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Proceedings Paper

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model
Author(s): Xiaoguang Lu; Hui Xue; Marie-Pierre Jolly; Christoph Guetter; Peter Kellman; Li-Yueh Hsu; Andrew Arai M.D.; Sven Zuehlsdorff; Arne Littmann; Bogdan Georgescu; Jens Guehring
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Paper Abstract

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Paper Details

Date Published: 9 March 2011
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796205 (9 March 2011); doi: 10.1117/12.878529
Show Author Affiliations
Xiaoguang Lu, Siemens Corp. Research (United States)
Hui Xue, Siemens Corp. Research (United States)
Marie-Pierre Jolly, Siemens Corp. Research (United States)
Christoph Guetter, Siemens Corp. Research (United States)
Peter Kellman, National Institutes of Health (United States)
Li-Yueh Hsu, National Institutes of Health (United States)
Andrew Arai M.D., National Institutes of Health (United States)
Sven Zuehlsdorff, Siemens Medical Research (United States)
Arne Littmann, Siemens AG (Germany)
Bogdan Georgescu, Siemens Corp. Research (United States)
Jens Guehring, Siemens Corp. Research (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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