MRI Analysis Research Articles

Alzheimer’s and neurodegenerative disease biomarkers in blood predict brain atrophy and cognitive decline

BackgroundAlthough blood-based biomarkers have been identified as cost-effective and scalable alternatives to PET and CSF markers of neurodegenerative disease, little is known about how these biomarkers predict future brain atrophy and cognitive decline in cognitively unimpaired individuals. Using data from the Baltimore Longitudinal Study of Aging (BLSA), we examined whether plasma biomarkers of Alzheimer’s disease (AD) pathology (amyloid-β [Aβ42/40], phosphorylated tau [pTau-181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) were associated with longitudinal brain volume loss and cognitive decline. Additionally, we determined whether sex, APOEε4 status, and plasma amyloid-β status modified these associations.MethodsPlasma biomarkers were measured using Quanterix SIMOA assays. Regional brain volumes were measured by 3T MRI, and a battery of neuropsychological tests assessed five cognitive domains. Linear mixed effects models adjusted for demographic factors, kidney function, and intracranial volume (MRI analyses) were completed to relate baseline plasma biomarkers to baseline and longitudinal brain volume and cognitive performance.ResultsBrain volume analyses included 622 participants (mean age ± SD: 70.9 ± 10.2) with an average of 3.3 MRI scans over 4.7 years. Cognitive performance analyses included 674 participants (mean age ± SD: 71.2 ± 10.0) with an average of 3.9 cognitive assessments over 5.7 years. Higher baseline pTau-181 was associated with steeper declines in total gray matter volume and steeper regional declines in several medial temporal regions, whereas higher baseline GFAP was associated with greater longitudinal increases in ventricular volume. Baseline Aβ42/40 and NfL levels were not associated with changes in brain volume. Lower baseline Aβ42/40 (higher Aβ burden) was associated with a faster decline in verbal memory and visuospatial performance, whereas higher baseline GFAP was associated with a faster decline in verbal fluency. Results were generally consistent across sex and APOEε4 status. However, the associations of higher pTau-181 with increasing ventricular volume and memory declines were significantly stronger among individuals with higher Aβ burden, as was the association of higher GFAP with memory decline.ConclusionsAmong cognitively unimpaired older adults, plasma biomarkers of AD pathology (pTau-181) and astrogliosis (GFAP), but not neuronal injury (NfL), serve as markers of future brain atrophy and cognitive decline.

Investigating Descending Pain Regulation in Fibromyalgia and the Link to Altered Autonomic Regulation by Means of Functional MRI Data.

Fibromyalgia syndrome (FM) is a chronic pain condition that affects a significant portion of the population; yet, this condition is still poorly understood. Prior research has suggested that individuals with FM display a heightened sensitivity to pain and signs of autonomic dysfunction. Recent advances in functional MRI analysis methods to model blood-oxygenation-level-dependent (BOLD) responses across networks of regions, and structural and physiological modeling (SAPM) have shown the potential to provide more detailed information about altered neural activity than was previously possible. Therefore, this study aimed to apply novel analysis methods to investigate altered neural processes underlying pain sensitivity in FM in functional magnetic resonance imaging (fMRI) data from the brainstem and spinal cord. Prior fMRI studies have shown evidence of functional differences in fibromyalgia (FM) within brain regions associated with pain's motivational aspects, as well as differences in neural activity related to pain regulation, arousal, and autonomic homeostatic regulation within the brainstem and spinal cord regions. We, therefore, hypothesized that nociceptive processing is altered in FM compared to healthy controls (HCs) in the brainstem and spinal cord areas linked to autonomic function and descending pain regulation, including the parabrachial nuclei (PBN) and nucleus tractus solitarius (NTS). We expected that new details of this altered neural signaling would be revealed with SAPM. The results provide new evidence of altered neural signaling in FM related to arousal and autonomic homeostatic regulation. This further advances our understanding of the altered neural processing that occurs in women with FM.

Unveiling the potential of breast MRI: a game changer for BI-RADS 4A microcalcifications.

To assess the diagnostic performance of breast MRI for BI-RADS 4A microcalcifications on mammography and propose a potential clinical pathway to avoid unnecessary biopsies. Bibliometrics analysis of breast MRI and BI-RADS 4 was provided. A retrospective analysis was conducted on 139 women and 142 cases of BI-RADS 4A microcalcifications on mammography from Fudan University Shanghai Cancer Center. The mammographic BI-RADS level and the MRI reports were compared with the final pathological diagnosis. Much attention has been given to breast MRI and BI-RADS 4 in the literature. However, studies on BI-RADS 4A are limited. Pathological results showed 117 cases (82.4%) were benign lesions, malignant cases of 25 (17.6%) in our study. The positive predictive values (PPV), specificity, sensitivity and negative predictive values (NPV) of MRI were 44.2% (23/52), 75.2% (88/117), 92.0% (23/25), and 97.8% (88/90), respectively. Therefore, 75.2% (88/117) of biopsies for benign lesions could potentially be avoided. There were 2.2% (2/90) malignant lesions missed. Logistic regression indicated that patients who are postmenopausal (HR = 2.655, p = 0.012), have a history of breast cancer (family history) (HR = 2.833, p = 0.029), and exhibit clustered microcalcifications (HR = 2.179, p = 0.046) are more likely to have a higher MRI BI-RADS level. Breast MRI has the potential to improve the diagnosis of BI-RADS 4A microcalcifications on mammography. We propose a potential clinical pathway that patients with BI-RADS 4A on mammography who are premenopausal, have no personal history of breast cancer (family history) or have non-clustered distribution of calcifications can undergo MRI to avoid unnecessary biopsies.

Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis

BackgroundImplantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. ObjectiveThe purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. MethodsADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. ResultsPatients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44–5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04–2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11–4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19–3.81; P = .01). ConclusionA detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model.

Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.

A clinical case report of HIV-associated encephalopathy

Due to the fact that neurological and psychiatric symptoms are frequent manifestations of NeuroAIDS, a neurologist and a psychiatrist may be the first to meet with an HIV-infected patient in clinical practice. The article presents a clinical case of HIV-associated encephalopathy identified in a patient in a psychiatric hospital; the clinical picture and diagnosis of dementia caused by the human immunodeficiency virus are described. The case study method and MRI analysis were used. It has been concluded that there is a need for more careful monitoring of patients registered with AIDS centers, achieving 100 % HAART coverage of HIV patients at the early stages of the disease, which will significantly reduce residual neurological and psychiatric consequences, as well as mortality from HIV-associated lesions of the central nervous system.

Parameters That Can Be Used to Quantify Reduction Accuracy in Talar Neck Fractures and Malunions: A PRISMA-Compliant Scoping Review and Meta-Analysis.

Understanding the three-dimensional anatomy of the talar neck is essential in assessing the accuracy of reduction in talar neck fractures as well as for planning surgical correction for talar malunions. However, the geometrical parameters that describe this anatomy are sparsely reported in the orthopedics literature. We aimed to identify from the existing literature, geometrical parameters that describe the anatomy of the talar neck, determine how these are measured, and their normative values. A scoping literature review was conducted in accordance with thePreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)extension for scoping reviews (PRISMA-ScR) guidelines. The primary searches were conducted on the PubMed, Embase, and Scopus databases. Any original research study looking at the human talus neck geometry was included. Parameters that described the anatomy of the talar neck were identified, andpooled estimates were determined by the random-effects meta-analysis model. Heterogeneity was assessed by the I2 test and leave-one-out meta-analysis. Subgroup analysis was done to compare the values of parameters between the Asian and Non-Asian populations. The risk of bias was assessed by the National Institutes of Health (NIH) Case Series Tool. The combined searches yielded 6326 results, of which 21 studies were included in the review and 15 in six different sets of metanalysis. The majority of the studies (n=19, 90.5%) evaluated adult tali, and only two (9.5%) evaluated pediatric tali.In most of the studies (n=13, 61.9%), talus neck geometry was evaluated on dry bones or anatomical specimens; evaluation by imaging techniques (radiographs, CT, MRI, and radiostereometric analysis) was used in eight studies, (39.1%). A total of eight different geometrical parameters (neck length, height, width, declination angle, inclination angle, torsion angle, circumference, and cross-sectional area) were identified. Except for talar torsion, variability was noted in methods of measurement of all other parameters. Subgroup analysis revealed that Asians had a higher neck height as compared to non-Asians; other parameters were not significantly different. Although the literature reports geometrical parameters to assess the talar geometry, the methods of measurement of these parameters are variable. Most of the available literature describes measurement techniques on cadaveric tali, and there is no literature on how these parameters should be measured on conventional CT or MRI slices. Further research needs to focus on the standardization of measurement techniques for these parameters on conventional CT and/or MRI scans.

Resting-state Functional MRI and Motion Analysis Characteristics in Patients with Isolated REM Sleep Behavior Disorder (P11-3.004)

Resting-state Functional MRI and Motion Analysis Characteristics in Patients with Isolated REM Sleep Behavior Disorder (P11-3.004)

Specificities in the Structure of the Cartilage of Patients with Advanced Stages of Developmental Dysplasia of the Hip.

Developmental dysplasia of the hip (DDH) presents varying degrees of femoral head dislocation, with severe cases leading to the formation of a new articular surface on the external side of the iliac bone-the neoacetabulum. Despite conventional understanding suggesting otherwise, a tissue resembling hyaline cartilage is found in the neoacetabulum and acetabulum of Crowe III and IV patients, indicating a potential for hyaline cartilage development without mechanical pressure. To test this theory, acetabular and femoral head cartilage obtained from patients with DDH was stained with hematoxylin-eosin and toluidine blue. The immunohistochemical analysis for collagen types II and VI and aggrecan was performed, as well as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) analysis on a 7.0 T micro-MRI machine. The results obtained from DDH patients were compared to those of the control groups. Hyaline cartilage was found in the neoacetabulum and the acetabulum of patients with DDH. The nature of the tissue was confirmed with both the histological and the MRI analyses. The results of this study proved the presence of hyaline cartilage in patients with DDH at anatomical regions genetically predisposed to be bone tissue and at regions that are not subjected to mechanical stress. This is the first time that the neoacetabular cartilage of patients with advanced stages of DDH has been characterized in detail.

Tumor Treating Fields for Treatment of Spinal Metastasis

INTRODUCTION: Tumor treating fields(TTF) has never been described for treatment of spinal metastasis. We investigate the anti-mitotic effect of TTF to inhibit the growth of human cancer using cells in vitro and in vivo models of spinal metastasis. METHODS: In-vitro: human derived osteosarcoma (Krib-Luc-1) and lund adenocarcinoma (A549-Luc) cells were seeded in 5x5mm human demineralized bone grafts kept in culture while treated with 0 KHz (control) or 150 KHz of TTF for 14 days. Bioluminescence images (BLI) were obtained at baseline and 14 days. In-vivo: Krib-Luc-1 cells were surgically implanted via trasperitoneal approach to the L3 vertebra in nude mice. Animals were treated with 0 KHz or 150 KHz of TTF for 30 days or until paralysis. BLI were obtained at day 0 and every 7 days. MRI was obtained at day 22. Histological analysis was performed at the end of the experiment. Control and TTF treated groups were compared with two-tailed t-tests. RESULTS: In vitro: BLI of the control grafts demonstrated a median value of 1×10^10 photons/sec at 14 days, significantly higher than 1×10^8 photons/sec observed in the 150 KHz group (p &lt; 0.0001). In Vivo: median time to paralysis in control animals was 25 days, all animals treated with TTF at 150 KHz reached the 30 day mark without deficits. BLI of the control group was 3.5×10^9 photons/sec significantly higher than 1×10^9 photons/sec in the 150 KHz TTF group (p = 0.0212). MRI and histological analysis confirmed significant smaller tumors in the treated group when compared to controls. CONCLUSIONS: Application of TTF 150 KHz reduced cellular proliferation in both in vitro and in vivo models of spinal metastasis. We believe TTF could become a groundbreaking treatment for spinal metastasis.

C-reactive protein concentration has limited value in the diagnosis of meningoencephalitis of unknown origin in dogs.

To evaluate blood and cerebrospinal fluid (CSF) concentrations of C-reactive protein (CRP) in dogs with meningoencephalitis of unknown origin (MUO); to evaluate whether blood CRP concentration is associated with epidemiological, clinicopathologic, and MRI findings; and to investigate blood CRP predictive power in survival. 30 client-owned dogs with MUO, 15 client-owned dogs with steroid-responsive meningitis arteritis (SRMA; positive control group), and 15 healthy dogs (negative control group). Blood CRP concentration was measured in each group, while it was performed in CSF only in the MUO and SRMA groups. The analysis of epidemiological data included breed, age, sex, duration of clinical signs, and history of seizures. Blinded analysis of MRI was performed based on a classification grid, and traditional CSF analysis parameters were assessed. The predictive power of blood CRP concentration regarding survival at 6 months was investigated. Of the 30 dogs with MUO, 9 (30%) had an increased CRP concentration in blood, and 3 (10%) showed a measurable CRP in CSF. Median blood CRP concentration in dogs with MUO was 0.1 mg/L (range, 0.1 to 102 mg/L), which was not statistically different from the healthy dog group but significantly lower than the SRMA control group. Only the duration of clinical signs was positively associated with an increased blood CRP level. Blood CRP concentration was not associated with survival at 6 months. Blood CRP concentration is of limited value for the diagnosis and prognosis of dogs with MUO. Chronicity of the disease may be associated with an increased concentration of blood CRP.

Treatment effect of trauma-focused treatment and/or integrated trauma-focused and personality disorder treatment on brain activation during an emotional face task

IntroductionPost-traumatic stress disorder (PTSD) and personality disorders are highly comorbid. There is some evidence that trauma-focused treatment normalises activation in brain areas involved in the fear circuit and regions involved in emotion regulation in people with PTSD. Although we assume that working mechanism of personality disorder treatments relies on improving emotion regulation and associated brain regions, there is as of yet little evidence of neurobiological effects of personality treatment on people with PTSD and comorbid PD.ObjectivesTo 1) study the effect of trauma-focused and/or trauma-focused and personality disorder treatment n brain activation in participants with PTSD and comorbid personality disorders and 2) relate change in brain activation to symptom improvement.MethodsParticipants with PTSD and comorbid borderline and/or cluster c personality disorders from the PROSPER-trials (Prediction and Outcome Study for PTSD and personality disorders) were randomized to either trauma-focused treatment (TFT) or TFT with personality disorder treatment (TFT+PT). Brain activation was measured with an emotional face task during functional magnetic resonance imaging scanning before and after treatment. Regions of interest for the analyses were the amygdala, dorsal ACC, insula, ventromedial prefrontal cortex (PFC), ventrolateral PFC and dorsolateral PFC. Bayesian multilevel analyses were conducted to analyze change in brain activation. Clinical measures were clinician-administered PTSD severity, self-rated emotion regulation problems, depression severity and dissociation severity.ResultsWe included 42 participants with a pre- and posttreatment scan (24 with TFT, 18 TFT+PT). Analyses on the pre-post data are currently being run and will be presented in April.ConclusionsThis is one of the first studies to conduct functional MRI analyses on treatment in participants with both PTSD and personality disorders.Disclosure of InterestNone Declared

159 PD-1 Agonism as a Strategy to Prevent Neuroinflammation After Acute Large Vessel Occlusion

INTRODUCTION: Stroke is a leading cause of death and long-term disability worldwide. Acute ischemia results in a profound inflammatory response that is counterproductive to short- and long-term recovery. Programmed Death Protein 1 (PD-1) is an immune checkpoint that has been shown to be upregulated after acute cerebral injury, and—upon binding its ligand PD-L1—negatively regulates immune cells to limit the inflammatory response. METHODS: C57BL/6J mice underwent middle cerebral artery occlusion (MCAO) and were randomized into three arms: (1)sham surgery, (2)MCAO control, and (3)MCAO+PD-L1. Survival, brain water content, and volumetric MRI analysis of cerebral edema were measured. Brains were analyzed by flow cytometry to quantify PD-1 expression on infiltrating immune cells. Peripheral blood was harvested for single-cell RNA-sequencing. Blood from stroke patients was collected, and the frequency of PD-1+ monocytes was correlated to clinical outcomes. RESULTS: PD-L1 significantly decreased brain edema as measured by brain water content (p = 0.0277) and high-resolution MRI (p &lt; 0.0001), and improved overall survival (p = 0.0022). PD-1+ monocytes are upregulated in the ischemic hemisphere, and appear to be critical mediators of edema. PD-L1 treatment decreased PD-1+ monocyte infiltration , while global PD-1 (PD-1-/-) and myeloid-specific knockout of PD-1 (PD-1f/fLysMcre) abrogated the treatment effect on edema. Single-cell RNA sequencing and differential expression analysis of peripheral monocytes revealed downregulation of multiple pro-inflammatory pathways in the treated mice versus the controls. Human correlative studies confirmed that PD-1+ monocytes were present in the peripheral blood of MCA stroke patients, and the frequency of this cell population correlated with post-stroke cerebral edema. CONCLUSIONS: Our results support PD-L1 as a novel therapeutic strategy for neuroinflammation and cerebral edema after acute ischemic stroke.

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

423 Innovation in MS Patient Care: Linking Cognitive Health and Myelin Integrity

OBJECTIVES/GOALS: Our objective is to develop a patient-friendly application addressing the progression of cognitive impairments in multiple sclerosis (MS) patients. This initiative aims to augment individualized care and precision management of a major MS comorbidity by generating a cognitive health brain map for each patient. METHODS/STUDY POPULATION: Using the UAMS COMS Database, featuring high-resolution multi-contrast MRIs, and a comprehensive clinical, behavioral, and demographic dataset, we are developing a hierarchical learning-based software tool to compute maps correlating brain structure-function and individual cognitive function. Our MRI analysis employs a three-compartment model (NNLS&gt;0.96). Functional scores are defined by individualized accuracy during the modified information processing speed task (e.g., m-SDMT). We utilize a Bayesian classifier with explicit Pearson’s correlation for tissue classification (BF10&gt;100) to compute an index of the likelihood of correlation with cognitive impairment throughout brain tissue. RESULTS/ANTICIPATED RESULTS: This approach allows us to reveal subtle cognitive changes and their potential links to myelin integrity, offering vital insights into disease progression and management. The m-SDMT strongly correlates with the standard SDMT (r=0.79, p&lt;0.001), confirming reliability as a cognitive assessment tool in clinical and research contexts. Analysis of the COMS dataset emphasized insights into the role of fine myelin structure in MS patients' cognitive functionality. Our findings heightened the pivotal significance of myelin integrity in preserving cognitive abilities and identify disruptions in myelin synthesis and homeostasis as primary contributors to cognitive decline. This discovery stresses the critical role that specialized brain pathways, influenced by myelin integrity, play in the pathology of MS. DISCUSSION/SIGNIFICANCE: This development bridges advanced neuroimaging techniques with practical clinical applications, emphasizing the nuanced role of myelin integrity in MS-related cognitive deficits. Our findings advocate for a multidisciplinary approach to MS management, demanding collaborative workforce development and education in translational science.

Associations between 3D-based Left Atrial Volumetric and Blood Flow Parameters in a Single-Site Cohort of the Multi-Ethnic Study of Atherosclerosis.

Purpose To investigate associations between left atrial volume (LAV) and function with impaired three-dimensional hemodynamics from four-dimensional flow MRI. Materials and Methods A subcohort of participants from the Multi-Ethnic Study of Atherosclerosis from Northwestern University underwent prospective 1.5-T cardiac MRI including whole-heart four-dimensional flow and short-axis cine imaging between 2019 and 2020. Four-dimensional flow MRI analysis included manual three-dimensional segmentations of the LA and LA appendage (LAA), which were used to quantify LA and LAA peak velocity and blood stasis (% voxels < 0.1 m/sec). Short-axis cine data were used to delineate LA contours on all cardiac time points, and the resulting three-dimensional-based LAVs were extracted for calculation of LA emptying fractions (LAEFtotal, LAEFactive, LAEFpassive). Stepwise multivariable linear models were calculated for each flow parameter (LA stasis, LA peak velocity, LAA stasis, LAA peak velocity) to determine associations with LAV and LAEF. Results This study included 158 participants (mean age, 73 years ± 7 [SD]; 83 [52.5%] female and 75 [47.4%] male participants). In multivariable models, a 1-unit increase of LAEFtotal was associated with decreased LA stasis (β coefficient, -0.47%; P < .001), while increased LAEFactive was associated with increased LA peak velocity (β coefficient, 0.21 cm/sec; P < .001). Furthermore, increased minimum LAV indexed was most associated with impaired LAA flow (higher LAA stasis [β coefficient, 0.65%; P < .001] and lower LAA peak velocity [β coefficient, -0.35 cm/sec; P < .001]). Conclusion Higher minimum LAV and reduced LA function were associated with impaired flow characteristics in the LA and LAA. LAV assessment might therefore be a surrogate measure for LA and LAA flow abnormalities. Keywords: Atherosclerosis, Left Atrial Volume, Left Atrial Blood Flow, 4D Flow MRI Supplemental material is available for this article. © RSNA, 2024.

Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models

Cancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography, and MRI analysis. However, accurately identifying and outlining the boundaries of tumor regions remains challenging due to their unpredictable shapes, random variations, and blurry outlines. Currently, the CAD based image analysis systems have gained huge attention in biomedical image processing systems along with Machine Learning (ML) and Deep Learning (DL) methods. The conventional machine learning methods suffer from accuracy related issues therefore deep learning-based schemes have been adopted. Specifically, UNet based approach is widely employed for biomedical image segmentation. This research focuses on development of a UNet based deep learning architecture for breast cancer image analysis and segmentation. The proposed model improves the segmentation accuracy by adding saliency model, channel and spatial attention model to consider the low- and high-level features. These modules help to achieve the clear boundary during segmentation. The performance of proposed model is evaluated by using publicly available mammogram dataset known as DDSM dataset.

Artificial intelligence and machine learning in axial spondyloarthritis.

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Non-invasive perfusion MR imaging of the human brain via breath-holding

Dynamic susceptibility contrast (DSC) MRI plays a pivotal role in the accurate diagnosis and prognosis of several neurovascular diseases, but is limited by its reliance on gadolinium, an intravascularly injected chelated metal. Here, we determined the feasibility of measuring perfusion using a DSC analysis of breath-hold-induced gradient-echo-MRI signal changes. We acquired data at both 3 T and 7 T from ten healthy participants who engaged in eight consecutive breath-holds. By pairing a novel arterial input function strategy with a standard DSC MRI analysis, we measured the cerebral blood volume, flow, and transit delay, and found values to agree with those documented in the literature using gadolinium. We also observed voxel-wise agreement between breath-hold and arterial spin labeling measures of cerebral blood flow. Breath-holding resulted in significantly higher contrast-to-noise (6.2 at 3 T vs. 8.5 at 7 T) and gray matter-to-white matter contrast at higher field strength. Finally, using a simulation framework to assess the effect of dynamic vasodilation on perfusion estimation, we found global perfusion underestimation of 20–40%. For the first time, we have assessed the feasibility of and limitations associated with using breath-holds for perfusion estimation with DSC. We hope that the methods and results presented in this study will help pave the way toward contrast-free perfusion imaging, in both basic and clinical research.

Brain Tumor Detection based on Multiple Deep Learning Models for MRI Images

INTRODUCTION: Medical imaging techniques are used to analyze the inner workings of the human body. In today's scientific world, medical image analysis is the most demanding and rising discipline, with brain tumor being the most deadly and destructive kind of malignancy. A brain tumor is an abnormal growth of cells within the skull that disrupts normal brain function by damaging neighboring cells. Brain tumors are regarded as one of the most dangerous, visible, and potentially fatal illnesses in the world. Because of the fast proliferation of tumor cells, brain tumors kill thousands of people each year all over the world. To save the lives of thousands of individuals worldwide, prompt analysis and automated identification of brain tumors are essential.&#x0D; OBJECTIVES: To design a enhanced deep learning model for brain tumor detection and classification from MRI analysis.&#x0D; METHODS: The proposed models Densenet-121, Resnet-101 Mobilenet-V2 is used to perform the task of Brain tumor detection for multi- class classification.&#x0D; RESULTS: The proposed models achieved an accuracy of up to 99% in our evaluations, and when compared to competing models, they yield superior results.&#x0D; CONCLUSION: The MRI image collection has been used to train deep learning models. The experimental findings show that the Densnet-121 model delivers the highest accuracy (99%) compared to other models. The system will have significant applications in the medical field. The presence or absence of a tumour can be ascertained using the proposed method.