The parametric image of Volume Transfer Coefficient (Ktrans) in MRI has been used to guide image reconstruction of Near-Infrared Spectral Tomography (NIRST). The image reconstruction used direct regularization, in which no segmentation has been involved. A total of 24 patients were involved in this study and the reconstructed results show that the tumor total hemoglobin (HbT) contrast could be used to differentiate the malignant from the benign cases (p-value= 0.018). The addition of the MRI information allows more accurate and definitive HbT values from the NIRST.
A portable, 12-wavelength hybrid frequency domain (FD) and continuous wave (CW) near-infrared spectral tomography (NIRST) system was developed for efficient characterization of breast cancer in a clinical oncology setting. Two sets of three FD and three CW measurements were acquired simultaneously. The imaging time was reduced from 90 to 55 seconds with a new gain adjustment scheme of the optical detector. The study of integrating this system into the workflow of clinical oncology practice is ongoing.
An image reconstruction regularization approach for magnetic resonance imaging-guided near-infrared spectral tomography has been developed to improve quantification of total hemoglobin (HbT) and water. By combining prior information from dynamic contrast enhanced (DCE) and diffusion weighted (DW) MR images, the absolute bias errors of HbT and water in the tumor were reduced by 22% and 18%, 21% and 6%, and 10% and 11%, compared to that in the no-prior, DCE- or DW-guided reconstructed images in three-dimensional simulations, respectively. In addition, the apparent contrast values of HbT and water were increased in patient image reconstruction from 1.4 and 1.4 (DCE) or 1.8 and 1.4 (DW) to 4.6 and 1.6.
An optimized approach to nonlinear iterative reconstruction of magnetic resonance imaging (MRI)–guided near-infrared spectral tomography (NIRST) images was developed using an L-curve-based algorithm for the choice of regularization parameter. This approach was applied to clinical exam data to maximize the reconstructed values differentiating malignant and benign lesions. MRI/NIRST data from 25 patients with abnormal breast readings (BI-RADS category 4-5) were analyzed using this optimal regularization methodology, and the results showed enhanced p values and area under the curve (AUC) for the task of differentiating malignant from benign lesions. Of the four absorption parameters and two scatter parameters, the most significant differences for benign versus malignant were total hemoglobin (HbT) and tissue optical index (TOI) with pvalues=0.01 and 0.001, and AUC values=0.79 and 0.94, respectively, in terms of HbT and TOI. This dramatically improved the values relative to fixed regularization (pvalue=0.02 and 0.003; AUC=0.75 and 0.83) showing that more differentiation was possible with the optimal method. Through a combination of both biomarkers, HbT and TOI, the AUC increased from 82.9% (fixed regulation=0.1) to 94.3% (optimal method).
A hybrid frequency domain (FD)-continuous wave (CW) MRI/NIRS system was validated in a clinical trial involving patients with at least ACR 4 radiologic findings in Xi’an, China. In this study, MRI guided nonlinear iterative reconstruction of near-infrared spectroscopy (NIRS) images with limited phase data is investigated. In addition, a systematic optimization of the system hardware design has been conducted as well. We are able to get less than 3% variation in tumor contrast to the surrounding normal tissue, by reducing the number of FD detectors from 16 to 6, showing the potential of reducing the FD detectors. Furthermore, a lookup table of the scattering properties has been made by averaging four MRI-identified breast density groups. By using this look-up table for the patient with the noisy phase data, similar AUCs and p-values are achieved for differentiating the malignant from benign patients.
A portable hybrid frequency domain (FD)-continuous wave (CW) Near-Infrared spectroscopy NIRS system has been developed for quantifying changes in total hemoglobin, oxygen saturation and water content in the breast during neoadjuvant chemotherapy. Simultaneous acquisition of two sets of 3 FD channels and 3 CW channels could be completed within 1 min. System calibration and homogeneous phantom measurement show phase variation less than 3% when PMT gain from 0.7 to 1.1 was used. The study of integrating this system into the workflow of clinical oncology practice is ongoing.
Dosimetry for aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) photodynamic therapy of actinic keratosis was examined with an optimized fluorescence dosimeter to measure PpIX during treatment. While insufficient PpIX generation may be an indicator of incomplete response, there exists no standardized method to quantitate PpIX production at depths in the skin during clinical treatments. In this study, a spectrometer-based point probe dosimeter system was used to sample PpIX fluorescence from superficial (blue wavelength excitation) and deeper (red wavelength excitation) tissue layers. Broadband white light spectroscopy (WLS) was used to monitor aspects of vascular physiology and inform a correction of fluorescence for the background optical properties. Measurements in tissue phantoms showed accurate recovery of blood volume fraction and reduced scattering coefficient from WLS, and a linear response of PpIX fluorescence versus concentration down to 1.95 and 250 nM for blue and red excitations, respectively. A pilot clinical study of 19 patients receiving 1-h ALA incubation before treatment showed high intrinsic variance in PpIX fluorescence with a standard deviation/mean ratio of >0.9. PpIX fluorescence was significantly higher in patients reporting higher pain levels on a visual analog scale. These pilot data suggest that patient-specific PpIX quantitation may predict outcome response.
Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.
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