Feasibility of multireferencetissue normalization of T2weighted prostate MRI

L. Stoilescu and H. Huisman

Annual Meeting of the Radiological Society of North America 2017.


To explore a novel multireferencetissue normalization method applied to t2weighted prostate MRI.


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.


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.


Semiautomatic multireferencetissue normalization shows reduced interpatient variability and may allow better quantitative discrimination between

tissue types.


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