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.
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
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