Spectral-profile-based algorithm for hemoglobin oxygen saturation determination from diffuse reflectance spectra

Effects of volume management on free flap perfusion and metabolism in a large animal model study

Free flap failure represents a substantial clinical burden. The role of intraoperative volume management remains controversial, with valid studies lacking. Here, using a large animal model, we investigated the influence of volume management on free flap perfusion and metabolism. Autotransfer of a musculocutaneous gracilis flap was performed on 31 German domestic pigs, with arterial anastomosis and catheterization of the pedicle vein for sequential blood sampling. Flap reperfusion was followed by induction of a hemorrhagic shock with maintenance for 30 min and subsequent circulation stabilization with crystalloid solution, crystalloid solution and catecholamine, autotransfusion or colloidal solution. Flap perfusion and oxygenation were periodically assessed using hyperspectral imaging. Flap metabolism was assessed via periodic blood gas analyses. Hyperspectral imaging revealed no difference in either superficial or deep tissue oxygen saturation, tissue hemoglobin or tissue water content between the test groups at any time point. Blood gas analyses showed that lactate levels were significantly increased in the group that received crystalloid solution and catecholamine, after circulatory stabilization and up to 2 h after. We conclude that, in hemorrhagic shock, volume management impacts acid-base balance in free flaps. Crystalloid solutions with norepinephrine increase lactate levels, yet short-term effects on flap perfusion seem minimal, suggesting that vasopressors are not detrimental.

Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology

Complete mesocolic excision (CME), which involves the adequate resection of the tumor-bearing colonic segment with “en bloc” removal of its mesocolon along embryological fascial planes is associated with superior oncological outcomes. However, CME presents a higher complication rate compared to non-CME resections due to a higher risk of vascular injury. Hyperspectral imaging (HSI) is a contrast-free optical imaging technology, which facilitates the quantitative imaging of physiological tissue parameters and the visualization of anatomical structures. This study evaluates the accuracy of HSI combined with deep learning (DL) to differentiate the colon and its mesenteric tissue from retroperitoneal tissue. In an animal study including 20 pig models, intraoperative hyperspectral images of the sigmoid colon, sigmoid mesentery, and retroperitoneum were recorded. A convolutional neural network (CNN) was trained to distinguish the two tissue classes using HSI data, validated with a leave-one-out cross-validation process. The overall recognition sensitivity of the tissues to be preserved (retroperitoneum) and the tissues to be resected (colon and mesentery) was 79.0 ± 21.0% and 86.0 ± 16.0%, respectively. Automatic classification based on HSI and CNNs is a promising tool to automatically, non-invasively, and objectively differentiate the colon and its mesentery from retroperitoneal tissue.

New Approach to the Old Challenge of Free Flap Monitoring-Hyperspectral Imaging Outperforms Clinical Assessment by Earlier Detection of Perfusion Failure

In reconstructive surgery, free flap failure, especially in complex osteocutaneous reconstructions, represents a significant clinical burden. Therefore, the aim of the presented study was to assess hyperspectral imaging (HSI) for monitoring of free flaps compared to clinical monitoring. In a prospective, non-randomized clinical study, patients with free flap reconstruction of the oro-maxillofacial-complex were included. Monitoring was assessed clinically and by using hyperspectral imaging (TIVITA™ Tissue-System, DiaspectiveVision GmbH, Pepelow, Germany) to determine tissue-oxygen-saturation [StO₂], near-infrared-perfusion-index [NPI], distribution of haemoglobin [THI] and water [TWI], and variance to an adjacent reference area (Δreference). A total of 54 primary and 11 secondary reconstructions were performed including fasciocutaneous and osteocutaneous flaps. Re-exploration was performed in 19 cases. A total of seven complete flap failures occurred, resulting in a 63% salvage rate. Mean time from flap inset to decision making for re-exploration based on clinical assessment was 23.1 ± 21.9 vs. 18.2 ± 19.4 h by the appearance of hyperspectral criteria indicating impaired perfusion (StO₂ ≤ 32% OR StO₂Δreference > −38% OR NPI ≤ 32.9 OR NPIΔreference ≥ −13.4%) resulting in a difference of 4.8 ± 5 h (p < 0.001). HSI seems able to detect perfusion compromise significantly earlier than clinical monitoring. These findings provide an interpretation aid for clinicians to simplify postoperative flap monitoring.

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.

Functional Imaging in Wounds: Imaging Modalities of Today and Tomorrow

Wound care is a multidisciplinary field with significant economic burden to our healthcare system. Not only does wound care cost the US healthcare system $20 billion annually, but wounds also remarkably impact the quality of life of patients; wounds pose significant risk of mortality, as the five-year mortality rate for diabetic foot ulcers (DFUs) and ischemic ulcers is notably higher compared to commonly encountered cancers such as breast and prostate. Although it is important to measure how wounds may or may not be improving over time, the only relative "marker" for this is wound area measurement-area measurements can help providers determine if a wound is on a healing or non-healing trajectory. Because wound area measurements are currently the only readily available "gold standard" for predicting healing outcomes, there is a pressing need to understand how other relative biomarkers may play a role in wound healing. Currently, wound care centers across the nation employ various techniques to obtain wound area measurements; length and width of a wound can be measured with a ruler, but this carries a high amount of inter- and intrapersonal error as well as uncertainty. Acetate tracings could be used to limit the amount of error but do not account for depth, thereby making them inaccurate. Here, we discuss current imaging modalities and how they can serve to accurately measure wound size and serve as useful adjuncts in wound assessment. Moreover, new imaging modalities are also discussed and how up-and-coming technologies can provide important information on "biomarkers" for wound healing.

Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer

BACKGROUND AND PURPOSE

Breast cancer is one of the most widely recognized tumors. .Diagnosis made in the early stage of disease may imporve outcomes. The discovery of malignant growth utilizing noninvasive light intrusive methods in lieu of conventional excisional biopsy may assist in achieving this goal.

MATERIALS AND METHODS

The change of the optical properties of ex-vivo breast tissues provides different responses to light transmission, absorption, and particularly the reflection over the spectrum range. We offer the use of Hyperspectral imaging (HSI) with advanced image processing and pattern recognition in order to analyze HSI data for breast cancer detection. The spectral signatures were mined and evaluated in both malignant and normal tissue. K-mean clustering was designed for classifying hyperspectral data in order to evaluate and detection of cancer tissue. This method was used to detect ex-vivo breast cancer. Spatial spectral images were created to high spot the differences in the reflectance properties of malignant versus normal tissue.

RESULTS

Trials showed that the superficial spectral reflection images within 500 nm wavelength showed high variance (214.65) between cancerous and normal breast tissues. On the other hand, image within 620 nm wavelength showed low variance (0.0020).However, the superimposed of spectral region 420-620 nm was proposed as the optimum bandwidth. Finally, the proposed HS imaging system was capable to discriminate the tumor region from normal tissue of the ex-vivo breast sample with sensitivity and a specificity of 95 % and 96 %.

CONCLUSIONS

High sensitivity and specificity were achieved, which proposes potential for HSI as an edge evaluation method to enhance the surgical outcome compared to the presently available techniques in the clinics.

O’Shanahan A, Carrera D, Bisshopp S, Sosa C, Marquez M, Camacho R, Plaza MDLL, Morera J, M. Callico G. Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

Algorithms for mapping kidney tissue oxygenation during normothermic machine perfusion using hyperspectral imaging

The lack of donor grafts is a severe problem in transplantation medicine. Hence, the improved preservation of existing and the usage of organs that were deemed untransplantable is as urgent as ever. The development of novel preservation techniques has come into focus. A promising alternative to traditional cold storage is normothermic machine perfusion (NMP), which provides the benefit of improving the organs' viability and of assessing the organs' status under physiological conditions. For this purpose, methods for evaluating organ parameters have yet to be developed. In a previous study, we determined the tissue oxygen saturation (StO2) of kidneys during NMP with hyperspectral imaging (HSI) based on a discrete wavelength (DW) algorithm. The aim of the current study was to identify a more accurate algorithm for StO2 calculation. A literature search revealed three candidates to test: a DW algorithm and two full spectral algorithms - area under a curve and partial least square regression (PLSR). After obtaining suitable calibration data to train each algorithm, they were evaluated during NMP. The wavelength range from 590 to 800 nm was found to be appropriate for analyzing StO2 of kidneys during NMP. The PLSR method shows good results in analyzing the tissues' oxygen status in perfusion experiments.

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.

Cutaneous Imaging Technologies in Acute Burn and Chronic Wound Care

BACKGROUND

Wound assessment relies on visual evaluation by physicians. Such assessment is largely subjective and presents the opportunity to explore the use of emergent technologies.

METHODS

Emergent and powerful noninvasive imaging technologies applicable to assess burn and chronic wounds are reviewed.

RESULTS

The need to estimate wound depth is critical in both chronic wound and burn injury settings. Harmonic ultrasound technology is powerful to study wound depth. It addresses the limitations of optical imaging with limited depth of penetration. What if a wound appears epithelialized by visual inspection, which shows no discharge yet is covered by repaired skin that lacks barrier function? In this case although the wound is closed as defined by current standards, it remains functionally open, presenting the risk of infection and other postclosure complications. Thus, assessment of skin barrier function is valuable in the context of assessing wound closure. Options for the study of tissue vascularization are many. If noncontact and noninvasive criteria are of importance, laser speckle imaging is powerful. Fluorescence imaging is standard in several clinical settings and is likely to serve the wound clinics well as long as indocyanine green injection is not of concern. A major advantage of harmonic ultrasound imaging of wound depth is that the same system is capable of providing information on blood flow dynamics in arterial perforators.

CONCLUSION

With many productive imaging platforms to choose from, wound care is about to be transformed by technology that would help assess wound severity.

Wavelet based feature extraction and visualization in hyperspectral tissue characterization

Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p <0.02) between the chosen tissue parameters and the selected wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition.