Feasibility of multi­reference­tissue normalization of t2­weighted prostate MRI

L. Stoilescu and H. Huisman

in: Annual Meeting of the Radiological Society of North America, 2017

Abstract

PURPOSE To explore a novel multi­reference­tissue normalization method applied to t2­weighted prostate MRI. METHOD AND MATERIALS Assuming the availability of a set of distinct reference tissue segmentations, the hypothesis is that it allows computing a patient specific sequence model that can normalize MRI. The normalization should produce similar scalar values in the same reference regions for different patients/scanners/sequences and interpolate in between reference values for other tissue areas. Regions of interest (ROI) were drawn in four distinct tissue types in a cohort of sixtyfive t2­weighted images from regular multiparametric prostate MRI (mpMRI). The four reference tissue types were: skeletal muscle, body fat, femur head, bladder lumen. Four average ROI signals were computed per patient. Each reference tissue was assigned a fixed reference value (t2 relaxation found in literature). Per patient, a smooth sequence model was fitted to the (average, reference) pairs. The estimated sequence model was then inverted to map patients' raw t2­weighted image scalar values to normalized values. To test the method, the effect of normalization on observed variance and tissue discriminability was analyzed. A leave­one­out experiment was performed in which for each ROI its normalized value was computed using the sequence model estimate using the three remaining reference ROIs. The difference between original t2­weighted and normalized scalar MRI was analyzed by means of variability and ROC analysis. RESULTS Multi­reference­tissue normalization significantly (p<0.05) decreased variability and increased the area under the ROC curve for discriminating each reference tissue combination. The ROC curves in the figure show the effect of the normalization (T2­n) on the discrimination between body fat and femur head tissue. CONCLUSION Semi­automatic multi­reference­tissue normalization shows reduced inter­patient variability and may allow better quantitative discrimination between tissue types. CLINICAL RELEVANCE/APPLICATION Multi­reference­tissue t2­weighted MRI normalization seems feasible. In combination with automatic segmentation, this could be providing clinical quantitative imaging support to mpMRI diagnosis of prostate cancer. This result motivates us to continue to explore the ability of this novel method to help detect and discriminate prostate cancer in mpMR