KEYWORDS: Computed tomography, Image registration, Magnetic resonance imaging, 3D image processing, Image segmentation, Tissues, In vivo imaging, Optical coherence tomography, Positron emission tomography, Medical imaging
Histopathology is the accepted gold standard for identifying cancerous tissues. Validation of in vivo imaging signals with precisely correlated histopathology can potentially improve the delineation of tumors in medical images for focal therapy planning, guidance, and assessment. Registration of histopathology with other imaging modalities is challenging due to soft tissue deformations that occur between imaging and histological processing of tissue. In this paper, a framework for precise registration of medical images and pathology using white-light images (photographs) is presented. A euthanized normal mouse was imaged using four imaging modalities: CBCT, PET-CT, MRI and micro CT. The mouse was then fixed in an embedding medium, optical cutting temperature (OCT) compound, with co-registration markers and sliced at 50 m intervals in a cryostatmicrotome. The device automatically photographed each slice with a mounted camera and reconstructed the 3D white-light image of the mouse through co-registering of consecutive slices. The white-light image was registered to the four imaging modalities based on the external contours of the mouse. Six organs (brain, liver, stomach, pancreas, kidneys and bladder) were contoured on the MR image while the skeletal structure and lungs were segmented on the CBCT image. The contours of these structures were propagated to the additional imaging modalities based on the registrations to the white-light image and were analyzed qualitatively by developing an anatomical atlas of normal mouse defined using three imaging modalities. This work will serve as the foundation to include histopathology through the transfer of the imaged slice onto tape for histological processing.
Treatment of hepatocellular carcinoma (HCC) with sorafenib, a multikinase inhibitor, results in decreased microvessel density associated with increased levels of tumor hypoxia. However, the response rate is relatively poor, and recently it has been shown that tumor hypoxia and perfusion have predictive correlations with HCC response to sorafenib. In this study, we have investigated the correlation of oxygen saturation (SO2) and perfusion, estimated using photoacoustic-ultrasonic (PAUS) imaging, to the sorafenib treatment response in an orthotopic rat model of HCC. Following spectroscopic photoacoustic (sPA) imaging, microbubble contrast was introduced and harmonic imaging data were acquired for perfusion measurements. An FEM-based fluence correction model based on the diffusion approximation with empirically estimated tissue surface fluence and an SNR-based thresholding approach have been developed and validated on ex vivo and in vivo rat data to estimate SO2 using sPA imaging. The SO2 estimate has been obtained by solving an iterative minimization problem and then thresholded based on a pixel-wise empirically estimated SNR mask. For the treated cohort, the results show that the change in SO2 during an oxygen challenge is positively correlated with disease progression, while it is negatively correlated for the untreated cohort. Additionally, perfusion was significantly decreased in the treated group compared to baseline pretreatment and untreated cohort measurements. The reduced treatment-mediated perfusion leads to lack of oxygen supply and thus reduced oxygen levels. This study shows the potential of PAUS estimation of SO2 and perfusion to monitor and predict HCC sorafenib treatment response, ultimately leading to improved future treatment.
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