Abstract #2736

Breast MRI Tissue Classification and Partial Volume Estimation using Different Methods: Evaluation on T1, T2 and PD-weighted TSE Images

Subhajit Chatterjee^1,2,3, Snekha Thakran¹, Rakesh Kumar Gupta⁴, and Anup Singh^1,5

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ²C-DOT India, New Delhi, India, ³Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India, ⁴Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India, New Delhi, India, ⁵Department of Biomedical Engineering, All India Institute of Medical Sciences Delhi, New Delhi, India

Partial volume effect(PVE) is caused by the insufficient spatial resolution of MRI images. Boundaries of different tissue-types are considered as partial volume(PV) prone area where each voxel can be mixture more than one tissue-type. PVE can introduce errors in inner segmentation and Breast density estimation. In this study we have identified PV voxels and estimated the proportion of each tissue-type within a PV voxel using fat and nonfat saturated MRI data. Experimental results revealed that difference method (difference between nonfat and fat saturated images) can provide similar tissue classification and estimation accuracy as compared to existing methods.

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