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Machine learning techniques have been recently applied for discriminating between Viable and Non-Viable tissues in animal wounds, to help surgeons to identify areas that need to be excised in the process of burn debridement. However, the presence of outliers in the training data set can degrade the performance of that discrimination. This paper presents an outlier removal technique based on the Mahalanobis distance to improve the accuracy detection of Non-Viable skin in human injuries. The iteratively application of this technique improves the accuracy results of the Non-Viable skin in a 13.6% when applying K-fold cross-validation.

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Non-invasive optical imaging techniques have been recently proposed for distinguishing between different types of tissue in burns generated in porcine models. These techniques are designed to assist surgeons during the process of burn debridement, to identify regions requiring excision and their appropriate excision depth. This paper presents a machine learning tool for discriminating between Viable and Non- Viable tissues in human injuries. This tool merges a supervised (QDA) with an unsupervised (k-means clustering) classification algorithms. This combination improves the Non-Viable tissue detection in 23.7% with respect to a simple QDA classifier.

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The use of solid cavities around electromagnetic sources has been recently reported as a mechanism to provide enhanced images at microwave frequencies. These cavities are used as measurement randomizers; and they compress the wave fields at the physical layer. As a result of this compression, the amount of information collected by the sensing array through the different excited modes inside the resonant cavity is increased when compared to that obtained by no-cavity approaches. In this work, a two-dimensional cavity, having multiple openings, is used to perform such a compression for ultrasound imaging. Moreover, compressive sensing techniques are used for sparse signal retrieval with a limited number of operating transceivers. As a proof-of-concept of this theoretical investigation, two point-like targets located in a uniform background medium are imaged in the presence and the absence of the cavity. In addition, an analysis of the sensing capacity and the shape of the point spread function is also carried out for the aforementioned cases. The cavity is designed to have the maximum sensing capacity given different materials and opening sizes. It is demonstrated that the use of a cavity, whether it is made of plastic or metal, can significantly enhance the sensing capacity and the point spread function of a focused beam. The imaging performance is also improved in terms cross-range resolution when compared to the no-cavity case.

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The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

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Accurate and early detection of breast cancer is of high importance, as it is directly associated with the patients' overall well-being during treatment and their chances of survival. Uncertainties in current breast imaging methods can potentially cause two main problems: (1) missing newly formed or small tumors; and (2) false alarms, which could be a source of stress for patients. A recent study at the Massachusetts General Hospital (MGH) indicates that using Digital Breast Tomosynthesis (DBT) can reduce the number of false alarms, when compared to conventional mammography. Despite the image quality enhancement DBT provides, the accurate detection of cancerous masses is still limited by low radiological contrast (about 1%) between the fibro-glandular tissue and affected tissue at X-ray frequencies. In a lower frequency region, at microwave frequencies, the contrast is comparatively higher (about 10%) between the aforementioned tissues; yet, microwave imaging suffers from low spatial resolution. This work reviews conventional X-ray breast imaging and describes the preliminary results of a novel near-field radar imaging mechatronic system (NRIMS) that can be fused with the DBT, in a co-registered fashion, to combine the advantages of both modalities. The NRIMS consists of two antipodal Vivaldi antennas, an XY positioner, and an ethanol container, all of which are particularly designed based on the DBT physical specifications. In this paper, the independent performance of the NRIMS is assessed by (1) imaging a bearing ball immersed in sunflower oil and (2) computing the heat Specific Absorption Rate (SAR) due to the electromagnetic power transmitted into the breast. The preliminary results demonstrate that the system is capable of generating images of the ball. Furthermore, the SAR results show that the system complies with the standards set for human trials. As a result, a configuration based on this design might be suitable for use in realistic clinical applications.

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Burn excision, a difficult technique owing to the training required to identify the extent and depth of injury, will benefit from a tool that can cue the surgeon as to where and how much to resect. We explored two rapid and noninvasive optical imaging techniques in their ability to identify burn tissue from the viable wound bed using an animal model of tangential burn excision. Photoplethysmography (PPG) imaging and multispectral imaging (MSI) were used to image the initial, intermediate, and final stages of burn excision of a deep partial-thickness burn. PPG imaging maps blood flow in the skin's microcirculation, and MSI collects the tissue reflectance spectrum in visible and infrared wavelengths of light to classify tissue based on a reference library. A porcine deep partial-thickness burn model was generated and serial tangential excision accomplished with an electric dermatome set to 1.0 mm depth. Excised eschar was stained with hematoxylin and eosin to determine the extent of burn remaining at each excision depth. We confirmed that the PPG imaging device showed significantly less blood flow where burn tissue was present, and the MSI method could delineate burn tissue in the wound bed from the viable wound bed. These results were confirmed independently by a histological analysis. We found these devices can identify the proper depth of excision, and their images could cue a surgeon as to the preparedness of the wound bed for grafting. These image outputs are expected to facilitate clinical judgment in the operating room.

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Currently X-ray-based imaging systems suffer from low contrast between malignant and healthy fibrous tissues in breast. Microwave Near-field Radar Imaging (NRI) shows a higher contrast between the aforementioned tissues and therefore can enhance tumor detection and diagnosis accuracy. In this work, we present the first imaging results of our developed NRI system that is equipped with a pair of Antipodal Vivaldi Antennas. We used a metal bearing ball immersed in oil as our object of interest, to keep the first measurement configuration simple. Moreover, to demonstrate the safety of our system for human subject tests, we simulated the Specific Absorption Rate (SAR) in a realistic breast tissue model and compared the resulted values with both the USA and Europe standards. The results show that firstly the imaging results from the measurements and simulations are comparable, and secondly the antennas radiations meet the SAR criteria.

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Burn debridement is a challenging technique that requires significant skill to identify regions requiring excision and appropriate excision depth. A machine learning tool is being developed in order to assist surgeons by providing a quantitative assessment of burn-injured tissue. Three noninvasive optical imaging techniques capable of distinguishing between four kinds of tissue-healthy skin, viable wound bed, deep burn, and shallow burn-during serial burn debridement in a porcine model are presented in this paper. The combination of all three techniques considerably improves the accuracy of tissue classification, from 0.42 to almost 0.77.

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