Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

OBJECTIVE: Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. STUDY DESIGN: Case series. SETTING: Tertiary Academic Center. METHODS: Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. RESULTS: Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. CONCLUSION: Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

Restoration of the anterior compartment after robotic total knee arthroplasty significantly improves functional outcome and range of motion at 1 year.

PURPOSE: This study aims to assess the functional outcomes based on restoring the anterior compartment after total knee arthroplasty (TKA). METHODS: This retrospective study included 96 primary TKAs performed between 2021 and 2022. Functional positioning principles were applied using an image-based robotic-assisted system. The mean age was 69.2 ± 7.9 years. Knee Society Score (KSS), Kujala score, Forgotten Joint Score (FJS) and knee flexion were collected preoperatively and at 1 year. The depth difference between native and prosthetic trochlea was measured to assess anterior compartment restoration at full extension, 30°, 70° and 90° flexion. The global anterior compartment restoration combined the anterior compartment restoration and the patellar thickness restoration. RESULTS: The trochlear offset was mostly understuffed after TKA compared to the native anatomy, mainly for medial and lateral condyles at 30° and 70° of flexion. The global anterior compartment restoration was understuffed in full extension (-0.7 mm ± 2), at 30° (-4.4 mm ± 2) and 70° of flexion (-3.6 mm ± 2.5). At 90°, the global anterior compartment restoration was overstuffed (2.2 mm ± 1.8). Functional scores were not significantly influenced by the anterior compartment stuffing at 0° and 30° (n.s.). The anterior compartment overstuffing at 70° and 90° was associated with decreased KSS function score (p = 0.009) and flexion (p = 0.04). CONCLUSION: Moderate anterior understuffing was frequently observed after TKA performed with functional positioning and an image-based robotic-assisted system. This understuffing did not influence the functional outcomes. The overstuffing of the anterior compartment led to a reduction in KSS function score and flexion measurements at 1 year. LEVEL OF EVIDENCE: Level III, retrospective cohort study.

Image-based molecular representation learning for drug development: a survey.

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development.

"I Did Not Want to Make a Bigger Deal Out of It than It Was": A Mixed-Method Study on the Help-Seeking Behavior of Victims of Image-Based Sexual Harassment and Abuse.

Experiencing image-based sexual harassment and abuse (IBSHA), or the sending of unsolicited nude or sexual images and the nonconsensual taking, sharing, or threats to share nude or sexual images, may have severe consequences for victims' well-being. While seeking help may be beneficial, not every victim seeks help. Little research has been conducted on IBSHA victims' help-seeking behavior. Therefore, the present mixed-method study examined the help-seeking behavior of IBSHA victims, particularly the barriers and facilitators to seeking help. The study used data from an online survey (N = 163) and interviews (N = 6) among 12-to-25-year-old victims. The quantitative data indicated that the majority of victims disclosed the incident (72.8%), but only a third received help (34.4%). Help-seekers reported greater experienced barriers to help-seeking, when compared to non-help-seekers. The experienced barriers positively predicted victims' reported help-seeking behavior. From the qualitative data, four types of barriers were identified: (a) individual barriers (e.g., feelings of shame), (b) practical barriers (e.g., affordability of help), (c) interpersonal barriers (e.g., fear of negative reactions from one's social environment), and (d) sociocultural barriers (e.g., normalization of IBSHA). Moreover, two types of facilitators were identified: (a) individual facilitators (e.g., the impact of victimization) and (b) interpersonal facilitators (e.g., social support). This suggests victims experience multiple barriers, which altogether can influence their help-seeking behavior. As a help-seeking trajectory is complex and unique to the individual, victims may encounter multiple new hurdles along the way. This may explain why help-seekers reported more barriers. The findings and corresponding implications highlight the importance of providing education on IBSHA and help-seeking on various levels in society.

High-Throughput All-Optical Determination of Nanorod Size and Orientation.

As a single-particle characterization technique, optical microscopy has transformed our understanding of structure-function relationships of plasmonic nanoparticles, but the need for ex-situ-correlated electron microscopy to obtain structural information handicaps an otherwise exceptional high-throughput technique. Here, we present an all-optical alternative to electron microscopy to accurately and quickly extract structural information about single gold nanorods (Au NRs) using calcite-assisted localization and kinetics (CLocK) microscopy. Color CLocK images of single Au NRs allow scattering from the longitudinal and transverse plasmon modes to be imaged simultaneously, encoding spectral data in CLocK images that can then be extracted to obtain Au NR size and orientation. Moreover, through the use of convolutional neural networks, Au NR length, width, and aspect ratio can be predicted directly from color CLocK images within ∼10% of the true value measured by electron microscopy.

Systematic review and meta-analysis of cryopreserved bovine sperm assessment: harnessing imaging flow cytometry for multi-parametric analysis.

Cryopreservation of sperm is an essential technique in assisted reproduction in cattle. The objective of the study was to systematically review and synthesize the literature on bull semen quality evaluation based on the comparison of morphological and metabolic parameters of cryopreserved bovine spermatozoa such as DNA integrity, mitochondrial status, plasma membrane alterations, total motility, and morphology (% of abnormal cells). The electronic databases PubMed, Web of Sciences, Scopus, and Google Scholar were searched up to December 2023. Studies and references were included if they reported the following parameters: DNA integrity, mitochondrial status, plasma membrane alterations, total motility, and morphological aberrations (% of abnormal cells) for conventional cryopreserved bovine spermatozoa. After an electronic search, out of 1,526 original studies, only 40 were included in the meta-analysis. Standardized mean differences (SMD) with 95% confidence intervals were estimated for the chosen studies, and a meta-analysis was performed using a random effects model. The tau-squared (tau2) and inconsistency index (I2) quantified heterogeneity among different studies. The regression analysis for the evaluated parameters showed a positive correlation between mitochondrial membrane potential (MMP), total motility, and abnormal morphology and a negative correlation between DNA fragmentation index (DFI) and total motility and MMP. Moreover, subgroup analysis demonstrated similar associations for dairy and non-dairy bull breeds, albeit with lower I2 values. The presence of publication bias was confirmed by Egger's test, except for the MMP parameter. A multi-parametric analysis of morphological and metabolic parameters can address the existing limitations of cryopreserved bovine spermatozoa quality assessment. Combining imaging flow cytometry (IFC) with standardization of sperm pre-processing and optimization of the experimental protocols may help to differentiate sperm from cellular debris and cytoplasmic droplets of similar size and alleviate limitations demonstrated by conventional sperm analysis.

Assessment of Open Surgery Suturing Skill: Image-based Metrics Using Computer Vision.

OBJECTIVE: This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons. DESIGN: A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle." PARTICIPANTS AND RESULTS: Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length. CONCLUSION AND SIGNIFICANCE: These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.

A smartphone image-based method for the colorimetric characterization of historical pigments in mural paintings.

In this article, the detailed description of a smartphone-based analytical method to turn a smartphone into a colorimeter is described and applied to mural painting replicas. The method consists of a camera characterization process, to estimate CIELAB values from RGB camera responses, correcting for potential experimental interferences, like the effect of the lighting/capture geometry or software for color modification and enhancement. Next, the estimated CIELAB descriptors were used to build a chemometric Support Vector Classifier to objectively distinguish among the colors of the different pigments. The method was validated in terms of color reproduction and classification performance.•A smartphone-based method is described to capture mural painting color.•A color gamut-specific camera characterization procedure is developed and the performance of the smartphone-based colorimeter is compared with a spectrophotometer.•A classification method based on the estimated CIELAB descriptors is validated in terms of classification performance.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography.

BACKGROUND AND OBJECTIVE: Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value. METHODS: Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG. RESULTS: The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from -27.7 to 18.7, outperforming MPGEcho, which ranged from -40.1 to 18.0. CONCLUSIONS: The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

An Image-Based Augmented Reality System for Achieving Accurate Bone Resection in Total Knee Arthroplasty.

Background and objective With the steady advancement of computer-assisted surgical techniques, the importance of assessing and researching technology related to total knee arthroplasty (TKA) procedures has increased. Augmented reality (AR), a recently proposed next-generation technology, is expected to enhance the precision of orthopedic surgery by providing a more efficient and cost-effective approach. However, the accuracy of image-based AR in TKA surgery has not been established. Therefore, this study aimed to determine whether accurate bone resection can be achieved in TKA surgery using image-based AR. Methods In this study, we replaced traditional CT imaging and reconstructions for creating a bone 3D model by direct 3D scanning of the femur and tibia. The preoperative planning involved identifying anatomical landmarks and determining the surgical details. During surgery, markers were employed to create a local coordinate system for an AR-assisted surgical system using a Polaris camera. This approach helped minimize discrepancies between the 3D model and actual positioning, ensuring accurate alignment. Results The AR-assisted surgery using the image method resulted in fewer errors [average error: 0.32 mm; standard deviation (SD): 0.143] between the bone resection depth of the preoperative surgical plan and the bone model test results. Conclusions Our findings demonstrated the accuracy of bone resectioning by using image-based AR-assisted navigation for TKA surgery. Image-based AR-assisted navigation in TKA surgery is a valuable tool not only for enhancing accuracy by using smart glasses and sensors but also for improving the efficiency of the procedure. Therefore, we anticipate that image-based AR-assisted navigation in TKA surgery will gain wide acceptance in practice.

Image-based assessment of natural killer cell activity against glioblastoma stem cells.

Glioblastoma (GBM) poses a significant challenge in oncology and stands as the most aggressive form of brain cancer. A primary contributor to its relentless nature is the stem-like cancer cells, called glioblastoma stem cells (GSCs). GSCs have the capacity for self-renewal and tumorigenesis, leading to frequent GBM recurrences and complicating treatment modalities. While natural killer (NK) cells exhibit potential in targeting and eliminating stem-like cancer cells, their efficacy within the GBM microenvironment is limited due to constrained infiltration and function. To address this limitation, novel investigations focusing on boosting NK cell activity against GSCs are imperative. This study presents two streamlined image-based assays assessing NK cell migration and cytotoxicity towards GSCs. It details protocols and explores the strengths and limitations of these methods. These assays could aid in identifying novel targets to enhance NK cell activity towards GSCs, facilitating the development of NK cell-based immunotherapy for improved GBM treatment.

Improved Preservation of Mouse Intestinal Tissue Using a Formalin/Acetic Acid Fixative and Quantitative Histological Analysis Using QuPath.

The architecture and morphology of the intestinal tissue from mice or other small animals are difficult to preserve for histological and molecular analysis due to the fragile nature of this tissue. The intestinal mucosa consists of villi and crypts lined with epithelial cells. In between the epithelial folds extends the lamina propria, a loose connective tissue that contains blood and lymph vessels, fibroblasts, and immune cells. Underneath the mucosa are two layers of contractile smooth muscle and nerves. The tissue experiences significant changes during fixation, which can impair the reliability of histologic analysis. Poor-quality histologic sections are not suitable for quantitative image-based tissue analysis. This article offers a new fixative composed of neutral buffered formalin (NBF) and acetic acid, called FA. This fixative significantly improved the histology of mouse intestinal tissue compared to traditional NBF and enabled precise, reproducible histologic molecular analyses using QuPath software. Algorithmic training of QuPath allows for automated segmentation of intestinal compartments, which can be further interrogated for cellular composition and disease-related changes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Improved preservation of mouse intestinal tissue using a formalin/acetic acid fixative Support Protocol: Quantitative tissue analysis using QuPath.

OneSLAM to map them all: a generalized approach to SLAM for monocular endoscopic imaging based on tracking any point.

PURPOSE: Monocular SLAM algorithms are the key enabling technology for image-based surgical navigation systems for endoscopic procedures. Due to the visual feature scarcity and unique lighting conditions encountered in endoscopy, classical SLAM approaches perform inconsistently. Many of the recent approaches to endoscopic SLAM rely on deep learning models. They show promising results when optimized on singular domains such as arthroscopy, sinus endoscopy, colonoscopy or laparoscopy, but are limited by an inability to generalize to different domains without retraining. METHODS: To address this generality issue, we propose OneSLAM a monocular SLAM algorithm for surgical endoscopy that works out of the box for several endoscopic domains, including sinus endoscopy, colonoscopy, arthroscopy and laparoscopy. Our pipeline builds upon robust tracking any point (TAP) foundation models to reliably track sparse correspondences across multiple frames and runs local bundle adjustment to jointly optimize camera poses and a sparse 3D reconstruction of the anatomy. RESULTS: We compare the performance of our method against three strong baselines previously proposed for monocular SLAM in endoscopy and general scenes. OneSLAM presents better or comparable performance over existing approaches targeted to that specific data in all four tested domains, generalizing across domains without the need for retraining. CONCLUSION: OneSLAM benefits from the convincing performance of TAP foundation models but generalizes to endoscopic sequences of different anatomies all while demonstrating better or comparable performance over domain-specific SLAM approaches. Future research on global loop closure will investigate how to reliably detect loops in endoscopic scenes to reduce accumulated drift and enhance long-term navigation capabilities.

Recent advances in algal bloom detection and prediction technology using machine learning.

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure.

The research on acceleration-level visual servoing of manipulators is crucial yet insufficient, which restricts the potential application range of visual servoing. To address this issue, this paper proposes a quadratic programming-based acceleration-level image-based visual servoing (AIVS) scheme, which considers joint constraints. Besides, aiming to address the unknown problems in visual servoing systems, a data-driven learning algorithm is proposed to facilitate estimating structural information. Building upon this foundation, a data-driven acceleration-level image-based visual servoing (DAIVS) scheme is proposed, integrating learning and control capabilities. Subsequently, a recurrent neural network (RNN) is developed to tackle the DAIVS scheme, followed by theoretical analyses substantiating its stability. Afterwards, simulations and experiments on a Franka Emika Panda manipulator with eye-in-hand structure and comparisons among the existing methods are provided. The obtained results demonstrate the feasibility and practicality of the proposed schemes and highlight the superior learning and control ability of the proposed RNN. This method is particularly well-suited for visual servoing applications of manipulators with unknown structure.

Tailored Prompting to Improve Adherence to Image-Based Dietary Assessment: Mixed Methods Study.

Background: Accurately assessing an individual's diet is vital in the management of personal nutrition and in the study of the effect of diet on health. Despite its importance, the tools available for dietary assessment remain either too imprecise, expensive, or burdensome for clinical or research use. Image-based methods offer a potential new tool to improve the reliability and accessibility of dietary assessment. Though promising, image-based methods are sensitive to adherence, as images cannot be captured from meals that have already been consumed. Adherence to image-based methods may be improved with appropriately timed prompting via text message. Objective: This study aimed to quantitatively examine the effect of prompt timing on adherence to an image-based dietary record and qualitatively explore the participant experience of dietary assessment in order to inform the design of a novel image-based dietary assessment tool. Methods: This study used a randomized crossover design to examine the intraindividual effect of 3 prompt settings on the number of images captured in an image-based dietary record. The prompt settings were control, where no prompts were sent; standard, where prompts were sent at 7:15 AM, 11:15 AM, and 5:15 PM for every participant; and tailored, where prompt timing was tailored to habitual meal times for each participant. Participants completed a text-based dietary record at baseline to determine the timing of tailored prompts. Participants were randomized to 1 of 6 study sequences, each with a unique order of the 3 prompt settings, with each 3-day image-based dietary record separated by a washout period of at least 7 days. The qualitative component comprised semistructured interviews and questionnaires exploring the experience of dietary assessment. Results: A total of 37 people were recruited, and 30 participants (11 male, 19 female; mean age 30, SD 10.8 years), completed all image-based dietary records. The image rate increased by 0.83 images per day in the standard setting compared to control (P=.23) and increased by 1.78 images per day in the tailored setting compared to control (P≤.001). We found that 13/21 (62%) of participants preferred to use the image-based dietary record versus the text-based dietary record but reported method-specific challenges with each method, particularly the inability to record via an image after a meal had been consumed. Conclusions: Tailored prompting improves adherence to image-based dietary assessment. Future image-based dietary assessment tools should use tailored prompting and offer both image-based and written input options to improve record completeness.

S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the multi-scale normalized cross correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated.

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

Inversion recovery and saturation recovery pulmonary vein MR angiography using an image based navigator fluoro trigger and variable-density 3D cartesian sampling with spiral-like order.

Contrast enhanced pulmonary vein magnetic resonance angiography (PV CE-MRA) has value in atrial ablation pre-procedural planning. We aimed to provide high fidelity, ECG gated PV CE-MRA accelerated by variable density Cartesian sampling (VD-CASPR) with image navigator (iNAV) respiratory motion correction acquired in under 4 min. We describe its use in part during the global iodinated contrast shortage. VD-CASPR/iNAV framework was applied to ECG-gated inversion and saturation recovery gradient recalled echo PV CE-MRA in 65 patients (66 exams) using .15 mmol/kg Gadobutrol. Image quality was assessed by three physicians, and anatomical segmentation quality by two technologists. Left atrial SNR and left atrial/myocardial CNR were measured. 12 patients had CTA within 6 months of MRA. Two readers assessed PV ostial measurements versus CTA for intermodality/interobserver agreement. Inter-rater/intermodality reliability, reproducibility of ostial measurements, SNR/CNR, image, and anatomical segmentation quality was compared. The mean acquisition time was 3.58 ± 0.60 min. Of 35 PV pre-ablation datasets (34 patients), mean anatomical segmentation quality score was 3.66 ± 0.54 and 3.63 ± 0.55 as rated by technologists 1 and 2, respectively (p = 0.7113). Good/excellent anatomical segmentation quality (grade 3/4) was seen in 97% of exams. Each rated one exam as moderate quality (grade 2). 95% received a majority image quality score of good/excellent by three physicians. Ostial PV measurements correlated moderate to excellently with CTA (ICCs range 0.52-0.86). No difference in SNR was observed between IR and SR. High quality PV CE-MRA is possible in under 4 min using iNAV bolus timing/motion correction and VD-CASPR.

Insights into metastatic roadmap of head and neck cancer squamous cell carcinoma based on clinical, histopathological and molecular profiles.

The incidence of head and neck cancer (HNC), constituting approximately one in ten cancer cases worldwide, affects approximately 644,000 individuals annually. Managing this complex disease involves various treatment modalities such as systemic therapy, radiation, and surgery, particularly for patients with locally advanced disease. HNC treatment necessitates a multidisciplinary approach due to alterations in patients' genomes affecting their functionality. Predominantly, squamous cell carcinomas (SCCs), the majority of HNCs, arise from the upper aerodigestive tract epithelium. The epidemiology, staging, diagnosis, and management techniques of head and neck squamous cell carcinoma (HNSCC), encompassing clinical, image-based, histopathological and molecular profiling, have been extensively reviewed. Lymph node metastasis (LNM) is a well-known predictive factor for HNSCC that initiates metastasis and significantly impacts HNSCC prognosis. Distant metastasis (DM) in HNSCC has been correlated to aberrant expression of cancer cell-derived cytokines and growth factors triggering abnormal activation of several signaling pathways that boost cancer cell aggressiveness. Recent advances in genetic profiling, understanding tumor microenvironment, oligometastatic disease, and immunotherapy have revolutionized treatment strategies and disease control. Future research may leverage genomics and proteomics to identify biomarkers aiding individualized HNSCC treatment. Understanding the molecular basis, genetic landscape, atypical signaling pathways, and tumor microenvironment have enhanced the comprehension of HNSCC molecular etiology. This critical review sheds light on regional and distant metastases in HNSCC, presenting major clinical and laboratory features, predictive biomarkers, and available therapeutic approaches.