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OBJECTIVES: To evaluate longitudinal placental perfusion using pseudo-continuous arterial spin-labeled (pCASL) MRI in normal pregnancies and in pregnancies affected by chronic hypertension (cHTN), who are at the greatest risk for placental-mediated disease conditions. METHODS: Eighteen normal and 23 pregnant subjects with cHTN requiring antihypertensive therapy were scanned at 3 T using free-breathing pCASL-MRI at 16-20 and 24-28 weeks of gestational age. RESULTS: Mean placental perfusion was 103.1 ± 48.0 and 71.4 ± 18.3 mL/100 g/min at 16-20 and 24-28 weeks respectively in normal pregnancies and 79.4 ± 27.4 and 74.9 ± 26.6 mL/100 g/min in cHTN pregnancies. There was a significant decrease in perfusion between the first and second scans in normal pregnancies (p = 0.004), which was not observed in cHTN pregnancies (p = 0.36). The mean perfusion was not statistically different between normal and cHTN pregnancies at both scans, but the absolute change in perfusion per week was statistically different between these groups (p = 0.044). Furthermore, placental perfusion was significantly lower at both time points (p = 0.027 and 0.044 respectively) in the four pregnant subjects with cHTN who went on to have infants that were small for gestational age (52.7 ± 20.4 and 50.4 ± 20.9 mL/100 g/min) versus those who did not (85 ± 25.6 and 80.0 ± 25.1 mL/100 g/min). CONCLUSION: pCASL-MRI enables longitudinal assessment of placental perfusion in pregnant subjects. Placental perfusion in the second trimester declined in normal pregnancies whereas it remained unchanged in cHTN pregnancies, consistent with alterations due to vascular disease pathology. Perfusion was significantly lower in those with small for gestational age infants, indicating that pCASL-MRI-measured perfusion may be an effective imaging biomarker for placental insufficiency. CLINICAL RELEVANCE STATEMENT: pCASL-MRI enables longitudinal assessment of placental perfusion without administering exogenous contrast agent and can identify placental insufficiency in pregnant subjects with chronic hypertension that can lead to earlier interventions. KEY POINTS: ⢠Arterial spin-labeled (ASL) magnetic resonance imaging (MRI) enables longitudinal assessment of placental perfusion without administering exogenous contrast agent. ⢠ASL-MRI-measured placental perfusion decreased significantly between 16-20 week and 24-28 week gestational age in normal pregnancies, while it remained relatively constant in hypertensive pregnancies, attributed to vascular disease pathology. ⢠ASL-MRI-measured placental perfusion was significantly lower in subjects with hypertension who had a small for gestational age infant at 16-20-week gestation, indicating perfusion as an effective biomarker of placental insufficiency.
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Hipertensão , Insuficiência Placentária , Gravidez , Feminino , Humanos , Lactente , Placenta/diagnóstico por imagem , Marcadores de Spin , Meios de Contraste , Imageamento por Ressonância Magnética/métodos , Perfusão , BiomarcadoresRESUMO
BACKGROUND: Although radiation therapy (RT) has been recognized for contributing to cardiovascular disease (CVD), it is unknown whether specific doses received by cardiovascular tissues influence development. OBJECTIVE: In this pilot study, we examined the contribution of RT dose distribution on the development of CVD events in patients with cancer within 5 years of RT. METHODS: A retrospective case-controlled design was used matching 28 cases receiving thoracic RT who subsequently developed an adverse CVD event with 28 controls based upon age, gender, and cancer type. Dose volume histograms of nongated computed tomography scans received during RT characterized the dose delivered to the heart. Heart chambers were segmented using an atlas approach, and radiomics features for the segmentation as well as planning dose in each chamber were tabulated for analysis. RESULT: No significant differences were observed in the RT dose statistics between groups, preexisting CVD, nor significant differences of RT doses delivered to distinct chambers of the heart. Cases were found to have greater CVD risk factors at the time of cancer diagnosis. Morphological significant differences for perimeter on border ( P = .043), equivalent spherical radius ( P = .050), and elongation ( P = .038) were observed, with preexisting CVD having the highest values (ie, larger hearts). CONCLUSION: Traditional CVD risk factors were more prevalent in the cases who developed CVD. No differences were observed in doses of RT. Of note, we observed significant differences in heart morphology and mass in known diseased hearts on the pretreatment scans. These new metrics may have implications for the measurement and quantification of CVD.
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Sobreviventes de Câncer , Doenças Cardiovasculares , Neoplasias , Doenças Cardiovasculares/epidemiologia , Doenças Cardiovasculares/etiologia , Humanos , Neoplasias/complicações , Neoplasias/radioterapia , Projetos Piloto , Doses de Radiação , Estudos RetrospectivosRESUMO
Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.
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Encéfalo/diagnóstico por imagem , Glioblastoma/diagnóstico , Imageamento Hiperespectral , Rede Nervosa , Algoritmos , Encéfalo/patologia , Aprendizado Profundo , Glioblastoma/patologia , Humanos , Processamento de Imagem Assistida por Computador , Redes Neurais de ComputaçãoRESUMO
PURPOSE: We assessed the feasibility and cancer detection rate of fluciclovine (18F) positron emission tomography-ultrasound fusion targeted biopsy vs standard template biopsy in the same patient with biochemical failure after nonsurgical therapy for prostate cancer. MATERIALS AND METHODS: A total of 21 patients with a mean ± SD prostate specific antigen of 7.4 ± 6.8 ng/ml and biochemical failure after nonoperative prostate cancer treatment underwent fluciclovine (18F) positron emission tomography-computerized tomography (mean 364.1 ± 37.7 MBq) and planning transrectal prostate ultrasound with 3-dimensional image reconstruction. Focal prostatic activity on positron emission tomography was delineated and co-registered with planning ultrasound. During the subsequent biopsy session computer generated 12-core template biopsies were performed and then fluciclovine defined targets were revealed and biopsied. Histological analysis of template and targeted cores were completed. RESULTS: Template biopsy was positive for malignancy in 6 of 21 patients (28.6%), including 10 of 124 regions and 11 of 246 cores, vs targeted biopsy in 10 of 21 (47.6%), including 17 of 50 regions and 40 of 125 cores. Five of 21 patients had positive findings on targeted biopsy only and 1 of 21 had positive findings on template biopsy only. An additional case was upgraded from Grade Group 2 to 3 on targeted biopsy. Extraprostatic disease was detected in 8 of 21 men (38.1%) with histological confirmation in all 3 who underwent lesion biopsy. CONCLUSIONS: Fluciclovine positron emission tomography real-time ultrasound fusion guidance for biopsy is feasible in patients with biochemical failure after nonsurgical therapy for prostate cancer. It identifies more recurrent prostate cancer using fewer cores compared with template biopsy in the same patient. Further study is required to determine in what manner targeted biopsy may augment template biopsy of recurrent prostate cancer.
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Ácidos Carboxílicos , Ciclobutanos , Biópsia Guiada por Imagem , Recidiva Local de Neoplasia/diagnóstico por imagem , Recidiva Local de Neoplasia/patologia , Tomografia por Emissão de Pósitrons , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/patologia , Idoso , Idoso de 80 Anos ou mais , Estudos de Viabilidade , Humanos , Masculino , Pessoa de Meia-Idade , Tomografia por Emissão de Pósitrons/instrumentação , Tomografia por Emissão de Pósitrons/métodos , Estudos Prospectivos , Resultado do Tratamento , Ultrassonografia/instrumentaçãoRESUMO
This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.
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The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.
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Aprendizado Profundo , Glioblastoma/diagnóstico por imagem , Algoritmos , Encéfalo/diagnóstico por imagem , Biologia Computacional , Humanos , Processamento de Imagem Assistida por Computador , Medicina de Precisão , Máquina de Vetores de SuporteRESUMO
There has been an increasing interest in studying cardiac fibers in order to improve the current knowledge regarding the mechanical and physiological properties of the heart during heart failure (HF), particularly early HF. Having a thorough understanding of the changes in cardiac fiber orientation may provide new insight into the mechanisms behind the progression of left ventricular (LV) remodeling and HF. We conducted a systematic review on various technologies for imaging cardiac fibers and its link to HF. This review covers literature reports from 1900 to 2017. PubMed and Google Scholar databases were searched using the keywords "cardiac fiber" and "heart failure" or "myofiber" and "heart failure." This review highlights imaging methodologies, including magnetic resonance diffusion tensor imaging (MR-DTI), ultrasound, and other imaging technologies as well as their potential applications in basic and translational research on the development and progression of HF. MR-DTI and ultrasound have been most useful and significant in evaluating cardiac fibers and HF. New imaging technologies that have the ability to measure cardiac fiber orientations and identify structural and functional information of the heart will advance basic research and clinical diagnoses of HF.
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Diagnóstico por Imagem/métodos , Insuficiência Cardíaca/diagnóstico , Coração/diagnóstico por imagem , Ecocardiografia , Humanos , Imagem Cinética por Ressonância Magnética , Reprodutibilidade dos TestesRESUMO
OBJECTIVE: The purpose of this review is to summarize the applications of PET molecular imaging-directed biopsy of a variety of organs in the management of various diseases with a focus on cancers. CONCLUSION: PET can yield metabolic information at the cellular and molecular levels, and PET-directed biopsy is playing an increasing role in the diagnosis and staging of diseases.
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Biópsia Guiada por Imagem , Imagem Molecular , Neoplasias/diagnóstico por imagem , Tomografia por Emissão de Pósitrons , Fluordesoxiglucose F18 , Humanos , Estadiamento de Neoplasias , Neoplasias/patologia , Compostos RadiofarmacêuticosRESUMO
This paper developed and evaluated a quantitative image analysis method to measure the concentration of the nanoparticles on which alkaline phosphatase (AP) was immobilized. These AP-labeled nanoparticles are widely used as signal markers for tagging biomolecules at nanometer and sub-nanometer scales. The AP-labeled nanoparticle concentration measurement can then be directly used to quantitatively analyze the biomolecular concentration. Micro-droplets are mono-dispersed micro-reactors that can be used to encapsulate and detect AP-labeled nanoparticles. Micro-droplets include both empty micro-droplets and fluorescent micro-droplets, while fluorescent micro-droplets are generated from the fluorescence reaction between the APs adhering to a single nanoparticle and corresponding fluorogenic substrates within droplets. By detecting micro-droplets and calculating the proportion of fluorescent micro-droplets to the overall micro-droplets, we can calculate the AP-labeled nanoparticle concentration. The proposed micro-droplet detection method includes the following steps: (1) Gaussian filtering to remove the noise of overall fluorescent targets, (2) a contrast-limited, adaptive histogram equalization processing to enhance the contrast of weakly luminescent micro-droplets, (3) an red maximizing inter-class variance thresholding method (OTSU) to segment the enhanced image for getting the binary map of the overall micro-droplets, (4) a circular Hough transform (CHT) method to detect overall micro-droplets and (5) an intensity-mean-based thresholding segmentation method to extract the fluorescent micro-droplets. The experimental results of fluorescent micro-droplet images show that the average accuracy of our micro-droplet detection method is 0.9586; the average true positive rate is 0.9502; and the average false positive rate is 0.0073. The detection method can be successfully applied to measure AP-labeled nanoparticle concentration in fluorescence microscopy.
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Nanopartículas , Fosfatase Alcalina , Corantes Fluorescentes , Humanos , Microscopia de FluorescênciaRESUMO
Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.
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Laparoscopic and robotic surgery, as one type of minimally invasive surgery (MIS), has gained popularity due to the improved surgeon ergonomics, instrument precision, operative time, and postoperative recovery. Hyperspectral imaging (HSI) is an emerging medical imaging modality, which has proved useful for intraoperative image guidance. Snapshot hyperspectral cameras are ideal for intraoperative laparoscopic imaging because of their compact size and light weight, but low spatial resolution can be a limitation. In this work, we developed a dual-camera laparoscopic imaging system that consists of a high-resolution color camera and a snapshot hyperspectral camera, and we employed super-resolution reconstruction to fuse the images from both cameras to generate high-resolution hyperspectral images. The experimental results show that our method can significantly improve the resolution of hyperspectral images without compromising the image quality or spectral signatures. The proposed super-resolution reconstruction method is promising to promote the employment of high-speed hyperspectral imaging in laparoscopic surgery.
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In this study, we developed an imaging system that can acquire and produce high-resolution hyperspectral images of the retina. Our system combines the view from a high-resolution RGB camera and a snapshot hyperspectral camera together. The method is fast and can be constructed into a compact imaging device. We tested our system by imaging a calibrated color chart, biological tissues ex vivo, and a phantom of the human retina. By using image pansharpening methods, we were able to produce a high-resolution hyperspectral image. The images from the hyperspectral camera alone have a spatial resolution of 0.2 mm/pixel, whereas the pansharpened images have a spatial resolution of 0.1 mm/pixel, a 2x increase in spatial resolution. Our method has the potential to capture images of the retina rapidly. Our method preserves both the spatial and spectral fidelity, as shown by comparing the original hyperspectral images with the pansharpened images. The high-resolution hyperspectral imaging device can have a variety of applications in retina examinations.
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Neuroblastoma is the most common type of extracranial solid tumor in children and can often result in death if not treated. High-intensity focused ultrasound (HIFU) is a non-invasive technique for treating tissue that is deep within the body. It avoids the use of ionizing radiation, avoiding long-term side-effects of these treatments. The goal of this project was to develop the rendering component of an augmented reality (AR) system with potential applications for image-guided HIFU treatment of neuroblastoma. Our project focuses on taking 3D models of neuroblastoma lesions obtained from PET/CT and displaying them in our AR system in near real-time for use by physicians. We used volume ray casting with raster graphics as our preferred rendering method, as it allows for the real-time editing of our 3D radiologic data. Some unique features of our AR system include intuitive hand gestures and virtual user interfaces that allow the user to interact with the rendered data and process PET/CT images for optimal visualization. We implemented the feature to set a custom transfer function, set custom intensity cutoff points, and region-of-interest extraction via cutting planes. In the future, we hope to incorporate this work as part of a complete system for focused ultrasound treatment by adding ultrasound simulation, visualization, and deformable registration.
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We are designing a real-time spectral imaging system using micro-LEDs and a high-speed micro-camera for potential endoscopic applications. Currently, gastrointestinal (GI) endoscopic imaging has largely been limited to white light imaging (WLI), while other endoscopic approaches have seen advancements in imaging techniques including fluorescence imaging, narrow-band imaging, and stereoscopic visualization. To further advance GI endoscopic imaging, we are working towards a high-speed spectral imaging system that can be implemented with a chip-on-tip design for a flexible endoscope. Hyperspectral imaging has potential applications in a variety of imaging procedures with its ability to discern spectral footprints of the imaging field in a series of two-dimensional images at different wavelengths. For investigating the feasibility of a real-time LED-based hyperspectral imaging system for endoscopic applications we designed and developed a large-scale prototype using through-hole LEDs, which is further analyzed as we design our future system. For high quality imaging, the LED array must be designed with the specific illumination patterns and intensities of each LED considered. We present our work in optimizing our current LED array through optical simulations performed in silico. Damped least squares and orthogonal descent algorithms are implemented to maximize irradiance power and improve illumination homogeneity at several working distances by adjusting radial distances of each LED from the camera. With our prototype LED-based hyperspectral imaging system, the simulation and optimization approach achieved an average increase of 8.36 ± 8.79% in irradiance and a 4.3% decrease in standard deviation at multiple working distances and field-of-views for each LED, as compared to the original design, leading to improved image quality and maintained acquisition speeds. This work highlights the value of in silico optical simulation and provides a framework for improved optical system design and will inform design decisions in future works.
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Significance: Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. Aim: The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Approach: Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension 250 × 250 × 84 pixels . We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention. Results: In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted F 1 score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted F 1 score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted F 1 score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features. Conclusions: The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the F 1 score, and the sensitivity score.
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Imageamento Hiperespectral , Neoplasias da Glândula Tireoide , Humanos , Neoplasias da Glândula Tireoide/diagnóstico por imagem , Neoplasias da Glândula Tireoide/patologia , Imageamento Hiperespectral/métodos , Algoritmos , Microscopia/métodos , Glândula Tireoide/diagnóstico por imagem , Glândula Tireoide/patologia , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Interpretação de Imagem Assistida por Computador/métodosRESUMO
Significance: Polarized hyperspectral microscopes with the capability of full Stokes vector imaging have potential for many biological and medical applications. Aim: The aim of this study is to investigate polarized hyperspectral imaging (PHSI) for improving the visualization of collagen fibers, which is an important biomarker related to tumor development, and improving the differentiation of normal and tumor cells on pathologic slides. Approach: We customized a polarized hyperspectral microscopic imaging system comprising an upright microscope with a motorized stage, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a compact SnapScan hyperspectral camera. The polarizers and LCVRs worked in tandem with the hyperspectral camera to acquire polarized hyperspectral images, which were further used to calculate four Stokes vectors: S0, S1, S2, and S3. Synthetic RGB images of the Stokes vectors were generated for the visualization of cellular components in PHSI images. Regions of interest of collagen, normal cells, and tumor cells in the synthetic RGB images were selected, and spectral signatures of the selected components were extracted for comparison. Specifically, we qualitatively and quantitatively investigated the enhanced visualization and spectral characteristics of dense fibers and sparse fibers in normal stroma tissue, fibers accumulated within tumors, and fibers accumulated around tumors. Results: By employing our customized polarized hyperspectral microscope, we extract the spectral signatures of Stokes vector parameters of collagen as well as of tumor and normal cells. The measurement of Stokes vector parameters increased the image contrast of collagen fibers and cells in the slides. Conclusions: With the spatial and spectral information from the Stokes vector data cubes (S0, S1, S2, and S3), our PHSI microscope system enhances the visualization of tumor cells and tumor microenvironment components, thus being beneficial for pathology and oncology.
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Neoplasias de Cabeça e Pescoço , Microscopia , Humanos , Carcinoma de Células Escamosas de Cabeça e Pescoço , Microscopia/métodos , Colágeno , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Microambiente TumoralRESUMO
Significance: Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. Aim: We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Approach: Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Results: Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51 ± 2.03 % , 3.43 ± 0.84 % , and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. Conclusions: We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.
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Desenho de Equipamento , Imageamento Hiperespectral , Laparoscopia , Laparoscopia/métodos , Imageamento Hiperespectral/métodos , Animais , Humanos , Imagens de Fantasmas , Processamento de Imagem Assistida por Computador/métodos , Reprodutibilidade dos Testes , Procedimentos Cirúrgicos Minimamente Invasivos/instrumentação , Procedimentos Cirúrgicos Minimamente Invasivos/métodos , SuínosRESUMO
Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.
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Zebrafish is a well-established animal model for developmental and disease studies. Its optical transparency at early developmental stages is ideal for tissue visualization. Interaction of light with zebrafish tissues provides information on their structure and properties. In this study, we developed a microscopic imaging system for improving the visualization of unstained zebrafish tissues on tissue slides, with two different setups: polarized light imaging and polarized hyperspectral imaging. Based on the polarized light imaging setup, we collected the RGB images of Stokes vector parameters (S0, S1, S2, and S3), and calculated the Stokes vector derived parameters: the degree of polarization (DOP), the degree of linear polarization (DOLP)). We also calculated Stokes vector data based on the polarized hyperspectral imaging setup. The preliminary results demonstrate that Stokes vector data in two imaging setups (polarized light imaging and polarized hyperspectral imaging) are capable of improving the visualization of different types of zebrafish tissues (brain, muscle, skin cells, blood vessels, and yolk). Using the images collected from larval zebrafish samples by polarized light imaging, we found that DOP and DOLP could show clearer structural information of the brain and of skin cells, muscle and blood vessels in the tail. Furthermore, DOP and DOLP parameters derived from images collected by polarized hyperspectral imaging could show clearer structural information of skin cells developing around yolk as well as the surrounding blood vessel network. In addition, polarized hyperspectral imaging could provide complementary spectral information to the spatial information on Stokes vector data of zebrafish tissues. The polarized light imaging & polarized hyperspectral imaging systems provide a better insight into the microstructures of zebrafish tissues.
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During surgery of delicate regions, differentiation between nerve and surrounding tissue is crucial. Hyperspectral imaging (HSI) techniques can enhance the contrast between types of tissue beyond what the human eye can differentiate. Whereas an RGB image captures 3 bands within the visible light range (e.g., 400 nm to 700 nm), HSI can acquire many bands in wavelength increments that highlight regions of an image across a wavelength spectrum. We developed a workflow to identify nerve tissues from other similar tissues such as fat, bone, and muscle. Our workflow uses spectral angle mapper (SAM) and endmember selection. The method is robust for different types of environment and lighting conditions. We validated our workflow on two samples of human tissues. We used a compact HSI system that can image from 400 to 1700 nm to produce HSI of the samples. On these two samples, we achieved an intersection-over-union (IoU) segmentation score of 84.15% and 76.73%, respectively. We showed that our workflow identifies nerve segments that are not easily seen in RGB images. This method is fast, does not rely on special hardware, and can be applied in real time. The hyperspectral imaging and nerve detection approach may provide a powerful tool for image-guided surgery.