PurposeDiffusion magnetic resonance imaging (dMRI) quantitatively estimates brain microstructure, diffusion tractography being one clinically utilized framework. To advance such dMRI approaches, direct quantitative comparisons between microscale anisotropy and orientation are imperative. Complete backscattering Mueller matrix polarized light imaging (PLI) enables the imaging of thin and thick tissue specimens to acquire numerous optical metrics not possible through conventional transmission PLI methods. By comparing complete PLI to dMRI within the ferret optic chiasm (OC), we may investigate the potential of this PLI technique as a dMRI validation tool and gain insight into the microstructural and orientational sensitivity of this imaging method in different tissue thicknesses.ApproachPost-mortem ferret brain tissue samples (whole brain, n=1 and OC, n=3) were imaged with both dMRI and complete backscattering Mueller matrix PLI. The specimens were sectioned and then reimaged with PLI. Region of interest and correlation analyses were performed on scalar metrics and orientation vectors of both dMRI and PLI in the coherent optic nerve and crossing chiasm.ResultsOptical retardance and dMRI fractional anisotropy showed similar trends between metric values and were strongly correlated, indicating a bias to macroscale architecture in retardance. Thick tissue displays comparable orientation between the diattenuation angle and dMRI fiber orientation distribution glyphs that are not evident in the retardance angle.ConclusionsWe demonstrate that backscattering Mueller matrix PLI shows potential as a tool for microstructural dMRI validation in thick tissue specimens. Performing complete polarimetry can provide directional characterization and potentially microscale anisotropy information not available by conventional PLI alone.
Pancreatic neuroendocrine tumors (PNETs) are a relatively rare type of cancer whose preferred method of treatment is surgery, however current intraoperative guidance techniques have poor contrast. Multiphoton microscopy (MPM) is an imaging technique capable of capturing many biomarkers indicative of cancer; this project examines whether MPM images may provide a basis for a robust method of PNET localization. 14 fixed frozen and 57 formalin-fixed paraffin-embedded samples were imaged using MPM and classified using linear discriminant analysis. The model performed well across both sample preparations, indicating our approach could be applied to improve surgical localization of PNETs.
Cryopreservation is routine in biomedical research and clinical practice for various purposes, including sample transportation, RNA preservation, and long-term storage. However, freezing poses risks of tissue damage due to ice crystal formation and cell lysis. The effects of tissue freezing and thawing on microstructural image features are not fully understood, and determining a freezing protocol that best preserves tissue integrity is essential for maximizing the transferability of imaging studies using previously frozen tissues. This study investigates the impact of freeze-thaw protocols on tissue microstructure using optical coherence tomography (OCT), an imaging technique that provides detailed 3D images of biological structures. Tissue specimens from three organs – lung, liver, and duodenum – were collected from six mice and imaged before and after freeze-thawing using different protocols. We tested protocols including slow freezing to -20 °C, slow freezing to -80 °C, and liquid nitrogen submersion. We examined immersion in both phosphate buffered saline and routine cryopreservation compounds for all methods. Using images from each specimen before and after freeze-thawing, differences in structural features were analyzed qualitatively and by using texture analysis. Texture features were extracted from OCT images using Haralick’s method, and statistical analysis was performed to compare the different protocols and tissue types. Results show that flash freezing methods and the use of cryopreservation compounds cause fewer alterations in tissue microstructure compared to slow freezing. This study provides insight into the effects of common freezing protocols on tissue integrity, which may inform the optimization of tissue preservation techniques across many disciplines.
KEYWORDS: Education and training, Tumors, Image classification, Deep learning, Data modeling, Multiphoton microscopy, Machine learning, Second harmonic generation, RGB color model, Tissues
Pancreatic neuroendocrine tumors (PNETs) present significant diagnostic and therapeutic challenges due to their heterogeneity and complex nature as a subtype of pancreatic cancer. The treatment approach varies considerably based on the tumor's location, grading, and focality. Accurate prognosis and management typically necessitate the expertise of a pathologist to evaluate histological slides of the tissue, a process that is often time-consuming and labor-intensive. Developing point-of-care techniques for automatic classification of PNETs would greatly improve the ability to treat and manage this disease by providing real-time decision-making information. In response to these challenges, our study introduces a highly efficient and versatile diagnostic strategy. This innovative approach synergistically integrates label-free multiphoton microscopy with finely adjusted, pre-trained deep learning models, optimized for performance even with limited data availability. We have meticulously optimized four pre-trained convolutional neural networks, utilizing a dataset comprising only 49 images, which includes both two-photon excitation fluorescence and second-harmonic generation imaging. This refined approach has resulted in an impressive average classification accuracy of over 95% for the development dataset and more than 90% for the test dataset. These results are significantly superior when compared to the preoperative misdiagnosis rates of conventional diagnostic modalities such as ultrasound (US) and computed tomography (CT), which stand at 81.8% and 61.5%, respectively. This methodology represents a significant advancement in the diagnostic process for PNETs, promising a more streamlined, rapid, and accurate approach to treatment. Furthermore, it opens substantial potential for the automated classification of various tumor types using multiphoton microscopic imaging, even in scenarios characterized by limited data availability.
SignificanceMultiphoton microscopy (MPM) is a useful biomedical imaging tool for its ability to probe labeled and unlabeled depth-resolved tissue biomarkers at high resolution. Automated MPM tile scanning allows for whole-slide image acquisition but can suffer from tile-stitching artifacts that prevent accurate quantitative data analysis.AimWe have investigated postprocessing artifact correction methods using ImageJ macros and custom Python code. Quantitative and qualitative comparisons of these methods were made using whole-slide MPM autofluorescence and second-harmonic generation images of human duodenal tissue.ApproachImage quality after artifact removal is assessed by evaluating the processed image and its unprocessed counterpart using the root mean square error, structural similarity index, and image histogram measurements.ResultsConsideration of both quantitative and qualitative results suggest that a combination of a custom flat-field-based correction and frequency filtering processing step provide improved artifact correction when compared with each method used independently to correct for tiling artifacts of tile-scan MPM images.ConclusionsWhile some image artifacts remain with these methods, further optimization of these processing steps may result in computational-efficient methods for removing these artifacts that are ubiquitous in large-scale MPM imaging. Removal of these artifacts with retention of the original image information would facilitate the use of this imaging modality in both research and clinical settings, where it is highly useful in collecting detailed morphologic and optical properties of tissue.
SignificanceLineage tracing using fluorescent reporters is a common tool for monitoring the expression of genes and transcription factors in stem cell populations and their progeny. The zinc-binding protein 89 (ZBP-89/Zfp148 mouse gene) is a transcription factor that plays a role in gastrointestinal (GI) stem cell maintenance and cellular differentiation and has been linked to the progression of colon cancer. While lineage tracing is a useful tool, it is commonly performed with high-magnification microscopy on a small field of view within tissue sections, thereby limiting the ability to resolve reporter expression at the organ level. Furthermore, this technique requires extensive tissue processing, which is time consuming and requires euthanizing the animal. Further knowledge could be elucidated by measuring the expression of fluorescent reporters across entire organs with minimal tissue processing.AimWe present the application of wide-field fluorescence imaging for whole-organ lineage tracing of an inducible Zfp148-tdTomato-expressing transgenic mouse line to assess the expression of ZBP-89/Zfp148 in the GI tract.ApproachWe measured tdTomato fluorescence in ex vivo organs at time points between 24 h and 6 months post-induction. Fluctuations in tdTomato expression were validated by fluorescence microscopy of tissue sections.ResultsQuantification of the wide field-of-view images showed a statistically significant increase in fluorescent signal across the GI tract between transgenic mice and littermate controls. The results also showed a gradient of decreasing reporter expression from proximal to distal intestine, suggesting a higher abundance of ZBP-89 expressing stem cells, or higher expression of ZBP-89 within the stem cells, in the proximal intestine.ConclusionsWe demonstrate that wide-field fluorescence imaging is a valuable tool for monitoring whole-organ expression of fluorescent reporters. This technique could potentially be applied in vivo for longitudinal assessment of a single animal, further enhancing our ability to resolve rare stem cell lineages spatially and temporally.
Pancreatic neuroendocrine tumors (PNETs) are a rare but increasingly more prevalent cancer with heterogeneous clinical and pathological expression. Surgery is the preferred treatment for most PNETs, but existing intraoperative localization imaging techniques have poor tumor contrast and resolution. Our work tests the suitability of combined somatostatin receptor imaging (SRI) and multiphoton microscopy (MPM) for localizing PNETs, combining the labeled technique of SRI with the label-free technique of MPM for enhanced contrast and sensitivity. Our results suggest that this approach could be a valuable clinical tool for surgical localization and treatment of PNETs.
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