extent on the accuracy with which the patient can be registered to preoperative image data. This work proposes a method for the registration of the temporal bone based on surface matching and investigates the achievable accuracy of the technique. METHODS Fourteen human temporal bones were utilized for evaluation; incisions were made, fiducial screws were implanted to act as a ground truth, and imaging was performed. The positions of the fiducials and surface of the mastoid were extracted from image data and reference positions defined at the round window and the mastoid surface. The surface of the bone was then digitized using a tracked pointer within the region exposed by the incisions and the physical and image point clouds registered, with the result compared to the fiducial-based registration. RESULTS Results of one case were excluded due to a problem with the ground truth registration. In the remaining cases an accuracy of [Formula: see text] and [Formula: see text] mm was observed relative to the ground truth at the surface of the mastoid and round window, respectively. CONCLUSIONS A technique for the registration of the temporal bone was proposed, based on surface matching after exposure of the mastoid surface, and evaluated on human temporal bone specimens. The results reveal that high-accuracy patient-to-image registration is possible without the use of fiducial screws