Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model
2011
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.
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