Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Chondrogenesis and mineralization during in vitro culture of human mesenchymal stem cells on three-dimensional woven scaffolds.

Human mesenchymal stem cells (hMSCs) and three-dimensional (3D) woven poly(ɛ-caprolactone) (PCL) scaffolds are promising tools for skeletal tissue engineering. We hypothesized that in vitro culture duration and medium additives can individually and interactively influence the structure, composition, mechanical, and molecular properties of engineered tissues based on hMSCs and 3D poly(ɛ-caprolactone). Bone marrow hMSCs were suspended in collagen gel, seeded on scaffolds, and cultured for 1, 21, or 45 days under chondrogenic and/or osteogenic conditions. Structure, composition, biomechanics, and gene expression were analyzed. In chondrogenic medium, cartilaginous tissue formed by day 21, and hypertrophic mineralization was observed in the newly formed extracellular matrix at the interface with underlying scaffold by day 45. Glycosaminoglycan, hydroxyproline, and calcium contents, and alkaline phosphatase activity depended on culture duration and medium additives, with significant interactive effects (all p < 0.0001). The 45-day constructs exhibited mechanical properties on the order of magnitude of native articular cartilage (aggregate, Young's, and shear moduli of 0.15, 0.12, and 0.033 MPa, respectively). Gene expression was characteristic of chondrogenesis and endochondral bone formation, with sequential regulation of Sox-9, collagen type II, aggrecan, core binding factor alpha 1 (Cbfα1)/Runx2, bone sialoprotein, bone morphogenetic protein-2, and osteocalcin. In contrast, osteogenic medium produced limited osteogenesis. Long-term culture of hMSC on 3D scaffolds resulted in chondrogenesis and regional mineralization at the interface between soft, newly formed engineered cartilage, and stiffer underlying scaffold. These findings merit consideration when developing grafts for osteochondral defect repair.

Direct Carbon-Carbon Bond Formation via Reductive Soft Enolization: A Kinetically Controlled syn-Aldol Addition of alpha-Halo Thioesters and Enolizable Aldehydes

The direct addition of enolizable aldehydes and a-halo thioesters to produce beta-hydroxy thioesters enabled by reductive soft enolization is reported. The transformation is operationally simple and efficient and has the unusual feature of giving high syn-selectivity, which is the opposite of that produced for (thio)esters under conventional conditions. Moreover, excellent diastereoselectivity results when a chiral nonracemic alpha-hydroxy aldehyde derivative is used.

Animal models of soft-tissue sarcoma.

Soft-tissue sarcomas (STSs) are rare mesenchymal tumors that arise from muscle, fat and connective tissue. Currently, over 75 subtypes of STS are recognized. The rarity and heterogeneity of patient samples complicate clinical investigations into sarcoma biology. Model organisms might provide traction to our understanding and treatment of the disease. Over the past 10 years, many successful animal models of STS have been developed, primarily genetically engineered mice and zebrafish. These models are useful for studying the relevant oncogenes, signaling pathways and other cell changes involved in generating STSs. Recently, these model systems have become preclinical platforms in which to evaluate new drugs and treatment regimens. Thus, animal models are useful surrogates for understanding STS disease susceptibility and pathogenesis as well as for testing potential therapeutic strategies.

A low-cost, portable, and quantitative spectral imaging system for application to biological tissues.

The ability of diffuse reflectance spectroscopy to extract quantitative biological composition of tissues has been used to discern tissue types in both pre-clinical and clinical cancer studies. Typically, diffuse reflectance spectroscopy systems are designed for single-point measurements. Clinically, an imaging system would provide valuable spatial information on tissue composition. While it is feasible to build a multiplexed fiber-optic probe based spectral imaging system, these systems suffer from drawbacks with respect to cost and size. To address these we developed a compact and low cost system using a broadband light source with an 8-slot filter wheel for illumination and silicon photodiodes for detection. The spectral imaging system was tested on a set of tissue mimicking liquid phantoms which yielded an optical property extraction accuracy of 6.40 +/- 7.78% for the absorption coefficient (micro(a)) and 11.37 +/- 19.62% for the wavelength-averaged reduced scattering coefficient (micro(s)').

A soft robust model for optimization under ambiguity

In this paper, we propose a framework for robust optimization that relaxes the standard notion of robustness by allowing the decision maker to vary the protection level in a smooth way across the uncertainty set. We apply our approach to the problem of maximizing the expected value of a payoff function when the underlying distribution is ambiguous and therefore robustness is relevant. Our primary objective is to develop this framework and relate it to the standard notion of robustness, which deals with only a single guarantee across one uncertainty set. First, we show that our approach connects closely to the theory of convex risk measures. We show that the complexity of this approach is equivalent to that of solving a small number of standard robust problems. We then investigate the conservatism benefits and downside probability guarantees implied by this approach and compare to the standard robust approach. Finally, we illustrate theme thodology on an asset allocation example consisting of historical market data over a 25-year investment horizon and find in every case we explore that relaxing standard robustness with soft robustness yields a seemingly favorable risk-return trade-off: each case results in a higher out-of-sample expected return for a relatively minor degradation of out-of-sample downside performance. © 2010 INFORMS.

Mechanical constraints enhance electrical energy densities of soft dielectrics

Thesis

Strategic non-cooperation as soft balancing: Why Iraq was not just about Iraq

Many commentators explain recent transatlantic rifts by pointing to diverging norms, interests and geopolitical preferences. This paper proceeds from the premise that not all situations of conflict are necessarily due to underlying deadlocked preferences. Rather, non-cooperation may be a strategic form of soft balancing. That is, more generally, if they believe that they are being shortchanged in terms of influence and payoffs, weaker states may deliberately reject possible cooperation in the short run to improve their influence vis-à-vis stronger states in the long run. This need not be due to traditional relative gains concern. States merely calculate that their reputation as a weak negotiator will erode future bargaining power and subsequently their future share of absolute gains. Strategic non-cooperation is therefore a rational signal of resolve. This paper develops the concept of strategic non-cooperation as a soft balancing tool and applies it to the Iraq case in 2002-2003. © 2005 Palgrave Macmillan Ltd.

Differential Apaf-1 levels allow cytochrome c to induce apoptosis in brain tumors but not in normal neural tissues.

Brain tumors are typically resistant to conventional chemotherapeutics, most of which initiate apoptosis upstream of mitochondrial cytochrome c release. In this study, we demonstrate that directly activating apoptosis downstream of the mitochondria, with cytosolic cytochrome c, kills brain tumor cells but not normal brain tissue. Specifically, cytosolic cytochrome c is sufficient to induce apoptosis in glioblastoma and medulloblastoma cell lines. In contrast, primary neurons from the cerebellum and cortex are remarkably resistant to cytosolic cytochrome c. Importantly, tumor tissue from mouse models of both high-grade astrocytoma and medulloblastoma display hypersensitivity to cytochrome c when compared with surrounding brain tissue. This differential sensitivity to cytochrome c is attributed to high Apaf-1 levels in the tumor tissue compared with low Apaf-1 levels in the adjacent brain tissue. These differences in Apaf-1 abundance correlate with differences in the levels of E2F1, a previously identified activator of Apaf-1 transcription. ChIP assays reveal that E2F1 binds the Apaf-1 promoter specifically in tumor tissue, suggesting that E2F1 contributes to the expression of Apaf-1 in brain tumors. Together, these results demonstrate an unexpected sensitivity of brain tumors to postmitochondrial induction of apoptosis. Moreover, they raise the possibility that this phenomenon could be exploited therapeutically to selectively kill brain cancer cells while sparing the surrounding brain parenchyma.

Induced pluripotent stem cell-derived cardiac progenitors differentiate to cardiomyocytes and form biosynthetic tissues.

The mammalian heart has little capacity to regenerate, and following injury the myocardium is replaced by non-contractile scar tissue. Consequently, increased wall stress and workload on the remaining myocardium leads to chamber dilation, dysfunction, and heart failure. Cell-based therapy with an autologous, epigenetically reprogrammed, and cardiac-committed progenitor cell source could potentially reverse this process by replacing the damaged myocardium with functional tissue. However, it is unclear whether cardiac progenitor cell-derived cardiomyocytes are capable of attaining levels of structural and functional maturity comparable to that of terminally-fated cardiomyocytes. Here, we first describe the derivation of mouse induced pluripotent stem (iPS) cells, which once differentiated allow for the enrichment of Nkx2-5(+) cardiac progenitors, and the cardiomyocyte-specific expression of the red fluorescent protein. We show that the cardiac progenitors are multipotent and capable of differentiating into endothelial cells, smooth muscle cells and cardiomyocytes. Moreover, cardiac progenitor selection corresponds to cKit(+) cell enrichment, while cardiomyocyte cell-lineage commitment is concomitant with dual expression of either cKit/Flk1 or cKit/Sca-1. We proceed to show that the cardiac progenitor-derived cardiomyocytes are capable of forming electrically and mechanically coupled large-scale 2D cell cultures with mature electrophysiological properties. Finally, we examine the cell progenitors' ability to form electromechanically coherent macroscopic tissues, using a physiologically relevant 3D culture model and demonstrate that following long-term culture the cardiomyocytes align, and form robust electromechanical connections throughout the volume of the biosynthetic tissue construct. We conclude that the iPS cell-derived cardiac progenitors are a robust cell source for tissue engineering applications and a 3D culture platform for pharmacological screening and drug development studies.

Efects of mineral suspension and dissolution on strength and compressibility of soft carbonate rocks

© 2014 Elsevier B.V.Calcarenites are highly porous soft rocks formed of mainly carbonate grains bonded together by calcite bridges. The above characteristics make them prone to water-induced weathering, frequently featuring large caverns and inland natural underground cavities. This study is aimed to determine the main physical processes at the base of the short- and long-term weakening experienced by these rocks when interacting with water. We present the results of microscale experimental investigations performed on calcarenites from four different sites in Southern Italy. SEM, thin sections, X-ray CT observations and related analyses are used for both the interpretation-definition of the structure changes, and the identification-quantification of the degradation mechanisms. Two distinct types of bonding have been identified within the rock: temporary bonding (TB) and persistent bonding (PB). The diverse mechanisms linked to these two types of bonding explain both the observed fast decrease in rock strength when water fills the pores (short-term effect of water), identified with a short-term debonding (STD), and a long-term weakening of the material, when the latter is persistently kept in water-saturated conditions (long-term effect of water), identified with a long-term debonding (LTD). To highlight the micro-hydro-chemo-mechanical processes of formation and annihilation of the TB bonds and their role in the evolution of the mechanical strength of the material, mechanical tests on samples prepared by drying partially saturated calcarenite powder, or a mix of glass ballotini and calcarenite powder were conducted. The long-term debonding processes have also been investigated, using acid solutions in order to accelerate the reaction rates. This paper attempts to identify and quantify differences between the two types of bonds and the relative micro-scale debonding processes leading to the macro-scale material weakening mechanisms.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.