Non-invasive Imaging Technique Research Articles

Retinal Vessel Analysis and Microvascular Abnormalities in Patients with Philadelphia-Negative Chronic Myeloproliferative Neoplasms.

Background: Philadelphia-negative chronic myeloproliferative neoplasms are a group of clonal hematopoietic disorders including polycythemia vera, essential thrombocythemia, and primary myelofi-brosis. These neoplasms are characterized by an increased risk of thrombotic complications. Several studies have highlighted that the study of vessels of the retina offers the opportunity to visualize, in vivo, the damage to microcirculation that is common in various systemic pathologies. Methods: in our study, forty patients underwent an ophthalmological examination, using non-invasive imaging tech-niques, for analyses of their retinal vascularization. The objective was to correlate the findings ob-tained from this study of the retina with different markers of thrombotic risk, to demonstrate the usefulness of studying retinal vessels as a possible new prognostic biomarker of thrombotic risk in patients affected by Philadelphia-negative chronic myeloproliferative neoplasms. Results: retinal imaging demonstrated changes in the microcirculation, with a reduced vascular density of the deep and superficial capillary plexuses with respect to a normal group, and a correlation between retinal changes and blood parameters. Conclusions: additional research will allow us to determine whether retinal changes in individuals with chronic myeloproliferative neoplasms could be predictive of the development of thrombotic events in these subjects.

OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

A New Method for In-Situ Characterization of Solid-State Batteries Based on Optical Coherence Tomography.

With the in-depth study of solid-state batteries (SSBs), various in situ and ex situ characterization technologies have been widely used to study them. The performance and reliability of SSBs are limited by the formation and evolution of lithium dendrites at the interfaces between solid electrodes and solid electrolytes. We propose a new method based on optical coherence tomography (OCT) for in situ characterization of the internal state of solid-state batteries. OCT is a low-loss, high-resolution, non-invasive imaging technique that can provide real-time monitoring of cross-sectional images of internal structures of SSBs. The morphology, growth, and evolution of lithium dendrites at different stages of cycling under various conditions can be visualized and quantified by OCT. Furthermore, we validate and correlate the OCT results with scanning electron microscopy (SEM) and XPS, proving the accuracy and effectiveness of the OCT characterization method. We reveal the interfacial phenomena and challenges in SSBs and demonstrate the feasibility and advantages of OCT as a powerful tool for in situ and operando imaging of battery interfaces. This study provides new insights into the mechanisms and factors that affect SSB performance, safety, and lifetime, and suggests possible solutions for improvement and application in the field of applied energy.

New non-local mean methods for MRI denoising based on global self-similarity between values

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high-resolution 3D images and valuable insights into human tissue conditions. Even at present, the refinement of denoising methods for MRI remains a crucial concern for improving the quality of the images. This study aims to improve the prefiltered rotationally invariant non-local principal component analysis (PRI-NL-PCA) algorithm. We relaxed the original restrictions using particle swarm optimization to determine optimal parameters for the PCA part of the original algorithm. In addition, we adjusted the prefiltered rotationally invariant non-local mean (PRI-NLM) part by traversing the signal intensities of voxels instead of their spatial positions to reduce duplicate calculations and expand the search volume to the whole image when estimating voxels’ signal intensities. The new method demonstrated superior denoising performance compared to the original approach. Moreover, in most cases, the new algorithm ran faster. Furthermore, our proposed method can also be applied to process Gaussian noise in natural images and has the potential to enhance other NLM-based denoising algorithms.

Morphological Differentiation of Corneal Inflammatory Cells: Proposal of Pragmatic Protocol.

Corneal confocal microscopy is a noninvasive imaging technique to analyze corneal nerve fibers and corneal inflammatory cells (CICs). The amount of CICs is a potential biomarker of disease activity in chronic autoinflammatory diseases. To date, there are no standardized criteria for the morphological characterization of CICs. The aim was to establish a protocol for a standardized morphological classification of CICs based on a literature search and to test this protocol for applicability and reliability. A systematic review of the literature about definitions of CICs was conducted. Existing morphological descriptions were translated into a structured algorithm and applied by raters. Subsequently, the protocol was optimized by reducing and defining the criteria of the cell types. The optimized algorithm was applied by 4 raters. The interrater reliability was calculated using Fleiss kappa (K). A systematic review of the literature revealed no uniform morphological criteria for the differentiation of the individual cell types in CICs. Our first protocol achieved only a low level of agreement between 3 raters (K = 0.09; 1062 rated cells). Our revised protocol was able to achieve a higher interrater reliability with 3 (K = 0.64; 471 rated cells) and 4 (K = 0.61; 628 rated cells) raters. The indirect use of criteria from the literature leads to a high error rate. By clearly defining the individual cell types and standardizing the protocol, reproducible results were obtained, allowing the introduction of this protocol for the future evaluation of CICs in the corneal confocal microscopy.

Human biodistribution and radiation dosimetry for the tau tracer [18F]Florzolotau in healthy subjects

BackgroundTau pathology plays a crucial role in neurodegeneration diseases including Alzheimer’s disease (AD) and non-AD diseases such as progressive supranuclear palsy. Tau positron emission tomography (PET) is an in-vivo and non-invasive medical imaging technique for detecting and visualizing tau deposition within a human brain. In this work, we aim to investigate the biodistribution of the dosimetry in the whole body and various organs for the [18F]Florzolotau tau-PET tracer. A total of 12 healthy controls (HCs) were enrolled at Chang Gung Memorial Hospital. All subjects were injected with approximately 379.03 ± 7.03 MBq of [18F]Florzolotau intravenously, and a whole-body PET/CT scan was performed for each subject. For image processing, the VOI for each organ was delineated manually by using the PMOD 3.7 software. Then, the time-activity curve of each organ was acquired by optimally fitting an exponential uptake and clearance model using the least squares method implemented in OLINDA/EXM 2.1 software. The absorbed dose for each target organ and the effective dose were finally calculated.ResultsFrom the biodistribution results, the elimination of [18F]Florzolotau is observed mainly from the liver to the intestine and partially through the kidneys. The highest organ-absorbed dose occurred in the right colon wall (255.83 μSv/MBq), and then in the small intestine (218.67 μSv/MBq), gallbladder wall (151.42 μSv/MBq), left colon wall (93.31 μSv/MBq), and liver (84.15 μSv/MBq). Based on the ICRP103, the final computed effective dose was 34.9 μSv/MBq with CV of 10.07%.ConclusionsThe biodistribution study of [18F]Florzolotau demonstrated that the excretion of [18F]Florzolotau are mainly through the hepatobiliary and gastrointestinal pathways. Therefore, a routine injection of 370 MBq or 185 MBq of [18F]Florzolotau leads to an estimated effective dose of 12.92 or 6.46 mSv, and as a result, the radiation exposure to the whole-body and each organ remains within acceptable limits and adheres to established constraints.Trial registrationRetrospectively Registered at Clinicaltrials.gov (NCT03625128) on 12 July, 2018, https://clinicaltrials.gov/study/NCT03625128.

ASL MRI Denoising via Multi Channel Collaborative Low-Rank Regularization.

Arterial spin labeling (ASL) perfusion MRI is the only non-invasive imaging technique for quantifying regional cerebral blood flow (CBF), which is a fundamental physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR). In this study, we proposed a novel ASL denoising method by simultaneously exploiting the inter- and intra-receive channel data correlations. MRI including ASL MRI data have been routinely acquired with multi-channel coils but current denoising methods are designed for denoising the coil-combined data. Indeed, the concurrently acquired multi-channel images differ only by coil sensitivity weighting and random noise, resulting in a strong low-rank structure of the stacked multi-channel data matrix. In our method, this matrix was formed by stacking the vectorized slices from different channels. Matrix rank was then approximately measured through the logarithm-determinant of the covariance matrix. Notably, our filtering technique is applied directly to complex data, avoiding the need to separate magnitude and phase or divide real and imaginary data, thereby ensuring minimal information loss. The degree of low-rank regularization is controlled based on the estimated noise level, striking a balance between noise removal and texture preservation. A noteworthy advantage of our framework is its freedom from parameter tuning, distinguishing it from most existing methods. Experimental results on real-world imaging data demonstrate the effectiveness of our proposed approach in significantly improving ASL perfusion quality. By effectively mitigating noise while preserving important textural information, our method showcases its potential for enhancing the utility and accuracy of ASL perfusion MRI, paving the way for improved neuroimaging studies and clinical diagnoses.

Clinicopathological Pattern of Endometrial Specimens in Women with Abnormal Uterine Bleeding and Ultrasonography Correlation.

Abnormal uterine bleeding (AUB) refers to any symptomatic deviation from normal menstruation. AUB is a common gynecological disorder in non-pregnant women of reproductive age, accounting for approximately 33% of gynecological outpatient visits. The early diagnosis and management cause of AUB is important because of increased incidence of endometrial carcinoma with rapid growth. Transvaginal ultrasound is non-invasive imaging technique used to find endometrial carcinoma before referring patients for invasive techniques. Dilatation and curettage (D&C) and endometrial biopsy are surgical procedures that scrape the endometrial lining of the uterus for diagnosis and treatment. The aim of this study is to describe the clinicopathologic pattern of endometrial specimens in women with AUB and ultrasonographic correlation. Tissues from endometrial biopsy and curettage of 411 patients with AUB who referred to Shahid Mohammadi hospital were prospectively selected from 2021 to 2023. Patients were divided into three groups based on age and menstrual status including: premenopausal (18-39 years), perimenopausal (40-49 years) and postmenopausal (≥50 years). The results were correlated to patient's age and other data and evaluated with statistical analysis. During the two-year study period, a total of 411 endometrial specimens with clinical diagnosis of AUB were submitted and the results were analyzed. The youngest patient presenting with AUB was 21 years old, while the oldest was 77 years old. The most common complaint was menorrhagia in 201 (48.0%) out of 411 patients. The most common pathology finding in three groups was polyp in 100 (24.3%) cases. Hormonal effect was the next commonly observed pattern seen in 70 (17.0%) cases. P value was calculated as 0.003 which was significant using chi-square for the trend seen in age. Endometrial sampling is a useful tool for evaluation of women with AUB and referring patients for treatment. Histopathological evaluation of the endometrium is very useful in detecting the etiology of AUB. Transvaginal sonography has high sensitivity in detecting polyps.

Quantum dots in noninvasive imaging of oral squamous cell carcinomas: A scoping literature review.

The current scoping review's objective was to outline existing applications, recent breakthroughs, and quantum dots' applicability in imaging of oral squamous cell cancer. Quantum dots are nanometric semiconductor crystals with customizable optical characteristics and intense, stable fluorescence suited for bioimaging and labeling. We used the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) recommendations for conducting our systematic search. An analysis of the properties and applications of quantum dots in noninvasive detection of oral squamous cell cancer is presented in this study, which comprehensively explores the available evidence. Following searches in the databases PubMed, Ovid SP, and Cochrane using the search terms quantum dots AND oral squamous cell cancer, 55 published publications were chosen for this review. The review identified a total of eight papers that met the criteria. In noninvasive detection of oral squamous cell carcinoma, quantum dots have the potential to offer an array of therapeutic and diagnostic applications. Furthermore, quantum dots emit near-infrared and visible light, which is advantageous in biological imaging since it reduces light dispersion and absorption of tissue. The future may see quantum dots become a popular noninvasive imaging technique for oral squamous cell cancer. The number of studies accessible is quite limited, and further research is required.

Comparison of Ga-68 PSMA PET/CT and Multiparametric MRI for Initial Detection and Staging of Prostate Cancer

Abstract Background Multiparametric magnetic resonance imaging (mpMRI) is widely used for the evaluation of prostate cancer and is known to have better accuracy. Gallium-68 prostate-specific membrane antigen (Ga-68 PSMA) is a radiotracer that shows high localization in prostate cancer cells. Purpose The purpose of this study was to assess the sensitivity and utility of Ga-68 PSMA positron emission tomography/computed tomography (PET/CT) in comparison with mpMRI as a noninvasive imaging technique for the initial diagnosis and locoregional staging of prostate cancer using transrectal ultrasound (TRUS)-guided biopsy as gold standard. Materials and Methods This prospective observational study conducted from August 2017 to April 2020 evaluated 60 men (n = 60) with biopsy-proven prostate carcinoma. They underwent mpMRI and Ga-68 PSMA PET/CT scans within 14 days with TRUS biopsy being gold standard. T staging of disease, N staging of lymph nodes within the pelvis, and M staging of lesions in pelvic bones (within the imaging field of mpMRI) were compared using PSPP version 1.0.1 statistical software. Results All 60 men with a mean age of 69.9 ± 9.35 years showed Ga-68 PSMA avid disease, whereas 55 were detected by mpMRI. The sensitivity in detection of prostate lesions (with 95% confidence interval) was 99.08% for Ga-68 PSMA PET/CT and 84.40% for mpMRI. Ga-68 PSMA PET/CT detected greater number of patients with regional lymph nodal involvement (19/60) as compared with mpMRI (12/60). Ga-68 PSMA PET/CT showed PSMA avid pelvic skeletal lesions in nine patients, whereas mpMRI detected pelvic lesions in six patients. In addition, four other patients showed extrapelvic skeletal lesions on Ga-68 PSMA PET/CT. Conclusion Ga-68 PSMA PET/CT has superior sensitivity in detection of primary prostate tumor, as compared with mpMRI. Both modalities correlate well in detection of seminal vesicle involvement. Ga-68 PSMA PET/CT outperformed mpMRI in detection of lymph nodal and skeletal metastases. Hence, Ga-68 PSMA PET/CT should be considered as first-line diagnostic modality for carcinoma prostate. Summary Statement: Ga-68 PSMA PET/CT shows superior diagnostic performance than mpMRI in the evaluation of prostate cancer.

Central Corneal Thickness and Endothelial Cell Changes in Diabetics and Age-Matched Non-diabetics in a Tertiary Care Hospital in Central India.

Background Diabetes has become an epidemic, significantly impacting ocular health as one of its end-organ responses. Among the various ocular complications, alterations in corneal morphology stand out. Central corneal thickness (CCT) and endothelial cell function are vital parameters in assessing intraocular pressure, conducting pre-refractive surgery evaluations, and maintaining corneal transparency. Understanding these changes in diabetic individuals compared to non-diabetics is crucial for managing ocular health in this population. Aim and objective This study evaluates and compares CCT and endothelial cell changes between diabetic individuals and age-matched non-diabetics. By analyzing these parameters, the study seeks to provide insights into the impact of diabetes on corneal morphology and its implications for ocular health. Methods The study recruited 124 patients from the Ophthalmology Outpatient Department (OPD) at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi. A cross-sectional research design was employed to collect data over six months. Patients were carefully selected, and informed consent was obtained from all participants. CCT and endothelial cell parameters were assessed using specular microscopy, a non-invasive imaging technique. Statistical analysis was done using the software Statistical Package for the Social Sciences (SPSS) for inferential statistics, such as t-tests and ANOVA, and comparing parameters between diabetic and non-diabetic groups.Findings were interpreted based on both statistical significance and clinical relevance. Results In diabetic patients, the mean CCT was 547.91 µm, while it was 523.62 µm in non-diabetic individuals.The T statistic for this variable was 5.14, indicating a17 significant differencebetween the two groups. Similarly, significant differences were found between diabetics and non-diabetics for endothelial cell density, coefficient of variation, and hexagonality, as evidenced by their respective T statistics of 7.46, 5.17, and 4.91. Endothelial cell density averaged 2375 cells/mm2 in diabetics and 2666.95 cells/mm2 in non-diabetics. Additionally, the coefficient of variation was higher among people with diabetes (40.87%) compared to non-diabetics (35.09%). Hexagonality, a measure of endothelial cell shape, was lower in diabetic corneas (40.48%) than in non-diabetic corneas (46.46%). Conclusion The study observed significant differences in corneal morphology, including central thickness and endothelial cell changes, between diabetic and non-diabetic individuals. These findings underscore the impact of diabetes on ocular health and emphasize the importance of monitoring corneal parameters in diabetic patients. Understanding these changes can aid in better management and treatment strategies for ocular complications associated with diabetes.

18F]-Radiolabelled Nanoplatforms: A Critical Review of Their Intrinsic Characteristics, Radiolabelling Methods, and Purification Techniques.

A wide range of nano-objects is found in many applications of our everyday life. Recognition of their peculiar properties and ease of functionalization has prompted their engineering into multifunctional platforms that are supposed to afford efficient tools for the development of biomedical applications. However, bridging the gap between bench to bedside cannot be expected without a good knowledge of their behaviour in vivo, which can be obtained through non-invasive imaging techniques, such as positron emission tomography (PET). Their radiolabelling with [18F]-fluorine, a technique already well established and widely used routinely for PET imaging, with [18F]-FDG for example, and in preclinical investigation using [18F]-radiolabelled biological macromolecules, has, therefore, been developed. In this context, this review highlights the various nano-objects studied so far, the reasons behind their radiolabelling, and main in vitro and/or in vivo results obtained thereof. Then, the methods developed to introduce the radioelement are presented. Detailed indications on the chemical steps involved are provided, and the stability of the radiolabelling is discussed. Emphasis is then made on the techniques used to purify and analyse the radiolabelled nano-objects, a point that is rarely discussed despite its technical relevance and importance for accurate imaging. The pros and cons of the different methods developed are finally discussed from which future work can develop.

Reduced Retinal Blood Vessel Densities Measured by Optical Coherence Tomography Angiography in Keratoconus Patients Are Negatively Correlated with Keratoconus Severity.

Keratoconus (KC) is the most common corneal ectasia. Optical coherence tomography angiography (OCT-A) is a relatively new non-invasive imaging technique that allows the visualization and quantification of retinal and choriocapillary blood vessels. The aim of this study is to assess retinal and choriocapillary vessel density (VD) differences between KC patients and healthy controls and to investigate correlations between VD and KC severity. Fifty-two eyes were included in this exploratory study: twenty-six eyes from 26 KC patients and twenty-six eyes from 26 age- and gender-matched healthy controls. All patients underwent Scheimpflug corneal topography with Pentacam, axis lengths measurement and optical coherence tomography angiography (OCT-A). The thinnest spot in corneal pachymetry, maximum K (Kmax) and KC severity indices from the Belin/Ambrósio enhanced ectasia display (BAD) were also assessed. There was a distinct reduction particularly in the retinal VD of the superficial capillary plexus (SCP). Correlation analyses showed strong and moderate negative correlations between the VD in the macular SCP and BAD KC scores and between the SCP VD and Kmax. There was no difference in retinal thickness between the KC and healthy controls. With this study, further evidence for altered VD measurements by OCT-A in KC patients is given. For the first time, we demonstrated negative correlations between BAD KC scores and retinal blood vessel alterations. A major limitation of the study is the relatively small sample size. Since an artefactual reduction of the quantitative OCT-A measurements due to irregular corneal topography in KC must be assumed, it remains to be investigated whether there are also actual changes in the retinal microcirculation in KC.

Quantitative 1H Magnetic Resonance Imaging on Normal and Pathologic Rat Bones by Solid-State 1H ZTE Sequence with Water and Fat Suppression.

Osteoporosis (OP) and osteomalacia (OM) are metabolic bone diseases characterized by mineral and matrix density changes. Quantitative bone matrix density differentiates OM from OP. MRI is a noninvasive and nonionizing imaging technique that can measure bone matrix density quantitatively in ex vivo and in vivo. To demonstrate water + fat suppressed 1H MRI to compute bone matrix density in ex vivo rat femurs in the preclinical model. Prospective. Fifteen skeletally mature female Sprague-Dawley rats, five per group (normal, ovariectomized (OVX), partially nephrectomized/vitamin D (Vit-D) deficient), 250-275 g, ∼15 weeks old. 7T, zero echo time sequence with water + fat (VAPOR) suppression capability, μCT imaging, and gravimetric measurements. Cortical and trabecular bone segments from normal and disease models were scanned in the same coil along with a dual calibration phantom for quantitative assessment of bone matrix density. ANOVA and linear regression were used for data analysis, with P-values <0.05 statistically significant. The MRI-derived three-density PEG pellet densities have a strong linear relationship with physical density measures (r2 = 0.99). The Vit-D group had the lowest bone matrix density for cortical bone (0.47 ± 0.16 g cm-3), whereas the OVX had the lowest bone matrix density for trabecular bone (0.26 ± 0.04 g cm-3). Gravimetry results confirmed these MRI-based observations for Vit-D cortical (0.51 ± 0.07 g cm-3) and OVX trabecular (0.26 ± 0.03 g cm-3) bone groups. Rat femur images were obtained using a modified pulse sequence and a custom-designed double-tuned (1H/31P) transmit-receive solenoid-coil on a 7T preclinical MRI scanner. Phantom experiments confirmed a strong linear relation between MRI-derived and physical density measures and quantitative bone matrix densities in rat femurs from normal, OVX, and Vit-D deficient/partially nephrectomized animals were computed. 2 TECHNICAL EFFICACY: Stage 2.

Alzheimer's MRI Recognition Based on Convolutional Neural Networks

Alzheimer's disease (AD) is a progressively worsening neurodegenerative disorder, where early diagnosis is crucial for implementing effective disease management strategies. In multimodal diagnostic pathways, Magnetic Resonance Imaging (MRI) plays a pivotal role as a non-invasive imaging technique in disease identification and progression monitoring. Deep learning, particularly Convolutional Neural Networks (CNN), can extract key biomarkers from complex imaging data. By training CNN models to automatically interpret MRI scans, radiology experts can utilize these advanced analytical tools for more efficient and consistent pathological assessments, thereby enhancing clinical decision support systems. This study develops a deep learning-based method for the automatic classification of Alzheimer's brain MRI images, using convolutional neural networks to categorize MRI images into four stages: no dementia, very mild dementia, mild dementia, and moderate dementia. A CNN model is constructed, learning distinctive features of the images through multi-level feature extraction and performing feature map visualization. Early stopping is employed to prevent overfitting. The model is trained on a training set and evaluated on a test set, with performance metrics including confusion matrix, accuracy, precision, recall, F1 score, Kappa coefficient, Matthew’s coefficient, ROC curve with AUC value, and PQ curve with AP value. The results show that the proposed model effectively differentiates between various categories of MRI images, providing valuable tools for early diagnosis and condition monitoring.

A longitudinal MRI and TSPO PET-based investigation of brain region-specific neuroprotection by diazepam versus midazolam following organophosphate-induced seizures

Acute poisoning with organophosphorus cholinesterase inhibitors (OPs), such as OP nerve agents and pesticides, can cause life threatening cholinergic crisis and status epilepticus (SE). Survivors often experience significant morbidity, including brain injury, acquired epilepsy, and cognitive deficits. Current medical countermeasures for acute OP poisoning include a benzodiazepine to mitigate seizures. Diazepam was long the benzodiazepine included in autoinjectors used to treat OP-induced seizures, but it is now being replaced in many guidelines by midazolam, which terminates seizures more quickly, particularly when administered intramuscularly. While a direct correlation between seizure duration and the extent of brain injury has been widely reported, there are limited data comparing the neuroprotective efficacy of diazepam versus midazolam following acute OP intoxication. To address this data gap, we used non-invasive imaging techniques to longitudinally quantify neuropathology in a rat model of acute intoxication with the OP diisopropylfluorophosphate (DFP) with and without post-exposure intervention with diazepam or midazolam. Magnetic resonance imaging (MRI) was used to monitor neuropathology and brain atrophy, while positron emission tomography (PET) with a radiotracer targeting translocator protein (TSPO) was utilized to assess neuroinflammation. Animals were scanned at 3, 7, 28, 65, 91, and 168 days post-DFP and imaging metrics were quantitated for the hippocampus, amygdala, piriform cortex, thalamus, cerebral cortex and lateral ventricles. In the DFP-intoxicated rat, neuroinflammation persisted for the duration of the study coincident with progressive atrophy and ongoing tissue remodeling. Benzodiazepines attenuated neuropathology in a region-dependent manner, but neither benzodiazepine was effective in attenuating long-term neuroinflammation as detected by TSPO PET. Diffusion MRI and TSPO PET metrics were highly correlated with seizure severity, and early MRI and PET metrics were positively correlated with long-term brain atrophy. Collectively, these results suggest that anti-seizure therapy alone is insufficient to prevent long-lasting neuroinflammation and tissue remodeling.

Remotely Triggered LAD Occlusion Using a Balloon Catheter in Spontaneously Breathing Mice.

Acute myocardial infarction (AMI) is a prevalent and high-mortality cardiovascular condition. Despite advancements in revascularization strategies for AMI, it frequently leads to myocardial ischemia-reperfusion injury (IRI), amplifying cardiac damage. Murine models serve as vital tools for investigating both acute injury and chronic myocardial remodeling in vivo. This study presents a unique closed-chest technique for remotely inducing myocardial IRI in mice, enabling the investigation of the very early phase of occlusion and reperfusion using in-vivo imaging such as MRI or PET. The protocol utilizes a remote occlusion method, allowing precise control over ischemia initiation after chest closure. It reduces surgical trauma, enables spontaneous breathing, and enhances experimental consistency. What sets this technique apart is its potential for simultaneous noninvasive imaging, including ultrasound and magnetic resonance imaging (MRI), during occlusion and reperfusion events. It offers a unique opportunity to analyze tissue responses in almost real-time, providing critical insights into processes during ischemia and reperfusion. Extensive systematic testing of this innovative approach was conducted, measuring cardiac necrosis markers for infarction, assessing the area at risk using contrast-enhanced MRI, and staining infarcts at the scar maturation stage. Through these investigations, emphasis was placed on the value of the proposed tool in advancing research approaches to myocardial ischemia-reperfusion injury and accelerating the development of targeted interventions. Preliminary findings demonstrating the feasibility of combining the proposed innovative experimental protocol with noninvasive imaging techniques are presented herein. These initial results highlight the benefit of utilizing the purpose-built animal cradle to remotely induce myocardial ischemia while simultaneously conducting MRI scans.

DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image

Breast cancer is a common malignancy, ranking as one of the most prevalent cancers among women globally. Improving treatment success rates for breast cancer are crucially dependent on early detection and accurate diagnosis. Ultrasound imaging, as a non-invasive and low-radiation imaging technique, plays a significant role in early breast cancer detection. However, due to the characteristics of fuzzy tumor boundaries and irregular shapes of the tumor, accurate breast tumor classification and segmentation remain challenging tasks. This paper introduces a novel approach for breast ultrasound image segmentation and classification tasks. Firstly, we study the potential value of frequency domain information in breast image analysis and propose the DBL-Net, a multi-task learning network for simultaneous tumor segmentation and tumor classification. Secondly, we customize a Spatial-Frequency Domain Feature Encoding module (S-FEM) to better encode and fuse spatial and frequency domain features of breast ultrasound images. Furthermore, we design a Representational Perception Enhancer (RPE) to improve the feature representation capabilities and prediction accuracy of the model. Finally, We have carried out a comprehensive series of tests on the BUS dataset, and the findings clearly indicate that our method outperforms the current state-of-the-art methods.

Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Population-level task-evoked functional connectivity via Fourier analysis

Abstract Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.