SignificanceHyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.AimWe expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.ApproachBreast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.ResultsThe manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas’ unique endmembers produced by the two methods agree with each other within <2% residual error margin.ConclusionsOur report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas’ unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.
Hyperspectral dark-field microscopy (HSDFM) and analysis algorithms demonstrate classification of various tissue types, including carcinoma in human post-lumpectomy breast tissues. Performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with Monte Carlo simulations of the experimental data. For classification algorithms, two approaches, a supervised spectral angle mapper (SAM) algorithm and an unsupervised K-means algorithm, are used. The manually extracted endmembers of known tissue types were determined by the histopathology reading of the hematoxylin and eosin (H&E)-stained slides. Their associated threshold spectral correlation angles from the SAM algorithm for supervised classification make a good reference library that validates endmembers from the unsupervised algorithm. For unsupervised classification, a K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The endmembers extracted by the two methods agree with each other within less than 2% residual
Hyperspectral dark-field microscopy of resected breast tissues is being developed to assess tumor margins in breast-conserving surgery. Classification between normal/benign and malignancy subtypes is achieved by a spectral angle mapper algorithm, quantifying the similarity of an unclassified spectrum to a known type from a reference spectral library. A library of reference spectra of various tissue types was established by extracting spectra from pathology-confirmed regions of interest in resected breast tissues. The tumor margin analysis is performed by calculating the tissue-dependent angle threshold between the unknown sample and a reference spectrum, treating them as vectors in multi-dimensional hyperspectral space. This work presents methods to determine and validate the tissue type-dependent threshold angles, the reference spectral library of various tissue types, and tumor margin detection in resected human breast tissues.
This paper reports the development and system analysis of a laparoscopic system based on structured illumination technique capable of three-dimensional (3-D) reconstruction of porcine intestine during surgical anastomosis (connection of tubular structures). A calibration target is used to validate the system performance and results show a depth of field of 20 mm with an accuracy of 0.008 mm and precision of 0.25 mm. The imaging system is used to reconstruct a quantitative 3-D depth measurement of ex vivo porcine bowel tissues to mimic an end-to-end bowel anastomosis scenario. We demonstrate that the system can detect a suture in the tissue and map homogeneous surfaces of the intestine with different tissue pigments, affirming the feasibility for depth quantization for guiding and assisting medical diagnostic decisions in anastomosis surgery.
Surgical 3D endoscopy based on structured illumination has been built and evaluated for application in minimally invasive anastomosis surgery which offers advantages of smaller incision, low risk of infection, quick recovery times and reduced blood loss. When combined with robotic manipulations, surgeons can perform surgical tasks with higher precision and repeatability. For reconstructive surgery such as anastomosis, a supervised laparoscopic anastomosis using a surgical robot has recently been reported with an open-surgery approach using a large 3D camera. To push the technology into minimally-invasive setting, we report an endoscopic 3D system based on structured illumination technique to assist the surgical robot, particularly in anastomosis surgery. The recorded structural profile achieves a high depth quantification of 250 um for static objects, with 25 mm depth of field. The proposed system can be integrated into a flexible holding arm to move in accordance with the surgical robotic arm. We characterize the system performance using multiple porcine intestinal tissue samples with variations in surface textures, tissue pigmentation and thickness.
Localized, non-invasive cell stimulation has many applications. Here we show the initial proof-of-concept in vitro study
of the photoacoustic cell stimulation approach, which is amenable to high-throughput screening applications. The
proposed method is implemented as follows: 1) the localized excitation using the focused pulsed laser delivery on an
absorptive material placed inside the well containing the cells, 2) cell stimulation by the photoacoustic pressure
generated, and 3) fluorescence quantification of the membrane potential change over time. The preliminary proof-ofconcept
in vitro study is conducted with primary neurons isolated from mouse cerebral cortex. The absorptive rubber
media generates the photoacoustic pressure by the pulsed laser excitation. The experimental results show the feasibility
of photoacoustic cell stimulation approach by indicating the significant membrane potential chance from the
photoacoustically-stimulated primary neurons. Otherwise, the sham control without any photoacoustic stimulation shows
minimal membrane potential change. We envisage that the proposed approach can allow broad strategies for noninvasive
cell stimulation by using the photoacoustic contrasts situated at inside or outside of the body such as external
absorptive materials or intravascularly-injected photoacoustic contrast particles.
Surgeons have been increasingly relying on minimally invasive surgical guidance techniques not only to reduce surgical trauma but also to achieve accurate and objective surgical risk evaluations. A typical minimally invasive surgical guidance system provides visual assistance in two-dimensional anatomy and pathology of internal organ within a limited field of view. In this work, we propose and implement a structure illumination endoscope to provide a simple, inexpensive 3D endoscopic imaging to conduct high resolution 3D imagery for use in surgical guidance system. The system is calibrated and validated for quantitative depth measurement in both calibrated target and human subject. The system exhibits a depth of field of 20 mm, depth resolution of 0.2mm and a relative accuracy of 0.1%. The demonstrated setup affirms the feasibility of using the structured illumination endoscope for depth quantization and assisting medical diagnostic assessments
Voltage-sensitive dyes (VSDs) are designed to monitor membrane potential by detecting fluorescence changes in response to neuronal or muscle electrical activity. However, fluorescence imaging is limited by depth of penetration and high scattering losses, which leads to low sensitivity in vivo systems for external detection. By contrast, photoacoustic (PA) imaging, an emerging modality, is capable of deep tissue, noninvasive imaging by combining near-infrared light excitation and ultrasound detection. Here, we show that voltage-dependent quenching of dye fluorescence leads to a reciprocal enhancement of PA intensity. We synthesized a near-infrared photoacoustic VSD (PA-VSD), whose PA intensity change is sensitive to membrane potential. In the polarized state, this cyanine-based probe enhances PA intensity while decreasing fluorescence output in a lipid vesicle membrane model. A theoretical model accounts for how the experimental PA intensity change depends on fluorescence and absorbance properties of the dye. These results not only demonstrate PA voltage sensing but also emphasize the interplay of both fluorescence and absorbance properties in the design of optimized PA probes. Together, our results demonstrate PA sensing as a potential new modality for recording and external imaging of electrophysiological and neurochemical events in the brain.
Monitoring of the membrane potential is possible using voltage sensitive dyes (VSD), where fluorescence intensity changes in response to neuronal electrical activity. However, fluorescence imaging is limited by depth of penetration and high scattering losses, which leads to low sensitivity in vivo systems for external detection. In contrast, photoacoustic (PA) imaging, an emerging modality, is capable of deep tissue, noninvasive imaging by combining near infrared light excitation and ultrasound detection. In this work, we develop the theoretical concept whereby the voltage-dependent quenching of dye fluorescence leads to a reciprocal enhancement of PA intensity. Based on this concept, we synthesized a novel near infrared photoacoustic VSD (PA-VSD) whose PA intensity change is sensitive to membrane potential. In the polarized state, this cyanine-based probe enhances PA intensity while decreasing fluorescence output in a lipid vesicle membrane model. With a 3-9 μM VSD concentration, we measured a PA signal increase in the range of 5.3 % to 18.1 %, and observed a corresponding signal reduction in fluorescence emission of 30.0 % to 48.7 %. A theoretical model successfully accounts for how the experimental PA intensity change depends on fluorescence and absorbance properties of the dye. These results not only demonstrate the voltage sensing capability of the dye, but also indicate the necessity of considering both fluorescence and absorbance spectral sensitivities in order to optimize the characteristics of improved photoacoustic probes. Together, our results demonstrate photoacoustic sensing as a potential new modality for sub-second recording and external imaging of electrophysiological and neurochemical events in the brain.
Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm−1, absorption coefficient: 0.1 cm−1 and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.
We describe a scanning near-infrared fluorescence imager for through-skull non-invasive brain
imaging on live murine models. The captured photoluminescence feature through scattering media
was enhanced using a high sensitivity scientific CMOS sensor with the obtained spatial resolution of
15.63 μm, depth of field of 5 mm and an average local signal-to-noise ratio of 37.5 dB.
Anastomosis, the connection of two structures, is a critical procedure for reconstructive surgery with over 1 million cases/year for visceral indication alone. However, complication rates such as strictures and leakage affect up to 19% of cases for colorectal anastomoses and up to 30% for visceral transplantation anastomoses. Local ischemia plays a critical role in anastomotic complications, making blood perfusion an important indicator for tissue health and predictor for healing following anastomosis. In this work, we apply a real time multispectral imaging technique to monitor impact on tissue perfusion due to varying interrupted suture spacing and suture tensions. Multispectral tissue images at 470, 540, 560, 580, 670 and 760 nm are analyzed in conjunction with an empirical model based on diffuse reflectance process to quantify the hemoglobin oxygen saturation within the suture site. The investigated tissues for anastomoses include porcine small (jejunum and ileum) and large (transverse colon) intestines. Two experiments using interrupted suturing with suture spacing of 1, 2, and 3 mm and tension levels from 0 N to 2.5 N are conducted. Tissue perfusion at 5, 10, 20 and 30 min after suturing are recorded and compared with the initial normal state. The result indicates the contrast between healthy and ischemic tissue areas and assists the determination of suturing spacing and tension. Therefore, the assessment of tissue perfusion will permit the development and intra-surgical monitoring of an optimal suture protocol during anastomosis with less complications and improved functional outcome.
Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement.
In this study, a photoacoustic detector integrated with Fourier-transform infrared spectroscopy was used to measure
biomarkers in gas samples independently. Simulated exhaled breath samples were created by mixing varying
concentrations of acetone, ammonia and ethane. The results of these measurements demonstrate the potential of
photoacoustic spectroscopy to detect biomarkers from human breath.
Microbial biofilm is a colony of single bacteria cells (planktonic) that attached to surfaces, attract other microorganisms
to attach and grow, and together they build an extracellular matrix composed of polysaccharides, protein, and DNA.
Eventually, some cells will detach and spread to other surface. Biofilm on medical devices can cause severe infection to
all age ranges from infant to adult. Therefore, it is important to detect biofilm in a fast and efficient manner.
Hyperspectral imaging was utilized for distinguishing wide area of biofilm coverage on various materials and on
different textures of stainless steeltest coupons. Not only is the coverage of biofilm important, but also the shear stress of
biofilm on the attached surfaces is significant. This study investigates the effects of shear stress on the adhesion of
biofilms on common medical device surfaces such as glass, polycarbonate, polytetrafluoroethylene, and stainless steel
with different textures. Biofilm was grown using Ps. aeruginosa and growth was monitored after 24 and 48 hours at 37°
C. The coupons covered with biofilm were tilted at 45 degrees and 90 degrees for 30 seconds to induce shear stress and
Hyperspectral images were taken. We hypothesize that stronger attachment on rough surface would be able to withstand
greater shear stress compared to smooth surface.
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