Registration methodology for histological sections and in vivo imaging of human prostate.

RATIONALE AND OBJECTIVES Registration enables quantitative spatial correlation of features from different imaging modalities. Our objective is to register in vivo imaging with histologic sections of the human prostate so that histologic truth can be correlated with in vivo imaging features. MATERIALS AND METHODS In vivo imaging of the prostate included T2-weighted anatomic and diffusion weighted 3-T magnetic resonance imaging (MRI) as well as 11C-choline positron emission tomography (PET). In addition, ex vivo 3-T MRI of the prostate specimen, histology, and associated block face photos of the prostate specimen were obtained. A standard registration method based on mutual information (MI) and thin-plate spline (TPS) was applied. Registration among in vivo imaging modalities is well established; however, accurate registration involving histology is difficult. Our approach breaks up the difficult direct registration of histology and in vivo imaging into achievable subregistration tasks involving intermediate ex vivo modalities like block face photography and specimen MRI. Results of subregistration tasks are combined to compute the intended, final registration between in vivo imaging and histology. RESULTS The methodology was applied to two patients and found to be clinically feasible. Overall registered anatomic MRI, diffusion MRI, and 11C-choline PET aligned well with histology qualitatively for both patients. There is no ground truth of registration accuracy as the scans are real patient scans. An indirect validation of the registration accuracy has been proposed comparing tumor boundary markings found in diffusion MRI and histologic sections. Registration errors for two patients between diffusion MRI and histology were 3.74 and 2.26 mm. CONCLUSION This proof of concept paper demonstrates a method based on intrinsic image information content for successfully registering in vivo imaging of the human prostate with its post-resection histology, which does not require the use of extrinsic fiducial markers. The methodology successfully mapped histology onto the in vivo imaging space, allowing the observation of how well different in vivo imaging features correspond to histologic truth. The methodology is therefore the basis for a systematic comparison of in vivo imaging for staging of human prostate cancer.

[1]  T. Hara,et al.  PET imaging of prostate cancer using carbon-11-choline. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[3]  M. Picchio,et al.  Value of 11C-choline PET and contrast-enhanced CT for staging of bladder cancer: correlation with histopathologic findings. , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[4]  Bradford A Moffat,et al.  A Methodology for Registration of a Histological Slide and In Vivo MRI Volume Based on Optimizing Mutual Information , 2006, Molecular imaging.

[5]  David L Wilson,et al.  Three‐dimensional method for comparing in vivo interventional MR images of thermally ablated tissue with tissue response , 2003, Journal of magnetic resonance imaging : JMRI.

[6]  Charles R. Meyer,et al.  Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations , 1997, Medical Image Anal..

[7]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[8]  C. Zarow,et al.  A standardized method for brain-cutting suitable for both stereology and MRI-brain co-registration , 2004, Journal of Neuroscience Methods.

[9]  D. Peck,et al.  Registration and warping of magnetic resonance images to histological sections. , 1999, Medical physics.

[10]  M. Coel,et al.  Prostate cancer localization with 18fluorine fluorocholine positron emission tomography. , 2005, The Journal of urology.

[11]  Morand Piert,et al.  Hypoxia-specific tumor imaging with 18F-fluoroazomycin arabinoside. , 2005, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[12]  B. McNeil,et al.  Comparison of Magnetic Resonance Imaging and Ultrasonography in Staging Early Prostate Cancer. Results of a Multi-Institutional Cooperative Trial , 1991, Investigative Radiology.

[13]  J. M. Taylor,et al.  Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. , 2000, Journal of the National Cancer Institute.

[14]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[15]  Balraj Naren,et al.  Medical Image Registration , 2022 .

[16]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[17]  Charles R. Meyer,et al.  Mutual Information for Automated Unwarping of Rat Brain Autoradiographs , 1997, NeuroImage.

[18]  Sven N. Reske,et al.  Carbon-11 acetate positron emission tomography can detect local recurrence of prostate cancer , 2002, European Journal of Nuclear Medicine and Molecular Imaging.

[19]  J. Bourland,et al.  Morphology-guided radiosurgery treatment planning and optimization for multiple isocenters. , 1999, Medical physics.

[20]  D. Piwnica-Worms,et al.  Permeation Peptide Conjugates for In Vivo Molecular Imaging Applications , 2006, Molecular imaging.

[21]  Robert Bartha,et al.  Registration of in vivo magnetic resonance T 1-weighted brain images to triphenyltetrazolium chloride stained sections in small animals , 2006, Journal of Neuroscience Methods.

[22]  N. Avril,et al.  Molecular positron emission tomography and PET/CT imaging in urological malignancies. , 2007, European urology.