Suboptimal Image Research Articles

Left Ventricle Myocardial Work Correlated with Functional Capacity in Severe Rheumatic Mitral Stenosis with Preserved Left Ventricular Ejection Fraction.

Functional capacity is reduced in mitral stenosis (MS) patients. Previous studies showed a correlation between left atrial strain and functional capacity in this population. However, currently, no left ventricle (LV) echocardiographic parameters were associated with functional capacity in patients with MS. Noninvasive LV pressure-strain loop analysis is a new echocardiographic method for evaluating LV function, integrating longitudinal strain from speckle-tracking analysis and noninvasively measured blood pressure to estimate myocardial work (MW) that overcomes the preload-dependent characteristics conventional parameters by integrating afterload. This study aimed to evaluate the association between MW and functional capacity measured using exercise tests in patients with severe MS and preserved LV ejection fraction (LVEF). Adult patients with symptomatic severe rheumatic MS (mitral valve area <1.5 cm2), and preserved LVEF (>50%) and sinus rhythm who underwent echocardiography and exercise stress test in our hospital from 2019 to 2021 were included. Exclusion criteria were suboptimal image quality for myocardial deformation analysis, significant mitral regurgitation or aortic valve lesions, coronary artery disease, intracardiac shunt, and atrial fibrillation. Standard echocardiographic parameters were measured, and all MW parameters were included. Exercise treadmill testing was performed using the modified Bruce protocol. A total of 33 individuals with isolated severe rheumatic MS in sinus rhythm (age 39.8 ± 9.8 years) were included in the study. Patients with severe isolated MS showed significantly impaired LV-global longitudinal strain values compared to normal reference values. Furthermore, patients with severe MS showed significantly lower values of global work index, global constructive work, and efficiency compared to normal values and higher wasted work. Global work efficiency was significantly correlated to the duration of exercise (P = 0.025, Pearson's r = 0.389). In stable patients with isolated severe mitral stenosis, MW efficiency significantly correlated with functional capacity measured objectively through exercise testing.

A multi-task segmentation and classification network for remote ship hull inspection

In contrast to manual close-up ship hull inspection methods, Remote Inspection Technology (RIT) offers the potential to improve performance while minimising costs. Nevertheless, the effectiveness of the RIT is still subject to the experience and expertise of the inspectors. As a result, development has focused on applying automated data processing methods to RIT itself. However, in the hull inspection scenario, several challenges remain, including suboptimal hull imaging conditions, disparities in the distribution of defect categories, and significant variations in defect size. To overcome these challenges, we introduce a multi-task hull inspection network (MTHI-Net) that leverages the principles of multi-task learning to improve hull inspection accuracy. This network addresses two problems associated with RIT: image-level defect classification and pixel-level defect segmentation. Specifically, MTHI-Net exploits the advantages of spatial and channel self-attention mechanisms, a residual refinement module, and a feature fusion module to enhance the feature representation capability for defect classification and mitigate the probability of mis-segmentation. In addition, a lightweight MTHI-Net called MTHI-Net-Lite was developed. The experimental results show that the proposed networks fulfil the hull inspection task better than the baselines in both real underwater and in-dock RIT scenarios.

A modified atmospheric scattering model and degradation image clarification algorithm for haze environments

Addressing the limitation of existing image dehazing algorithms based on atmospheric scattering models, which often overlook the underlying physical causes of color distortion in hazy images, resulting in suboptimal algorithm robustness and image clarity when faced with varying levels of haze pollution, this study introduces a refined atmospheric scattering model. It also presents a haze image clarity algorithm based on this modified atmospheric scattering model by analyzing the fundamental factors contributing to image degradation and distortion caused by haze. Initially, the hazy image undergoes conversion to a new HSI color space. The hazy image is aligned more closely with human visual perception characteristics by implementing an adaptive saturation enhancement technique and adjusting contribution coefficients for the three RGB channels to the luminance component. Subsequently, the hazy image is partitioned into dense haze and non-dense haze regions based on haze distribution characteristics within the image. A global light intensity computation method is devised for the hazy image utilizing a quad-tree segmentation algorithm. Following this, calculations are performed to determine the atmospheric light value and transmittance for dense and non-dense haze regions separately. The global atmospheric light and transmittance values are then derived based on the probability density function of the dense haze region using Alpha Fusion. Finally, corrections are applied to the global atmospheric light values using an atmospheric light correction vector, while denoising the global transmittance is achieved through a fast non-local mean filtering algorithm. Experimental findings demonstrate that the proposed haze image clarity algorithm, grounded in the modified atmospheric scattering model, exhibits adaptability across various degrees and types of haze images. Comparative analysis against other prominent image dehazing algorithms reveals its efficacy in addressing severe color distortion in dense haze scenarios, achieving a more natural clarity effect, and showcasing superior performance across all numerical evaluation metrics.

Generative Adversarial Networks with Learnable Auxiliary Module for Image Synthesis

Training generative adversarial networks (GANs) for noise-to-image synthesis is a challenge task, primarily due to the instability of GANs’ training process. One of the key issues is the generator’s sensitivity to input data, which can cause sudden fluctuations in the generator’s loss value with certain inputs. This sensitivity suggests an inadequate ability to resist disturbances in the generator, causing the discriminator’s loss value to oscillate and negatively impacting the discriminator. Then, the negative feedback of discriminator is also not conducive to updating generator’s parameters, leading to suboptimal image generation quality. In response to this challenge, we present an innovative GANs model equipped with a learnable auxiliary module that processes auxiliary noise. The core objective of this module is to enhance the stability of both the generator and discriminator throughout the training process. To achieve this target, we incorporate a learnable auxiliary penalty and an augmented discriminator, designed to control the generator and reinforce the discriminator’s stability, respectively. We further apply our method to the Hinge and LSGANs loss functions, illustrating its efficacy in reducing the instability of both the generator and the discriminator. The tests we conducted on LSUN, CelebA, Market-1501 and Creative Senz3D datasets serve as proof of our method’s ability to improve the training stability and overall performance of the baseline methods.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

BackgroundA seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. MethodsForty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. ResultsWe observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = −0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = −0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = −0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error–corrected p < .05). ConclusionsSubgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Incidence of Suboptimal Fluoroscopic Outlet Imaging of the Sacrum and Pelvic Retroversion Necessary for Optimal Views.

To estimate the prevalence of suboptimal fluoroscopy of sacral outlet images due to anatomic and equipment dimensions. Pelvic retroversion is hypothesized to mitigate this issue. In silico simulations using retrospectively collected computed tomography (CT) data from human patients. Level I trauma center. Adults with OTA/AO 61 pelvic ring disruptions treated with posterior pelvic fixation between July and December 2021. C-arm tilt angles required to obtain 3 optimal fluoroscopic sacral outlet images, defined as vectors from pubic symphysis to S2 and parallel to the first and second sacral neural foramina, were calculated from sagittal CT images. A suboptimal view was defined as collision of the C-arm radiation source or image intensifier with the patient/operating table at the required tilt angle simulated using the dimensions of 5 commercial C-arm models and trigonometric calculations. Incidence of suboptimal outlet views and pelvic retroversion necessary to obtain optimal views without collision, which may be obtained by placement of a sacral bump, was determined for each view for all patients and C-arm models. CT data from 72 adults were used. Collision between patient and C-arm would occur at the optimal tilt angle for 17% of simulations and at least 1 view in 68% of patients. Greater body mass index was associated with greater odds of suboptimal imaging (standard outlet: odds ratio [OR] 0.84, confidence interval [CI] 0.79-0.89, P < 0.001; S1: OR 0.91, CI 0.87-0.97, P = 0.002; S2: OR 0.85, CI 0.80-0.91, P < 0.001). S1 anterior sacral slope was associated with suboptimal S1 outlet views (OR 1.12, Cl 1.07-1.17, P < 0.001). S2 anterior sacral slope was associated with suboptimal standard outlet (OR 1.07, Cl 1.02-1.13, P = 0.004) and S2 outlet (OR 1.16, Cl 1.09-1.23, P < 0.001) views. Retroversion of the pelvis 15-20 degrees made optimal outlet views possible without collision in 95%-99% of all simulations, respectively. Suboptimal outlet imaging of the sacrum is associated with greater body mass index and sacral slope at S1 and S2. Retroversion of the pelvis by 15-20 degrees with a bump under the distal sacrum may offer a low-tech solution to ensure optimal fluoroscopic imaging for percutaneous fixation of the posterior pelvic ring. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

Serious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types and stages, and potential errors in interpretation hinder the achievement of precise and prompt diagnoses. The rapid identification of brain tumors plays a pivotal role in ensuring patient safety. Deep learning-based systems hold promise in aiding radiologists to make diagnoses swiftly and accurately. In this study, we present an advanced deep learning approach based on the Swin Transformer. The proposed method introduces a novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along with a rescaled model. This enhancement aims to improve classification accuracy, reduce memory usage, and simplify training complexity. The Residual-based MLP (ResMLP) replaces the traditional MLP in the Swin Transformer, thereby improving accuracy, training speed, and parameter efficiency. We evaluate the Proposed-Swin model on a publicly available brain MRI dataset with four classes, using only test data. Model performance is enhanced through the application of transfer learning and data augmentation techniques for efficient and robust training. The Proposed-Swin model achieves a remarkable accuracy of 99.92%, surpassing previous research and deep learning models. This underscores the effectiveness of the Swin Transformer with HSW-MSA and ResMLP improvements in brain tumor diagnosis. This method introduces an innovative diagnostic approach using HSW-MSA and ResMLP in the Swin Transformer, offering potential support to radiologists in timely and accurate brain tumor diagnosis, ultimately improving patient outcomes and reducing risks.

"Two-Stage Rocket-Propelled" Strategy Boosting Theranostic Efficacy with Mitochondria-Specific Type I-II Photosensitizers.

Imaging-guided photodynamic therapy (PDT) holds great potential for tumor therapy. However, achieving the synergistic enhancement of the reactive oxygen species (ROS) generation efficiency and fluorescence emission of photosensitizers (PSs) remains a challenge, resulting in suboptimal image guidance and theranostic efficacy. The hypoxic tumor microenvironment also hinders the efficacy of PDT. Herein, we propose a "two-stage rocket-propelled" photosensitive system for tumor cell ablation. This system utilizes MitoS, a mitochondria-targeted PS, to ablate tumor cells. Importantly, MitoS can react with HClO to generate a more efficient PS, MitoSO, with a significantly improved fluorescence quantum yield. Both MitoS and MitoSO exhibit less O2-dependent type I ROS generation capability, inducing apoptosis and ferroptosis. In vivo PDT results confirm that this mitochondrial-specific type I-II cascade phototherapeutic strategy is a potent intervention for tumor downstaging. This study not only sheds light on the correlation between the PS structure and the ROS generation pathway but also proposes a novel and effective strategy for tumor downstaging intervention.

Ultrasound-Enhancing Agent and IV Administration in Cardiac Sonography: How Sonographers Play a Key Role in Positively Affecting Key Performance Indicators

As the health system in the United States continuously searches for ways to reduce costs and improve the health care experience, cardiac sonographers are uniquely positioned to add to this initiative. Tasked with providing high-quality imaging studies in outpatient and inpatient areas, the scope of practice for sonographers continues to evolve. To ensure quality imaging, ultrasound-enhancing agents (UEAs) are needed in patients with suboptimal images to improve delineation of left ventricular endocardial borders. This process involves intravenous (IV) access and occurs in both patients in ambulatory clinics and hospitals. Historically, health care professionals delegated to perform these duties have been nursing, radiology, and nuclear medicine staff. Because these professionals have their own job responsibilities, the potential to complete a transthoracic echocardiogram (TTE) with UEA, in a timely fashion, may be compromised. Expanding the practice of cardiac sonographers to include these duties should be supported to facilitate uninterrupted patient care, efficiency, and stakeholder satisfaction.

Crop Guidance Photography Algorithm for Mobile Terminals

The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile devices during image capture. An application for crop guidance photography was developed, which involved classifying and identifying crops from various orientations and providing guidance prompts. Three steps were executed, including increasing sample randomness, model pruning, and knowledge distillation, to improve the MobileNet model for constructing a smartphone-based orientation detection model with high accuracy and low computational requirements. Subsequently, the application was realized by utilizing the classification results for guidance prompts. The test demonstrated that this method effectively and seamlessly guided agricultural practitioners in capturing high-quality crop images, providing effective photographic guidance for farmers.

Contrast Echocardiography in Heart Failure: Update 2023.

The application of ultrasound-enhancing agents (contrast agents) has improved the accuracy and reproducibility of echocardiography. The review focuses on the currently approved and evolving indications for contrast echocardiography in patients with heart failure, specifically examining clinical studies conducted after the publication of the guidelines in 2017 and 2018. The current ASE/EACVI recommendations for contrast echocardiography are based on its accuracy and reproducibility in comparison to non-enhanced echocardiography or other imaging modalities like cardiac MRI. However, tissue characterization remains limited with contrast echocardiography. During the last few years, several studies have demonstrated the clinical impact of using contrast agents on the management of patients with heart failure. There is growing evidence on the benefit of using contrast echocardiography in critically ill patients where echocardiography without contrast agents is often suboptimal and other imaging methods are less feasible. There is no risk of worsening renal function after the administration of ultrasound-enhancing agents, and these agents can be administered even in patients with end-stage renal disease. Contrast echocardiography has become a valuable tool for first-line imaging of patients with heart failure across the spectrum of patients with chronic heart failure to critically ill patients.

Sensitivity Testing of Stereophotoclinometry for the OSIRIS-REx Mission. II. Effective Observation Geometry for Digital Terrain Modeling

The OSIRIS-REx mission used stereophotoclinometry (SPC) to generate digital terrain models (DTMs) of its target asteroid, Bennu. Here we present a suite of preflight tests conducted to identify the observing geometry and number of images needed to create DTMs that would enable successful navigation around and to the surface of the asteroid. We demonstrate that high-quality DTMs can be generated by using only five images: four that are focused on topography, in which the spacecraft’s viewing geometry brackets the target (north, south, east, and west), and a fifth that measures the target’s albedo variation, taken from near local noon. We further show that the first 10 iterations of the SPC process can meaningfully improve DTM quality, including in the case of a suboptimal input image set, whereas after 10 iterations the DTM quality approaches an asymptotic maximum. We distill our findings into recommendations for observation planning that can be applied by other missions intending to use SPC to model the shape and terrain of their target.

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring.

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Multifunctional Ablative Gastrointestinal Imaging Capsule (MAGIC) for Esophagus Surveillance and Interventions.

Objective and Impact Statement: A clinically viable technology for comprehensive esophagus surveillance and potential treatment is lacking. Here, we report a novel multifunctional ablative gastrointestinal imaging capsule (MAGIC) technology platform to address this clinical need. The MAGIC technology could also facilitate the clinical translation and adoption of the tethered capsule endomicroscopy (TCE) technology. Introduction: Recently developed optical coherence tomography (OCT) TCE technologies have shown a promising potential for surveillance of Barrett's esophagus and esophageal cancer in awake patients without the need for sedation. However, it remains challenging with the current TCE technology for detecting early lesions and clinical adoption due to its suboptimal resolution, imaging contrast, and lack of visual guidance during imaging. Methods: Our technology reported here integrates dual-wavelength OCT imaging (operating at 800 and 1300 nm), an ultracompact endoscope camera, and an ablation laser, aiming to enable comprehensive surveillance, guidance, and potential ablative treatment of the esophagus. Results: The MAGIC has been successfully developed with its multimodality imaging and ablation capabilities demonstrated by imaging swine esophagus ex vivo and in vivo. The 800-nm OCT imaging offers exceptional resolution and contrast for the superficial layers, well suited for detecting subtle changes associated with early neoplasia. The 1300-nm OCT imaging provides deeper penetration, essential for assessing lesion invasion. The built-in miniature camera affords a conventional endoscopic view for assisting capsule deployment and laser ablation. Conclusion: By offering complementary and clinically viable functions in a single device, the reported technology represents an effective solution for endoscopic screening, diagnosis, and potential ablation treatment of the esophagus of a patient in an office setting.

Deep learning based retinal vessel segmentation and hypertensive retinopathy quantification using heterogeneous features cross-attention neural network.

Retinal vessels play a pivotal role as biomarkers in the detection of retinal diseases, including hypertensive retinopathy. The manual identification of these retinal vessels is both resource-intensive and time-consuming. The fidelity of vessel segmentation in automated methods directly depends on the fundus images' quality. In instances of sub-optimal image quality, applying deep learning-based methodologies emerges as a more effective approach for precise segmentation. We propose a heterogeneous neural network combining the benefit of local semantic information extraction of convolutional neural network and long-range spatial features mining of transformer network structures. Such cross-attention network structure boosts the model's ability to tackle vessel structures in the retinal images. Experiments on four publicly available datasets demonstrate our model's superior performance on vessel segmentation and the big potential of hypertensive retinopathy quantification.

Investigating Haptic Feedback in Vision-Deficient Millirobot Telemanipulation.

The evolution of magnetically actuated millirobots gives rise to unique teleoperation challenges due to their non-traditional kinematic and dynamic architectures, as well as their frequent use of suboptimal imaging modalities. Recent investigations into haptic interfaces for millirobots have shown promise but lack the clinically motivated task scenarios necessary to justify future development. In this work, we investigate the utility of haptic feedback on bilateral teleoperation of a magnetically actuated millirobot in visually deficient conditions. We conducted an N=23 user study in an aneurysm coiling inspired procedure, which required participants to navigate the robot through a maze in near total darkness to manipulate beads to a target under simulated fluoroscopy. We hypothesized that users will be better able to complete the telemanipulation task with haptic feedback while reducing excess forces on their surroundings compared to the no feedback conditions. Our results showed an over 40% improvement in participants' bead scoring, a nearly 10% reduction in mean force, and 13% reduction in maximum force with haptic feedback, as well as significant improvements in other metrics. Results highlight that benefits of haptic feedback are retained when haptic feedback is removed. These findings suggest that haptic feedback has the potential to significantly improve millirobot telemanipulation and control in traditionally vision deficient tasks.

Clinical validation of enhanced CT imaging for distal radius fractures through conditional Generative Adversarial Networks (cGAN).

Distal radius fractures (DRFs) account for approximately 18% of fractures in patients 65 years and older. While plain radiographs are standard, the value of high-resolution computed tomography (CT) for detailed imaging crucial for diagnosis, prognosis, and intervention planning, and increasingly recognized. High-definition 3D reconstructions from CT scans are vital for applications like 3D printing in orthopedics and for the utility of mobile C-arm CT in orthopedic diagnostics. However, concerns over radiation exposure and suboptimal image resolution from some devices necessitate the exploration of advanced computational techniques for refining CT imaging without compromising safety. Therefore, this study aims to utilize conditional Generative Adversarial Networks (cGAN) to improve the resolution of 3 mm CT images (CT enhancement). Following institutional review board approval, 3 mm-1 mm paired CT data from 11 patients with DRFs were collected. cGAN was used to improve the resolution of 3 mm CT images to match that of 1 mm images (CT enhancement). Two distinct methods were employed for training and generating CT images. In Method 1, a 3 mm CT raw image was used as input with the aim of generating a 1 mm CT raw image. Method 2 was designed to emphasize the difference value between the 3 mm and 1 mm images; using a 3 mm CT raw image as input, it produced the difference in image values between the 3 mm and 1 mm CT scans. Both quantitative metrics, such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM), and qualitative assessments by two orthopedic surgeons were used to evaluate image quality by assessing the grade (1~4, which low number means high quality of resolution). Quantitative evaluations showed that our proposed techniques, particularly emphasizing the difference value in Method 2, consistently outperformed traditional approaches in achieving higher image resolution. In qualitative evaluation by two clinicians, images from method 2 showed better quality of images (grade: method 1, 2.7; method 2, 2.2). And more choice was found in method 2 for similar image with 1 mm slice image (15 vs 7, p = 201). In our study utilizing cGAN for enhancing CT imaging resolution, the authors found that the method, which focuses on the difference value between 3 mm and 1 mm images (Method 2), consistently outperformed.

Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach.

One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo signals. Obtaining non-aberrated ground truths in real-world scenarios can be extremely challenging, if not impossible. This challenge hinders the performance of deep learning-based techniques due to the domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require ground truth to correct the phase aberration problem and, as such, can be directly trained on real data. We train a network wherein both the input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training such a network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Finally, we publicly release our dataset, comprising over 180,000 aberrated single plane-wave images (RF data), wherein phase aberrations are modeled as near-field phase screens. Although not utilized in the proposed method, each aberrated image is paired with its corresponding aberration profile and the non-aberrated version, aiming to mitigate the data scarcity problem in developing deep learning-based techniques for phase aberration correction. Source code and trained model are also available along with the dataset at http://code.sonography.ai/main-aaa.

Added Value of Computed Tomography Angiography Prior to Bronchial Artery Embolization for Hemoptysis: A Retrospective Two-Center Study

Objectives: The aim of this retrospective study was to evaluate the added value of pre-procedural computed tomography angiography (CTA) prior to bronchial artery embolization for patients presenting with hemoptysis.&#x0D; Materials and Methods: In this retrospective study, we evaluated patients admitted for hemoptysis from 2010 to 2021 and treated by catheter-directed embolization. After establishing quality criteria for pre-procedural computed tomography (CT), patients were divided into two groups depending on their pre-procedural imaging assessment: Quality CT-angiography (QCTA group) and suboptimal pre-procedural imaging (suboptimal CTA, unenhanced or no CT evaluation; control group). Groups were compared based on radiological success, procedure-related complications, and clinical success, including cessation of hemoptysis, recurrence rates, and overall mortality.&#x0D; Results: We included 31 patients in the QCTA group, and 35 in the control group. Clinical success was n = 24/31 (77.4%) in the QCTA group and n = 27/35 (77.1%) in the control group (p = 0.979). Technical success was n = 37/42 (88.1%) in the QCTA group and n = 39/42 (92.86%) in the control group (p = 0.820). Overall recurrence was 10.6%. Minor complications occurred in 27.3%, and one major complication was reported.&#x0D; The concordance between the affected bleeding lung and the identification of pathological arteries during angiography was better in the QCTA group (p = 0.045).&#x0D; The average number of culprit arteries (bronchial, non-bronchial systemic arteries [NBSA] or pulmonary) in the QCTA group was not significantly higher than that in the control group.&#x0D; Conclusions: Preprocedural QCTA better identifies the affected bleeding lung and bleeding vessels compared to direct angiography. No difference in clinical success, complications, recurrence rates, or mortality was observed.

Influence of ultrasound transmit frequency on measurement of global longitudinal strain on 2D speckle tracking echocardiography

The reproducibility of longitudinal strain measured by 2D speckle tracking echocardiography (2DSTE) may be affected by ultrasound settings. This study investigated the effect of transmit ultrasound frequency on global longitudinal strain (GLS) by 2DSTE. Apical, 2- and 4-chamber, and long-axis views were obtained in consecutive 162 patients using Philips ultrasound devices. Three different frequency presets were used sequentially: high resolution (HRES, 1.9 to 2.1 MHz), general (HGEN, 1.6 to 1.8 MHz), and penetration mode (HPEN, 1.3 to 1.6 MHz). GLS values were determined for each preset using the Philips Q-station software, resulting in GLS-HRES, GLS-HGEN, and GLS-HPEN. Among the 151 patients with successfully measured GLS, a significant difference in GLS was observed among the three presets (p < 0.0001). GLS-HRES (− 17.9 ± 4.4%) showed a slightly smaller magnitude compared to GLS-HGEN (− 18.8 ± 4.5%, p < 0.0001) and GLS-HPEN (− 18.8 ± 4.5%, p < 0.0001), with absolute differences of 1.1 ± 1.0% and 1.1 ± 1.2%, respectively. This variation in GLS with frequency was evident in patients with both optimal (n = 104) and suboptimal (n = 47) image quality and remained consistent regardless of ultrasound devices, ischemic etiology, or ejection fraction. In conclusion, ultrasound frequency had only a modest effect on GLS measurements. GLS may be reliably assessed in most cases regardless of the ultrasound frequency used.