Hyperspectral imaging (HSI) in abdominal surgery is a new non-invasive tool for the assessment of the perfusion and oxygenation of various tissues and organs. Its benefit in pancreatic surgery is still unknown. The aim of this study was to evaluate the key impact of using HSI during pancreatoduodenectomy (PD). In total, 20 consecutive patients were included. HSI was recorded during surgery as part of a pilot study approved by the local Ethics Committee. Data were collected prospectively with the TIVITA® Tissue System. Intraoperative HS images were recorded before and after gastroduodenal artery (GDA) clamping. We detected four patients with celiac artery stenosis (CAS) caused by a median arcuate ligament (MAL). In two of these patients, a reduction in liver oxygenation (StO2) was discovered 15 and 30 min after GDA clamping. The MAL was divided in these patients. HSI showed an improvement of liver StO2 after MAL division (from 61% to 73%) in one of these two patients. There was no obvious decrease in liver StO2 in the other two patients with CAS. HSI, as a non-invasive procedure, could be helpful in evaluating liver and gastric perfusion during PD, which might assist surgeons in choosing the best surgical approach and in improving patients’ outcomes.

To reduce the risk of pancreatic fistula after pancreatectomy, a satisfactory blood flow at the pancreatic stump is considered crucial. Our group has developed and validated a real-time computational imaging analysis of tissue perfusion, using fluorescence imaging, the fluorescence-based enhanced reality (FLER). Hyperspectral imaging (HSI) is another emerging technology, which provides tissue-specific spectral signatures, allowing for perfusion quantification. Both imaging modalities were employed to estimate perfusion in a porcine model of partial pancreatic ischemia. Perfusion quantification was assessed using the metrics of both imaging modalities (slope of the time to reach maximum fluorescence intensity and tissue oxygen saturation (StO2), for FLER and HSI, respectively). We found that the HSI-StO2 and the FLER slope were statistically correlated using the Spearman analysis (R = 0.697; p = 0.013). Local capillary lactate values were statistically correlated to the HSI-StO2 and to the FLER slope (R = −0.88; p < 0.001 and R = −0.608; p = 0.0074). HSI-based and FLER-based lactate prediction models had statistically similar predictive abilities (p = 0.112). Both modalities are promising to assess real-time pancreatic perfusion. Clinical translation in human pancreatic surgery is currently underway.

Organ transplantation survival rates have continued to improve over the last decades, mostly due to reduction of mortality early after transplantation. The advancement of the field is facilitating a liberalization of the access to organ transplantation with more patients with higher risk profile being added to the waiting list. At the same time, the persisting organ shortage fosters strategies to rescue organs of marginal donors. In this regard, hypothermic and normothermic machine perfusion are recognized as one of the most important developments in the modern era. Owing to these developments, novel non-invasive tools for the assessment of organ quality are on the horizon. Hyperspectral imaging represents a potentially suitable method capable of evaluating tissue morphology and organ perfusion prior to transplantation. Considering the changing environment, we here discuss the hypothetical combination of organ machine perfusion and hyperspectral imaging as a prospective feasibility concept in organ transplantation.

Melanomas account for more than 1.7% of global cancer diagnoses and about 1% of all skin cancer diagnoses in the United States. This type of cancer occurs in the melanin-producing cells in the epidermis and exhibits distinctive variations in melanin and blood concentration values in the form of skin lesions. The current approach for evaluating skin cancer lesions involves visual inspection with a dermatoscope, typically followed by biopsy and histopathological analysis. However, this process, to decrease the risk of misdiagnosis, results in unnecessary biopsies, contributing to the emotional and financial distress of patients. The implementation of a non-invasive imaging technique to aid the analysis of skin lesions in the early stages can potentially mitigate these consequences. Hyperspectral imaging (HSI) has shown promise as a non-invasive technique to analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and examined with an HSI camera. In this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of melanin concentration, blood volume fraction and blood oxygen saturation in the skin. The human skin is modelled as a bi-layer planar system, whose surface reflectance is theoretically calculated using the Kubelka-Munk theory and absorption laws by Beer and Lambert. Hyperspectral images of the dorsal portion of three volunteer subjects' hands 400 - 1000 nm range, were used to estimate the contributing parameters. The mean and standard deviation of these estimates are reported compared with theoretical values from the literature. The model is also evaluated for its sensitivity with respect to these parameters, and then fitted to measured hyperspectral data of three volunteer subjects in different conditions. The wavelengths and wavelength groups which were identified to result in the maximum change in percentage reflectance calculated from the model were 450 and 660 nm for melanin, 500 - 520 nm and 590 - 625 nm for blood volume fraction and 606, 646 and 750 nm for blood oxygen saturation. / Master of Science / Melanoma, the most serious type of skin cancer, develops in the melanin-producing cells in the epidermis. A characteristic marker of skin lesions is the abrupt variations in melanin and blood concentration in areas of the lesion. The present technique to inspect skin cancer lesions involves dermatoscopy, which is a qualitative visual analysis of the lesion's features using a few standardized techniques such as the 7-point checklist and the ABCDE rule. Typically, dermatoscopy is followed by a biopsy and then a histopathological analysis of the biopsy. To reduce the possibility of misdiagnosing actual melanomas, a considerable number of dermoscopically unclear lesions are biopsied, increasing emotional, financial, and medical consequences. A non-invasive imaging technique to analyze skin lesions during the dermoscopic stage can help alleviate some of these consequences. Hyperspectral imaging (HSI) is a promising methodology to non-invasively analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and analyzed with an HSI camera. In this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of melanin concentration, blood volume fraction and blood oxygen saturation. The mean and standard deviation of these estimates are reported compared with theoretical values from the literature. The model is also evaluated for its sensitivity with respect to these parameters, and then fitted to measured hyperspectral data of six volunteer subjects in different conditions. Wavelengths which capture the most influential changes in the model response are identified to be 450 and 660 nm for melanin, 500 - 520 nm and 590 - 625 nm for blood volume fraction and 606, 646 and 750 nm for blood oxygen saturation.

Single-cell bio-analytical techniques play a pivotal role in contemporary biological and biomedical research. Among current high-throughput single-cell imaging methods, coherent Raman imaging offers both high bio-compatibility and high-throughput information-rich capabilities, offering insights into cellular composition, dynamics, and function. Coherent Raman imaging finds its value in diverse applications, ranging from live cell dynamic imaging, high-throughput drug screening, fast antimicrobial susceptibility testing, etc. In this thesis, I first present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-shot femtosecond stimulated Raman scattering (SRS) image. This method allows high-speed, high-throughput tracking of lipid droplet dynamics and drug response in live cells. Second, I provide image-based single-cell analysis in an engineered Escherichia coli (E. coli) population, confirming the chemical composition and subcellular structure organization of individual engineered E. coli cells. Additionally, I unveil metabolon formation in engineered E. coli by high-speed spectroscopic SRS and two-photon fluorescence imaging. Lastly, I present stimulated Raman-activated cell ejection (S-RACE) by integrating high-throughput SRS imaging, in situ image decomposition, and high-precision laser-induced cell ejection. I demonstrate the automatic imaging-identification-sorting workflow in S-RACE and advance its compatibility with versatile samples ranging from polymer particles, single live bacteria/fungus, and tissue sections. Collectively, these efforts demonstrate the valuable capability of SRS in high-throughput single-cell imaging and sorting, opening opportunities for a wide range of biological and biomedical applications.

No description available.

No description available.

<p>In recent years, hyperspectral imaging technologies have been broadly applied to evaluate complex plant physiological features such as leaf moisture content, nutrient level and disease stress. A  critical  component  of  this  technique  is  white  referencing  used  to  remove  the  effect  of  non-uniform  lighting  intensity  in  different  wavelengths  on  raw  hyperspectral  images. Based  on  the  literature,  the leaf  geometry (e.g.,  tilt  angles)  and its interaction  with  the  illumination  severely impact  the  plant  reflectance  spectra  and vegetation  indices  such  as  the  normalized  difference  vegetation index (NDVI).  This thesis is  aimed to address the issues caused by the tilt angles across the leaf surface. To achieve this, two methods based on the fusion of the hyperspectral images and 3D  point  clouds  were  proposed.  The  first  method  was  to  build  a  3D  white  reference  library  in  which a point with almost the same tilt angle, height and position with the pixel on the plant leaf can be found, and then the white reference spectrum at that point can be used to calibrate the raw spectrum of the leaf pixel. The second method was to observe and summarize how the plant spectra and NDVI values changed with the leaf angles. Using the changing trends, the original NDVI and spectra  of  leaf  pixels  at  different  angles  can  be  calibrate  to  a  same  standard  as  if  the  leaf  was  imaged  at  a  flat  and  horizontal  surface.  The  approach  was  called  3D  calibration.  The  results  showed  that  the NDVI  values significantly  changed  with  leaf  angles  and  the  changing  trends differed  between  the  corn  and  soybean  species.  To evaluate the  performance  of  3D  calibration, 180 soybean plants with different genotypes, nitrogen (N), phosphorus (P) and water treatments were  grown  in  the  greenhouse. Each  plant  was  imaged  in three systems:  the high-throughput greenhouse hyperspectral imaging system, the indoor desktop imaging system with a visible-near infrared  (VINIR)  hyperspectral camera and  an  Intel  RealSense  depth  camera  and  the handheld device hyperspectral imaging system. In the greenhouse system, the whole canopy was captured. In the indoor desktop system, the partial canopy was captured because of the space limitation and the  top-matured  leaf  (the  middle  leaf  of  the  uppermost  matured  trifoliate)  was  focused.  The proposed  3D  calibration  was  applied  on  the  top-matured  leaf  to  remove  angle  impacts.  In  the  handheld device system, the flat top-matured leaf was captured. After done with imaging work, the plants were harvested to collect the ground truth data such as relative water content (RWC), N content and P content. Combined with the ground truth data, the NDVI values from three systems were  used  to  discriminate  different  genotypes  and  biochemical treatments,  whereas,  the  spectra from three systems were used to build partial least squares regression (PLSR) models for N, P and RWC. The results showed that the averaged tilt angles of top-matured leaves were impacted by different treatments. For instance, the low-nitrogen (LN) plants showed significantly higher leaf angles than high-nitrogen (HN) plants; the leaf angles on water-stressed (WS) plants were higher than those on well-watered (WW) plants. The leaf angles carried some signals that influenced not only the NDVI discrimination but also the PLSR modelling results. The signals were lost after 3D calibration.  For  the  top-matured  leaves,  the  discrimination  and  modelling  results  after  3D  calibration in the indoor desktop system were close to those from the flat leaves in the handheld device  system.  The  proposed  3D  calibration  approach  has  a  potential  to  eliminate  leaf  angle  impacts.</p>

ABSTRACT Erythema is a well-documented early indicator of tissue injury resulting from exposure to high doses of ionizing radiation. Close monitoring of radiation-induced injury to the skin can help identify opportunities for early interventions that may prevent or reduce more severe reactions. The gold standard for monitoring erythema is visual assessment (VA) by a trained clinician. This method has been criticized for being subjective and designed with very broad categorical descriptors. This work introduces a newly developed VA scale called the clinician erythema assessment for radiation therapy (CEA-RT).The reliability and accuracy of the CEA-RT scale was tested among 20 radiation therapists who trained to use the scale on digital images of radiation induced erythema. CEA-RT demonstrated to be highly reliable when therapist’s grades were compared to themselves, but moderately accurate when therapist’s grades were compared to each. A follow-up study with real patients and fewer but more extensively trained raters was proposed to demonstrate the grading accuracy of the CEA-RT scale. As an alternatively to VA, spectroscopy has the ability to monitor erythema by measuring the change in concentration of hemoglobin (Hb) within the vessels of the skin. These changes in Hb concentration are linked to the degree of erythema. This thesis also investigated the use of hyperspectral imaging (HSI) and diffuse reflectance spectroscopy (DRS) as potential technological alternatives for evaluating erythema. In a second study, Erythema was artificially induced in 3 volunteers who participated in a pilot study designed to assess the ability of an experimental HSI camera to detect skin changes. The data extracted from the hyperspectral images was found to effectively yield spectral profiles and Dawson’s erythema indices (EI) in agreement with the erythema grades assigned by the gold standard therefore showing HSI to be a viable alternative of assessing erythema. Finally, a third study compared DRS measurements to VA using the CEA-RT scale. The DRS system was previously used to measure in vivo erythema but did not compare spectral measurements to an accepted standard. Ten patient volunteers received daily DRS and VA evaluations for a period of 2 to 4 weeks. The results demonstrated that the Dawson’s EI calculated from the spectral data correlated well with the gold standard (VA grades) and that DRS is able to detect changes in the skin throughout the course of radiation treatments. / Thesis / Master of Science (MSc)

Doctor of Philosophy / The digital imaging technology, geographical analyses tool, and computer vision (a technique that enables computers and systems to get meaningful information from images) methods can be used to extract traits-related branching pattern, canopy cover, and pod location in edamame for many plant populations in short time using less labor and resources. Using genome-wide association study, we identified several genetic markers that were associated with those traits. These markers can be used in marker-assisted selection to develop the edamame varieties that are more adaptable to mechanical harvesting and give more yield, along with understanding the physiological mechanisms for better shoot architecture traits and better yield. We used spectral signatures of different edamame at several harvesting time along with machine learning methods to identify the optimal harvest time of edamame. Hyperspectral imaging (a technique that analyzes a wide spectrum of light instead of just assigning primary colors (red, green, blue) to each pixel) when combined with computer vision and machine learning methods can be used to quantify the levels of vomitoxin (chemical that causes vomiting and feed refusal in animal and humans) for larger wheat kernel samples in a cheaper and faster way.