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Purpose: To present and evaluate a method for registration of whole-mount prostate digital histology images to ex vivo magnetic resonance (MR) images. Materials and Methods: Nine radical prostatectomy specimens were marked with 10 strand-shaped fiducial markers per specimen, imaged with T1- and T2-weighted 3T MRI protocols, sliced at 4.4-mm intervals, processed for whole-mount histology, and the resulting histological sections (3–5 per specimen, 34 in total) were digitized. The correspondence between fiducial markers on histology and MR images yielded an initial registration, which was refined by a local optimization technique, yielding the least-squares best-fit affine transformation between corresponding fiducial points on histology and MR images. Accuracy was quantified as the postregistration 3D distance between landmarks (3–7 per section, 184 in total) on histology and MR images, and compared to a previous state-of-the-art registration method. Results: The proposed method and previous method had mean (SD) target registration errors of 0.71 (0.38) mm and 1.21 (0.74) mm, respectively, requiring 3 and 11 hours of processing time, respectively. Conclusion: The proposed method registers digital histology to prostate MR images, yielding 70% reduced processing time and mean accuracy sufficient to achieve 85% overlap on histology and ex vivo MR images for a 0.2 cc spherical tumor. J. Magn. Reson. Imaging 2012; 36:1402–1412. © 2012 Wiley Periodicals, Inc.

volume is critical in treatment planning. Whether by expert guidelines, retrograde urethrography, bony landmark, ultrasound, or CT-based planning, there is discordant anatomic information regarding the distance between apex and the bulb, also known as the genitourinary diaphragm (GUD) or membranous urethra length (MUL). In this study, we use MRI with endorectal coil in a large series of patients to better characterize the MUL in radiation therapy planning. Materials/Methods: Patients with early stage, clinically localized prostate cancer (Gleason 6 and 7, cT1c-cT2b, PSA 85% interobserver agreement within 2 mm measurement. There was no association between MUL and patient age, T-stage, Gleason score, patient height, weight, or NCCN risk group. Conclusions: Although there remains variability, the median MUL, or GUD length, is 15 mm. This report likely represents the largest evaluation of the pre-treatment MUL in men with prostate cancer treated with external beam radiation. Our data challenges traditional guidelines between the apex and bulb from smaller series and adds to critical anatomic information of male urethral length and prostate apex anatomy. Prostate MRI with endorectal coil produces a reliable MUL and should be used to optimize target volume delineation at the prostate apex. Author Disclosure: N.K. Taunk: None. O. Akin: None. X. Pei: None. M. Zelefsky: None.

Purpose: Evaluation ofin vivo prostate imaging modalities for determining the spatial distribution and aggressiveness of prostate cancer ideally requires accurate registration of images to an accepted reference standard, such as histopathological examination of radical prostatectomy specimens. Three-dimensional (3D) reconstruction of prostate histology facilitates these registration-based evaluations by reintroducing 3D spatial information lost during histology processing. Because the reconstruction accuracy may constrain the clinical questions that can be answered with these data, it is important to assess the tradeoffs between minimally disruptive methods based on intrinsic image information and potentially more robust methods based on extrinsic fiducial markers. Methods: Ex vivo magnetic resonance (MR) images and digitized whole-mount histology images from 12 radical prostatectomy specimens were used to evaluate four 3D histology reconstruction algorithms. 3D reconstructions were computed by registering each histology image to the corresponding ex vivo MR image using one of two similarity metrics (mutual information or fiducial registration error) and one of two search domains (affine transformations or a constrained subset thereof). The algorithms were evaluated for accuracy using the mean target registration error (TRE) computed from homologous intrinsic point landmarks (3–16 per histology section; 232 total) identified on histology and MR images, and for the sensitivity of TRE to rotational, translational, and scaling initialization errors. Results: The algorithms using fiducial registration error and mutual information had mean ± standard deviation TREs of 0.7 ± 0.4 and 1.2 ± 0.7 mm, respectively, and one algorithm using fiducial registration error and affine transforms had negligible sensitivities to initialization errors. The postoptimization values of the mutual information-based metric showed evidence of errors due to both the optimizer and the similarity metric, and variation of parameters of the mutual information-based metric did not improve its performance. Conclusions: The extrinsic fiducial-based algorithm had lower mean TRE and lower sensitivity to initialization than the intrinsic intensity-based algorithm using mutual information. A model relating statistical power to registration error for certain imaging validation study designs estimated that a reconstruction algorithm with a mean TRE of 0.7 mm would require 27% fewer subjects than the method used to initialize the algorithms (mean TRE 1.3 ± 0.7 mm), suggesting the choice of reconstruction technique can have a substantial impact on the design of imaging validation studies, and on their overall cost.