Search Results (103)

Background and Clinical Significance: Thoracic dorsal arachnoid web (DAW) is a rare intradural extramedullary condition characterized by a thin band of arachnoid tissue compressing the dorsal spinal cord. A hallmark imaging feature is the “scalpel sign”, which refers to anterior displacement of the thoracic spinal cord with dorsal cerebrospinal fluid (CSF) accumulation, producing a sagittal profile resembling a surgical scalpel. Although highly specific for DAW, this sign may also appear in other intradural conditions such as idiopathic ventral spinal cord herniation and arachnoid cysts. The clinical presentation is typically progressive and nonspecific, including lower limb weakness, sensory changes, gait disturbances, and, less frequently, sphincter dysfunction. Diagnosis is often delayed due to the subtle nature of the lesion and limited resolution of conventional Magnetic Resonance Imaging (MRI). High-resolution Three-Dimensional Constructive Interference in Steady State (3D-CISS) sequences improve diagnostic accuracy by highlighting indirect signs such as spinal cord deformation and dorsal CSF flow obstruction. Case Presentation: We report the case of a 57-year-old woman presenting with chronic cervico-dorsalgia, bilateral lower limb weakness, paresthesia, and progressive gait instability. Neurological examination revealed spastic paraparesis and hyperreflexia. Conventional MRI was inconclusive. However, sagittal T2-weighted and 3D-CISS sequences demonstrated the scalpel sign at the T4–T5 level, with anterior cord displacement and dorsal subarachnoid space enlargement. Surgical exploration confirmed the presence of a dorsal arachnoid web, which was resected. Postoperative follow-up showed clear improvement in motor function and gait. Conclusions: DAW should be considered in cases of unexplained thoracic myelopathy or cervico-dorsalgia with neurological signs. Early recognition of the scalpel sign using advanced MRI sequences is critical for timely diagnosis and surgical planning, which may lead to significant clinical improvement. Full article

Atrial fibrillation (AF) is a prevalent heart arrhythmia, characterized by an irregularly irregular rhythm and the absence of identifiable P waves on ECG. Given the loss of effective atrial contraction, AF carries a risk of serious complications. If untreated, AF can promote thrombogenesis, leading to stroke, systemic embolism (e.g., limb or organ ischemia), and myocardial infarction. These serious complications highlight the importance of understanding AF and assessing stroke risk to guide optimal management of this chronic arrhythmia. Congruent with recent technological developments, advanced imaging has emerged as a modality to better understand AF. This review highlights advanced imaging techniques and their advantages, with a focus on 4D flow MRI, a novel modality that enables visualization of blood flow patterns in three dimensions and provides unique insights into cardiac hemodynamics. It also synthesizes the current literature on key hemodynamic markers identified by 4D flow MRI, including blood flow stasis, wall shear stress, and vorticity. Quantifying these markers has improved predictive accuracy of future stroke risk in AF patients, allowing clinicians to risk stratifying their patients and optimize management. Finally, the review discusses potential future markers that may further refine our understanding of AF and inform patient care. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

Pulse wave velocity (PWV) is a key marker of aortic stiffness and cardiovascular risk, yet current methods typically offer only global or regional estimates and lack the possibility to measure local variations along the thoracic aorta. This study aimed to develop and evaluate a pipeline for assessing local aortic PWV using the flow–area (QA) method (PWV_QA) by combining high-resolution 4D MRI techniques. A 3D cine balanced steady-state free precession (bSSFP) sequence was used to capture dynamic changes in aortic geometry, while 4D flow MRI measured time-resolved blood flow. The QA method was applied during the reflection-free early systolic phase. Scan–rescan reproducibility was assessed in six healthy volunteers, and feasibility was additionally explored in Marfan syndrome patients. The mean ± SD values of the Pearson correlation coefficients for per-slice maximum area, velocity, flow, and PWV_QA were 0.99 ± 0.00, 0.82 ± 0.11, 0.96 ± 0.01, and 0.20 ± 0.35, respectively. The median (Q1–Q3) average PWV_QA was 6.6 (5.4–9.4) m/s for scan 1 and 9.1 (6.7–11.3) m/s for scan 2 (p = 0.16) in healthy volunteers and 7.1 (6.9–8.0) m/s in Marfan patients. Combining 4D bSSFP and 4D flow MRI is technically feasible, but the derived PWV_QA maps show high variability, particularly in the aortic root and descending aorta, requiring further optimization. Full article

Background/Objectives: To validate an artificial intelligence-based arterial input function (AI-AIF) deep learning model for myocardial blood flow (MBF) quantification during stress perfusion and assess its extension to rest perfusion, enabling myocardial perfusion reserve (MPR) calculation. Methods: Sixty patients with or at risk for vascular cognitive impairment, prospectively enrolled in the CRUCIAL consortium, underwent quantitative stress and rest myocardial perfusion imaging using a 3 T MRI system. Perfusion imaging was performed using a dual-sequence (DS) protocol after intravenous administration of 0.05 mmol/kg gadobutrol. Retrospectively, the AI-AIF was estimated from standard perfusion images using a 1-D U-Net model trained to predict an unsaturated AIF from a saturated input. MBF was quantified using Fermi function-constrained deconvolution with motion compensation. MPR was calculated as the stress-to-rest MBF ratio. MBF and MPR estimates from both AIF methods were compared using Bland–Altman analyses. Results: Complete stress and rest perfusion datasets were available for 31 patients. A bias of −0.07 mL/g/min was observed between AI-AIF and DS-AIF for stress MBF (median 2.19 vs. 2.30 mL/g/min), with concordant coronary artery disease classification based on the optimal MBF threshold in over 92% of myocardial segments and coronary arteries. Larger biases of 0.12 mL/g/min and −0.30 were observed for rest MBF (1.12 vs. 1.02 mL/g/min) and MPR (2.31 vs. 1.84), respectively, with lower concordance using the optimal MPR threshold (85% of segments, 72% of arteries). Conclusions: The AI-AIF model showed comparable performance to DS-AIF for stress MBF quantification but requires further training for accurate rest MBF and MPR assessment. Full article

Background: Regional cerebral blood flow (rCBF) is a putative biomarker for neuropsychiatric disorders, including major depressive disorder (MDD). Methods: Here, we show that rCBF can be predicted from resting-state functional MRI (rsfMRI) at the voxel level while correcting for partial volume averaging (PVA) artifacts. Cortical patterns of MDD-related CBF differences decoded from rsfMRI using a PVA-corrected approach showed excellent agreement with CBF measured using single-photon emission computed tomography (SPECT) and arterial spin labeling (ASL). A support vector machine algorithm was trained to decode cortical voxel-wise CBF from temporal and power-spectral features of voxel-level rsfMRI time series while accounting for PVA. Three datasets, Amish Connectome Project (N = 300; 179 M/121 F, both rsfMRI and ASL data), UK Biobank (N = 8396; 3097 M/5319 F, rsfMRI data), and Amen Clinics Inc. datasets (N = 372: N = 183 M/189 F, SPECT data), were used. Results: PVA-corrected CBF values predicted from rsfMRI showed significant correlation with the whole-brain (r = 0.54, p = 2 × 10⁻⁵) and 31 out of 34 regional (r = 0.33 to 0.59, p < 1.1 × 10⁻³) rCBF measures from 3D ASL. PVA-corrected rCBF values showed significant regional deficits in the UKBB MDD group (Cohen’s d = −0.30 to −0.56, p < 10⁻²⁸), with the strongest effect sizes observed in the frontal and cingulate areas. The regional deficit pattern of MDD-related hypoperfusion showed excellent agreement with CBF deficits observed in the SPECT data (r = 0.74, p = 4.9 × 10⁻⁷). Consistent with previous findings, this new method suggests that perfusion signals can be predicted using voxel-wise rsfMRI signals. Conclusions: CBF values computed from widely available rsfMRI can be used to study the impact of neuropsychiatric disorders such as MDD on cerebral neurophysiology. Full article

Background: Pulmonary arterial pressure is a key parameter for diagnosing cardiovascular and pulmonary diseases. Its measurement through right heart catheterization is considered the gold standard, and it is an invasive procedure that entails significant risks for patients. This has motivated the development of non-invasive techniques based on patient-specific imaging, such as Physics-Informed Neural Networks (PINNs), which integrate clinical measurements with physical models, such as the 1D reduced Navier–Stokes model, enabling biologically plausible predictions with limited data. Methods: This work implements a PINN model that uses velocity and area measurements in the main bifurcation of the pulmonary artery, comprising the main artery and its secondary branches, to predict pressure, velocity, and area variations throughout the bifurcation. The model training includes penalties to satisfy the laws of flow and momentum conservation. Results: The results show that, using 4D Flow MRI images from a healthy patient as clinical data, the pressure estimates provided by the model are consistent with the expected ranges reported in the literature, reaching a mean arterial pressure of 21.5 mmHg. Conclusions: This model presents an innovative approach that avoids invasive methods, being the first study to apply PINNs to estimate pulmonary arterial pressure in bifurcations. In future work, we aim to validate the model in larger populations and confirm pulmonary hypertension cases diagnosed through catheterization. Full article

Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a fully automated artificial intelligence (AI) 4D flow analysis pipeline was developed and evaluated in a cohort of over 350 subjects. The 4D flow MRI analysis pipeline integrated a series of previously developed and validated deep learning networks, which replaced traditionally manual processing tasks (background-phase correction, noise masking, velocity anti-aliasing, aorta 3D segmentation). Hemodynamic parameters (global aortic pulse wave velocity (PWV), peak velocity, flow energetics) were automatically quantified. The pipeline was evaluated in a heterogeneous single-center cohort of 379 subjects (age = 43.5 ± 18.6 years, 118 female) who underwent 4D flow MRI of the thoracic aorta (n = 147 healthy controls, n = 147 patients with a bicuspid aortic valve [BAV], n = 10 with mechanical valve prostheses, n = 75 pediatric patients with hereditary aortic disease). Pipeline performance with BAV and control data was evaluated by comparing to manual analysis performed by two human observers. A fully automated 4D flow pipeline analysis was successfully performed in 365 of 379 patients (96%). Pipeline-based quantification of aortic hemodynamics was closely correlated with manual analysis results (peak velocity: r = 1.00, p < 0.001; PWV: r = 0.99, p < 0.001; flow energetics: r = 0.99, p < 0.001; overall r ≥ 0.99, p < 0.001). Bland–Altman analysis showed close agreement for all hemodynamic parameters (bias 1–3%, limits of agreement 6–22%). Notably, limits of agreement between different human observers’ quantifications were moderate (4–20%). In addition, the pipeline 4D flow analysis closely reproduced hemodynamic differences between age-matched adult BAV patients and controls (median peak velocity: 1.74 m/s [automated] or 1.76 m/s [manual] BAV vs. 1.31 [auto.] vs. 1.29 [manu.] controls, p < 0.005; PWV: 6.4–6.6 m/s all groups, any processing [no significant differences]; kinetic energy: 4.9 μJ [auto.] or 5.0 μJ [manu.] BAV vs. 3.1 μJ [both] control, p < 0.005). This study presents a framework for the complete automation of quantitative 4D flow MRI data processing with a failure rate of less than 5%, offering improved measurement reliability in quantitative 4D flow MRI. Future studies are warranted to reduced failure rates and evaluate pipeline performance across multiple centers. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Background/Objectives: Isocitrate dehydrogenase (IDH) mutations are hallmark features in subsets of gliomas, producing the oncometabolite D-2-hydroxyglutarate (2HG). Although IDH mutations are associated with better clinical outcomes, their relationship with tumor progression is complex. This study aimed to investigate, in vitro and in vivo, the phenotypic consequences of IDH mutation and 2HG exposure in glioblastoma (GBM) under normoxic and hypoxic conditions and under temozolomide (TMZ) and radiation exposure. Methods: Experiments were conducted using IDH-wildtype (IDH-wt) and IDH-mutant (IDH-mut) glioma cell lines under controlled oxygen conditions. Functional assays included cell viability, cell cycle analysis, apoptosis profiling, migration, and surface marker expression via flow cytometry. Orthotopic xenografts were established in immunocompromised mice to assess in vivo tumor growth and morphology, followed by MRI and histological analysis. Treatments included TMZ, radiation, and 2HG at varying concentrations. Statistical analyses were performed using SPSS and RStudio. Results:IDH-wt cells exhibited faster proliferation and greater adaptability under hypoxia, while IDH-mut cells showed cell cycle arrest and limited growth. 2HG recapitulated IDH-mut features in IDH-wt cells, including increased apoptosis under TMZ, reduced proliferation, and altered CD24/CD44 expression. In vivo, IDH-wt tumors were larger and more infiltrative, while 2HG administration reduced tumor volume and promoted compact morphology. Notably, migration was initially similar across genotypes but increased in IDH-mut and 2HG-treated IDH-wt cells over time, though suppressed under therapeutic stress. Conclusions: IDH mutation and 2HG modulate glioma cell biology, including cell cycle dynamics, proliferation rates, migration, and apoptosis. While the IDH mutation and its metabolic product confer initial growth advantages, they enhance treatment sensitivity and reduce invasiveness, highlighting potential vulnerabilities for targeted therapy. Full article

Staged palliation with the creation of a Fontan circulation is the standard surgical approach in patients with a single ventricle. The Fontan circulation is a complex circuit that is associated with various complications that may present early or later in life and can limit life quality and expectancy. In this context, a good understanding of the Fontan physiology is important to improve outcomes for single-ventricle patients. Cardiovascular magnetic resonance (CMR) is recommended for the long-term follow-up of Fontan patients, as it provides functional and haemodynamic information. Four-dimensional (4D) Flow MRI is a time-resolved, three-dimensional, velocity-encoded cardiovascular magnetic resonance technique that is increasingly used in Fontan patients because it not only enables measuring blood flow within a three-dimensional (3D) volume, but also allows for assessing more advanced haemodynamic parameters that may help in understanding the Fontan physiology and pathophysiology. Furthermore, 4D Flow is used for image-based simulations using computational fluid dynamics. In this review, we provide an overview of the use of cardiovascular magnetic resonance flow assessment, with a focus on four-dimensional flow (‘4D Flow’). Full article

Pathophysiological differences separating small vessel disease (SVD) from Alzheimer’s disease (AD) may alter treatment approach. Investigating peak-arterial and late-capillary perfusion may differentiate SVD from AD. 14 Subjects with MoCA scores of 11–24 were divided into 2 groups. Group one: 6 AD likely subjects positive for 1 or 2 copies of APOE 4+. Group two: 8 SVD likely subjects APOE−. Group three: 7 age-matched controls (MoCA 26–30). All underwent 3D PASL MRI, FLAIR, and SWI axial MRI. Arterial phase peak amplitude and latency, late capillary inflow/clearance rates, and anatomic abnormalities quantitated using microhemorrhage count, Fazekas, Koedam, and Schelton scales were compared. Arterial perfusion demonstrated no statistical differences among SVD, AD, and controls, suggesting normal arterial flow. Late phase perfusion showed significant localized reduction in capillary flow/clearance rates in SVD and AD compared to controls. Absent arterial phase but significant capillary inflow/clearance differences from controls suggest SVD and AD share common impaired blood–brain barrier origins. Full article

Intracranial aneurysms (IAs) are present in approximately 3–5% of the global population and carry a significant risk of rupture, leading to subarachnoid haemorrhage (SAH), a condition associated with high morbidity and mortality. Even with developments in neuroimaging, fundamental clinical difficulty remains in precisely predicting which aneurysms will rupture. Although aneurysm size, location, and patient history define traditional risk assessment, these elements by themselves have insufficient predictive ability. Key elements in rupture risk are aneurysm wall biology, haemodynamics, and inflammation; recent developments in magnetic resonance imaging (MRI) including high-resolution vascular wall imaging (VWI), 4D flow MRI, and quantitative susceptibility mapping (QSM) provide fresh insights on these aspects. The present evidence on these sophisticated MRI techniques is synthesised in this review of the literature, which also analyses their clinical relevance and addresses newly developed computational methods like machine learning for better risk stratification. I underline important studies showing the diagnostic and prognostic worth of MRI-based biomarkers, discuss present constraints, and suggest future lines of research. Personalised aneurysm care could benefit from the combination of multiparametric MRI data with artificial intelligence (AI), hence improving patient outcomes. Full article

Background: Recent studies suggest that any degree of patient-prosthesis mismatch (PPM) increases morbidity and mortality after surgical aortic valve replacement (SAVR). We used computational fluid dynamics simulations to test the influence of prosthesis size and physical activity after SAVR. Methods: In 10 patients with aortic valve stenosis, virtual SAVR was performed. Left ventricular outflow tract stroke volume and flow direction information (4D Flow) were used, and an increase in stroke volume of 25% was chosen for simulating physical activity. Pressure gradients (DP max) across the aortic valve and blood flow profiles in the ascending aorta were calculated and predicted for three different valve sizes at rest and under stress in every patient. Results: Gradients across the aortic valve were significantly lower using larger valves; however, they were not normalized after SAVR (DP max [mmHg] norm/smaller/reference/larger valve = 6/14/12/9 mmHg, <0.01 compared to norm). Physical activity simulation increased DP max in all patients and across all valve sizes (DP max [mmHg] rest versus stress for the smaller/reference/larger valve = 14 vs. 23, 12 vs. 18, 9 vs. 14). Blood flow profiles did not normalize after SAVR and remained unaffected by physical activity. Gradients differed between mild and moderate stenosis between different therapy options and even showed moderate to severe stenosis under simulated physical activity. Conclusions: Prosthesis size and physical activity simulation have a significant influence on gradients across the aortic valve. Virtual therapy planning using patient-specific data might help to improve outcomes after SAVR in the future. Full article

Background and Aims: Elevated lipoprotein(a) [Lp(a)] and familial hypercholesterolemia (FH) are both inherited dyslipidemias that are independently associated with cardiovascular disease. Surrogate markers to assess signs of atherosclerosis, such as arterial stiffness, might be useful to evaluate the cardiovascular risk in young patients. The aim of this study is to evaluate the contribution of Lp(a) to arterial stiffness, as measured by carotid pulse wave velocity (cPWV) in young adults with FH. Methods: For this cross-sectional study, 214 children with FH who participated in a randomized controlled trial between 1997 and 1999 on the efficacy and safety of pravastatin were eligible. After 20 years, these patients were invited for a hospital visit, including cPWV assessment (by 4D flow MRI) and Lp(a) measurement. Linear mixed-effects models were used to evaluate the association between Lp(a) and cPWV. Results: We included 143 patients (mean [standard deviation] age: 31.8 [3.2] years) from 108 families. Median (interquartile range) cPWV was 1.62 (1.31–2.06) m/s. Both the unadjusted (ß = −0.0014 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0052 to 0.0023, p = 0.455) and adjusted model (ß = −0.0005 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0042 to 0.0032, p = 0.785) showed no significant association between Lp(a) and cPWV. Conclusions: Our findings indicate that Lp(a) levels are not associated with carotid arterial stiffness in young adults with FH. Possibly, High Lp(a) might cause atherosclerosis by mechanisms beyond arterial stiffness in young adults. Other surrogate markers of early signs of atherosclerosis may be more suitable to evaluate the Lp(a)-mediated contribution to atherosclerosis in young FH patients. Full article