Cross-modality assessment and planning for pulmonary trunk treatment using CT and MRI imaging

Authors:
Dime Vitanovski;Alexey Tsymbal;Razvan Ioan Ionasec;Bogdan Georgescu;Martin Huber;Andrew Taylor;Silvia Schievano;Shaohua Kevin Zhou;Joachim Hornegger;Dorin Comaniciu
Affiliations:
Integrated Data Systems, Siemens Corporate Research, Princeton and Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton;Integrated Data Systems, Siemens Corporate Research, Princeton;Integrated Data Systems, Siemens Corporate Research, Princeton;Integrated Data Systems, Siemens Corporate Research, Princeton;Great Ormond Street Hospital for Children, London, England;Great Ormond Street Hospital for Children, London, England;Integrated Data Systems, Siemens Corporate Research, Princeton;Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

Congenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting. For advanced evaluation and therapy planning purposes, cardiac Computed Tomography (CT) and cardiac Magnetic Resonance Imaging (cMRI) are important non-invasive investigation techniques with complementary properties. Although, characterized by high temporal resolution, cMRI does not cover the full motion of the pulmonary trunk. The sparse cMRI data acquired in this context include only one 3D scan of the heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. In this paper we present a cross-modality framework for the evaluation of the pulmonary trunk, which combines the advantages of both, cardiac CT and cMRI. A patient-specific model is estimated from both modalities using hierarchical learning-based techniques. The pulmonary trunk model is exploited within a novel dynamic regression-based reconstruction to infer the incomplete cMRI temporal information. Extensive experiments performed on 72 cardiac CT and 74 cMRI sequences demonstrated the average speed of 110 seconds and accuracy of 1.4mm for the proposed approach. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from sparse 4D cMRI data.