3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy

Abstract

Purpose To assess a novel method of 3D co-registration of prostate MRI exams performed before and after prostate cancer focal therapy. Material and method We developed a software platform for automatic 3D deformable coregistration of prostate MRI at different time points and applied this method to ten patients who underwent focal ablative therapy. MRI exams were performed preoperatively, as well as one week and six months posttreatment. Rigid registration served as reference for assessing coregistration accuracy and precision. Results Segmentation of preoperative and postoperative prostate revealed a significant post-operative volume decrease of the gland that averaged 6.49 cc (p=0.017). Applying deformable transformation based upon Mutual Information (MI) from 120 pairs of MRI slices, we refined by 2.9 mm (max 6.25mm) the alignment of the ablation zone (AZ), segmented from contrastenhanced images on the one-week post-operative exam, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (p=0.001), corrected by volume. The dissimilarity by Dice index of the mapped AZ using deformable transformation vs. rigid control was significantly (p=0.04) higher at the ablation site compared to the whole gland. Conclusion Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from MI of preoperative and follow-up MRI is accurate in co-registration of MRI exams performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

“…In recent years, deep learning has also been employed for registration [206] , including in the prostate [207] . Image registration is also important with regard to therapy [208] ; neural networks are currently being explored for this purpose as well [209][210][211] .…”

Role of MRI for the detection of prostate cancer

“…[70,71] MRI is also critical in post FT follow-up, where the DCE sequence becomes the dominant sequence although the number of studies is few. [72] FT has achieved good short-term outcomes but still lacks long-term evidence, is composed of relatively small cohorts and lack comparative treatment outcomes. [73] Further high level trials providing evidence of outcomes and patient selection is requisite before FT becoming widely adopted.…”

Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation

“…Therefore, in this work, we focus on testing a proposed forecasting approach using registrationquantified morphological changes, in which gland masks are used. However, we acknowledge that, in future studies, direct utilising or combining image intensities [3,10] and other forms of features should be investigated.…”