Blood Vessels In Images Research Articles

DynaCT biliary reconstruction via a 3D C-arm cholangiography system: clinical application in hepatolithiasis.

Dyna computed tomography (DynaCT) is an innovative clinical imaging tool used to obtain three-dimensional (3D) images of biliary structures via the Artis Zee DSA system (SIEMENS Company, Germany). DynaCT is a type of 3D cone beam computed tomography (CBCT) reconstruction produced from a two-dimensional (2D) cholangiography system by rotating the C-arm without moving the patient. The aim of this study was to evaluate the technical approach and application value of DynaCT to diagnosis hepatolithiasis and biliary stenosis. This was a retrospective single-centre series of 37 hepatolithiasis patients with tubes receiving one-step percutaneous transhepatic cholangioscopic lithotripsy (one-step PTCSL) between October 2021 and October 2022: twenty-one patients were guided by CT (CT group) and sixteen by DynaCT biliary reconstruction (DynaCT group). We compared DynaCT biliary reconstruction technology with computed tomography (CT) in the application of bile ducts. DynaCT biliary reconstruction was successfully performed in 37 patients. Biliary stenosis, including anatomy, morphology, and size, was visualized via DynaCT. Compared with the CT group, the DynaCT group was characterized by significantly more target biliary branches with stones (92 vs. 48, P < 0.05), a higher percentage of secondary stenosis (75.76% vs. 24.24%, p < 0.05), a greater percentage of biliary infection (37.5% vs. 9.5%, P = 0.041), a shorter overall stone clearance time (26.38 ± 13.49 vs. 52.67 ± 30.10, P = 0.001), and a lower rate of reoperation for residual stones (25.00% vs. 66.67%, P = 0.012). DynaCT had a lower contrast agent (25.61 ± 5.13 vs. 42.69 ± 11.15, p < 0.05). However, DynaCT increased radiation exposure (38.12 ± 10.59 vs. 25.79 ± 4.76, p < 0.05). There were no significant differences between the two groups regarding the clearance ratio of the calculus or several postoperative complications. DynaCT for biliary reconstruction has the potential to be a powerful evaluation tool for one-step PTCSL surgery and could lead to new possibilities for hepatobiliary surgery. DynaCT was used for the first time in patients with hepatolithiasis and biliary stenosis. Compared with CT, DynaCT for biliary reconstruction results in higher-quality 3D biliary, blood vessel and liver images. On the basis of the DynaCT biliary model, one-step PTCSL has the potential to improve the stone clearance ratio and shorten the stone clearance time and reoperation ratio.

SFNet: Spatial and Frequency Domain Networks for Wide-Field OCT Angiography Retinal Vessel Segmentation.

Automatic segmentation of blood vessels in fundus images is important to assist ophthalmologists in diagnosis. However, automatic segmentation for Optical Coherence Tomography Angiography (OCTA) blood vessels has not been fully investigated due to various difficulties, such as vessel complexity. In addition, there are only a few publicly available OCTA image data sets for training and validating segmentation algorithms. To address these issues, we constructed a wild-field retinal OCTA segmentation data set, the Retinal Vessels Images in OCTA (REVIO) dataset. Second, we propose a new retinal vessel segmentation network based on spatial and frequency domain networks (SFNet). The proposed model are tested on three benchmark data sets including REVIO, ROSE and OCTA-500. The experimental results show superior performance on segmentation tasks compared to the representative methods.

From Optical Fiber Communications to Bioimaging: Wavelength Division Multiplexing Technology for Simplified in vivo Large-depth NIR-IIb Fluorescence Confocal Microscopy.

Near-infrared II (NIR-II, 900-1880nm) fluorescence confocal microscopy offers high spatial resolution and extensive in vivo imaging capabilities. However, conventional confocal microscopy requires precise pinhole positioning, posing challenges due to the small size of the pinhole and invisible NIR-II fluorescence. To simplify this, a fiber optical wavelength division multiplexer (WDM) replaces dichroic mirrors and traditional pinholes for excitation and fluorescence beams, allowing NIR-IIb (1500-1700 nm) fluorescence and excitation light to be coupled into the same optical fiber. This streamlined system seamlessly integrates key components-excitation light, detector, and scanning microscopy-via optical fibers. Compared to traditional NIR-II confocal systems, the fiber optical WDM configuration offers simplicity and ease of adjustment. Notably, this simplified system successfully achieves optical sectioning imaging of mouse cerebral blood vessels up to 1000µm in depth. It can discern tiny blood vessels (diameter: 4.57µm) at 800µm depth with a signal-to-background ratio (SBR) of 5.34. Additionally, it clearly visualizes liver vessels, which are typically challenging to image, down to a depth of 300µm.

Preparation and Complex Characterisation of Stabilised Gold Nanoparticles: Biodistribution and Application for High Resolution In Vivo Imaging

The Turkevich method was optimized to prepare gold nanoparticles (AuNP) stabilized by polyethyleneglycol (PEG) for µCT. Using various independent modalities, we thoroughly characterized the optimized PEG-AuNPs. Here, we show that PEG-AuNPs are retained in the blood and provide a high contrast in the high-resolution µCT imaging of blood vessels and inner organs. The biodistribution is characterized by prolonged circulation in the blood and accumulation in the liver, spleen and skin. The accumulation of AuNP in the skin resulted in the blue discoloration of eyes and the whole skin. In vitro experiments using a leukemic monocyte THP-1 cell line model expressing high levels of NLRP3 demonstrated that the NLRP3inflammasome was not activated by PEG AuNP. Over 9 months, the mice were scanned by µCT and were in good health. Scans in mice using PEG-stabilized AuNPs in this study were sharper, with a higher contrast, when compared to a commercial contrasting agent at the same dose. The PEG-AuNPs were morphologically and chemically stable for at least two years when stored in the refrigerator.

Two‐Photon Clusteroluminescence Enabled by Through‐Space Conjugation for In Vivo Bioimaging

AbstractClusteroluminescence (CL) materials without largely conjugated structures have gained significant attention due to their unique photophysical properties and potential in bioimaging. However, low luminescence efficiency and short emission wavelength limit their development. This work designs three luminogens with CL properties (CLgens) by introducing n‐electron‐involved through‐space conjugation (TSC) into diarylmethane. Apart from single‐photon excited long‐wavelength (686 nm) and high‐efficiency (29 %) CL, two‐photon clusteroluminescence (TPCL) is successfully achieved in such small luminogens with only two isolated heteroatomic units. TSC stabilized in the aggregate state has been proven to realize efficient spatial electron delocalization similar to conventionally conjugated compounds. Encouraged by the excellent TPCL properties, two‐photon imaging of blood vessels in vivo and biocompatibility verification utilizing CLgens are also achieved. This work illustrates the essential role of TSC in promoting nonlinear optical properties of CLgens and may facilitate further design and development of the next generation of bioprobes with excellent biocompatibility.

Facilely Achieving Near-Infrared-II J-Aggregates through Molecular Bending on a Donor-Acceptor Fluorophore for High-Performance Tumor Phototheranostics.

Constructing J-aggregated organic dyes represents a promising strategy for obtaining biomedical second near-infrared (NIR-II) emissive materials, as they exhibit red-shifted spectroscopic properties upon assembly into nanoparticles (NPs) in aqueous environments. However, currently available NIR-II J-aggregates primarily rely on specific molecular backbones with intricate design strategies and are susceptible to fluorescence quenching during assembly. A facile approach for constructing bright NIR-II J-aggregates using prevalent donor-acceptor (D-A) molecules is still lacking. In this study, we present a facile method that transforms D-A molecules into J-aggregates by simply bending the molecule through introducing a methyl group, enabling high-performance NIR-II phototheranostics. The TAA-BT-CN molecule exhibits hypsochromic-shift absorption upon forming H-aggregated NPs, while the designed mTAA-BT-CN with a bent structure demonstrates a bathochromic shift of over 100 nm in absorption upon forming J-aggregated NPs, leading to much enhanced NIR-II emission beyond 1100 nm. With respect to its H-aggregated counterpart with the aggregation-caused quenching (ACQ) phenomenon, the J-aggregated mTAA-BT-CN NPs exhibit a 7-fold increase in NIR-II fluorescence owing to their aggregation-induced emission (AIE) property as well as efficient generation of heat and reactive oxygen species under 808 nm light excitation. Finally, the mTAA-BT-CN NPs are employed for whole-body blood vessel imaging using NIR-II technology as well as imaging-guided tumor phototherapies. This study will facilitate the flourishing advancement of J-aggregates based on prevalent D-A-type molecules.

One-Pot Fabrication of Kinetically Inert Ultrasmall Manganese(II) Chelate-Backboned Polymer Contrast Agents for High-Performance Magnetic Resonance Imaging.

Traditional macromolecules or nanoscale Mn2+ chelate-based magnetic resonance imaging (MRI) contrast agents (CAs) suffer from complicated and laborious synthesis processes, relatively low kinetic stability and T1 relaxivity, limiting their clinical applications. Herein, we fabricated a series of kinetically inert Mn2+ chelate-backboned polymers, P(MnL-PEG), through a facile and one-pot polymerization process. Particularly, P(MnL-PEG)-3 demonstrates a significantly higher T1 relaxivity of 23.9 Mn mM-1 s-1 at 1.5 T than that of previously reported small molecules and macromolecules or nanoscale Mn2+ chelate-based CAs. Due to its high T1 relaxivity, extended blood circulation, hepatocyte-specific uptake, and kidneys metabolism, P(MnL-PEG)-3 presents significantly enhanced contrast in blood vessel, liver, and kidneys imaging compared to clinical Gd3+-based CAs (Gd-EOB-DTPA and Gd-DOTA) at a dosage of 0.05 mmol Mn/Gd kg-1 BW, and can accurately diagnose orthotopic H22 liver tumors in vivo in animal models. We anticipate that this work will promote the development of clinically relevant MRI CAs.

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model

The incidence of diabetes has increased in recent times due to factors such as obesity and genetic predisposition. Diabetes wears out the eye vessels over time. Diabetic retinopathy (DR) is a serious disease that leads to vision problems. DR can be diagnosed by specialists who examine the fundus images of the eye at regular intervals. With 537 million diabetics in 2021, this method can be time-consuming, costly and inadequate. Artificial intelligence algorithms can provide fast and cost-effective solutions for DR diagnosis. In this study, the noise of blood vessels in fundus images was eliminated using the LinkNet-RCB7 model, and diabetic retinopathy was categorized into five classes using a machine learning-based ensemble model. Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). By using Gray Level Cooccurrence Matrix (GLCM) attributes in the classification phase, a lower resource requirement was aimed for. A Dice coefficient of 85.95% was achieved for the segmentation of blood vessels in the Stare dataset, in addition to 97.46% accuracy for binary classification and 96.10% accuracy for classifying DR into five classes in the dataset APTOS 2019.

Multi-GlaucNet: A multi-task model for optic disc segmentation, blood vessel segmentation and glaucoma detection

Glaucoma is a common and severe ocular disease that often leads to vision loss. The information on the optic disc (OD) and blood vessels in fundus images can significantly aid in glaucoma detection. In addition, the use of deep learning models for glaucoma detection is a highly effective approach. We propose a multi-task deep learning model called Multi-GlaucNet that can simultaneously segment the OD and blood vessels, thereby assisting doctors in diagnosing glaucoma. Multi-GlaucNet consists of three modules: the OD segmentation module, blood vessel segmentation module and glaucoma detection module. The OD segmentation module and blood vessel segmentation module both adopt an encoder–decoder structure to segment OD and blood vessels, respectively. The segmentation module is constructed using bottleneck layers in the encoding process and uses Pixel Shuffle and channel attention mechanisms in the decoding process. The detection module uses the ResNet50 network to perform glaucoma detection based on the features extracted from the segmentation modules. Multi-GlaucNet demonstrates outstanding performance in three areas. It achieves a Dice coefficient of 96.7% for OD segmentation on the ORIGA dataset, accuracy of 0.9798 and F1 score of 0.8562 for blood vessel segmentation on a mixed dataset. For glaucoma detection on the REFUGE dataset, it attains the highest accuracy of 0.967 and an area under the curve (AUC) of 0.950. These results validate the effectiveness of Multi-GlaucNet for glaucoma detection. The model’s ability to perform multiple tasks with high accuracy and efficiency demonstrates that it is a valuable aiding tool for glaucoma diagnosis.

Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.

Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.

Peripheral Non-Contrast MR Angiography Using FBI: Scan Time and T2 Blurring Reduction with 2D Parallel Imaging.

Non-contrast magnetic resonance angiography (NC-MRA), including fresh blood imaging (FBI), is a suitable choice for evaluating patients with peripheral artery disease (PAD). We evaluated standard FBI (sFBI) and centric ky-kz FBI (cFBI) acquisitions, using 1D and 2D parallel imaging factors (PIFs) to assess the trade-off between scan time and image quality due to blurring. The bilateral legs of four volunteers (mean age 33 years, two females) were imaged in the coronal plane using a body array coil with a posterior spine coil. Two types of sFBI and cFBI sequences with 1D PIF factor 5 in the phase encode (PE) direction (in-plane) and 2D PIF 3 (PE) × 2 (slice encode (SE)) (in-plane, through-slice) were studied. Image quality was evaluated by a radiologist, the vessel's signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured, and major vessel width was measured on the coronal maximum intensity projection (MIP) and 80-degree MIP. Results showed significant time reductions from 184 to 206 s on average when using sFBI down to 98 to 162 s when using cFBI (p = 0.003). Similar SNRs (averaging 200 to 370 across all sequences and PIF) and CNRs (averaging 190 to 360) for all techniques (p > 0.08) were found. There was no significant difference in the image quality (averaging 4.0 to 4.5; p > 0.2) or vessel width (averaging 4.1 to 4.9 mm; p > 0.1) on coronal MIP due to sequence or PIF. However, vessel width measured using 80-degree MIP demonstrated a significantly wider vessel in cFBI (5.6 to 6.8 mm) compared to sFBI (4.5 to 4.7 mm) (p = 0.022), and in 1D (4.7 to 6.8 mm) compared to 2D (4.5 to 5.6 mm) (p < 0.05) PIF. This demonstrated a trade-off in T2 blurring between 1D and 2D PIF: 1D using a PIF of 5 shortened the acquisition window, resulting in sharper arterial blood vessels in coronal images but significant blur in the 80-degree MIP. Two-dimensional PIF for cFBI provided a good balance between shorter scan time (relative to sFBI) and good sharpness in both in- and through-plane, while no benefit of 2D PIF was seen for sFBI. In conclusion, this study demonstrated the usefulness of FBI-based techniques for peripheral artery imaging and underscored the need to strike a balance between scan time and image quality in different planes through the use of 2D parallel imaging.

Does Artificial Intelligence Change How We Design User Interfaces? A Case Study from Representatives in the Medical Device Industry

Intravascular ultrasound (IVUS) is an imaging technique that allows interventional cardiologists (ICs) to capture and visually display images of blood vessels during Percutaneous Coronary Interventions (PCI). IVUS images allow ICs to: (i) evaluate the type and characteristics of lesions present, (ii) assess the appropriate intervention, (iii) determine the size of the stent or balloon to be used if they were needed, and (iv) examine if the intervention was successful. The size of the stent or balloon is determined by identifying, and then measuring, the diameters of vessel or lumen borders at the treatment locations. Identification of vessel borders is a manual time-consuming process that is rigorous to accomplish. Alternatively, a machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed. This industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.

Pulmonary Embolism Risk Assessment in Acute Isolated Distal Deep Venous Thrombosis.

The clinical risk factors and risk of developing pulmonary embolism (PE) in patients with acute isolated distal deep venous thrombosis (IDDVT) were assessed using single complete-duplex ultrasound (CDUS) imaging to reduce over-testing and over-treatment. This observational study was conducted from January 2020 to December 2022. Retrospective analysis was performed on CT pulmonary angiography (CTPA), blood coagulation markers, myocardial injury markers, blood gas analysis, and CDUS imaging of the lower extremity blood vessels of 146 patients with newly diagnosed IDDVT. Binary logistic regression was used to evaluate the relationship between these indicators and PE. After stepwise regression analysis, the predictors included in the regression model were D-dimer (DD), the sum of the thrombus length, and the maximum value of the thrombus width, with odds ratios (ORs) of 1.307 (p <0.001), 1.018 (p = 0.005), and 1.613 (p = 0.018), respectively. The combined prediction model achieved an area under the receiver operating characteristic curve (AUC) of 0.832 [95% confidence interval (CI): 0.761, 0.902]. By balancing the sensitivity and specificity of DD, combined single CDUS improves the predictive value for PE in patients with IDDVT. Key Words: Venous thrombosis, Pulmonary embolism, Ultrasonography, D-dimer, Diagnosis.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

Two-photon Clusteroluminescence Enabled by Through-Space Conjugation for In Vivo Bioimaging.

Clusteroluminescence (CL) materials without largely conjugated structures have gained significant attention due to their unique photophysical properties and potential in bioimaging. However, low luminescence efficiency and short emission wavelength limit their development. This work designs three luminogens with CL properties (CLgens) by introducing n-electron-involved through-space conjugation (TSC) into diarylmethane. Apart from single-photon excited long-wavelength (688 nm) and high-efficiency (29%) CL, two-photon clusteroluminescence (TPCL) is successfully achieved in such small luminogens with only two isolated heteroatomic units. TSC stabilized in the aggregate state has been proven to realize efficient spatial electron delocalization similar to conventionally conjugated compounds. Encouraged by the excellent TPCL properties, two-photon imaging of blood vessels in vivo and biocompatibility verification utilizing CLgens are also achieved. This work illustrates the essential role of TSC in promoting nonlinear optical properties of CLgens and may facilitate further design and development of the next generation of bioprobes with excellent biocompatibility.

Imaging arterial and venous vessels using Iron Dextran enhanced multi-echo 3D gradient echo MRI at 7T.

Iron Dextran is a widely used iron oxide compound to treat iron-deficiency anemia patients in the clinic. Similar to other iron oxide compounds such as Ferumoxytol, it can also be used off-label as an intravascular magnetic resonance imaging (MRI) contrast agent due to its strong iron-induced T2 and T2* shortening effects. In this study, we seek to evaluate the feasibility of using Iron Dextran enhanced multi-echo susceptibility weighted imaging (SWI) MRI at 7T to image arterial and venous blood vessels in the human brain. Phantom experiments were performed to measure the r2* relaxivity for Iron Dextran in blood, based on which the SWI sequence was optimized. Pre- and post-infusion MR images were acquired in human subjects from which maps of arteries and veins were extracted. The post-contrast SWI images showed enhanced susceptibility difference between blood and the surrounding tissue in both arteries and veins. Our results showed that the proposed Iron Dextran enhanced multi-echo SWI approach allowed the visualization of blood vessels with diameters down to ~100 μm, including small blood vessels supplying and draining small brain structures such as the hippocampus. We conclude that Iron Dextran can be an alternative iron-based MRI contrast agent for blood vessel imaging in the human brain.

Vessel-promoted OCT to OCTA image translation by heuristic contextual constraints

Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.

Imaging light fluence in blood vessels by combining photoacoustic fluctuation imaging and ultrasound power Doppler

Objectives. Numerous optical biomedical imaging or therapeutic modalities suffer from unknown light fluence distribution at depths. Photoacoustic (PA) imaging, which enables imaging blood vessels at the acoustic resolution, probes the product between the fluence and effective optical absorption that depends on the size or density of blood vessels. In the case of unresolved vessels, fluence and absorption can not be decoupled using PA imaging alone without the use of inverse problems. Thus, we propose combining two modalities that are sensitive to blood vessels to directly image fluence maps within vascularized areas, including in unresolved vessels. Approach. To achieve fluence imaging, the combination of photoacoustic fluctuation (PAFI) and Ultrasound Power Doppler (UPD) images is considered. After exposing a new theoretical expression of the UPD image, we establish a fluence imaging method giving quantitative fluence in blood vessels. Fluence imaging involves resolution compensation with a PSF filter that is compared to alternative simpler corrections. Main results. This method universally applies to arbitrary hematocrit and multi-scale vessel imaging. Using a spherical sparse array, we demonstrate 3D fluence imaging within blood vessels in simulation and experiments which is not possible with PAFI alone. Significance. Overall, we show that combining PAFI and UPD has the potential for real-time light dosimetry or could enhance quantitative inverse problems in PA imaging.

Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using Deep Learning.

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling.