Weighted MRI Scans Research Articles

Quantification of Collateral Supply with Local-AIF Dynamic Susceptibility Contrast MRI Predicts Infarct Growth.

In ischemic stroke, leptomeningeal collaterals can provide delayed and dispersed compensatory blood flow to tissue-at-risk despite an occlusion and can impact treatment response and infarct growth. The purpose of this work is to test the hypothesis that inclusion of this delayed and dispersed flow with an appropriately calculated Local Arterial Input Function (Local-AIF) is needed to quantify the degree of collateral blood supply in tissue distal to an occlusion. Seven experiments were conducted in a pre-clinical middle cerebral artery occlusion model. Dynamic susceptibility contrast MRI was imaged and post-processed to yield quantitative cerebral blood flow (qCBF) maps with both a traditionally chosen single arterial input function applied globally to the whole brain (i.e. "Global-AIF") and a delay and dispersion corrected AIF (i.e. "Local-AIF") that is sensitive to retrograde flow. Leptomeningeal collateral arterial recruitment was quantified with a pial collateral score from x-ray angiograms, and infarct growth calculated from serially acquired diffusion weighted MRI scans. The degree of collateralization at x-ray correlated more strongly with qCBF using the Local-AIF in the ischemic penumbra (R2=0.81) than traditionally chosen Global-AIF (R2=0.05). qCBF using a Local-AIF was negatively correlated (less infarct progression as perfusion increased) with infarct growth (R2 = 0.79) more strongly than a Global-AIF (R2=0.02). In acute stroke, qCBF calculated with a Local-AIF is more accurate for assessing tissue status and collateral supply than traditionally chosen Global-AIFs. These findings support use of a Local-AIF that corrects for delayed and dispersed retrograde flow in determining quantitative tissue perfusion with collateral supply in occlusive disease. MRI = magnetic resonance imaging; DSC = dynamic susceptibility contrast; PCS = pial collateral score; MCAO = middle cerebral artery occlusion; MCA = middle cerebral artery; AIF = arterial input function; rCBF = relative cerebral blood flow; qCBF = quantitative cerebral blood flow.

Relationship between Lumbar Foraminal Stenosis and Multifidus Muscle Atrophy - A Retrospective Cross-Sectional Study.

Retrospective cross-sectional study. To evaluate the relationship between lumbar foraminal stenosis (LFS) and multifidus muscle atrophy. The multifidus muscle is an important stabilizer of the lumbar spine. In LFS, the compression of the segmental nerve can give rise to radicular symptoms and back pain. LFS can impede function and induce atrophy of the segmentally innervated multifidus muscle. Patients with degenerative lumbar spinal conditions who underwent posterior spinal fusion for degenerative lumbar disease from December 2014 to February 2024 were analyzed. Multifidus fatty infiltration (FI) and functional cross-sectional area (fCSA) were determined at the L4 upper endplate axial level on T2- weighted MRI scans using dedicated software. Severity of LFS was assessed at all lumbar levels and sides using the Lee classification (Grade: 0 - 3). For each level, Pfirrmann and Weishaupt gradings were used to assess intervertebral disc disease (IVDD) and facet joint osteoarthritis (FJOA), respectively. Multivariable linear mixed models were run for the LFS grade of each level and side separately as the independent predictor of multifidus FI and fCSA. Each analysis was adjusted for age, sex, BMI, as well as FJOA and IVDD of the level corresponding to the LFS. A total of 216 patients (50.5% female) with a median age of 61.6 years (IQR=52.0 - 69.0) and a median BMI of 28.1kg/m2 (IQR=24.8 - 33.0) were included. Linear mixed model analysis revealed that higher multifidus FI (Estimate [Confidence interval]=1.7% [0.1 - 3.3], P=0.043) and lower fCSA (-18.6mm2 [-34.3 - -2.6], P=0.022) were both significantly predicted by L2-L3 level LFS severity. The observed positive correlation between upper segment LFS and multifidus muscle atrophy points towards compromised innervation. This necessitates further research to establish the causal relationship and guide prevention efforts.

Study to estimate the NIHS score and volume of stroke in predicting 30-day mortality in acute ischemic stroke patients

Background. Stroke is defined as a sudden onset of a neurological deficit attributed to a focal vascular cause. It is one of the leading causes of mortality and morbidity all over the world. The present study aimed to estimate the NIHS score and volume of stroke in predicting 30-day mortality in acute ischemic stroke patients. Material and methods. The present study was a prospective cross-sectional study conducted in 100 patients above 18 years old, of either gender, with symptoms of stroke for less than 24 hours, diagnosis of acute ischemic stroke based on history, physical examination, CT scan and/or diffusion weighted MRI scan performed during the first 24 hours. Results. In the present study, the mean age was 57.4±13.36 years. The volume of stroke on 1st day was 40.75±38.51, and on 30th day it was reduced to 31.94±33.20 which was statistically significant. NIHSS (National Institutes of Health Stroke Scale) score on day 1 was 9.70±5.22, and on day 30 it was 7.77±5.25, with a statistically significant difference (p=0.000). At the end of the 30 days, 10% mortality was observed. NIHSS score and volume of stroke were significantly associated with the 30-day outcome. Conclusion. In conclusion, integrating NIHSS scoring and stroke volume assessment into routine clinical practice enhances predictive accuracy for 30-day mortality in acute ischemic stroke patients. These findings advocate for early intervention strategies aimed at reducing stroke volume and improving neurological outcomes. Further research is recommended to validate these findings across diverse populations and settings, potentially refining acute stroke management protocol.

OBSERVATIONAL STUDY ON SILENT CEREBRAL INFARCT IN CHILDREN OF SICKLE CELL DISEASE IN ASSOCIATION WITH RISK FACTORS

Aim and Objectives: To find out the presence of silent cerebral infracts (SCI) among sickle cell diseased children and to identify risk factors and correlate risk factors with silent cerebral infracts in sickle cell diseased children.. Settings, Design. Methods and Materials: The children who came to paediatrics department of maharaja institute of medical sciences, with confirmed haemoglobin electrophoresis findings of sickle cell disease in steady-state are subjected to T2 weighted MRI scan of the brain using Philips ingenia CX 1.5Tesla MRI machine. Results: out of 50 children with sickle cell disease ,18(36%) children have silent cerebral infarct and it was observed that the lower hemoglobin concentration, Lower RBC count, Lower Serum Ferritin, and elevated WBC count were considered to be the potential risk factors for Silent cerebral infarcts in Sickle cell disease. Conclusion : Patients with risk factors for silent cerebral infarcts should be evaluated for cerebrovascular disease. If evidence of the presence of SCI, consideration must be given to therapeutic intervention.

Spatial distribution of cerebral microbleeds and FLAIR hyperintensities on follow-up MRI after radiotherapy for lower grade glioma.

Cerebral microbleeds (CMBs) and fluid-attenuated-inversion recovery (FLAIR) hyperintensities on brain MRI scans after radiotherapy (RT) are considered markers for microvascular damage and related cognitive changes. However, the spatial distribution using existing scoring systems as well as colocation of these imaging biomarkers remain unclear, hampering clinical interpretation. This study aims to elucidate the distribution and colocation of these markers in patients with lower grade glioma (LGG). CMBs were spatially classified on retrospective 1.5 T susceptibility weighted MRI scans according to the existing Microbleed Anatomical Rating Scale (MARS) and were additionally scored for being located in hippocampus, amygdala, cortex, white matter (WM), grey matter (GM), WM/GM junction and for their spatial relation to FLAIR hyperintensities. Scoring was performed for whole, ipsilateral and contralateral cerebrum (with respect to tumour bulk). Fifty-one scans were included of which 28 had at least one CMB. The majority of CMBs were localized in the lobar area and in deep and periventricular white matter (DPWM) - generally in WM. Only few CMBs were found in GM. In scans obtained up to 7 years after RT completion the majority of CMBs were not colocalized with FLAIR hyperintensities. CMBs and FLAIR hyperintensities appear to be separate imaging biomarkers for radiation therapy induced microvascular damage, as they are not colocalized in patients with LGG, especially not early on after completion of RT.

Measuring olfactory‐related brain region volume loss in MCI and AD using structural MRI

AbstractBackgroundAlzheimer disease (AD) is a chronic disorder that affects millions of individuals worldwide. Olfactory dysfunction is a common symptom of several neurological disorders including AD. Olfactory dysfunction can be an early alteration heralding the presence of AD. Studying the pattern of alterations in olfaction‐related brain regions might help detection of neurodegenerative diseases at an earlier stage. Objectives of this study are to assess the volume of olfactory areas associated with odour processing in healthy elderly, MCI and AD populations using structural MRI scans. To test whether there are any differences in the volume of olfactory related regions between patients and healthy controls. To determine whether there is an effect of sex on olfactory‐related brain regions’ volume loss.MethodNeurologically healthy participants aged 66‐85 (n = 95), MCI aged 58‐86 (n = 81) and mild probable AD aged 55‐88 (n = 37) participants were recruited. T1‐ weighted MRI scans acquired at 1.5T were processed using SPM12. Smoothed images were used to extract the volume of olfactory cortex, hippocampus, parahippocampus, amygdala, orbitofrontal cortex and entorhinal cortex.ResultOne‐way ANOVA analyses showed a significant reduction in all olfaction‐related brain regions (p≤0.0001). Post‐hoc multiple comparison tests showed a significant reduction in volume in the AD group in all olfaction‐related brain regions. In the MCI group, a significantly smaller volume than the healthy controls was found in four of the selected olfactory‐related brain regions (namely, hippocampus, parahippocampus, amygdala and entorhinal cortex) for the total and left volume in the female group only. Additionally in the female MCI, the volume of the right entorhinal cortex however, was also significantly smaller than that of the control group. When adjusting for MMSE, however, between group differences in all olfaction‐related brain regions were no longer significant.ConclusionThese preliminary data show a significant reduction in grey matter volume in all olfaction‐related brain regions. Grey matter volume loss in these regions appears to be a good proxy of disease severity.

RADIOLOGICAL SPINE ADIPOSE INDEX AN INDEPENDENT RISK FACTOR FOR DEEP INFECTION AFTER POSTERIOR INSTRUMENTED LUMBAR FUSION

Surgical site infections (SSIs) after spinal fusion surgery increase healthcare costs, morbidity and mortality. Routine measures of obesity fail to consider site specific fat distribution. We aimed to assess the association between the spine adipose index and deep surgical site infection and determine a threshold value for spine adipose index that can assist in preoperative risk stratification in patients undergoing posterior instrumented lumbar fusion (PILF).A multicentre retrospective case-control study was completed. We reviewed patients who underwent PILF from January 1, 2010 to December 31, 2018. All patients developing a deep primary incisional or organ-space SSI within 90 days of surgery as per US Centre for Disease Control and Prevention criteria were identified. We gathered potential pre-operative and intra-operative deep infection risk factors for each patient. Spine adipose index was measured on pre-operative mid-sagittal cuts of T2 weighted MRI scans. Each measurement was repeated twice by three authors in a blinded fashion, with each series of measurement separated by a period of at least six weeks.Forty-two patients were included in final analysis, with twenty-one cases and twenty-one matched controls. The spine adipose index was significantly greater in patients developing deep SSI (p =0.029), and this relationship was maintained after adjusting for confounders (p=0.046). Risk of developing deep SSI following PILF surgery was increased 2.0-fold when the spine adipose index was ≥0.51. The spine adipose index had excellent (ICC >0.9; p <0.001) inter- and intra-observer reliabilities.The spine adipose index is a novel radiographic measure and an independent risk factor for developing deep SSI, with 0.51 being the ideal threshold value for pre-operative risk stratification in patients undergoing PILF surgery.

A pilot study of MRI radiomics for high-risk prostate cancer stratification in 1.5 T MR-guided radiotherapy.

To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI-guided linear accelerator (MR-LINAC) for D'Amico high-risk prostate cancer (PC) classification in MR-guided radiotherapy (MRgRT). One hundred seventy-six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2 -weighted MRI scans on a 1.5 T MR-LINAC. The endpoint was to differentiate high-risk from low/intermediate-risk PC based on D'Amico criteria using MRI-radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan-rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI-radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference (P=0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV-based model; and 71.43%, 71.43%, and 71.43% for the CTV-based model. The corresponding AUCs were 0.718 and 0.750 (P=0.091) for CTV- and PTV-based models. MRI-radiomics obtained from a 1.5 T MR-LINAC showed promising results in D'Amico high-risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification.

Regional neuronal compensation in early and late stages of the Alzheimer’s trajectory

AbstractBackgroundGlucose hypermetabolism is suggested to occur as a compensatory response to early neuronal damage[1]. Hypermetabolism has been shown to arise in the prodromal phase of Alzheimer’s disease development as a mechanism to prevent against cognitive deficits or as a compensatory mechanism to maintain the neuronal function amidst of other neurodegenerative process. Here we aimed to evaluate whether there is regional glucose hypermetabolism as a proxy to neuronal function in response to tau deposition in different stages of Alzheimer’s disease.Method139 amyloid positive cognitively impaired subjects were evaluated from the Alzheimer’s Disease Neuroimaging Initiative (n = 44 diagnosed with Alzheimer’s disease, n = 95 mild cognitive impairment). All subjects had [18F]AV1451 and [18F]FDG PET imaging and an anatomical T1 weighted MRI scan. PET scans were coregistered to the respective anatomical scan and normalised to MNI space in SPM12. Standard uptake value ratio (SUVr) images were created using cerebellar grey matter and pons uptake as reference region for [18F]AV1451 and [18F]FDG, respectively. Voxel‐wise linear regression was conducted between[18F]AV1451 and [18F]FDG. Pearson’s correlations were conducted between regions displaying a positive association.ResultVoxel‐wise analysis revealed a correlation between higher glucose metabolism and tau deposition localised to the pre and post‐central gyrus in our cognitively impaired cohort. We found evidence that higher tau deposition and higher glucose metabolism were associated in the left and right post‐central gyrus in Alzheimer’s subjects (n=44, r=0.361 p=0.016 and r=0.327 p=0.030, respectively). A significant correlation was observed in both the left and right pre‐central gyrus (n=95, r=0.418 p<0.001 and r=0.361 p<0.001, respectively) as well as the left (r=0.384 p<0.001)and right post‐central gyrus (r=0.324 p=0.001) in subjects diagnosed with mild cognitive impairment.ConclusionThis correlation between higher tau and higher glucose metabolism in pre‐and post‐central gyrus implies that there is still neuronal compensation occurring in response to tau deposition both in early and later stages of the disease process (Braak stages V‐VI). This suggests that neuronal compensation occurs on a regional basis throughout the Alzheimer’s disease trajectory and may reflect the ability of neurons to still respond to an effective treatment.

Classification of Brain Tumor using Hybrid Deep Learning Approach

Diagnosis of tumor at its early stage is the most challenging task for its treatment in the area of neurology. As, brain tumor is the most common problem in the world, so tremendous research is being carried out to find out the cancer during its onset stages. The task of diagnosis as well as its automation has been extremely difficult using conventional image processing methods. In view of this, a novel technique has been proposed based on convolutional neural network architecture to classify the brain tumor which assists radiologists and physicians to make their decision fast and accurate. The proposed deep learning structure helps to analyze and produce better feature maps to classify the variations in the normal and malignant cases. The proposed method i.e. Hybrid Deep Neural Network (H-DNN) architecture is the combination of two different DNN. First Deep Neural Network (DNN-1) uses the spatial texture information of the cranial Magnetic Resonance (MR) images, whereas in the second method Deep Neural Network (DNN-2) uses the frequency domain information of the MRI scans. Finally, we combine both neural networks to produce better classification result based on prediction score. The training input to the DNN-1 is the texture which is computed by Local Binary Patterns, whereas the DNN-2 uses the frequencies, which have being calculated by Wavelet Transformation as its training input. Here two Dataset have been used for the evaluation of the proposed model i.e. Real MRI dataset and BraTS 2012 MRI Dataset for T2- weighted MRI scans. In this study, the proposed model provides 98.7% classification accuracy, which outperforms the other methods as reported in the related work. Also comparisons of Accuracy, Sensitivity and Specificity of the proposed method has been done with DNN-1 and DNN-2 architecture to indicate that the reported model gives better results when compared to the other methods.

No correlation identified between the proportional size of a prolapsed intravertebral disc with disability or leg pain.

The aim of the study was to determine if there was a direct correlation between the pain and disability experienced by patients and size of their disc prolapse, measured by the disc's cross-sectional area on T2 axial MRI scans. Patients were asked to prospectively complete visual analogue scale (VAS) and Oswestry Disability Index (ODI) scores on the day of their MRI scan. All patients with primary disc herniation were included. Exclusion criteria included recurrent disc herniation, cauda equina syndrome, or any other associated spinal pathology. T2 weighted MRI scans were reviewed on picture archiving and communications software. The T2 axial image showing the disc protrusion with the largest cross sectional area was used for measurements. The area of the disc and canal were measured at this level. The size of the disc was measured as a percentage of the cross-sectional area of the spinal canal on the chosen image. The VAS leg pain and ODI scores were each correlated with the size of the disc using the Pearson correlation coefficient (PCC). Intraobserver reliability for MRI measurement was assessed using the interclass correlation coefficient (ICC). We assessed if the position of the disc prolapse (central, lateral recess, or foraminal) altered the symptoms described by the patient. The VAS and ODI scores from central and lateral recess disc prolapses were compared. A total of 56 patients (mean age 41.1 years (22.8 to 70.3)) were included. A high degree of intraobserver reliability was observed for MRI measurement: single measure ICC was 0.99 (95% confidence interval (CI) from 0.97 to 0.99 (p < 0.001)). The PCC comparing VAS leg scores with canal occupancy for herniated disc was 0.056. The PCC comparing ODI for herniated disc was 0.070. We found 13 disc prolapses centrally and 43 lateral recess prolapses. There were no foraminal prolapses in this group. The position of the prolapse was not found to be related to the mean VAS score or ODI experienced by the patients (VAS, p = 0.251; ODI, p = 0.093). The results of the statistical analysis show that there is no direct correlation between the size or position of the disc prolapse and a patient's symptoms. The symptoms experienced by patients should be the primary concern in deciding to perform discectomy. Cite this article: Bone Joint J2022;104-B(6):715-720.

Deep learning architectures for Parkinson's disease detection by using multi-modal features

BackgroundThe use of multi-modal features for improving the diagnosing accuracy of Parkinson's disease (PD) is still under consideration. MethodEarly diagnosis of PD is very crucial for better management and treatment planning of PD, as the delay in the diagnosis may even lead to death of the patient. Two frameworks, feature-level and modal-level, both of which are based on deep learning, are presented to classify the given subjects into PD and healthy by using neuroimaging (T1 weighted MRI scans and SPECT) and biological (CSF) features as the dataset. In the feature-level framework, all these features are integrated to form a heterogeneous dataset which is then supplied to two deep learning models to diagnose PD. In the modal-level framework, the number of features from T1 weighted MRI scans is reduced first, by using the filter feature selection method ReliefF. Those reduced number of features from the MRI scans are integrated with SPECT and CSF features to form another heterogeneous dataset, which is then fed to a deep learning model. ResultsDue to imbalanced nature of the dataset (consists of 73 PD and 59 healthy subjects), F1-score, geometric-mean, sensitivity, and specificity are measured, in addition to measuring the accuracy, to evaluate the performance of the developed models. A maximum accuracy of 93.33% and 92.38% is observed, for CNN, in the feature-level framework and modal-level framework, respectively. ConclusionsThough the complexity of the approach based on multi-modal features is high as compared to an approach that uses only one type of feature, i.e., either neuroimaging or biological; the results prove that the approach based on multi-modal features is useful for classifying the given subjects into PD and healthy and can help the clinicians in accurately diagnosing the PD.

Longitudinal changes of connectomes and graph theory measures in aging.

Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that are correlated with neurobiological variations and can help guide quantitative study of brain function. However, the understanding of how connectomes change longitudinally is limited. In this work, we evaluate modern pipelines to obtain the connectomics data from diffusion weighted MRI scans across different sessions from control subjects (55-99 years old) in the Baltimore Longitudinal Study of Aging and Cambridge Centre for Ageing and Neuroscience. From the connectivity matrices, we compute graph theory measures to understand their brain networks and apply a linear mixed-effects model to study their longitudinal changes with respect to age. With this approach, we computed 14 graph theory measures of 1469 healthy subjects (2476 scans) and found statistically significant correlations between all 14 measures and age. In this analysis: 1) the brain becomes more segregated but less integrated in aging; 2) the overall network cost increases for older subjects; 3) the small-world organizations remain stable; and 4) due to high intra-subject variance, there is not clear trend for longitudinal changes of graph theory measures of individual subjects. Therefore, while useful to investigate brain evolution in aging at the population level, improvements in the connectome reconstruction are needed to decrease single subject variability for individual inference.

Abstract 70: Racial Differences In Small Vessel Disease Measured In MRI Of Stroke/ TIA Patients

Introduction: White matter hyperintensities (WMH) and cerebral microbleeds (CMB) are manifestations of small vessel disease (SVD), a surrogate for vascular cognitive impairment. Few studies have systematically investigated the racial differences in SVD among African American (AA) and white stroke/TIA patients. Methods: MRI scans of consecutive ischemic stroke and TIA patients admitted to our tertiary stroke center were assessed as a part of a stroke study. Deep and periventricular WMHs (DWMHs and PVWMHs, respectively) were rated visually on axial FLAIR images using the 4-point Fazekas scale and categorized into none/mild (grades 0 and 1) or moderate/severe (grades 2 and 3). Subsequently T2*-weighted MRI scans for the number, location, and topography of CMBs, as well as patients’ demographic, clinical information, and periodontal disease (PD) were evaluated. Student t-tests were performed to test continuous variables. X 2 tests were used to compare categorical variables. Multivariable analysis using multiple logistic regression was used to test the association between AA and SVD. Results: Among a total of 861 consecutive patients (741 stroke, 120 TIA), 469 AA patients were compared with 360 white for moderate/severe PVWMH (38%), DWMH (32%) and CMB≥5 (3%). Those with moderate/severe PVWMH were older (age 68±11 vs. 61±12, p&lt;0.001), more likely to be male (42% vs. 35%, p=0.02), hypertensive (40% vs. 26%, p=0.02), diabetic (43% vs. 34%, p=0.007), and have severe PD (41% vs. 35%, p=0.07). AA race was associated with PVWMH (Adj. OR 1.9, 95% 1.4-2.7) when adjusted for age, gender, hypertension, diabetes, and PD. Those with moderate/severe DWMH were older (age 68±10 vs. 62±13, p&lt;0.001), and more likely to be hypertensive (33% vs. 24%, p=0.09), diabetic (37% vs. 27%, p=0.003), and have severe PD (36% vs. 28%, p=0.01). AA race was associated with DWMH (Adj. OR 1.5, 95% 1.1-2.2) when adjusted for age, hypertension, diabetes, and PD. Those with CMB≥5 were more likely to be hypertensive (5% vs. 1%, p=0.008). AA race was associated with CMB≥5 (Adj. OR 4.1, 95% 1.3-12.6) when adjusted for age and hypertension. Conclusion: We report significant independent association between AA race and various manifestations of SVD including moderate/severe DWMH, PVWMH, and CMB≥5.

Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging.

Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1‐weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1‐FLAIR, T2, T2*, T2‐FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single‐contrast T1‐weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1‐FLAIR and single‐contrast T1‐weighted scans, using correlations between voxels and regions of interest across participants, measures of within‐ and between‐participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix‐derived data using test–retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.

Subcortical brain trajectories in later life between sexes and APOE genotypes: A UK Biobank study of individuals of self‐identified Indian ancestry

AbstractBackgroundIn the UK, individuals with Indian ancestry show lower rates of dementia compared to other ethnicities. Most dementia studies mainly examine individuals of European ancestry and/or higher socioeconomic status, making it unclear if effects generalize to other populations. Here we map age trends in subcortical brain morphometry in later life and investigate the modulating effects of sex and APOE genotype in people of self‐identified (SI) Indian ancestry.MethodT1‐weighted MRI scans from mostly healthy individuals with SI‐Indian ancestry from the UK Biobank (; N = 297; born in India = 43.1%) were processed using FreeSurfer v7.1 to derive bilaterally averaged hippocampus, thalamus, putamen, pallidum, amygdala, caudate, nucleus accumbens, and ventricle volumes. SI‐ancestry groups were confirmed with the first three genetic principal components ()4. Sex‐stratified normative centile reference curves (nomograms) of standardized residuals corrected for intracranial volume (ICV) and scanner were plotted for each structure. Linear mixed models were used to investigate a sex‐by‐age interaction adjusted for scanner and ICV. APOE ε4 carriers and non‐carriers were compared. All results were corrected for multiple comparisons.ResultNomograms () show a similar pattern for SI‐Indian and SI‐European ancestry groups (N= 35,608). All subcortical volumes generally declined significantly with increasing age, except for the ventricles which increased as expected (; Cohen’s d: 0.12 ‐ 0.93). No subcortical region showed a significant age by sex interaction (). Subcortical volumes did not differ between carriers and non‐carriers ().ConclusionNomograms and statistical analysis of subcortical brain volume trajectories showed similar patterns between SI‐Indian and SI‐European groups. In the current sample, we detected no sex by age interaction for any subcortical volume nor detrimental effect of APOE ε4 allele, likely due to lack of power. Larger samples of SI‐Indian ancestry individuals along with analyses accounting for life expectancy, short survival with dementia, and risk/protective factors compared with populations of higher prevalence will be valuable to assess whether differential subcortical trajectories may account for lower rates of AD in India compared to other countries.

Alterations in olfaction‐related brain regions in the ageing population: A structural MRI study

AbstractBackgroundAgeing plays a major role in neurodegenerative disorders such as Alzheimer’s disease. Olfactory dysfunction can be an early alteration heralding the presence of a neurodegenerative disorder in ageing. Studying alterations in olfaction‐related brain regions might help detection of neurodegenerative diseases at an earlier stage. Objectives: To assess the volume of olfactory cortex and areas associated with odour processing in different age groups in the cognitively healthy population using high resolution 3D structural MRI scans. To test whether normal ageing determines any volumetric alterations of olfactory cortex, hippocampus and other associated areas and whether there are any differential predictive values of these alterations for neurodegenerative disorders. To determine if atrophy of the olfactory cortex occurs prior to atrophy of the hippocampus and other areas involved in some aspects of odour processing in the healthy population.MethodNeurologically healthy participants were divided based on their age into three age groups: young (20–35 year; n=53), middle‐aged (36‐65 year; n=66) and elderly (66‐85 year; n=95). T1‐ weighted MRI scans acquired at 1.5T were processed using SPM12. Smoothed images were used to extract the volume of olfaction‐related regions including primary olfactory cortex, hippocampus, parahippocampus and amygdala.ResultOne‐way ANOVA analyses showed significant decreases in volume between age groups in all olfaction‐related brain regions (p≤0.0001). Post‐hoc multiple comparisons tests showed that significant differences were found between age groups except in the young vs the middle‐aged where no significant differences were found for hippocampal and amygdala volumes. Pairwise comparisons of groups following ANCOVA showed no significant difference in all olfaction‐related brain regions when controlling for age. Adjusting for education showed significant age‐group differences in olfactory cortex and parahippocampus volumes except for hippocampus in all groups, and for the amygdala for young versus middle‐aged groupsConclusionThese preliminary data showed significant reduction in the volume of olfaction‐related brain regions with ageing, but hippocampus and amygdala appeared to have a more limited volume reduction than primary olfactory cortex and parahippocampus. The results suggest that monitoring of volume of olfaction‐related brain regions in the ageing population may be valuable to detect risk of neurodegenerative diseases.

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance of 2D, 3D, and hybrid deep learning models for automatic segmentation of 11 lower leg muscles and two bones from MRI scans of children with and without cerebral palsy. All six models were trained and evaluated on manually segmented T1 -weighted MRI scans of the lower legs of 20 children, six of whom had cerebral palsy. The segmentation results were assessed using the median Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and volume error (VError) of all 13 labels of every scan. The best performance was achieved by H-DenseUNet, a hybrid model (DSC 0.90, ASSD 0.5 mm, and VError 2.6 cm3 ). The performance was equivalent to the inter-rater performance of manual segmentation (DSC 0.89, ASSD 0.6 mm, and VError 3.3 cm3 ). Models trained with the Dice loss function outperformed models trained with the cross-entropy loss function. Near-optimal performance could be attained using only 11 scans for training. Segmentation performance was similar for scans of typically developing children (DSC 0.90, ASSD 0.5 mm, and VError 2.8 cm3 ) and children with cerebral palsy (DSC 0.85, ASSD 0.6 mm, and VError 2.4 cm3 ). These findings demonstrate the feasibility of fully automatic segmentation of individual muscles and bones from MRI scans of children with and without cerebral palsy.

Analysis of the Effect of Incentive Nursing Intervention in Children with Severe Viral Encephalitis and Myocarditis during Rehabilitation Based on Diffusion Weighted MRI.

Background Severe viral encephalitis in children causes a viral infection that damages their central nervous system. This situation arises the mental abnormalities, sudden rise in body temperature, disturbance of consciousness, and so forth in children, which can be life-threatening. Objective This work aimed at exploring the effect of diffusion weighted MRI on children with severe viral encephalitis and myocarditis. Methods This work presents a diffusion weighted MRI scanning method that involves scanning through a serial imaging device, axial scanning, and sagittal and coronal scanning. 60 children with severe viral encephalitis and myocarditis who admitted to Brain Hospital of Hunan Province from April 2017 to May 2020 were deemed as research subjects. All the children underwent CT and MRI examination, blood routine examination, and cerebrospinal fluid examination after admission. This work uses the random number table method to classify the subjects into control group and observation group, each consisting of 30 cases. Children in the control group were provided with the routine nursing intervention, whereas children in the observation group were subjected to incentive nursing intervention. The baseline data, ECG monitoring indicators, body abnormalities, and clinical symptom relief time of the two groups of children were compared and the results of diffusion weighted MRI scans were analyzed and the ADC values were counted. Results The two groups of children were compared on the basis of baseline data, and the variation was not statistically substantial (P > 0.05). The cases of children in the control group had higher heart rate and respiration, and physical dysfunction, language dysfunction, unconsciousness, and nervous dysfunction were more than those in the observation group. However, the cases of blood oxygen saturation were less than those of the observation group. After nursing intervention done for the control group, remission time of clinical symptoms such as convulsion, physical dysfunction, unconsciousness, and nerve dysfunction was longer relative to the observation group (all P < 0.05 are considered). Conclusion The diffusion weighted MRI had diagnostic significance for severe viral encephalitis and myocarditis. For children with severe viral encephalitis and myocarditis, clinical incentive nursing intervention was particularly imperative. It can not only help children to relieve symptoms and control the deterioration of the disease in a short time but also help improve the quality of life of the children and the confidence of family members to cope with the disease.

Radiographic spine adipose index: an independent risk factor for deep surgical site infection after posterior instrumented lumbar fusion

BACKGROUND CONTEXTSurgical site infections (SSIs) after spinal fusion surgery increase healthcare costs, morbidity and mortality. Routine measures of obesity fail to consider site specific fat distribution. PURPOSETo assess the association between the spine adipose index and deep surgical site infection and determine a threshold value for spine adipose index that can assist in preoperative risk stratification in patients undergoing posterior instrumented lumbar fusion (PILF). STUDY DESIGN/SETTINGMulticenter retrospective case-control study. PATIENT SAMPLEWe reviewed patients who underwent PILF from January 1, 2010 to December 31, 2018. OUTCOME MEASURESAll patients developing a deep primary incisional or organ-space SSI within 90 days of surgery as per US Center for Disease Control and Prevention criteria were identified. We gathered potential pre-operative and intra-operative deep infection risk factors for each patient. A 1:1 match was performed using the following criteria: gender, age (±3 y), ethnicity, date of surgery (± 1 y), and hospital location of surgery. Spine adipose index was measured on pre-operative mid-sagittal cuts of T2 weighted MRI scans. Each measurement was repeated twice by three authors in a blinded fashion, with each series of measurement separated by a period of at least six weeks. MethodsStepwise binary logistic regression analysis was used to assess the association between SAI and the development of deep SSI. Separate logistic regression models were used for body mass index (BMI) and direct measures of subcutaneous fat thickness. Receiver Operating Characteristic analysis was used to determine the optimal value for SAI, and subsequent risk ratios were calculated using the identified threshold. Intra- and inter-observer reliabilities were assessed using intra-class coefficients. ResultsForty-two patients were included in final analysis, with twenty-one cases and twenty-one matched controls. The spine adipose index was significantly greater in patients developing deep SSI (p=.029), and this relationship was maintained after adjusting for confounders (p=.046). Risk of developing deep SSI following PILF surgery was increased 2.0-fold when the spine adipose index was ≥0.51. The spine adipose index had excellent (ICC >0.9; p<.001) inter- and intra-observer reliabilities. ConclusionThe spine adipose index is a novel radiographic measure and an independent risk factor for developing deep SSI, with 0.51 being the ideal threshold value for pre-operative risk stratification in patients undergoing PILF surgery.