Magnetic Resonance Angiography Data Research Articles

Is Common Trunk Anomaly Frequent in Hemifacial Spasm? Comparison with Normal Subjects using MRI

PurposeHemifacial spasm (HFS) results from vascular compression at the root exit of the facial nerve. Although the underlying etiology has yet to be identified, it has been suggested that congenital vascular anomalies are involved. We have hypothesized common trunk anomaly of the anterior inferior cerebellar artery (AICA) and posterior inferior cerebellar artery (PICA), may play a role in HFS. However, no previous studies have directly compared the incidence of this anomaly between HFS patients and normal subjects. The present study was designed to address this gap in the literature. MethodsThis case-control study compared magnetic resonance angiography data from 65 HFS patients and 65 normal subjects. Dominant AICA/PICA is defined as the absence of PICA/AICA, with the remaining AICA/PICA supplying both vascular territories. The term "common trunk" encompasses both dominant AICA and dominant PICA. The frequency of common trunk and dominant AICA/PICA was compared between the two groups. ResultsThere were no significant differences in the incidence of a common trunk (68.5% and 64.6%), dominant AICA (30.8% and 32.3%) and dominant PICA (37.7% and 32.3%) between the two groups. Additionally, no differences were found in the frequency of atherosclerosis risk factors. ConclusionsThere was no apparent association between common trunk anomaly and HFS. It is suspected that some vascular anomalies other than a common trunk are involved in HFS.

A New Preoperative Simulation Using Magnetic Resonance Imaging Bone-Like Imaging with Zero-Echo-Time Sequence

This study aimed to evaluate the clinical usefulness of zero-echo time(ZTE)-based magnetic resonance imaging(MRI)in planning an optimal surgical approach and applying ZTE for anatomical guidance during transcranial surgery. P atients who underwent transcranial surgery and carotid endarterectomy and for whom ZTE-based MRI and magnetic resonance angiography(MRA)data were obtained, were analyzed by creating ZTE/MRA fusion images and 3D-ZTE-based MRI models. We examined whether these images and models could be substituted for computed tomography imaging during neurosurgical procedures. Furthermore, the clinical usability of the 3D-ZTE-based MRI model was evaluated by comparing it with actual surgical views. ZTE/MRA fusion images and 3D-ZTE-based MRI models clearly illustrated the cranial and intracranial morphology without radiation exposure or the use of an iodinated contrast medium. The models allowed the determination of the optimum surgical approach for cerebral aneurysms, brain tumors near the brain surface, and cervical internal carotid artery stenosis by visualizing the relationship between the lesions and adjacent bone structures. However, ZTE-based MRI did not provide useful information for surgery for skull base lesions, such as vestibular schwannoma, because bone structures of the skull base often include air components, which cause signal disturbances in MRI. ZTE sequences on MRI allowed distinct visualization of not only the bone but also the vital structures around the lesion. This technology is minimally invasive and useful for preoperative planning and guidance of the optimum approach during surgery in a subset of neurosurgical diseases.

An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography.

Centerline tracking is useful in performing segmental analysis of vessel tortuosity in angiography data. However, a highly tortuous) artery can produce multiple centerlines due to over-segmentation of the artery, resulting in inaccurate path-finding results when using the shortest path-finding algorithm. In this study, the internal carotid arteries (ICAs) from three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF MRA) data were used to demonstrate the effectiveness of a new path-finding method. The method is based on a series of depth-first searches (DFSs) with randomly different orders of neighborhood searches and produces an appropriate path connecting the two endpoints in the ICAs. It was compared with three existing methods which were (a) DFS with a sequential order of neighborhood search, (b) Dijkstra algorithm, and (c) A* algorithm. The path-finding accuracy was evaluated by counting the number of successful paths. The method resulted in an accuracy of 95.8%, outperforming the three existing methods. In conclusion, the proposed method has been shown to be more suitable as a path-finding procedure than the existing methods, particularly in cases where there is more than one centerline resulting from over-segmentation of a highly tortuous artery.

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.

This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels' tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects' three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels' diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model's age predictions in patients with intracranial vessel diseases.

Diagnosis of spinal dural arteriovenous fistula: a multimodal MRI assessment strategy

To identify more specific screening indicators at magnetic resonance imaging (MRI) for the diagnosis of spinal dural arteriovenous fistulas (SDAVFs) and to determine an efficient diagnostic strategy. This retrospective study analysed clinical and imaging data of patients diagnosed with SDAVF and alternative myelopathy who underwent conventional MRI examinations. Additionally, three-dimensional (3D) T2-weighted sampling perfection with application-optimised contrasts using different flip-angle evolutions (3D-T2-SPACE) and contrast-enhanced magnetic resonance angiography (CE-MRA) data from patients with SDAVF were compared with digital subtraction angiography (DSA) data. The age of onset, perimedullary flow voids (PFV), distribution of lesions, syringomyelia, degree of spinal oedema, and cauda equina disorder (CED) were factors that showed statistically significance in the identification of SDAVF with alternative myelopathy. After controlling for age, gender, PFV, degree of spinal cord swelling, and syringomyelia, the multivariable ordinal logistic regression model showed that the CED sign (OR = 32.46; 95% confidence interval [CI]: 2.47-427.15; p=0.008) was an independent predictor for SDAVF. The diagnostic model constructed using the PFV and CED signs had better diagnostic performance, with an area under the curve of 0.957 (p<0.001), maximum Youden index of 0.844, sensitivity of 92.9%, and specificity of 91.5%. Both 3D-T2-SPACE (77.8%) and CE-MRA (83.3%) sequences had good localisation values for SDAVF. Combining the two imaging examinations had better diagnostic accuracy than that of DSA. CED and PFV on conventional MRI were specific indicators for the diagnosis of SDAVF. To compensate for the lack of fistula localisation on conventional MRI, 3D-T2-SPACE and CE-MRA can be used. Together they complement each other and have good diagnostic potential.

Contour attention network for cerebrovascular segmentation from TOF-MRA volumetric images.

Cerebrovascular segmentation is a crucial step in the computer-assisted diagnosis of cerebrovascular pathologies. However, accurate extraction of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is still challenging due to the complex topology and slender shape. The existing deep learning-based approaches pay more attention to the skeleton and ignore the contour, which limits the segmentation performance of the cerebrovascular structure. We aim to weight the contour of brain vessels in shallow features when concatenating with deep features. It helps to obtain more accurate cerebrovascular details and narrows the semantic gap between multilevelfeatures. This work proposes a novel framework for priority extraction of contours in cerebrovascular structures. We first design a neighborhood-based algorithm to generate the ground truth of the cerebrovascular contour from original annotations, which can introduce useful shape information for the segmentation network. Moreover, We propose an encoder-dual decoder-based contour attention network (CA-Net), which consists of the dilated asymmetry convolution block (DACB) and the Contour Attention Module (CAM). The ancillary decoder uses the DACB to obtain cerebrovascular contour features under the supervision of contour annotations. The CAM transforms these features into a spatial attention map to increase the weight of the contour voxels in main decoder to better restored the vessel contourdetails. The CA-Net is thoroughly validated using two publicly available datasets, and the experimental results demonstrate that our network outperforms the competitors for cerebrovascular segmentation. We achieved the average dice similarity coefficient ( ) of 68.15 and 99.92% in natural and synthetic datasets. Our method segments cerebrovascular structures with bettercompleteness. We propose a new framework containing contour annotation generation and cerebrovascular segmentation network that better captures the tiny vessels and improve vessel connectivity.

Using Mean Arterial Pressure in Hypertension Diagnosis versus Using Either Systolic or Diastolic Blood Pressure Measurements.

Hypertension is a severe and highly prevalent disease. It is considered a leading contributor to mortality worldwide. Diagnosis guidelines for hypertension use systolic and diastolic blood pressure (BP) together. Mean arterial pressure (MAP), which refers to the average of the arterial blood pressure through a single cardiac cycle, can be an alternative index that may capture the overall exposure of the person to a heightened pressure. A clinical hypothesis, however, suggests that in patients over 50 years old in age, systolic BP may be more predictive of adverse events, while in patients under 50 years old, diastolic BP may be slightly more predictive. In this study, we investigated the correlation between cerebrovascular changes, (impacted by hypertension), and MAP, systolic BP, and diastolic BP separately. Several experiments were conducted using real and synthetic magnetic resonance angiography (MRA) data, along with corresponding BP measurements. Each experiment employs the following methodology: First, MRA data were processed to remove noise, bias, or inhomogeneity. Second, the cerebrovasculature was delineated for MRA subjects using a 3D adaptive region growing connected components algorithm. Third, vascular features (changes in blood vessel's diameters and tortuosity) that describe cerebrovascular alterations that occur prior to and during the development of hypertension were extracted. Finally, feature vectors were constructed, and data were classified using different classifiers, such as SVM, KNN, linear discriminant, and logistic regression, into either normotensives or hypertensives according to the cerebral vascular alterations and the BP measurements. The initial results showed that MAP would be more beneficial and accurate in identifying the cerebrovascular impact of hypertension (accuracy up to 95.2%) than just using either systolic BP (accuracy up to 89.3%) or diastolic BP (accuracy up to 88.9%). This result emphasizes the pathophysiological significance of MAP and supports prior views that this simple measure may be a superior index for the definition of hypertension and research on hypertension.

Carotid Plaque Diagnosis With 3-Dimensional Computed Tomography Angiography: A Comparison With Magnetic Resonance Imaging-Based Plaque Diagnosis.

Although a qualitative diagnosis of plaque causing carotid stenosis has been attempted with carotid computed tomography angiography (CaCTA), no clear findings have been reported. We examined the correlation between the plaque CT values and plaque images obtained by magnetic resonance imaging to derive a qualitative diagnosis of the plaque using CaCTA. Preoperative CaCTA images acquired from patients stented for carotid stenosis were retrospectively analyzed with respect to magnetization-prepared rapid acquisition with gradient echo and time-of-flight magnetic resonance angiography data. Carotid plaques in the stenosed region were quantified in terms of CT density and the plaque/muscle ratio (magnetization-prepared rapid acquisition with gradient echo), and correlations between these 2 features were determined. Plaques were classified as stable or unstable based on the plaque/muscle ratio, with the smallest plaque/muscle ratio observed among plaques positive for intraplaque hemorrhage set as the cutoff value (1.76). A total of 165 patients (179 plaques) were included. Perioperative complications included minor stroke (n = 3), major stroke (n = 1, fatal), and hyperperfusion (n = 2). The correlation between CT density and the plaque/muscle ratio was nonlinear ( P = 0.0139) and negative ( P < 0.0001). The cutoff point (1.76) corresponded to a CT density of 83 HU, supporting this value as a standard reference for plaque stability. Computed tomography density exhibits a nonlinear ( P = 0.0139) and highly negative correlation ( P < 0.0001) with the plaque/muscle ratio. Our results demonstrate that plaque characteristics can be meaningfully diagnosed based on CaCTA image data.

Reproducibility of the computational fluid dynamic analysis of a cerebral aneurysm monitored over a decade

Computational fluid dynamics (CFD) simulations are increasingly utilised to evaluate intracranial aneurysm (IA) haemodynamics to aid in the prediction of morphological changes and rupture risk. However, these models vary and differences in published results warrant the investigation of IA-CFD reproducibility. This study aims to explore sources of intra-team variability and determine its impact on the aneurysm morphology and CFD parameters. A team of four operators were given six sets of magnetic resonance angiography data spanning a decade from one patient with a middle cerebral aneurysm. All operators were given the same protocol and software for model reconstruction and numerical analysis. The morphology and haemodynamics of the operator models were then compared. The segmentation, smoothing factor, inlet and outflow branch lengths were found to cause intra-team variability. There was 80% reproducibility in the time-averaged wall shear stress distribution among operators with the major difference attributed to the level of smoothing. Based on these findings, it was concluded that the clinical applicability of CFD simulations may be feasible if a standardised segmentation protocol is developed. Moreover, when analysing the aneurysm shape change over a decade, it was noted that the co-existence of positive and negative values of the wall shear stress divergence (WSSD) contributed to the growth of a daughter sac.

Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction. In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning. Perforator location was assessed using MR-A imaging (MR-A projection) and Doppler ultrasound data (3D distance) and confirmed intraoperatively in all cases. We evaluated usability (System Usability Scale, SUS), data transfer load and documented personnel hours for software development, correlation of image data, as well as processing duration to clinical readiness (time from MR-A to AR projections per scan). All perforator locations were confirmed intraoperatively, and we found a strong correlation between MR-A projection and 3D distance measurements (Spearman r = 0.894). The overall usability (SUS) was 67 ± 10 (=moderate to good). The presented setup for AR projections took 173 min to clinical readiness (=availability on AR device per patient). In this pilot, we calculated development investments based on project-approved grant-funded personnel hours with a moderate to good usability outcome resulting from some limitations: assessment was based on one-time testing with no previous training, a time lag of AR visualizations on the body and difficulties in spatial AR orientation. The use of AR systems can provide new opportunities for future surgical planning, but has more potential for educational (e.g., patient information) or training purposes of medical under- and postgraduates (spatial recognition of imaging data associated with anatomical structures and operative planning). We expect future usability improvements with refined user interfaces, faster AR hardware and artificial intelligence-enhanced visualization techniques.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data.

Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.

Clinical utility of new bone imaging using zero-echo-time sequence in neurosurgical procedures: Can zero-echo-time be used in clinical practice in neurosurgery?

The purpose of this study was to evaluate the clinical usefulness of zero-echo-time (ZTE)-based magnetic resonance imaging (MRI) in planning the optimum surgical approach and applying ZTE for anatomical guidance during transcranial surgery. Eleven of 26 patients who underwent transcranial surgery and carotid endarterectomy and in whom ZTE-based MRI and magnetic resonance angiography (MRA) data were obtained were analyzed by creating ZTE/MRA fusion images and 3D ZTE-based MRI models. We examined whether these images and models can be substituted for computed tomography imaging for neurosurgical procedures. Furthermore, the clinical usability of the 3D ZTE-based MRI models was evaluated by comparing them with actual surgical views. Zero-echo-time/MRA fusion images and 3D ZTE-based MRI models clearly illustrated the cranial and intracranial morphology without radiation exposure or the use of iodinated contrast medium. The models allowed determination of the optimum surgical approach to cerebral aneurysms, brain tumors near the brain surface, and cervical internal carotid artery stenosis by visualizing the relationship of lesions with adjacent bone structures. However, ZTE-based MRI did not provide useful information for surgery for skull base lesions such as vestibular schwannoma because bone structures of the skull base often include air components, which cause signal disturbance in MRI. Zero-echo-time sequences on MRI allowed distinct visualization of not only bone but also vital structures around the lesion. This technology has low invasiveness for patients and was useful for preoperative planning and guidance of the optimum approach during surgery in a subset of neurosurgical diseases.

An Oscillatory Shear Index-Based Model to Describe Progressive Carotid Artery Stenosis.

Background and aims: This study describes and demonstrates the applicability of a novel in silico method for modeling progressive carotid artery stenosis using the oscillatory shear index (OSI) as the basis of stenosis. Methods: Three-dimensional reconstructions of 11 carotid arteries were generated using patient-derived magnetic resonance angiography and duplex ultrasound data. Computational fluid dynamic simulations were sequentially generated following computational stenosis assessment, and corresponding changes in OSI were observed and used as measure of morphological stabilization. Results: 6 carotid models showed progressive stenosis with statistically significant increases in regions of high OSI (OSI >.2, P < .05) with eventual carotid occlusion in 1 of the cases. Three models remained free or nearly free of increased OSI, whereas 1 model showed an overall decrease in high OSI regions (P < .05) and another trended in that direction but did not achieve statistical significance (P = .145). Conclusions: To our knowledge, this is the first computational model describing progressive stenosis in any peripheral artery including the carotid. Taken together, this study provides a novel framework for computational hemodynamic investigations on progressive atherosclerosis in the carotid artery.

Diagnostic Performance of 0.55 T MRI for Intracranial Aneurysm Detection.

Intracranial aneurysm (IA) is the main cause of subarachnoid hemorrhages. Time-of-flight (TOF) magnetic resonance angiography (MRA) at 1.5 T or 3 T magnetic resonance imaging (MRI) is a well-established method for the diagnosis of IA. The aim of this prospective study was to evaluate the performance of a modern 0.55 T MRI in the diagnosis of IAs in comparison to digital subtraction angiography (DSA) as a standard of reference. Seventeen patients with suspicion of single or multiple IAs underwent TOF MRA at 0.55 T MRI 1 day before DSA. Two neuroradiologists independently measured the aneurysm neck, width, and height on 0.55 T, 1.5 T, and 3 T 3D-TOF MRA source images and 2D/3D rotational angiography. The main analysis assessed the intermodality agreement between 0.55 T TOF MRA and DSA using Bland-Altman plots, a Wilcoxon test, and the intraclass correlation coefficient (ICC). In a secondary analysis, aneurysm dimensions were compared between 0.55 T TOF MRA and 1.5/3 T TOF MRA. Interreader agreement was evaluated by ICC. A third neuroradiologist blinded to patient history screened 0.55 T TOF MRA data sets of the aforementioned 17 patients and 15 additional healthy patients for the presence and location of aneurysms. A total of 19 aneurysms in 16 patients were identified in both 0.55 T MRA and DSA. Measurements of the 2 nonblinded readers showed no significant differences between 0.55 T TOF MRA and DSA in the overall aneurysm size (calculated as the mean from height/width/neck) ( P = 0.178), as well as in the mean width ( P = 0.778) and neck values ( P = 0.190). The mean height was significantly larger in 0.55 T TOF MRA in comparison to DSA ( P = 0.020). Intermodality (1.5/3 T TOF MRA) and interrater agreement were excellent (ICC > 0.94). Of the 32 data sets of patients with and without IA, the blinded reader detected all aneurysms correctly by using 0.55 T images. TOF-MRA acquired with a modern 0.55 T MRI is a reliable tool for the detection and initial assessment of IAs.

Location matters: Offset in tissue-engineered vascular graft implantation location affects wall shear stress in porcine models

ObjectiveAlthough surgical simulation using computational fluid dynamics has advanced, little is known about the accuracy of cardiac surgical procedures after patient-specific design. We evaluated the effects of discrepancies in location for patient-specific simulation and actual implantation on hemodynamic performance of patient-specific tissue-engineered vascular grafts (TEVGs) in porcine models. MethodsMagnetic resonance angiography and 4-dimensional (4D) flow data were acquired in porcine models (n = 11) to create individualized TEVGs. Graft shapes were optimized and manufactured by electrospinning bioresorbable material onto a metal mandrel. TEVGs were implanted 1 or 3 months postimaging, and postoperative magnetic resonance angiography and 4D flow data were obtained and segmented. Displacement between intended and observed TEVG position was determined through center of mass analysis. Hemodynamic data were obtained from 4D flow analysis. Displacement and hemodynamic data were compared using linear regression. ResultsPatient-specific TEVGs were displaced between 1 and 8 mm during implantation compared with their surgically simulated, intended locations. Greater offset between intended and observed position correlated with greater wall shear stress (WSS) in postoperative vasculature (P < .01). Grafts that were implanted closer to their intended locations showed decreased WSS. ConclusionsPatient-specific TEVGs are designed for precise locations to help optimize hemodynamic performance. However, if TEVGs were implanted far from their intended location, worse WSS was observed. This underscores the importance of not only patient-specific design but also precision-guided implantation to optimize hemodynamics in cardiac surgery and increase reproducibility of surgical simulation.

Reversible cerebral artery constriction accompanied with stroke-like episode in MELAS: A case series

ObjectiveThe pathophysiology of stroke-like episode (SLE) in mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) was uncertain, though mitochondrial metabolic crisis of cortical neurons and mitochondrial proliferation in small vessels of brain have been considered. However, the involvement of major cerebral vessels was debated. We aimed to investigate whether major cerebral vessels participate in SLE. MethodsWe retrospectively collected the clinical and neuroimaging data of MELAS patients diagnosed in our center. Through follow-up, the cases harboring reversible cerebral artery constriction on brain magnetic resonance angiography (MRA) examination were included in this study. ResultsThere were 20 patients with intact brain MRA data at acute and non-acute phases. Only 3 cases with m.3243A > G mutation were enrolled. They suffered once or twice SLEs manifesting headache, blurred vision, seizures or mental and behavior disorder. New lesions were present in temporo-parietal and/or temporo-occipital regions. Segmental stenosis at middle cerebral artery and/or posterior cerebral artery, proximal portions in particular, was ipsilateral to the lesions at acute phase in all the 3 patients, which was resolved during the subacute or chronic stages. Moreover, the SLEs lesions were located within the stenotic arteries territory. In addition, dilation at distal portions of the stenotic arteries was observed at acute phase as well in 2 patients. ConclusionReversible constriction of cerebral arteries may contribute to SLE of MELAS. MELAS should be a differential diagnosis when stenosis of major cerebral vessels is present at acute phase of SLE.

Uncontrolled HIV and inflammation is associated with intracranial saccular aneurysm presence.

To study biomarkers of inflammation in cerebrovascular disease, exploring modifiable and non-modifiable biochemical and clinical risk factors associated with the presence of intracranial saccular aneurysms (ISAs) in an HIV-positive cohort. A cross-sectional community-based study was used to study blood biomarkers of inflammation as predictors of cerebrovascular disease, specifically the presence of ISAs in persons with HIV. Potential biochemical and clinical predictors of ISA presence were identified. Time of flight magnetic resonance angiography and magnetic resonance imaging data identified the presence of ISAs in an HIV-positive cohort. Quantitative assays for neuroinflammatory biomarkers were performed on plasma blood samples. Lasso regression models were used to identify neuroinflammatory biomarkers and clinical risk factors associated with ISAs. Eight of 72 participants had radiographically identified ISAs. ISAs were more common in non-Hispanic black participants (18.5% vs. 0% presence in nonblack patients). Participants with well controlled HIV (defined as CD4+ count >200 cells/ml and undetectable viral load at time of magnetic resonance imaging) had lower odds of ISAs (odds ratio: 0.19, 95% confidence interval 0.05-0.79) independent of age, sex, ethnicity and vascular risk factors. Macrophage inflammatory protein-1 p, an HIV- suppressive factor detected in participant blood samples, was inversely associated with aneurysm presence. Well controlled HIV is associated with fewer ISAs. The identification of non-modifiable and modifiable risk factors contributing to ISA formation may provide valuable insight to impact clinical practice and inform the pathophysiology underlying ISA formation.

DOP13 The Arborisation index: An MRI-based measure of mesenteric hyperaemia in Crohn’s Disease

Abstract Background Robust and sensitive therapeutic targets are key in effective management of Inflammatory Bowel Disease1. Mesenteric hyperaemia is a recognized sign of active disease and in cross-sectional image is described as the comb sign. Although it is subjectively described, no automated quantitative MRI-based measures have been developed. We aim to develop an automated methodology using contrast-less time of flight (TOF) Magnetic resonance angiography (MRA). Methods A MATLAB algorithm was developed to track the vessels on a 3D maximum intensity projection of a TOF MRA data set and calculate an arborization Index which is the number of branching points in the intrabdominal vessels (figure 1). 2D TOF scans were acquired in the transverse plane between the top of the hip joint and L4 vertebra using a 3T Ingenia Wide bore scanner (Philips, The Netherlands). The primary outcome was a comparison of the arborization index between Crohn’s disease (CD) and healthy volunteers (HV) groups. A planned sub-analysis was undertaken across CD and HV matched for BMI to investigate the effect of visceral fat on the arborization index. Repeated measures were undertaken to evaluate the variability of the quantification method. No contrast agents were used for the TOF MRA scans. Biological variations within each group and test-retest repeatability were assessed using the coefficient of variation (CV). Statistical analysis with unpaired, two-tailed t-tests were conducted and differences were considered significant when the p-value ≤0.05. All absolute values are presented as mean ±standard deviation (SD). Results In this prospective pilot study, 7 CD patients (C-Reactive Protein=5.2±6.1 mg/L, Faecal Calprotectin 611±981μg/g, BMI=23±3 kg/m2) and 15 HVs (BMI=29±7 kg/m2) were recruited. Patients showed a significantly higher arborization index when compared to HVs (mean arborization in HV=94±21 and CD=139±26; p-value=0.002). The difference in arborization index persisted in a sub-analysis of 7 HVs (BMI=24±2 kg/m2) and 7 CD patients (mean arborization in matched HVs=101±22 vs mean index in CD=139±26; p=0.01) (Figure 2). The CV was 23% for HVs and 18% for CD indicating biological variation. Test-retest variability calculated from multiple TOF scans of the same subjects gave a mean CV of 6±5% for both groups combined. Conclusion Our preliminary data suggest that the arborization index may be a useful measure of hypervascularity and hence intestinal inflammation in Inflammatory Bowel Disease. Further validation to standard disease activity measures is needed across larger cohorts to better investigate the utility of this potential biomarker as a non-invasive measure of disease activity and its reversibility to IBD therapies. Reference 1.Turner,D.,et al.Gastro.2021;160(5):1570–1583.

Choosing an appropriate size valve for transcatheter pulmonary valve implantation in a native right ventricle outflow tract.

Introduction:Transcatheter pulmonary valve implantation has been an effective treatment for dysfuntional right ventricular tract outflow tract (RVOT). Defining a landing zone before the intervention is crucial in patients with native RVOT. Improper sizing and undefined landing zone will lead to embolization.Methods:It is a retrospective observational study from August 2020 to December 2020 in native RVOT. Three patients who had significant Right ventricle dilatation were analyzed. The multi-slice computed tomography (MSCT) with magnetic resonance imaging and angiography data of all patients before the procedure were analyzed. All patients underwent an angiogram in the same sitting, before the procedure to assess the landing zone, valve diameter as well as the risk for coronary compression. We chose a valve based on valve area 23%–25% more than the area at the waist during balloon sizing.Results:All three patients underwent successful valve implantation. Valve sizes used were 27.5 mm in one and 32 mm in the other two. The mean RVOT gradient postprocedure was 11.5 mm Hg and pre procedure was 43 mmHg. There were no complications during the procedure or at a mean follow-up of 3.6 months.Conclusion:The balloon sizing gives the true narrowest diameter in comparison with MSCT, and increasing this area by 23%–25% will give the appropriate valve size for successful implantation.