نبذة مختصرة : PURPOSE Focal therapy has been proposed as an alternative method to whole-gland treatment for prostate cancer when aiming to reduce treatment side effects. The authors recently validated a radiobiological model which takes into account tumor location and tumor characteristics including tumor cell density, Gleason score, and hypoxia in order to plan optimal dose distributions for focal therapy. The authors propose that this model can be informed using multiparametric MRI (mpMRI) and in this study present a registration framework developed to map prostate mpMRI and histology data, where histology will provide the "ground truth" data regarding tumor location and biology. The authors aim to apply this framework to a growing database to develop a prostate biological atlas which will enable MRI based planning for prostate focal therapy treatment. METHODS Six patients scheduled for routine radical prostatectomy were used in this proof-of-concept study. Each patient underwent mpMRI scanning prior to surgery, after which the excised prostate specimen was formalin fixed and mounted in agarose gel in a custom designed sectioning box. T2-weighted MRI of the specimen in the sectioning box was acquired, after which 5 mm sections of the prostate were cut and histology sections were microtomed. A number of image processing and registration steps were used to register histology images with ex vivo MRI and deformable image registration (DIR) was applied to 3D T2w images to align the in vivo and ex vivo MRI data. Dice coefficient metrics and corresponding feature points from two independent annotators were selected in order to assess the DIR accuracy. RESULTS Images from all six patients were registered, providing histology and in vivo MRI in the ex vivo MRI frame of reference for each patient. Results demonstrated that their DIR methodology to register in vivo and ex vivo 3D T2w MRI improved accuracy in comparison with an initial manual alignment for prostates containing features which were readily visible on MRI. The average ...
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