Purpose: Develop a framework to register in vivo and ex vivo images with histology using a novel cryo-microtome for validation of anatomical structures to improve tissue modeling and imaging signals.
Methods: Breast tissue obtained from a mastectomy patient and a formalin-fixed entire breast including chest wall from a cadaver were imaged using magnetic resonance (MR) imaging (3D T1 and 3D T2 Dixon, fat/water-saturated, in/out-of-phase). Specimens were frozen and embedded in optical cutting temperature (OCT) compound. The OCT block was placed in a cryo-microtome mounted with an overhead camera (Xerra, Emit Imaging). Slices of 33µm and 50µm were successively shaved off the block for the tissue and cadaver specimens, respectively. After each shaving, the blockface was photographed. At select tissue sites (connective/adipose, muscle, skin, fibroglandular), 20µm sections were transferred onto cryotape for manual H&E staining and histological analysis. A 3D blockface image was automatically reconstructed from the photographs by aligning fiducial markers embedded into the OCT block. The 3D MR, blockface images, and histology images were rigidly registered. Target registration errors (TRE) were computed based on corresponding points marked at fibroglandular intersections (5 for tissue, 10 for cadaver). The overall MR-histology registration was used to compare the MR intensities at tissue extraction sites with a one-way ANOVA.
Results: The MRI-blockface TREs were 0.34±0.11mm and 0.73±25mm for the tissue and cadaver datasets, respectively (both less than respective MR slice thicknesses of 0.35 and 1.00mm). The blockface-histology registration showed alignment of anatomical structures and tissue boundaries. The MR intensities at the four tissue sites showed statistically significant differences (P<0.01). Each pair of tissues except skin-connective/adipose were also significantly different (P<0.01).
Conclusion: Fine sectioning in a highly controlled imaging/sectioning environment enabled accurate registration between the MR image and histology and differentiation of tissue types in the MR image.
Funding Support, Disclosures, and Conflict of Interest: Research reported was supported in part by resources of the Image Guided Cancer Therapy Research Program from The University of Texas MD Anderson Cancer Center and the Helen Black Image-Guided Fund.