Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, much recent research has focused on early disease detection Bronchoscopy is the procedure of choice for an effective noninvasive way of detecting early manifestations (bronchial lesions) of lung cancer. In particular, autofluorescence bronchoscopy (AFB) discriminates the autofluorescence properties of normal (green) and diseased tissue (reddish brown) with different colors. Because recent studies show AFB's high sensitivity in searching lesions, it has become a potentially pivotal method in bronchoscopic airway exams. Unfortunately, manual inspection of AFB video is extremely tedious and error prone, while limited effort has been expended toward potentially more robust automatic AFB lesion analysis. We propose a real-time (processing throughput of 27 frames/sec) deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in AFB video streams. The architecture features an encoder structure that exploits pretrained Mix Transformer (MiT) encoders and an efficient stage-wise feature pyramid (ESFP) decoder structure. Segmentation results from the AFB airway-exam videos of 20 lung cancer patients indicate that our approach gives a mean Dice index = 0.756 and an average Intersection of Union = 0.624, results that are superior to those generated by other recent architectures. Thus, ESFPNet gives the physician a potential tool for confident real-time lesion segmentation and detection during a live bronchoscopic airway exam. Moreover, our model shows promising potential applicability to other domains, as evidenced by its state-of-the-art (SOTA) performance on the CVC-ClinicDB, ETIS-LaribPolypDB datasets, and superior performance on the Kvasir, CVC-ColonDB datasets.
Oxygenation concentration of tissue is an important factor in culturing stem cells and in studying the therapy response of cancer cells. The hypoxia bone marrow is the site to harbor cancer cells. Thus, direct high-resolution measurements of molecular O2 would provide powerful means of monitoring cultured stem cells and therapied cancer cells. We proposed an imaging approach to measure oxygenation concentration in deep tissues, based on the XLCT, with combined strengths of high chemical sensitivity and high spatial resolution. We have developed different biosensing films for oxygenation measurements and tested these films with X-ray luminescent experiments. We have also performed phantom experiments with multiple targets to validate the XLCT imaging system with measurements at two channels.
1000 fps HSA enables visualization of flow details, which may be important in accurately guiding interventional procedures; however, single-plane imaging may lack clear visualization of vessel geometry and flow detail. The previously presented high-speed orthogonal biplane imaging may overcome these limitations but may still result in foreshortening of vessel morphology. In certain morphologies, acquiring two non-orthogonal biplane projections at multiple angles can provide better flow detail rather than a standard orthogonal biplane acquisition. Flow studies of aneurysm models were performed, where simultaneous biplane acquisitions at various angles separating the two detector views allowed for better evaluation of morphology and flow. 3D-printed, patient-specific internal carotid artery aneurysm models were imaged with various non-orthogonal angles between the two high-speed photon-counting detectors (7.5 cm x 5 cm FOV) to provide frame-correlated simultaneous 1000-fps image sequences. Fluid dynamics were visualized in multi-angled planes of each model using automated injections of iodine contrast media. The resulting dual simultaneous frame-correlated 1000-fps acquisitions from multiple planes of each aneurysm model provided improved visualization of complex aneurysm geometries and flow streamlines. Multi-angled biplane acquisitions with frame correlation allows for further understanding of aneurysm morphology and flow details: additionally, the ability to recover fluid dynamics at depth enables accurate analysis of 3D flow streamlines, and it is expected that multiple-planar views will enable better volumetric flow visualization and quantification. Such better visualization has the potential to improve interventional procedures.
Purpose: To advance the development of radiomic models of bone quality using the recently introduced Ultra-High Resolution CT (UHR CT), we investigate inter-scan reproducibility of trabecular bone texture features to spatially-variant azimuthal and radial blurs associated with focal spot elongation and gantry rotation. Methods: The UHR CT system features 250x250 μm detector pixels and an x-ray source with a 0.4x0.5 mm focal spot. Visualization of details down to ~150 μm has been reported for this device. A cadaveric femur was imaged on UHR CT at three radial locations within the field-of-view: 0 cm (isocenter), 9 cm from the isocenter, and 18 cm from the isocenter; we expect the non-stationary blurs to worsen with increasing radial displacement. Gray level cooccurrence (GLCM) and gray level run length (GLRLM) texture features were extracted from 237 trabecular regions of interest (ROIs, 5 cm diameter) placed at corresponding locations in the femoral head in scans obtained at the different shifts. We evaluated concordance correlation coefficient (CCC) between texture features at 0 cm (reference) and at 9 cm and 18 cm. We also investigated whether the spatially-variant blurs affect K-means clustering of trabecular bone ROIs based on their texture features. Results: The average CCCs (against the 0 cm reference) for GLCM and GLRM features were ~0.7 at 9 cm. At 18 cm, the average CCCs were reduced to ~0.17 for GLCM and ~0.26 for GLRM. The non-stationary blurs are incorporated in radiomic features of cancellous bone, leading to inconsistencies in clustering of trabecular ROIs between different radial locations: an intersection-over-union overlap of corresponding (most similar) clusters between 0 cm and 9 cm shift was >70%, but dropped to <60% for the majority of corresponding clusters between 0 cm and 18 cm shift. Conclusion: Non-stationary CT system blurs reduce inter-scan reproducibility of texture features of trabecular bone in UHR CT, especially for locations <15 cm from the isocenter. Radiomic models of bone quality derived from UHR CT measurements at isocenter might need to be revised before application in peripheral body sites such as the hips.
Tumor-node-metastasis (TNM) classification for lung cancer is essential for appropriate treatment strategies and has been used widely in the investigation and treatment of this cancer. In TNM classification, N descriptors are one of the most important prognostic indicators and are determined by the metastatic lymph node stations. Therefore, accurate classification of lymph nodes is crucial. Thoracic contrast-enhanced Computed Tomography (CT) images represent the gold-standard modality. However, manual segmentation and classification of lymph nodes are challenges that arise from the relatively similar attenuation between lymph nodes and surrounding structures. Recent progress of convolutional neural network (CNN) has spawned research on mediastinal lymph nodes segmentation on chest CT images using CNNs. However, the previous CNN-based method did not consider the relationship between airways and lymph node locations for segmenting the thoracic N1 lymph nodes group. In this study, we investigate whether distance maps based on tracheobronchial labeling can represent the anatomy properties of the N1 lymph nodes group in volumetric CT images using the NIH open-source dataset.
Aging of the thoracic musculoskeletal system can result in adverse impacts on lung function. Measurement of rib morphology in chest CT scans and assessment of their changes between full inspiratory, or total lung capacity (TLC), and full expiratory, or residual volume (RV), help examine the impacts of rib cage-related changes on lung function. We present new and automated methods using deep learning, multi-parametric thresholding, and topological analysis to segment and label individual ribs in chest CT scans, compute static morphological features at individual rib locations, and assess their lung volume related changes (ΔLV) between TLC and RV scans. The method was applied on TLC and RV scans from the COPDGene Iowa cohort at baseline visits, and accuracy of rib segmentation and computed metrics were examined by comparing with manually outlined results on TLC and RV scans (n=2×20). An average Dice score of 0.93 was observed in all TLC and RV rib segmentations, and root-mean-square errors for different static and ΔLV metrics were found between 0.7 and 4.9%. Application on a larger population (n=200) revealed a five-year loss of 6.2% (p<.001) in the trendline for ΔLV in the anterior-posterior diameter of the 5th rib with losses of 8.4 and 4.0% for males and females, respectively. Automation of CT-based static and ΔLV metrics of rib morphology and significant evidence of age-related changes and sex-bias establish a novel and effective tool to investigate the influence of different risk factors and comorbidities in patients with chronic lung disease and their impacts on disease progression and clinical outcomes.
Changes in brain network connectivity can be observed in schizophrenia and other psychiatric diseases. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source timeseries in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsXGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsXGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 100 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 87.40% ± 19.73% and mean Area Under the receiver operating characteristic Curve (AUC) values of 95.00% ± 13.69% across all tested numbers of features for lsXGC, which is significantly better than the results obtained with cross-correlation (AUC=54.75% ± 30.96%, f1-score=51.10% ± 27.54%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsXGC as a potential imaging biomarker for schizophrenia.
Breast-conserving surgery (BCS) is an alternative approach for tumor removal in breast tumor patients. This procedure requires confirmation of the tumor margin. In this study, a high-resolution cone-beam X-ray computed tomography (CBCT) was designed and evaluated for the application of breast tumor margin delineation. The imaging system was validated with a CBCT image quality phantom for spatial resolution and contrast. In addition, a breast phantom containing multiple pathological features was investigated. Finally, a breast tumor specimen was examined on the high-resolution CBCT. The results showed that the imaging system could provide a decent spatial resolution and great contrast. The pathological features of the breast phantom were clearly visible. The margin of the excised breast specimen was well defined by the system. Overall, the high-resolution CBCT provides a promising result for the clinical application.
Tissue engineering (TE) holds promise for generating lab-grown patient specific organs which can provide: (1) effective treatment for conditions that require volumetric tissue transplantation and (2) new platforms for drug testing. Even though volumetric structural information is essential for confirming successful organ maturation, TE protocol designs are currently informed through destructive and 2D construct assessment tools (e.g. histology). X-ray phase-contrast computed-tomography (PC-CT) can generate non-destructive, high resolution, 3D density maps of organ architecture. In this work, PC-CT is used as new imaging tool for guiding two TE protocols currently at the in-vitro testing stage. The first (1) involves cell-repopulation of an oesophageal scaffold, with the aim of using the regenerated construct for treating long-gap oesophageal atresia, whilst for the second (2) a lung-derived scaffold is populated with islets for regenerating a pancreas, with the “repurposed” lung offering a platform for diabetes drug testing. By combing 3D images and quantitative information, we were able to perform comprehensive construct evaluation. Specifically, we assessed volumetrically: (1) the cell-distribution within the regenerated oesophagi and (2) islet integration with the vascular tree of the lung-derived scaffold. This new information was proven to be essential for establishing corresponding TE protocols and enabled their progression to more advanced scale-up models. We are confident that PC-CT will provide the novel insights necessary to further progress TE protocols, with the next step being in-vivo testing. Crucially, the non-destructive nature of PC-CT will allow in-vivo assessments of TE constructs following their implantation into animal hosts, to investigate their successful integration.
A significant challenge regarding the treatment of aneurysms is the variability in morphology and analysis of abnormal flow. With conventional DSA, low frame rates limit the flow information available to clinicians at the time of the vascular intervention. With 1000 fps High-Speed Angiography (HSA), high frame rates enable flow details to be better resolved for endovascular interventional guidance. The purpose of this work is to demonstrate how 1000 fps biplane-HSA can be used to differentiate flow features, such as vortex formation and endoleaks, amongst patient-specific internal carotid artery aneurysm phantoms pre- and post-endovascular intervention using an in-vitro flow setup. The aneurysm phantoms were attached to a flow loop configured to a carotid waveform, with automated injections of contrast media. Simultaneous Biplane High-Speed Angiographic (SB- HSA) acquisitions were obtained at 1000 fps using two photon-counting detectors with the respective aneurysm and inflow/ outflow vasculature in the FOV. After x-rays were turned on, the detector acquisitions occurred simultaneously, during which iodine contrast was injected at a continuous rate. A pipeline stent was then deployed to divert flow from the aneurysm, and image sequences were once again acquired using the same parameters. Optical Flow, an algorithm that calculates velocity based on spatial-temporal intensity changes between pixels, was used to derive velocity distributions from HSA image sequences. Both the image sequences and velocity distributions indicate detailed changes in flow features amongst the aneurysms before and after deployment of the interventional device. SB-HSA can provide detailed flow analysis, including streamline and velocity changes, which may be beneficial for interventional guidance.
Osteoarthritis (OA) is a debilitating disease that results in cartilage loss and pain, but no drug is currently available for OA, largely due to the absence of a reliable index for measuring disease modifying effects of drug candidates in clinical trials. T1ρ MRI promises to be a reliable method because it provides a measure of cartilage degradation. However, it is critical to establish its reproducibility across MRI vendors before its universal use in clinical studies. Here we report the establishment of a clinically relevant reproducibility error in T1ρ in 25 healthy volunteer knees within and across 3T MRI scanners.
This study aimed to determine the potential of pericoronary adipose tissue (PCAT) radiomics in non-invasively identifying thin cap fibroatheroma (TCFA) in coronary CTA images. TCFA is a precursor of plaque rupture and can only be visualized using intravascular OCT (IVOCT). Our findings showed that PCAT radiomics can be used to identify TCFA as assessed by IVOCT, potentially improving the accuracy of risk assessment and treatment planning. This is the first study to demonstrate the use of PCAT radiomics for non-invasive identification of TCFA.
The literature suggests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Augmented Granger Causality (lsAGC) can capture such alterations using restingstate fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsAGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsAGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 30 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 82.89% ± 17.25% and mean Area Under the receiver operating characteristic Curve (AUC) values of 93.33% ± 12.81% across all tested numbers of features for lsAGC, which is significantly better than the results obtained with cross-correlation (AUC=78.33% ± 25.60%, f1-score=66.22% ± 24.73%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsAGC as a potential imaging biomarker for schizophrenia.
The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manual labor, but, in recent years, progress in deep learning techniques made the automation of this process viable. For this purpose, we tested different deep learning networks to segment the aortic vessel tree from computed tomography angiography (CTA) scans with a deep neural network consisting of an encoder-decoder architecture with skip connections and an optional self-attention block. The networks were trained on a dataset of 56 CTA scans from three different sources and resulted in Dice score similarities between 0.043−0.897. Generally, the classical U-Nets performed better than the ones containing a self-attention block, indicating that they might diminish performance for AVT segmentation. The quality of the resulting segmentations was highly dependent on the CTA image quality, especially on the contrast between the aorta and the surrounding tissues. However, the trained deep neural network can segment CTA scans well with limited computational resources and training data.
4D Flow Magnetic Resonance Imaging (MRI) allows non-invasive assessment of cardiovascular hemodynamics through the acquisition of three-dimensional pulsatile velocities in a single scan. However, this technique is often plagued by issues of noise and low resolution. In this paper, we employed a deep learning-based super-resolution method utilizing an SR residual network (ResNet) to enhance the measurement of hemodynamic indices at a higher resolution. Our approach enables the derivation of hemodynamic parameters dependent on spatiotemporal velocity derivatives such as vorticity, circulation, and turbulent kinetic energy, which were validated using a phantom model of arterial stenosis. We also compared the deep learning approach with linear, nearest neighbor, and natural interpolation methods with a 2x upsampling factor. The results were evaluated against Computational Fluid Dynamics simulations as a reference and showed that the deep learning approach improved the accuracy of turbulent kinetic energy (TKE) and viscous energy loss at peak systole by 7% and 9%, respectively, indicating a significant enhancement over traditional interpolation methods. Additionally, herein we introduce a novel hemodynamic parameter, enstrophy, as a potential diagnostic biomarker for assessing stenosis severity. Overall, our findings suggest that deep learning is a reliable and efficient approach for predicting hemodynamic parameters from 4Dflow MRI.
Evaluation of bone fracture risk is important for the diagnosis and treatment of osteoporosis. Bone stiffness is a major factor in determining overall bone strength and fracture risk. With recent improvements in the spatial resolution of CT systems, it is possible to visualize bone microstructure and extract texture features. It is hypothesized that bone texture can be used to improve the assessment of bone strength compared to using bone mineral density (BMD) alone. In this work, we develop image analysis models for bone stiffness estimation utilizing deep learning (DL) features, radiomics features, and gradient structure tensors (GSTs) to estimate trabecular bone stiffness for high-resolution CT. We base our analysis on a dataset containing micro-CT images of 70 individual lumbar vertebrae. Ten trabecular bone ROIs were extracted from each vertebral body and their bone structure was segmented. The mechanical stiffness of each ROI was estimated using micro-finite element (μFE) analysis. Blur and correlated noise derived from clinical high-resolution CT systems were then added to the trabecular bone ROIs to generate simulated high-resolution CT images. A 3D residual network (ResNet) was trained to extract DL features to predict μFE-derived bone stiffness from the simulated CT images. Radiomics and GST features of bone ROIs were also computed for the same task. The prediction results for DL, radiomics, and GST features combined showed the best performance with a root mean square error (RMSE) of 2.646 N/μm and an R2 of 0.881. The performance of DL features alone was better than using BMD alone or using radiomic features alone. Additionally, incorporating orientation information from GST into the models resulted in improved accuracy. We demonstrate that μFEestimated mechanical properties of lumbar vertebral trabecular bone can be inferred from high-resolution CT images and that a combination of DL, radiomic, and GST features provides the highest prediction performance.
Measurement of body composition, including multiple types of adipose tissue, skeletal tissue, and skeletal muscle, on computed tomography (CT) images is practical given the powerful anatomical structure visualization ability of CT, and is useful for clinical and research applications related to health care and underlying pathology. In recent years, deep learning-based methods have contributed significantly to the development of automatic body composition analysis (BCA). However, the unsatisfactory segmentation performance for indistinguishable boundaries of multiple body composition tissues and the need for large-scale datasets for training deep neural networks still need to be addressed. This paper proposes a deep learning-based approach, called Geographic Attention Network (GA-Net), for body composition tissue segmentation on body torso positron emission tomography/computed tomography (PET/CT) images which leverages the body area information. The representation ability of GA-Net is significantly enhanced with the body area information as it strongly correlates with the target body composition tissue. This method achieves precise segmentation performance for multiple body composition tissues, especially for boundaries that are hard to distinguish, and effectively reduces the data requirements for training the network. We evaluate the proposed model on a dataset that includes 50 body torso PET/CT scans for segmenting 4 key bodily tissues - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle tissue (SMT), and skeleton (Sk). Experiments show that our proposed method increases segmentation accuracy, especially with a limited training dataset, by providing geographic information of target body composition tissues.
Finite element (FE) analysis is an exciting computational technique that permits the collection of biomechanical data. It is widely utilized in industrial engineering, anthropology, comparative anatomy, and medicine. Unfortunately, there are still many aspects of FE analysis that need to be studied in order for this technique to more effectively support biomedical research. The current study examines how material property variation influences FE data to further advance FE analysis and augment biomedical data validity. Using standardized segmentation, 3D anatomical models of whole femur structure were obtained from cadaveric CT data provided by the University at Buffalo Anatomical Gift Program. FE analysis of the model experimental groups with different elastic properties was carried out simulating physiological loading of the femur consistent with previous biomechanical experiments on the femur model system. The results revealed that minor changes in material properties of FE models yield statistically significant differences in maximum displacement, average displacement, and average strain. Regional strain disparities were especially prominent at the inferior femoral neck, medial aspect of the femoral shaft, and the distolateral femur. The results indicate that Young’s modulus variation that is smaller than the variation in Young’s modulus values between FE studies leads to significant differences in biomechanical data. Therefore, these findings underscore the necessity for careful selection of exact elastic properties that are informed by validation data when feasible and consistent for particular anatomical structures across studies in order to advance FE modeling in biomedical research.
Photoacoustic imaging is an emerging preclinical modality that uses a combination of optics and acoustics mechanisms to visualize differences in optical absorption in target imaging objects. Photoacoustic imaging is potentially suitable for visualizing vasculature, as the hemoglobin in red blood cells is a prominent heat absorber and therefore serves as a great biomarker. Due to acoustic reflection, diffraction and scattering, photoacoustic imaging is subject to artifacts when the target soft tissue is close to bone. We construct an ex vivo phantom featuring vascularized soft tissue near long bone, to facilitate evaluation of photoacoustic images and to enable future research on artifact removal in photoacoustic imaging.
In this paper, we propose a scheme that includes automated extraction of thrombus regions and quantitative analysis of thrombosis in confocal laser scanning microscope (CLSM) blood flow image sequence. Making thrombosis model in animal models play an important role in the development of antithrombotic drugs and ascertaining thrombosis mechanisms. Making thrombosis model in cerebral cortex of mice is usually observed using a CLSM in the fluorescence mode. However, some small changes of thrombus regions are not easily observed in CLSM blood flow image sequences. In addition, it is not easy for researchers to quantitatively analyze the degree of thrombosis. Therefore, we propose a scheme to achieve automatic thrombosis region extraction and quantitative analysis. In which, our thrombosis region extraction method uses analysis of changing pattern of thrombosis regions in CLSM blood flow image sequence. Experimental results showed that our scheme can help biological researchers observe and analyze the changes of thrombosis in animal models and reduced the use of fluorescent thrombus markers.
