![]() ![]() © 2014 Wiley Periodicals, Inc.Įvaluation of in 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. Derived transformation matrices were used to map regions of pathologically defined disease to MRI.ConclusionLATIS was used to successfully coregister digital pathology with in vivo MRI to facilitate improved correlative studies between pathologically identified features of prostate cancer and multiparametric MRI. Image registration performed without the use of internal structures led to an 87% increase in landmark-based registration error. Registration accuracy was assessed by calculation of the Dice similarity coefficient (DSC) between transformed and target capsule masks and least-square distance between transformed and target landmark positions.ResultsLATIS registration resulted in a DSC value of 0.991 ± 0.004 and registration accuracy of 1.54 ± 0.64 mm based on identified landmarks common to both datasets. Manually annotated macro-structures on both pathology and MRI were used to assist registration using a relaxed local affine transformation approximation. Excised prostate specimens underwent quarter mount step-section pathologic processing, digitization, annotation, and assembly into a PWM. Thirty-five patients with biopsy-proven prostate cancer were imaged at 3T with an endorectal coil. PurposeTo present a novel registration approach called LATIS (Local Affine Transformation guided by Internal Structures) for coregistering post prostatectomy pseudo-whole mount (PWM) pathological sections with in vivo MRI (magnetic resonance imaging) images.Materials and Methods For the 7 studies, we obtained an average Hausdorff distance of 1.85 mm, mean absolute distance of 0.99 mm, RMS of 1.65 mm, and DICE of 0.83, when comparing the capsular alignment on MRI to histology. We quantitatively and qualitatively evaluated all aspects of our methodology in the multimodal registration of a total of 7 corresponding histology and MRI sections from 2 different patients. Our registration methodology comprises the following main steps, (1) affine registration of T2w and DCE MRI, (2) affine registration of stitched WMHS to multiprotocol T2w and DCE MRI, and (3) multimodal image registration of WMHS to multiprotocol T2w and DCE MRI using SWMI. Additionally, we leverage a program developed by our group, Histostitcher©, for interactive stitching of individual histology quadrants to digitally reconstruct the pseudo WMHS. ![]() The SWMI scheme obviates the need for pre-segmentation of the prostate capsule on MRI. The novel contribution of this paper is that it leverages a spatially weighted mutual information (SWMI) scheme to automatically register and map CaP extent from WMHS onto pre-operative, multiprotocol MRI. An additional challenge is that most registration techniques rely on availability of the pre-segmented prostate capsule on T2w MRI. This means they need to be reconstituted as a pseudo-whole mount histologic section (WMHS) prior to registration with MRI. Apart from the challenges in spatially registering multi-modal data (histology and MRI) on account of (a) modality specific differences, (b) deformation due to the endorectal coil and tissue loss on histology, another complication is that the ex vivo histological sections, in the lab, are usually obtained as quadrants. This may be done by spatially mapping delineated extent of disease on ex vivo histopathology onto pre-operative in vivo MRI via image registration. Spatial alignment of ex vivo histological sections to pre-operative in vivo MRI for prostate cancer (CaP) patients undergoing radical prostatectomy is a necessary first step in the discovery of quantitative multiprotocol MRI signatures for CaP. ![]() In this work, we present a scheme for the registration of digitally reconstructed whole mount histology (WMH) to pre-operative in vivo multiprotocol prostate MR imagery (T2w and DCE) using spatially weighted mutual information (SWMI). ![]()
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