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Purpose  In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified.    Methods  Multiparametric MRI, consisting of quantitative T 1 , T 2 , Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions.    Results  The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T 1 , T 2 , ADC}. R 1  (1/T 1 ), R 2  (1/T 2 ), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R 1 , ADC and MTR were also significantly increased at 72 h after HIFU.    Conclusions  This study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment.

Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue