Abstract #3430

Detection of Liver Fibrosis using Strain-Encoding MRI and Support Vector Machine

Inas A Yassine^1,2, Mai Wael¹, Mohamed Elmahdy², Tamer Basha², Ahmed S Fahmy^1,2, Ralph Sinkus³, Theo Heller⁴, Ahmed M Gharib⁵, and Khaled Z Abd-Elmoniem⁵

¹School of communication and Information Technology, Nile University, Cairo, Egypt, ²Systems and Biomedical Engineering Department, Cairo University, Cairo, Egypt, ³Biomedical Engineering Department, King’s College, London, United Kingdom, ⁴Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, United States, ⁵Biomedical and Metabolic Imaging Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, United States

This study proposes a device-free semi-automatic liver fibrosis identification system based on Strain Encoded (SENC) MRI. SENC-MRI was applied to quantify liver deformation induced by the heart motion over the cardiac cycle. Twenty-two patients with different stages of biopsy proven liver fibrosis and ten healthy subjects were imaged using SENC-MRI. A Support Vector Machine (SVM) classification system was used to classify the strain and strain rate for both the patients and healthy subjects. Based on leave-one-out cross validation. Strain and strain rate were more robust than the peak-to-peak value based classification, which has bias towards the sensitivity. The proposed method showed classification accuracy of 87.5% with sensitivity and specificity of 90.0% and 90%, respectively.

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