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Recent advancement in MRI established multi-parametric imaging for in vivo characterization of pathologic changes in brain cancer, which is expected to play a role in imaging biomarker development. Diffusion Tensor Imaging (DTI) is a prime example, which has been deployed for assessment of therapeutic response via analysis of apparent diffusion coefficient (ADC) / mean diffusivity (MD) values. They have been speculated to reflect apoptosis/necrosis. As newer medical imaging emerges, it is essential to verify that apparent abnormal features in imaging correlate with histopathology. Furthermore, the feasibility of imaging correlation with molecular profile should be explored in order to enhance the potential of biomedical imaging as a reliable biomarker. We focus on glioblastoma, which is an aggressive brain cancer. Despite the increased number of studies involving DTI in glioblastoma; however, little has been explored to bridge the gap between the molecular biomarkers and DTI data. Due to spatial heterogeneity in, MRI signals, pathologic change and protein expression, precise correlation is required between DTI, pathology and proteomics data in a histoanatomically identical manner. The challenge is obtaining an identical plane from in vivo imaging data that exactly matches with histopathology section. Thus, we propose to incorporate ex vivo tissue imaging to bridge between in vivo imaging data and histopathology. With ex vivo scan of removed tissue, it is feasible to use high-field 7T MRI scanner, which can achieve microscopic resolution. Once histology section showing the identical plane, it is feasible to correlate protein expression by a unique technology, “multiplex tissue immunoblotting”.

Purpose: To evaluate the feasibility of using strain-encoded (SENC) breast magnetic resonance images(MRI) for breast cancer detection by examining the compression and relaxation response properties in phantoms andex vivo breast samples. Methods: A tissue phantom was constructed to mimic different sizes of breast masses and tissue stiffness. In addition, five humanex vivo whole breast specimens with and without masses were studied. MR data was acquired on a 3T scanner consisting of T1-weighted, fat suppressed spin echo T2-weighted, and SENC breast images. Mechanical tissue characteristics (strain) of the phantoms and breast tissue samples were measured using SENC imaging in both compression and relaxation modes. The breast tissue specimens were sectioned and stained in the same plane as the MRI for histological evaluation. Results: For the phantom, SENC images showed soft masses with quantitative strain values between 35% and 50%, while harder masses had strain values between 0% and 20%. Combined compression (CMP) and relaxation (REX) breast SENC images separately categorized all masses into three different groups. For breast SENC, the signal intensities betweenex vivo breast mass and breast glandular tissue were significantly different (−7.6 ± 2.6 verses −20.6 ± 5.4 for SENC-CMP, and 4.2 ± 1.5 verses 22.6 ± 5 for SENC-REX, p < 0.05). Conclusions: We have demonstrated that SENC breast MRI can be used to obtain mechanical tissue properties and give quantitative estimates of strain in tumors. This feasibility study provides the basis for future clinical studies.

To advance magnetic resonance imaging (MRI) technologies further for in vivo tissue characterization with histopathologic validation, we investigated the feasibility of ex vivo tissue imaging of a surgically removed human brain tumor as a comprehensive approach for radiology–pathology correlation in histoanatomically identical fashion in a rare case of pigmented ganglioglioma with complex paramagnetic properties. Pieces of surgically removed ganglioglioma, containing melanin and hemosiderin pigments, were imaged with a small bore 7-T MRI scanner to obtain T1-, T2-, and T2*-weighted image and diffusion tensor imaging (DTI). Corresponding histopathological slides were prepared for routine hematoxylin and eosin stain and special stains for melanin and iron/hemosiderin to correlate with MRI signal characteristics. Furthermore, mean diffusivity (MD) maps were generated from DTI data and correlated with cellularity using image analysis. While the presence of melanin was difficult to interpret in in vivo MRI with certainty due to concomitant hemosiderin pigments and calcium depositions, ex vivo tissue imaging clearly demonstrated pieces of tissue exhibiting the characteristic MR signal pattern for melanin with pathologic confirmation in a histoanatomically identical location. There was also concordant correlation between MD and cellularity. Although it is still in an initial phase of development, ex vivo tissue imaging is a promising approach, which offers radiology–pathology correlation in a straightforward and comprehensive manner.