Fundamental Imaging Research Articles

Novel Quantitative Liver Steatosis Assessment Method With Ultrasound Harmonic Imaging.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder in Western countries, with approximately 20%-30% of the MASLD patients progressing to severe stages. There is an urgent need for noninvasive, cost-effective, widely accessible, and precise biomarkers to evaluate liver steatosis. This study aims to assess and compare the diagnostic performance of a novel reference frequency method-based ultrasound attenuation coefficient estimation (ACE) in both fundamental (RFM-ACE-FI) and harmonic (RFM-ACE-HI) imaging for detecting and grading liver steatosis. An Institutional Review Board-approved prospective study was carried out between December 2018 and October 2022. A total number of 130 subjects were enrolled in the study. The correlation between RFM-ACE-HI values and magnetic resonance imaging proton density fat fraction (MRI-PDFF), as well as between RFM-ACE-FI values and MRI-PDFF were calculated. The diagnostic performance of RFM-ACE-FI and RFM-ACE-HI was evaluated using receiver operating characteristic (ROC) curve analysis, as compared to MRI-PDFF. The reproducibility of RFM-ACE-HI was assessed by interobserver agreement between two sonographers. A strong correlation was observed between RFM-ACE-HI and MRI-PDFF, with R = 0.88 (95% confidence interval [CI]: 0.83-0.92; P < .001), while the correlation between RFM-ACE-FI and MRI-PDFF was R = 0.65 (95% CI: 0.50-0.76; P < .001). The area under the ROC (AUROC) curve for RFM-ACE-HI in staging liver steatosis grades of S ≥ 1 and S ≥ 2 was 0.97 (95% CI: 0.91-0.99; P < .001) and 0.98 (95% CI: 0.93-1.00; P < .001), respectively, and 0.76 (95% CI: 0.65-0.85) and 0.80 (95% CI: 0.70-0.88) for RFM-ACE-FI, respectively. Great reproducibility was achieved for RFM-ACE-HI, with an interobserver agreement of R = 0.97 (95% CI: 0.94-0.99; P < .001). The novel RFM-ACE-HI method offered high liver steatosis diagnostic accuracy and reproducibility, which has important clinical implications for early disease intervention and treatment evaluation.

Anxiety of Patients Undergoing Magnetic Resonance Imaging (MRI): The Effectiveness of Guided Mental Imagery.

Since magnetic resonance imaging (MRI) is an extensively used and fundamental diagnostic imaging method and anxiety is one of the most important confounding factors in its performance, using guided imagery is recommended. This study aimed to assess the effectiveness of guided imagery on the anxiety of patients undergoing MRI in 2023. 88 patients were randomly assigned to intervention and control groups. The intervention group listened to the nature-based guided imagery audio file during their scan, and the control group did not receive any intervention. Data were collected using demographic information and the Spielberger Anxiety Questionnaire before and after the scan. There was no significant difference between the 2 groups before the intervention regarding demographic data and anxiety. In the intervention group, the mean anxiety decreased from 104.0 ± 14.6 to 92.4 ± 9.0, showing a significant reduction in the level of anxiety in both subscales (state and trait) and the total score (P < 0.001), compared with the control group and before the intervention. The results showed that using guided imagery could decrease anxiety levels in patients undergoing MRI. Since patients' anxiety is one of the most important nursing diagnoses, performing cognitive methods, including guided imagery, as a simple, safe, inexpensive, and effective intervention should be considered.

Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose E2HQV, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40% for some evaluation metrics.

On the imaging depth limit of photoacoustic tomography in the visible and first near-infrared windows.

It is well known that photoacoustic tomography (PAT) can circumvent the photon scattering problem in optical imaging and achieve high-contrast and high-resolution imaging at centimeter depths. However, after two decades of development, the long-standing question of the imaging depth limit of PAT in biological tissues remains unclear. Here we propose a numerical framework for evaluating the imaging depth limit of PAT in the visible and the first near-infrared windows. The established framework simulates the physical process of PAT and consists of seven modules, including tissue modelling, photon transportation, photon to ultrasound conversion, sound field propagation, signal reception, image reconstruction, and imaging depth evaluation. The framework can simulate the imaging depth limits in general tissues, such as the human breast, the human abdomen-liver tissues, and the rodent whole body and provide accurate evaluation results. The study elucidates the fundamental imaging depth limit of PAT in biological tissues and can provide useful guidance for practical experiments.

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images.

When compared to fundamental B-mode imaging, coherence-based beamforming, and harmonic imaging are independently known to reduce acoustic clutter, distinguish solid from fluid content in indeterminate breast masses, and thereby reduce unnecessary biopsies during a breast cancer diagnosis. However, a systematic investigation of independent and combined coherence beamforming and harmonic imaging approaches is necessary for the clinical deployment of the most optimal approach. Therefore, we compare the performance of fundamental and harmonic images created with short-lag spatial coherence (SLSC), M-weighted SLSC (M-SLSC), SLSC combined with robust principal component analysis with no M-weighting (r-SLSC), and r-SLSC with M-weighting (R-SLSC), relative to traditional fundamental and harmonic B-mode images, when distinguishing solid from fluid breast masses. Raw channel data acquired from 40 total breast masses (28 solid, 7 fluid, 5 mixed) were beamformed and analyzed. The contrast of fluid masses was better with fundamental rather than harmonic coherence imaging, due to the lower spatial coherence within the fluid masses in the fundamental coherence images. Relative to SLSC imaging, M-SLSC, r-SLSC, and R-SLSC imaging provided similar contrast across multiple masses (with the exception of clinically challenging complicated cysts) and minimized the range of generalized contrast-to-noise ratios (gCNRs) of fluid masses, yet required additional computational resources. Among the eight coherence imaging modes compared, fundamental SLSC imaging best identified fluid versus solid breast mass contents, outperforming fundamental and harmonic B-mode imaging. With fundamental SLSC images, the specificity and sensitivity to identify fluid masses using the reader-independent metrics of contrast difference, mean lag one coherence (LOC), and gCNR were 0.86 and 1, 1 and 0.89, and 1 and 1, respectively. Results demonstrate that fundamental SLSC imaging and gCNR (or LOC if no coherence image or background region of interest is introduced) have the greatest potential to impact clinical decisions and improve the diagnostic certainty of breast mass contents. These observations are additionally anticipated to extend to masses in other organs.

Название «Преступление и Наказание» как ключ к целостному анализу романа Ф.М. Достоевского в школе. Статья 1. Преступление

The article is intended for teachers of literature and can help organize a conversation about the novel Crime and Punishment. It offers a variant of a modern reading of one of the most significant works of the school curriculum and summarizes the views of leading Dostoevsky’s scholars on the key problems of the novel, offering topical interpretations of its fundamental images and collisions. A new interpretation of Razumikhin’s image as a true “Russian personality” and the embodiment of the “brotherhood” that will save Raskolnikov is proposed here. The analysis of the multidimensional semantics of the concepts of “crime” and “punishment” included in the title allows the teacher to present a holistic analysis of the novel, organize a conversation about the key images and problems raised in it, help students grasp the depth and significance of the issues that can be “dug out” of it. Answering the questions proposed in the article and completing assignments, the students come to the conclusion that the true crime in the novel is represented by the idea, and the murder of an old woman is the result of its influence. Such concepts as “false” and “true” motives of crime, universal and actual concept of “algorithm of crime” are introduced into the methodology of school analysis of the novel, “dialectics” of a “ideologic crime” is shown. The article proposes a system of questions and tasks that can contribute to a deeper understanding of the great novel by students.

Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.

Meningiomas are the most common primary central nervous system tumors, and magnetic resonance imaging (MRI), especially contrast-enhanced T1 weighted image (CE T1WI), is used as a fundamental imaging modality for the detection and analysis of the tumors. In this study, we propose an automated deep-learning model for meningioma detection using the dural tail sign. The dataset included 123 patients with 3,824 dural tail signs on sagittal CE T1WI. The dataset was divided into training and test datasets based on specific time point, and 78 and 45 patients were comprised for the training and test dataset, respectively. To compensate for the small sample size of the training dataset, 39 additional patients with 69 dural tail signs from the open dataset were appended to the training dataset. A You Only Look Once (YOLO) v4 network was trained with sagittal CE T1WI to detect dural tail signs. The normal group dataset, comprised of 51 patients with no abnormal finding on MRI, was employed to evaluate the specificity of the trained model. The sensitivity and false positive average were 82.22% and 29.73, respectively, in the test dataset. The specificity and false positive average were 17.65% and 3.16, respectively, in the normal dataset. Most of the false-positive cases in the test dataset were enhancing vessels, misinterpreted as dural thickening. The proposed model demonstrates an automated detection system for the dural tail sign to identify meningioma in general screening MRI. Our model can facilitate and alleviate radiologists' reading process by notifying the possibility of incidental dural mass based on dural tail sign detection.

Absolute and Relative Washout Rates Associated With Parathyroid Adenoma.

Background Parathyroid adenoma is a benign parathyroid gland tumor that causes excessive parathyroid hormone production, leading to primary hyperparathyroidism. High serum calcium levels characterize it. Accurate diagnosis and localization of adenomas are crucial for effective surgical management. Computed tomography is a fundamental imaging technique used to identify and characterize parathyroid adenomas. This study aimed to provide a comprehensive overview of the absolute and relative contrast washout rates of parathyroid adenoma and the thyroid gland, and compare enhancement patterns to establish the absolute and relative washout rates of parathyroid adenoma. Materials & methods This retrospective study analyzes the CT findings of 33 patients with histopathologically proven parathyroid adenomas. All patients with 4D CT scans have been included with no exclusion criteria. The mean attenuation was measured in Hounsfield units for the parathyroid adenoma and thyroid gland in the non-enhancing, arterial, venous, and delayed phases, depending on the region of interest. All statistical analyses were performed using SPSS (IBM Corp., Armonk, NY, USA). Student's t-test was used to evaluate the differences in measurements between the parathyroid adenoma and thyroid tissue. One-way ANOVA was used to evaluate the difference in calculations between the parathyroid adenoma and thyroid tissue. P-values <0.001 were considered statistically significant. Results The most common location of parathyroid adenomas is inferior to the thyroid gland. The average pre-contrast attenuation of the parathyroid adenoma is 61.8 ± 15.5 HU compared to 105.5 ± 15.2 HU of the thyroid gland. The arterial attenuation of the parathyroid adenoma is 170.3 ± 40.7 HU, relatively comparable to the thyroid gland arterial attenuation, which is 188.0 ± 9.6 HU. The venous and delayed-phase attenuation of the parathyroid adenoma were 146.8 ± 37.5 and 96.8 ± 26.7 HU, respectively,and 178.8 ± 20.2 HU and 149.3 ± 15.2 HU for the thyroid gland, respectively. The calculated absolute and relative arterial washout rates for the parathyroid adenoma were 69.4 ± 13.4% and 43.2 ± 8.0%, respectively, as compared to 46.4 ± 9.9% and 20.6 ± 6.7% for the thyroid gland. The calculated absolute and relative venous washout rates for the parathyroid adenoma were 58.0 ± 21.4% and 33.0 ± 13.7%, respectively, as compared to 37.2 ± 17.2% and 15.9 ± 9.6% for the thyroid gland. Conclusions Parathyroid adenoma demonstrated a significantly higher washout rate than the thyroid gland tissue. Absolute arterial washout ≥69% and relative arterial washout ≥43% indicate parathyroid adenoma. Moreover, absolute venous washout ≥58% and relative venous washout ≥33% can be considered diagnostic factors for parathyroid adenoma. Further, pre-contrast attenuation of <60 Hounsfield units has a substantial predictive value for parathyroid adenoma in addition to the described washout rate. Increased awareness of the washout rate can increase the success rate of four-dimensional computed tomography interpretation.

Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way. We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM. Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method. The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.

Problems of institutionalization of higher professional education in sports ballroom dancing in Ukraine

The purpose of the study is to identify and systematize the features of modern sports ballroom dance, which should be the basis for the development of higher professional education in this field and its further specialization. The following tasks have been set: to determine the specifics of sports ballroom dance as a type of choreography, sport, and socio-cultural phenomenon.&#x0D; The methodology is based on a system-synergistic approach. The article is an analysis of the author’s own many years’ experience — sports, coaching, organization and teaching — by generalizing and systematizing observations.&#x0D; The results. New circumstances force further institutionalization of sports ballroom dancing, namely the definition and consolidation of social norms, rules, statuses and roles in this area, bringing them into a functioning system capable of self-development and self-reproduction. Sports ballroom dance has not lost its relevance even in the regions of Ukraine most affected by the armed aggression, proving once again its right to develop in the national cultural space. Its mass appeal, its reliance on fundamental ideas and images of life, and its potentially wide target audience of students will contribute to the resumption of competitions and the growing need for higher professional education. The profession of dance teachers is a promising area of work for universities. At the same time, the training of specialists in this field requires further specialization and institutionalization. It should be accepted as a fact that the career of ballroom dancing specialists will be in club and competition activities, and the educational process should be organized accordingly.&#x0D; The scientific novelty of the article is to clarify the peculiarities of the concept of sports ballroom dance in the system of choreographic art, its socio-cultural role, as well as to analyze dance competitions as the main sphere of their modern existence.&#x0D; The practical significance. Some provisions of the article can be used in education and special courses in ballroom choreography, theory and methods of dance sports and ballroom dance practices.

Quantification of Hepatocellular Carcinoma Vascular Dynamics With Contrast-Enhanced Ultrasound for LI-RADS Implementation.

The aim of this study is to describe a comprehensive contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to implement CEUS LI-RADS (Liver Imaging Reporting and Data System) in a quantifiable manner. The methods that are validated with a prospective single-center study aim to simplify CEUS LI-RADS evaluation, remove observer bias, and potentially improve the sensitivity of CEUS LI-RADS. This prospective single-center study enrolled patients with hepatocellular carcinoma (April 2021-June 2022; N = 31; mean age ± SD, 67 ± 6 years; 24 men/7 women). For each patient, at least 2 CEUS loops spanning over 5 minutes were collected for different lesion scan planes using an articulated arm to hold the transducer. Automatic respiratory gating and motion compensation algorithms removed errors due to breathing motion. The long axis of the lesion was measured in the contrast and fundamental images to capture nodule size. Parametric processing of time-intensity curve analysis on linearized data provided quantifiable information of the wash-in and washout dynamics via rise time ( RT ) and degree of washout ( DW ) parameters extracted from the time-intensity curve, respectively. A Welch t test was performed between lesion and parenchyma RT for each lesion to confirm statistically significant differences. P values for bootstrapped 95% confidence intervals of the relative degree of washout ( rDW ), ratio of DW between the lesion and surrounding parenchyma, were computed to quantify lesion washout. Coefficient of variation (COV) of RT , DW , and rDW was calculated for each patient between injections for both the lesion and surrounding parenchyma to gauge reproducibility of these metrics. Spearman rank correlation tests were performed among size, RT , DW , and rDW values to evaluate statistical dependence between the variables. The mean ± SD lesion diameter was 23 ± 8 mm. The RT for all lesions, capturing arterial phase hyperenhancement, was shorter than that of surrounding liver parenchyma ( P < 0.05). All lesions also demonstrated significant ( P < 0.05) but variable levels of washout at both 2-minute and 5-minute time points, quantified in rDW . The COV of RT for the lesion and surrounding parenchyma were both 11%, and the COV of DW and rDW at 2 and 5 minutes ranged from 22% to 31%. Statistically significant relationships between lesion and parenchyma RT and between lesion RT and lesion DW at the 2- and 5-minute time points were found ( P < 0.05). The imaging protocol and analysis method presented provide robust, quantitative metrics that describe the dynamic vascular patterns of LI-RADS 5 lesions classified as hepatocellular carcinomas. The RT of the bolus transit quantifies the arterial phase hyperenhancement, and the DW and rDW parameters quantify the washout from linearized CEUS intensity data. This unique methodology is able to implement the CEUS-LIRADS scheme in a quantifiable manner for the first time and remove its existing issues of currently being qualitative and suffering from subjective evaluations.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool.

Mass spectrometry imaging (MSI) is an analytical technique capable of measuring and visualizing the spatial distribution of thousands of ions across a sample. Measured ions can be putatively identified and annotated by comparing their mass-to-charge ratio (m/z) to a database of known compounds. For high-resolution, accurate mass (HRAM) imaging data sets, this is commonly performed by the annotation platform METASPACE. Annotations are reported with a metabolite-signal-match (MSM) score as a measure of the annotation's confidence level. However, the MSM scores reported by METASPACE often do not reflect a reasonable confidence level of an annotation and are not assigned consistently. The metabolite annotation confidence score (MACS) is an alternative scoring system based on fundamental mass spectrometry imaging metrics (mass measurement accuracy, spectral accuracy, and spatial distribution) to generate values that reflect the confidence of a specific annotation in HRAM-MSI data sets. Herein, the MACS system is characterized and compared to MSM scores from ions annotated by METASPACE.

Hypertrophic cardiomyopathy: a modern view of the problem

Cardiomyopathy is considered one of the main causes of heart failure and sudden cardiac death, at least in young people. Approximately 50% of patients who die suddenly in childhood or adolescence or undergo heart transplantation suffer from this condition. The purpose of this literature review is to study and highlight the issues of etiology, pathogenesis, clinical features, diagnosis and treatment of hypertrophic cardiomyopathy from the point of view of modern ideas. The search and analysis of domestic and foreign literature materials using the PubMed and eLibrary databases was carried out. Of particular interest is the etiology of primary congenital cardiomyopathies, in respect of which research continues. As a result of the implementation of genetic factors, multiple structural and functional changes in the myocardium develop, which lead to changes in hemodynamics. Cardiomyopathy is a clinically heterogeneous disease, and one of the factors that determine the clinical phenotype is the genotype. In addition to standard laboratory testing, patients with suspected hypertrophic cardiomyopathy are advised to undergo medical genetic counseling to identify the causative mutation, and often to obtain prognostic information. The fundamental imaging method is echocardiography, but the role of magnetic resonance imaging in the diagnosis of the disease is also considered. Patients with symptomatic obstructive hypertrophic cardiomyopathy are usually recommended first-line pharmacotherapy with -blockers or non-dihydropyridine calcium channel blockers. Currently, research on new drugs for the treatment of hypertrophic cardiomyopathy inhibitors of cardiac myosin is ongoing. Surgical methods of treatment are developing progressively, however, methods of conservative treatment require further active research of drugs that have not been used before.

Rank-One Prior: Real-Time Scene Recovery.

Scene recovery is a fundamental imaging task with several practical applications, including video surveillance and autonomous vehicles, etc. In this article, we provide a new real-time scene recovery framework to restore degraded images under different weather/imaging conditions, such as underwater, sand dust and haze. A degraded image can actually be seen as a superimposition of a clear image with the same color imaging environment (underwater, sand or haze, etc.). Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for real-time recovery. For general cases, we develop ROP + to further improve the recovery performance. Comprehensive experiments of the scene recovery illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

The Enhanced Resolution Imager and Spectrograph for the VLT

The Enhanced Resolution Imager and Spectrograph (ERIS) is an instrument that both extends and enhances the fundamental diffraction limited imaging and spectroscopy capability for the VLT. It replaces two instruments that were being maintained beyond their operational lifetimes, combines their functionality on a single focus, provides a new wavefront sensing module for natural and laser guide stars that makes use of the Adaptive Optics Facility, and considerably improves on their performance. The observational modes ERIS provides are integral field spectroscopy at 1–2.5 μm, imaging at 1–5 μm with several options for high-contrast imaging, and long-slit spectroscopy at 3–4 μm. The instrument is installed at the Cassegrain focus of UT4 at the VLT and, following its commissioning during 2022, has been made available to the community.

Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI: A Literature Review.

Brain and cardiac MRIs are fundamental noninvasive imaging tools, which can provide important clinical information and can be performed without or with gadolinium-based contrast agents (GBCAs), depending on the clinical indication. It is currently a topic of debate whether it would be feasible to extract information such as standard gadolinium-enhanced MRI while injecting either less or no GBCAs. Artificial intelligence (AI) is a great source of innovation in medical imaging and has been explored as a method to synthesize virtual contrast MR images, potentially yielding similar diagnostic performance without the need to administer GBCAs. If possible, there would be significant benefits, including reduction of costs, acquisition time, and environmental impact with respect to conventional contrast-enhanced MRI examinations. Given its promise, we believe additional research is needed to increase the evidence to make these AI solutions feasible, reliable, and robust enough to be integrated into the clinical framework. Here, we review recent AI studies aimed at reducing or replacing gadolinium in brain and cardiac imaging while maintaining diagnostic image quality.

A method to determine the bandwidth of imaging pulses for chirp-coded excitation imaging of histotripsy bubble clouds

Histotripsy is a noninvasive focused ultrasound therapy that utilizes bubble cloud activity for tissue ablation. Real-time ultrasound imaging is used to guide histotripsy, and subharmonic imaging with chirp-coded excitation has been shown to provide effective bubble cloud contrast for targets at depth (&amp;gt;5 cm). Choice of appropriate parameters for the chirped imaging pulse are dependent on a number of factors, including the imaging probe bandwidth. In this study, an analytic method was developed and tested to design chirped imaging pulses for fundamental and subharmonic imaging. In silico studies were conducted to estimate received signal based on the frequency response of a curvilinear imaging probe (C5-2v, Verasonics, Inc., Kirkland, WA). To enable assessment of both bubbles and anatomic information, criteria were set to maximize the probe sensitivity for both fundamental and contrast-specific signals. Based on these analyses, in vitro studies were conducted using a scattering tissue phantom for sequences that highlight fundamental, subharmonic, or equally between the two for bubble cloud visualization. Good qualitative agreement was observed between insilico prediction and in vitro assessment of bubble cloud generation, indicating the formalism developed here is a promising approach for the development of imaging sequences for histotripsy guidance.