Intramural spatial variation of optical tissue properties measured with fluorescence microsphere images of porcine cardiac tissue

A Comprehensive Study of Reactive Oxygen Species Explicit Dosimetry for Pleural Photodynamic Therapy

Photodynamic therapy (PDT) relies on the interactions between light, photosensitizers, and tissue oxygen to produce cytotoxic reactive oxygen species (ROS), primarily singlet oxygen (1O2) through Type II photochemical reactions, along with superoxide anion radicals (O2•-), hydrogen peroxide (H2O2), and hydroxyl radicals (•OH) through Type I mechanisms. Accurate dosimetry, accounting for all three components, is crucial for predicting and optimizing PDT outcomes. Conventional dosimetry tracks only light fluence rate and photosensitizer concentration, neglecting the role of tissue oxygenation. Reactive oxygen species explicit dosimetry (ROSED) quantifies the reacted oxygen species concentration ([ROS]rx) by explicit measurements of light fluence (rate), photosensitizer concentration, and tissue oxygen concentration. Here we determine tissue oxygenation from non-invasive diffuse correlation spectroscopy (DCS) measurement of tumor blood flow using a conversion factor established preclinically. In this study, we have enrolled 24 pleural PDT patients into the study. Of these patients, we are able to obtain data on 20. Explicit dosimetry of light fluence, Photofrin concentration, and tissue oxygenation concentrations were integrated into the ROSED model to calculate [ROS]rx across multiple sites inside the pleural cavity and among different patients. Large inter- and intra-patient heterogeneities in [ROS]rx were observed, despite identical 60 J/cm2 light doses, with mean [ROS]rx,meas of 0.56 ± 0.26 mM for 13 patients with 21 sites, and [ROS]rx,calc1 of 0.48 ± 0.23 mM for 20 patients with 76 sites. This study presented the first comprehensive analysis of clinical ROSED in pleural mesothelioma patients, providing valuable data on future ROSED based pleural PDT that can potentially produce uniform ROS and thus improve the PDT efficacy for Photofrin-mediated pleural PDT.

Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images

Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (I_fluo), effective tissue attenuation coefficient (μ_T), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.

Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.

euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling

The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.