Cardiac anchoring in MRI through context modeling

Authors:
Xiaoguang Lu;Bogdan Georgescu;Marie-Pierrex Jolly;Jens Guehring;Alistair Young;Brett Cowan;Arnex Littmann;Dorin Comaniciu
Affiliations:
Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Auckland MRI Research Group, University of Auckland, Auckland, New Zealand;Auckland MRI Research Group, University of Auckland, Auckland, New Zealand;Magnetic Resonance, Siemens Healthcare, Erlangen, Germany;Siemens Corporate Research, Princeton, NJ
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

Cardiac magnetic resonance imaging (MRI) has advanced to become a powerful diagnostic tool in clinical practice. Robust and fast cardiac modeling is important for structural and functional analysis of the heart. Cardiac anchors provide strong cues to extract morphological and functional features for diagnosis and disease monitoring. We present a fully automatic method and system that is able to detect these cues. The proposed approach explores expert knowledge embedded in a large annotated database. Exemplar cues in our experiments include left ventricle (LV) base plane and LV apex from long-axis images, and right ventricle (RV) insertion points from short-axis images. We evaluate the proposed approach on 8304 long-axis images from 188 patients and 891 short-axis images from 338 patients that are acquired from different vendors. In addition, another evaluation is conducted on an independent 7140 images from 87 patient studies. Experimental results show promise of the proposed approach.