title: Automatic 3D motion estimation of left ventricle from C-arm rotational angiocardiography using a prior motion model and learning based boundary detector.
abstract: Compared to pre-operative imaging modalities, it is more convenient to estimate the current cardiac physiological status from C-arm angiocardiography since C-arm is a widely used intra-operative imaging modality to guide many cardiac interventions. The 3D shape and motion of the left ventricle (LV) estimated from rotational angiocardiography provide important cardiac function measurements, e.g., ejection fraction and myocardium motion dyssynchrony. However, automatic estimation of the 3D LV motion is difficult since all anatomical structures overlap on the 2D X-ray projections and the nearby confounding strong image boundaries (e.g., pericardium) often cause ambiguities to LV endocardium boundary detection. In this paper, a new framework is proposed to overcome the aforementioned difficulties: (1) A new learning-based boundary detector is developed by training a boosting boundary classifier combined with the principal component analysis of a local image patch; (2) The prior LV motion model is learned from a set of dynamic cardiac computed tomography (CT) sequences to provide a good initial estimate of the 3D LV shape of different cardiac phases; (3) The 3D motion trajectory is learned for each mesh point; (4) All these components are integrated into a multi-surface graph optimization method to extract the globally coherent motion. The method is tested on seven patient scans, showing significant improvement on the ambiguous boundary cases with a detection accuracy of 2.87 +/- 1.00 mm on LV endocardium boundary delineation in the 2D projections.
authors: Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA.
authors: Mingqing
authors: M
authors: Chen
authors: Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA.
authors: Yefeng
authors: Y
authors: Zheng
authors: Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA.
authors: Yang
authors: Y
authors: Wang
authors: Healthcare Sector, Siemens AG, Forchheim, Germany.
authors: Kerstin
authors: K
authors: Mueller
authors: Healthcare Sector, Siemens AG, Forchheim, Germany.
authors: Guenter
authors: G
authors: Lauritsch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012399:Rotation; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*