3D Registration of Whole-Mount Prostate Histology Images to Ex Vivo Magnetic Resonance Images Using Strand-Shaped Fiducials

Determining the intra-prostatic spatial distribution and grade of prostate cancer before treatment may support improved diagnosis, therapy selection, or guidance of intra-prostatic lesion-focused therapies (e.g., radiation boosting or ablative focal therapy). Several in vivo imaging modalities are showing promise for imaging the intra-prostatic distribution of cancer. Evaluations of such imaging modalities ideally include comparisons to registered histological examinations of prostatectomy specimens, the clinically accepted “gold standard” for staging and grading prostate cancer. The registration of histology to ex vivo magnetic resonance (MR) images supports these challenging in vivo registrations by reconstructing 3D spatial information that is lost during the process of acquiring histology sections. In the work described in this chapter, ex vivo MR and histology images were acquired from nine formalin-fixed radical prostatectomy specimens which had been marked with extrinsic fiducials designed to be visible in these modalities. The histology images were registered retrospectively to the MR images using a novel algorithm based on the minimization of fiducial registration error between fiducial cross-sections on histology images and parametric fiducial curves on MR images. The 3D target registration error (TRE) was quantified based on the post-registration misalignment of manually identified homologous landmarks (3–7 per section, 184 in total), and was compared to two previously developed methods: (1) a method based on the guidance of the coarse slicing of specimens and (2) a method based on additional imaging of the images of the thick tissue slices. The proposed method yielded a mean ± standard deviation TRE of 0.71 ± 0.38 mm, 0.38–0.63 mm lower [95 % confidence interval (CI)] than the image-guided-slicing-based method, and within 0.13 mm (95 % CI) of the tissue-slice-imaging-based method. One component of the proposed method was able to refine the result from the image-guided-slicing-based method to within 0.13 mm (95 % CI) of the proposed method. The proposed method also resulted in a 70 % decrease in specimen processing time compared to the image-guided-slicing-based approach previously implemented at our center.