ArticleProceedings of SPIE--the International Society for Optical Engineering2012
Imaging of prostate cancer: a platform for 3D co-registration of in-vivo MRI ex-vivo MRI and pathology.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Deep Learning-based Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI.Radiology. Imaging cancer · 2025Article
- Registration of histopathology to magnetic resonance imaging of prostate cancer.Physics and imaging in radiation oncology · 2021Article
- Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax).PloS one · 2020Article
- Registration Accuracy of Patient-Specific, Three-Dimensional-Printed Prostate Molds for Correlating Pathology With Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2019Article
- Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology.Journal of ultrasound · 2018Review
- Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.Scientific reports · 2017Article
- Combined T2 and diffusion-weighted MR imaging with template prostate biopsies in men suspected with prostate cancer but negative transrectal ultrasound-guided biopsies.World journal of urology · 2017Article
- Apparatus for Histological Validation ofFrontiers in oncology · 2017Article
- Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.Radiology · 2016Article
- Semiautomatic registration of digital histopathology images to in vivo MR images in molded and unmolded prostates.Journal of magnetic resonance imaging : JMRI · 2014Article
- A system for evaluating magnetic resonance imaging of prostate cancer using patient-specific 3D printed molds.American journal of clinical and experimental urology · 2014Article
- Challenges in accurate registration of 3-D medical imaging and histopathology in primary prostate cancer.European journal of nuclear medicine and molecular imaging · 2013Review
- Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.International journal of molecular imaging · 2013Article
- 3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section location.Journal of pathology informatics · 2013Article
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6 authors.
Funding
Abstract
objectivesMulti-parametric MRI is emerging as a promising method for prostate cancer diagnosis. prognosis and treatment planning. However, the localization of in-vivo detected lesions and pathologic sites of cancer remains a significant challenge. To overcome this limitation we have developed and tested a system for co-registration of in-vivo MRI, ex-vivo MRI and histology. MATERIALS AND
methodsThree men diagnosed with localized prostate cancer (ages 54-72, PSA levels 5.1-7.7 ng/ml) were prospectively enrolled in this study. All patients underwent 3T multi-parametric MRI that included T2W, DCE-MRI, and DWI prior to robotic-assisted prostatectomy. Ex-vivo multi-parametric MRI was performed on fresh prostate specimen. Excised prostates were then sliced at regular intervals and photographed both before and after fixation. Slices were perpendicular to the main axis of the posterior capsule, i.e., along the direction of the rectal wall. Guided by the location of the urethra, 2D digital images were assembled into 3D models. Cancer foci, extra-capsular extensions and zonal margins were delineated by the pathologist and included in 3D histology data. A locally-developed software was applied to register in-vivo, ex-vivo and histology using an over-determined set of anatomical landmarks placed in anterior fibro-muscular stroma, central. transition and peripheral zones. The mean root square distance across corresponding control points was used to assess co-registration error.
resultsTwo specimens were pT3a and one pT2b (negative margin) at pathology. The software successfully fused in-vivo MRI. ex-vivo MRI fresh specimen and histology using appropriate (rigid and affine) transformation models with mean square error of 1.59 mm. Coregistration accuracy was confirmed by multi-modality viewing using operator-guided variable transparency.
conclusionThe method enables successful co-registration of pre-operative MRI, ex-vivo MRI and pathology and it provides initial evidence of feasibility of MRI-guided surgical planning.
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