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19 March 2015 Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model
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Abstract
Changes in tissue mechanical properties, measured non-invasively by elastography methods, have been shown to be an important diagnostic tool, particularly for cancer. Tissue elasticity information, tracked over the course of therapy, may be an important prognostic indicator of tumor response to treatment. While many elastography techniques exist, this work reports on the use of a novel form of elastography that uses image texture to reconstruct elastic property distributions in tissue (i.e., a modality independent elastography (MIE) method) within the context of a pre-clinical breast cancer system.1,2 The elasticity results have previously shown good correlation with independent mechanical testing.1 Furthermore, MIE has been successfully utilized to localize and characterize lesions in both phantom experiments and simulation experiments with clinical data.2,3 However, the reproducibility of this method has not been characterized in previous work. The goal of this study is to evaluate voxel-level reproducibility of MIE in a pre-clinical model of breast cancer. Bland-Altman analysis of co-registered repeat MIE scans in this preliminary study showed a reproducibility index of 24.7% (scaled to a percent of maximum stiffness) at the voxel level. As opposed to many reports in the magnetic resonance elastography (MRE) literature that speak to reproducibility measures of the bulk organ, these results establish MIE reproducibility at the voxel level; i.e., the reproducibility of locally-defined mechanical property measurements throughout the tumor volume.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Katelyn M. Flint, Jared A. Weis, Thomas E. Yankeelov, and Michael I. Miga "Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model", Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 941704 (19 March 2015); https://doi.org/10.1117/12.2082230
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