A denoising diffusion probabilistic model for metal artifact reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p < 10-26; PSNR: p < 10-21), the CNN (SSIM: p < 10-25; PSNR: p < 10-9) and the GAN (SSIM: p < 10-6; PSNR: p < 0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

AZGP1 deficiency promotes angiogenesis in prostate cancer.

BACKGROUND: Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive. METHOD: AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples. RESULT: Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues. CONCLUSION: AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

Objective. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial wall stiffness. Such techniques rely upon motion detection algorithms that provide arterial wall displacement estimation.Approach. In this study, we propose an unsupervised deep learning-based approach, originally proposed for image registration, in order to enable improved quality arterial wall displacement estimation at high temporal and spatial resolutions. The performance of the proposed network was assessed through phantom experiments, where various models were trained by using ultrasound RF signals, or B-mode images, as well as different loss functions.Main results. Using the mean square error (MSE) for the training process provided the highest signal-to-noise ratio when training on the B-modes images (30.36 ± 1.14 dB) and highest contrast-to-noise ratio when training on the RF signals (32.84 ± 1.89 dB). In addition, training the model on RF signals demonstrated the capability of providing accurate localized pulse wave velocity (PWV) maps, with a mean relative error (MREPWV) of 3.32 ± 1.80% and anR2 of 0.97 ± 0.03. Finally, the developed model was tested in human common carotid arteriesin vivo, providing accurate tracking of the distension pulse wave propagation, with an MREPWV= 3.86 ± 2.69% andR2 = 0.95 ± 0.03.Significance. In conclusion, a novel displacement estimation approach was presented, showing promise in improving vascular elasticity imaging techniques.

Pulse wave and vector flow Imaging for atherosclerotic disease progression in hypercholesterolemic swine.

Non-invasive monitoring of atherosclerosis remains challenging. Pulse Wave Imaging (PWI) is a non-invasive technique to measure the local stiffness at diastolic and end-systolic pressures and quantify the hemodynamics. The objective of this study is twofold, namely (1) to investigate the capability of (adaptive) PWI to assess progressive change in local stiffness and homogeneity of the carotid in a high-cholesterol swine model and (2) to assess the ability of PWI to monitor the change in hemodynamics and a corresponding change in stiffness. Nine (n=9) hypercholesterolemic swine were included in this study and followed for up to 9 months. A ligation in the left carotid was used to cause a hemodynamic disturbance. The carotids with detectable hemodynamic disturbance showed a reduction in wall shear stress immediately after ligation (2.12 ± 0.49 to 0.98 ± 0.47 Pa for 40-90% ligation (Group B) and 1.82 ± 0.25 to 0.49 ± 0.46 Pa for >90% ligation (Group C)). Histology revealed subsequent lesion formation after 8-9 months, and the type of lesion formation was dependent on the type of the induced ligation, with more complex plaques observed in the carotids with a more significant ligation (C: >90%). The compliance progression appears differed for groups B and C, with an increase in compliance to 2.09 ± 2.90×10-10 m2 Pa-1 for group C whereas the compliance of group B remained low at 8 months (0.95 ± 0.94×10-10 m2 Pa-1). In summary, PWI appeared capable of monitoring a change in wall shear stress and separating two distinct progression pathways resulting in distinct compliances.

Pulse wave imaging of a stenotic artery model with plaque constituents of different stiffnesses: Experimental demonstration in phantoms and fluid-structure interaction simulation.

Vulnerable plaques associated with softer components may rupture, releasing thrombotic emboli to smaller vessels in the brain, thus causing an ischemic stroke. Pulse Wave Imaging (PWI) is an ultrasound-based method that allows for pulse wave visualization while the regional pulse wave velocity (PWV) is mapped along the arterial wall to infer the underlying wall compliance. One potential application of PWI is the non-invasive estimation of plaque's mechanical properties for investigating its vulnerability. In this study, the accuracy of PWV estimation in stenotic vessels was investigated by computational simulation and PWI in validation phantoms to evaluate this modality for assessing future stroke risk. Polyvinyl alcohol (PVA) phantoms with plaque constituents of different stiffnesses were designed and constructed to emulate stenotic arteries in the experiment, and the novel fabrication process was described. Finite-element fluid-structure interaction simulations were performed in a stenotic phantom model that matched the geometry and parameters of the experiment in phantoms. The peak distension acceleration of the phantom wall was tracked to estimate PWV. PWVs of 2.57 ms-1, 3.41 ms-1, and 4.48 ms-1 were respectively obtained in the soft, intermediate, and stiff plaque material in phantoms during the experiment using PWI. PWVs of 2.10 ms-1, 3.33 ms-1, and 4.02 ms-1 were respectively found in the soft, intermediate, and stiff plaque material in the computational simulation. These results demonstrate that PWI can effectively distinguish the mechanical properties of plaque in phantoms as compared to computational simulation.

Adaptive Wall Shear Stress Imaging in Phantoms, Simulations and In Vivo.

WSS measurement is challenging since it requires sensitive flow measurements at a distance close to the wall. The aim of this study is to develop an ultrasound imaging technique which combines vector flow imaging with an unsupervised data clustering approach that automatically detects the region close to the wall with optimally linear flow profile, to provide direct and robust WSS estimation. The proposed technique was evaluated in phantoms, mimicking normal and atherosclerotic vessels, and spatially registered Fluid Structure Interaction (FSI) simulations. A relative error of 6.7% and 19.8% was obtained for peak systolic (WSSPS) and end diastolic (WSSED) WSS in the straight phantom, while in the stenotic phantom, a good similarity was found between measured and simulated WSS distribution, with a correlation coefficient, R, of 0.89 and 0.85 for WSSPS and WSSED, respectively. Moreover, the feasibility of the technique to detect pre-clinical atherosclerosis was tested in an atherosclerotic swine model. Six swines were fed atherogenic diet, while their left carotid artery was ligated in order to disturb flow patterns. Ligated arterial segments that were exposed to low WSSPS and WSS characterized by high frequency oscillations at baseline, developed either moderately or highly stenotic plaques (p < 0.05). Finally, feasibility of the technique was demonstrated in normal and atherosclerotic human subjects. Atherosclerotic carotid arteries with low stenosis had lower WSSPS as compared to control subjects (p < 0.01), while in one subject with high stenosis, elevated WSS was found on an arterial segment, which coincided with plaque rupture site, as determined through histological examination.

Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images.

Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.

Feasibility of longitudinal monitoring of atherosclerosis with pulse wave imaging in a swine model.

Objective.Atherosclerosis is a vascular disease characterized by compositional and mechanical changes in the arterial walls that lead to a plaque buildup. Depending on its geometry and composition, a plaque can ruptured and cause stroke, ischemia or infarction. Pulse wave imaging (PWI) is an ultrasound-based technique developed to locally quantify the stiffness of arteries. This technique has shown promising results when applied to patients. The objective of this study is to assess the capability of PWI to monitor the disease progression in a swine model that mimics human pathology.Approach.The left common carotid of three hypercholesterolemic Wisconsin miniature swines, fed an atherogenic diet, was ligated. Ligated and contralateral carotids were imaged once a month over 9 months, at a high-frame-rate, with a 5-plane wave compounding sequence and a 5 MHz linear array. Each acquisition was repeated after probe repositioning to evaluate the reproducibility. Wall displacements were estimated from the beamformed RF-data and were arranged as spatiotemporal maps depicting the wave propagation. The pulse wave velocity (PWV) estimated by tracking the 50% upstroke of the wave was converted in compliance using the Bramwell-Hill model. At the termination of the experiment, the carotids were extracted for histology analysis.Main results.PWI was able to monitor the evolution of compliance in both carotids of the animals. Reproducibility was demonstrated as the difference of PWV between cardiac cycles was similar to the difference between acquisitions (9.04% versus 9.91%). The plaque components were similar to the ones usually observed in patients. Each animal presented a unique pattern of compliance progression, which was confirmed by the plaque composition observed histologically.Significance.This study provides important insights on the vascular wall stiffness progression in an atherosclerotic swine model. It therefore paves the way for a thorough longitudinal study that examines the role of stiffness in both the plaque formation and plaque progression.

Pulse Wave Imaging Coupled With Vector Flow Mapping: A Phantom, Simulation, and In Vivo Study.

Pulse wave imaging (PWI) is an ultrasound imaging modality that estimates the wall stiffness of an imaged arterial segment by tracking the pulse wave propagation. The aim of the present study is to integrate PWI with vector flow imaging, enabling simultaneous and co-localized mapping of vessel wall mechanical properties and 2-D flow patterns. Two vector flow imaging techniques were implemented using the PWI acquisition sequence: 1) multiangle vector Doppler and 2) a cross-correlation-based vector flow imaging (CC VFI) method. The two vector flow imaging techniques were evaluated in vitro using a vessel phantom with an embedded plaque, along with spatially registered fluid structure interaction (FSI) simulations with the same geometry and inlet flow as the phantom setup. The flow magnitude and vector direction obtained through simulations and phantom experiments were compared in a prestenotic and stenotic segment of the phantom and at five different time frames. In most comparisons, CC VFI provided significantly lower bias or precision than the vector Doppler method ( ) indicating better performance. In addition, the proposed technique was applied to the carotid arteries of nonatherosclerotic subjects of different ages to investigate the relationship between PWI-derived compliance of the arterial wall and flow velocity in vivo. Spearman's rank-order test revealed positive correlation between compliance and peak flow velocity magnitude ( rs = 0.90 and ), while significantly lower compliance ( ) and lower peak flow velocity magnitude ( ) were determined in older (54-73 y.o.) compared with young (24-32 y.o.) subjects. Finally, initial feasibility was shown in an atherosclerotic common carotid artery in vivo. The proposed imaging modality successfully provided information on blood flow patterns and arterial wall stiffness and is expected to provide additional insight in studying carotid artery biomechanics, as well as aid in carotid artery disease diagnosis and monitoring.

Modeling Pulse Wave Propagation Through a Stenotic Artery With Fluid Structure Interaction: A Validation Study Using Ultrasound Pulse Wave Imaging.

Pulse wave imaging (PWI) is an ultrasound-based method that allows spatiotemporal mapping of the arterial pulse wave propagation, from which the local pulse wave velocity (PWV) can be derived. Recent reports indicate that PWI can help the assessment of atherosclerotic plaque composition and mechanical properties. However, the effect of the atherosclerotic plaque's geometry and mechanics on the arterial wall distension and local PWV remains unclear. In this study, we investigated the accuracy of a finite element (FE) fluid-structure interaction (FSI) approach to predict the velocity of a pulse wave propagating through a stenotic artery with an asymmetrical plaque, as quantified with PWI method. Experiments were designed to compare FE-FSI modeling of the pulse wave propagation through a stenotic artery against PWI obtained with manufactured phantom arteries made of polyvinyl alcohol (PVA) material. FSI-generated spatiotemporal maps were used to estimate PWV at the plaque region and compared it to the experimental results. Velocity of the pulse wave propagation and magnitude of the wall distension were correctly predicted with the FE analysis. In addition, findings indicate that a plaque with a high degree of stenosis (>70%) attenuates the propagation of the pulse pressure wave. Results of this study support the validity of the FE-FSI methods to investigate the effect of arterial wall structural and mechanical properties on the pulse wave propagation. This modeling method can help to guide the optimization of PWI to characterize plaque properties and substantiate clinical findings.

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo.

Imaging arterial mechanical properties may improve vascular disease diagnosis. Pulse wave velocity (PWV) is a marker of arterial stiffness linked to cardio-vascular mortality. Pulse wave imaging (PWI) is a technique for imaging the pulse wave propagation at high spatial and temporal resolution. In this paper, we introduce adaptive PWI, a technique for the automated partition of heterogeneous arteries into individual segments characterized by most homogeneous pulse wave propagation, allowing for more robust PWV estimation. This technique was validated in a silicone phantom with a soft-stiff interface. The mean detection error of the interface was 4.67 ± 0.73 mm and 3.64 ± 0.14 mm in the stiff-to-soft and soft-to-stiff pulse wave transmission direction, respectively. This technique was tested in monitoring the progression of atherosclerosis in mouse aortas in vivo ( n = 11 ). The PWV was found to already increase at the early stage of 10 weeks of high-fat diet (3.17 ± 0.67 m/sec compared to baseline 2.55 ± 0.47 m/sec, ) and further increase after 20 weeks of high-fat diet (3.76±1.20 m/sec). The number of detected segments of the imaged aortas monotonically increased with the duration of high-fat diet indicating an increase in arterial wall property inhomogeneity. The performance of adaptive PWI was also tested in aneurysmal mouse aortas in vivo. Aneurysmal boundaries were detected with a mean error of 0.68±0.44 mm. Finally, initial feasibility was shown in the carotid arteries of healthy and atherosclerotic human subjects in vivo ( n = 3 each). Consequently, adaptive PWI was successful in detecting stiffness inhomogeneity at its early onset and monitoring atherosclerosis progression in vivo.

Arterial wall mechanical inhomogeneity detection and atherosclerotic plaque characterization using high frame rate pulse wave imaging in carotid artery disease patients in vivo.

Pulse wave imaging (PWI) is a non-invasive, ultrasound-based technique, which provides information on arterial wall stiffness by estimating the pulse wave velocity (PWV) along an imaged arterial wall segment. The aims of the present study were to: (1) utilize the PWI information to automatically and optimally divide the artery into the segments with most homogeneous properties and (2) assess the feasibility of this method to provide arterial wall mechanical characterization in normal and atherosclerotic carotid arteries in vivo. A silicone phantom consisting of a soft and stiff segment along its longitudinal axis was scanned at the stiffness transition, and the PWV in each segment was estimated through static testing. The proposed algorithm detected the stiffness interface with an average error of 0.98 ± 0.49 mm and 1.04 ± 0.27 mm in the soft-to-stiff and stiff-to-soft pulse wave transmission direction, respectively. Mean PWVs estimated in the case of the soft-to-stiff pulse wave transmission direction were 2.47 [Formula: see text] 0.04 m s-1 and 3.43 [Formula: see text] 0.08 m s-1 for the soft and stiff phantom segments, respectively, while in the case of stiff-to-soft transmission direction PWVs were 2.60 [Formula: see text] 0.18 m s-1 and 3.72 [Formula: see text] 0.08 m s-1 for the soft and stiff phantom segments, respectively, which were in good agreement with the PWVs obtained through static testing (soft segment: 2.41 m s-1, stiff segment: 3.52 m s-1). Furthermore, the carotid arteries of N = 9 young subjects (22-32 y.o.) and N = 9 elderly subjects (60-73 y.o.) with no prior history of carotid artery disease were scanned, in vivo, as well as the atherosclerotic carotid arteries of N = 12 (59-85 y.o.) carotid artery disease patients. One-way ANOVA with Holm-Sidak correction showed that the number of most homogeneous segments in which the artery was divided was significantly higher in the case of carotid artery disease patients compared to young (3.25 [Formula: see text] 0.86 segments versus 1.00 [Formula: see text] 0.00 segments, p -value < 0.0001) and elderly non-atherosclerotic subjects (3.25 [Formula: see text] 0.86 segments versus 1.44 [Formula: see text] 0.51 segments p -value < 0.0001), indicating increased wall inhomogeneity in atherosclerotic arteries. The compliance provided by the proposed algorithm was significantly higher in non-calcified/high-lipid plaques as compared with calcified plaques (3.35 [Formula: see text] 2.45 *[Formula: see text] versus 0.22 [Formula: see text] 0.18 * [Formula: see text], p -value < 0.01) and the compliance estimated in elderly subjects (3.35 [Formula: see text] 2.45 * [Formula: see text] versus 0.79 [Formula: see text] 0.30 * [Formula: see text], p -value < 0.01). Moreover, lower compliance was estimated in cases where vulnerable plaque characteristics were present (i.e. necrotic lipid core, thrombus), compared to stable plaque components (calcification), as evaluated through plaque histological examination. The proposed algorithm was thus capable of evaluating arterial wall inhomogeneity and characterize wall mechanical properties, showing promise in vascular disease diagnosis and monitoring.

Atherosclerotic plaque mechanical characterization coupled with vector Doppler imaging in atherosclerotic carotid arteries in-vivo.

Methods used in clinical practice to diagnose and monitor atherosclerosis present limitations. Imaging the mechanical properties of the arterial wall has demonstrated the potential evaluate plaque vulnerability and assess the risk for stroke. Adaptive Pulse Wave Imaging (PWI) is a non-invasive ultrasound imaging technique, which automatically detects points of spatial mechanical inhomogeneity along the imaged artery and provides piecewise stiffness characterization. The aims of the present study are to: 1) demonstrate the initial feasibility of adaptive PWI to image the mechanical properties of an atherosclerotic plaque 2) demonstrate the feasibility to combine adaptive PWI with vector Doppler in a single imaging modality in order to simultaneously obtain information plaque mechanical properties and plaque hemodynamics. The common carotid arteries of 1 healthy subject and 2 carotid artery disease patients were scanned in vivo. One of the patients underwent carotid endarterectomy and a plaque sample was retrieved. In this patient, a higher compliance value of the stenotic segment was estimated by Adaptive PWI as compared with the adjacent arterial wall, and the healthy carotid artery. This was corroborated by histological staining of the plaque sample, which revealed the presence of a large necrotic core and a thrombus, characteristics associated with reduced stiffness. Moreover, the same sequence demonstrated the feasibility to obtain both stiffness maps and vector flow information, showing promise in atherosclerosis diagnosis and patient care.

The Promise of Mobile Technologies for the Health Care System in the Developing World: A Systematic Review.

Evolution of mobile technologies and their rapid penetration into people's daily lives, especially in the developing countries, have highlighted mobile health, or m-health, as a promising solution to improve health outcomes. Several studies have been conducted that characterize the impact of m-health solutions in resource-limited settings and assess their potential to improve health care. The aim of this review is twofold: 1) to present an overview of the background and significance of m-health and 2) to summarize and discuss the existing evidence for the effectiveness of m-health in the developing world. A systematic search in the literature was performed in Pubmed, Scopus, as well as reference lists, and a broad sample of 98 relevant articles was identified, which were then categorized into five wider m-health categories. Although statistically significant conclusions cannot be drawn since the majority of studies relied on small-scale trials and limited assessment of long-term effects, this review provides a systematic and extensive analysis of the advantages, disadvantages, and challenges of m-health in developing countries in an attempt to determine future research directions of m-health interventions.