Dual-source computed tomography coronary artery imaging in children.

Computed tomography (CT) has a well-established diagnostic role in the assessment of coronary arteries in adults. However, its application in a pediatric setting is still limited and often impaired by several technical issues, such as high heart rates, poor patient cooperation, and radiation dose exposure. Nonetheless, CT is becoming crucial in the noninvasive approach of children affected by coronary abnormalities and congenital heart disease. In some circumstances, CT might be preferred to other noninvasive techniques such as echocardiography and MRI for its lack of acoustic window influence, shorter acquisition time, and high spatial resolution. The introduction of dual-source CT has expanded the role of CT in the evaluation of pediatric cardiovascular anatomy and pathology. Furthermore, technical advances in the optimization of low-dose protocols represent an attractive innovation. Dual-source CT can play a key role in several clinical settings in children, namely in the evaluation of children with suspected congenital coronary artery anomalies, both isolated and in association with congenital heart disease. Moreover, it can be used to assess acquired coronary artery abnormalities, as in children with Kawasaki disease and after surgical manipulation, especially in case of transposition of the great arteries treated with arterial switch operation and in case of coronary re-implantation.

Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from CT data.

Pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data.