3D prostate histology reconstruction: an evaluation of image-based and fiducial-based algorithms

Imaging may enable the determination of the spatial distribution and aggressiveness of prostate cancer in vivo before treatment, possibly supporting diagnosis, therapy selection, and focal therapy guidance. 3D reconstruction of prostate histology facilitates the validation of such imaging applications. We evaluated four histology– ex vivo magnetic resonance (MR) image 3D reconstruction algorithms comprising two similarity metrics (mutual information M MI or fiducial registration error M FRE ) and two search domains (affine transformations T A or fiducial-constrained affine transformations T F ). Seven radical prostatectomy specimens were imaged with MR imaging, processed for whole-mount histology, and digitized as histology images. The algorithms were evaluated on the reconstruction error and the sensitivity of same to translational and rotational errors in initialization. Reconstruction error was quantified as the target registration error (TRE): the post-reconstruction distance between homologous point landmarks (7–15 per histology section; 132 total) identified on histology and MR images. Sensitivity to initialization was quantified using a linear model relating TRE to varied levels of translational/rotational initialization errors. The algorithm using M MI and T A yielded a mean TRE of 1.2±0.7 mm when initialized using an approach that assumes histology corresponds to the front faces of tissue blocks, but was sensitive to initialization error. The algorithm using M FRE and T A yielded a mean TRE of 0.8±0.4 mm with minimal sensitivity to initialization errors. Compared to the method used to initialize the algorithms (mean TRE 1.4±0.7 mm), a study using an algorithm with a mean TRE of 0.8 mm would require 27% fewer subjects for certain imaging validation study designs.