Volumetric Magnetic Resonance Imaging Research Articles

Structural correlations between brain magnetic resonance image-derived phenotypes and retinal neuroanatomy.

The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank. Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders. Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10-5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers. Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections.

Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study

BackgroundLimited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet.MethodsParticipants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman’s rank-order correlation coefficient.ResultsThirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$($$\\end{document}± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant.ConclusionsAcquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample.Trial registrationThe study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538).

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.

The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

The association between cannabis use and neuroimaging measures in older adults: findings from the UK biobank.

Cannabis use has increased in recent years. However, the long-term implications of cannabis use on brain health remain unknown. We explored the associations of cannabis use with volumetric brain magnetic resonance imaging (MRI) measures in dementia-free older adults. This cross-sectional and longitudinal study included dementia-free participants of the UK Biobank aged ≥60years. Linear regression models were used to evaluate the association of cannabis use and patterns of use with volumetric brain MRI measures. The association between cannabis use and change in brain MRI measures over time was also tested. All models were adjusted for potential confounders. The sample included 19,932 participants (mean age 68 ± 5years, 48% men), 3,800 (19%) reported lifetime use of cannabis. Cannabis use was associated with smaller total, white, grey and peripheral cortical grey matter volumes (B= -6,690 ± 1,157; P< 0.001, B= -4,396 ± 766; P< 0.001, B= -2,140 ± 690; P= 0.002 and B= -2,451 ± 606; P< 0.001, respectively). Among cannabis users, longer duration of use was associated with smaller total brain, grey and cortical grey matter volumes (B= -7,878 ± 2,396; P= 0.001, B= -5,411 ± 1,430; P< 0.001, B = -5,396 ± 1,254; P< 0.001, respectively), and with increased white matter hyperintensity volume (B= 0.09 ± 0.03; P= 0.008). Additionally, current vs. former users (B= -10,432 ± 4,395; P= 0.020) and frequent versus non-frequent users (B= -2,274 ± 1,125; P= 0.043) had smaller grey and cortical grey matter volumes, respectively. No significant associations were observed between cannabis use and change in brain MRI measures. Our findings suggest that cannabis use, particularly longer duration and frequent use, may be related to smaller grey and white matter volumes in older ages, but not to late-life changes in these measures over time.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. AHIs. Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

Optimizing Screening for Intrastriatal Interventions in Huntington's Disease Using Predictive Models.

Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD. The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions. We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to identify the 10 most important variables. A random forest algorithm was applied to build a predictive model for putamen volume >2500 mm3 and caudate volume >2000 mm3 bilaterally. Using the same 10 predictors, we constructed a logistic regression model with predictors significant at P < 0.05. The random forest model with 1000 trees and minimal terminal node size of 5 resulted in 83% area under the curve (AUC). The logistic regression model retaining age, CAG repeat size, and symbol digit modalities test-correct had 85.1% AUC. A probability cutoff of 0.8 resulted in 5.4% false positive and 66.7% false negative rates. Using easily obtainable clinical data and machine learning-identified initial predictor variables, random forest, and logistic regression models can successfully identify people with sufficient striatal volumes for inclusion cutoffs. Adopting these models in prescreening could accelerate clinical trial enrollment in HD and other neurodegenerative disorders when volume cutoffs are necessary enrollment criteria. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Psychosis in Alzheimer’s disease is associated with specific changes in brain MRI volume, cognition and neuropathology

Psychosis in Alzheimer’s Disease (AD) is prevalent and indicates poor prognosis. However, the neuropathological, cognitive and brain atrophy patterns underlying these symptoms have not been fully elucidated. In this study, we evaluated 178 patients with AD neuropathological change (ADNC) and ante-mortem volumetric brain magnetic resonance imaging (MRI). Presence of psychosis was determined using the Neuropsychiatric Inventory Questionnaire. Clinical Dementia Rating Sum-of-boxes (CDR-SB) was longitudinally compared between groups with a follow-up of 3000 days using mixed-effects multiple linear regression. Neuropsychological tests closest to the time of MRI and brain regional volumes were cross-sectionally compared. Psychosis was associated with lower age of death, higher longitudinal CDR-SB scores, multi-domain cognitive deficits, higher neuritic plaque severity, Braak stage, Lewy Body pathology (LB) and right temporal lobe regional atrophy. Division according to the presence of LB showed differential patterns of AD-typical pathology, cognitive deficits and regional atrophy. In conclusion, psychosis in ADNC with and without LB has clinical value and associates with subgroup patterns of neuropathology, cognition and regional atrophy.

Volumetric analysis of hippocampal subregions and subfields in left and right semantic dementia.

Two variants of semantic dementia are recognized based on the laterality of temporal lobe involvement: a left-predominant variant associated with verbal knowledge impairment and a right-predominant variant associated with behavioural changes and non-verbal knowledge loss. This cross-sectional clinicoradiologic study aimed to assess whole hippocampal, subregion, and/or subfield volume loss in semantic dementia versus controls and across its variants. Thirty-five semantic dementia participants and 15 controls from the Neurodegenerative Research Group at Mayo Clinic who had completed 3.0-T volumetric magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography were included. Classification as left-predominant (n = 25) or right-predominant (n = 10) variant was based on temporal lobe hypometabolism. Volumes of hippocampal subregions (head, body, and tail) and subfields (parasubiculum, presubiculum, subiculum, cornu ammonis 1, cornu ammonis 3, cornu ammonis 4, dentate gyrus, molecular layer, hippocampal-amygdaloid transition area, and fimbria) were obtained using FreeSurfer 7. Subfield volumes were measured separately from head and body subregions. We fit linear mixed-effects models using log-transformed whole hippocampal/subregion/subfield volumes as dependent variables; age, sex, total intracranial volume, hemisphere and a group-by-hemisphere interaction as fixed effects; and subregion/subfield nested within hemisphere as a random effect. Significant results (P < 0.05) are hereby reported. At the whole hippocampal level, the dominant (predominantly involved) hemisphere of both variants showed 23-27% smaller volumes than controls. The non-dominant (less involved) hemisphere of the right-predominant variant also showed volume loss versus controls and the left-predominant variant. At the subregional level, both variants showed 17-28% smaller dominant hemisphere head, body, and tail than controls, with the right-predominant variant also showing 8-12% smaller non-dominant hemisphere head than controls and left-predominant variant. At the subfield level, the left-predominant variant showed 12-36% smaller volumes across all dominant hemisphere subfields and 14-15% smaller non-dominant hemisphere parasubiculum, presubiculum (head and body), subiculum (head) and hippocampal-amygdaloid transition area than controls. The right-predominant variant showed 16-49% smaller volumes across all dominant hemisphere subfields and 14-22% smaller parasubiculum, presubiculum, subiculum, cornu ammonis 3, hippocampal-amygdaloid transition area (all from the head) and fimbria of non-dominant hemisphere versus controls. Comparison of dominant hemispheres showed 16-29% smaller volumes of the parasubiculum, presubiculum (head) and fimbria in the right-predominant than left-predominant variant; comparison of non-dominant hemispheres showed 12-15% smaller cornu ammonis 3, cornu ammonis 4, dentate gyrus, hippocampal-amygdaloid transition area (all from the head) and cornu ammonis 1, cornu ammonis 3 and cornu ammonis 4 (all from the body) in the right-predominant variant. All hippocampal subregion/subfield volumes are affected in semantic dementia, although some are more affected in both dominant and non-dominant hemispheres of the right-predominant than the left-predominant variant by the time of presentation. Involvement of hippocampal structures is apparently more subregion dependent than subfield dependent, indicating possible superiority of subregion volumes as disease biomarkers.

Abducens Nerve Duplication: Novel Intraoperative and Radiographic Observation of a Rare Anatomical Variant

Abstract Objectives The abducens nerve has a long, serpentine subarachnoid course with complex topographical relationships, rendering abducens nerve palsy the most common ocular motor cranial nerve palsy in adults and second most common in pediatric patients, with anatomical variants reported in the literature. Preoperative awareness of abducens nerve variant anatomy may help prevent inadvertent intraoperative injury. Design This study is a case report with a review of the abducens nerve anatomy and variants. Setting The study setting included outpatient, inpatient, and operating room in the neurosurgery department of a quaternary referral center. Participants The study included a woman in her early 30s with a diagnosis of petrous meningioma. Main Outcome Measures In vivo documentation of a type 3 abducens nerve duplication was carried out. Results A left extended retrosigmoid craniotomy was recommended for the petroclival meningioma resection. Intraoperatively, a complete duplication of the left abducens cisternal segment was encountered and photographed. The left unilateral abducens nerve duplication was confirmed with postoperative volumetric magnetic resonance imaging using the FIESTA (fast imaging employing steady-state acquisition) sequence, revealing the union of the duplicated cisternal abducens nerves into a single trunk from Dorello's canal distally. Conclusions Abducens nerve variants are uncommon, and although reported in the setting of cadaveric dissection, in vivo documentation of them is limited. This case report of an in vivo type 3 abducens nerve duplication with intraoperative photographic and radiographic images highlights the need for clinical awareness to avoid inadvertent intraoperative injury.

Assessment of resectability of pancreatic cancer using novel immersive high-performance virtual reality rendering of abdominal computed tomography and magnetic resonance imaging

PurposeVirtual reality (VR) allows for an immersive and interactive analysis of imaging data such as computed tomography (CT) and magnetic resonance imaging (MRI). The aim of this study is to assess the comprehensibility of VR anatomy and its value in assessing resectability of pancreatic ductal adenocarcinoma (PDAC).MethodsThis study assesses exposure to VR anatomy and evaluates the potential role of VR in assessing resectability of PDAC. Firstly, volumetric abdominal CT and MRI data were displayed in an immersive VR environment. Volunteering physicians were asked to identify anatomical landmarks in VR. In the second stage, experienced clinicians were asked to identify vascular involvement in a total of 12 CT and MRI scans displaying PDAC (2 resectable, 2 borderline resectable, and 2 locally advanced tumours per modality). Results were compared to 2D standard PACS viewing.ResultsIn VR visualisation of CT and MRI, the abdominal anatomical landmarks were recognised by all participants except the pancreas (30/34) in VR CT and the splenic (31/34) and common hepatic artery (18/34) in VR MRI, respectively. In VR CT, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 22/24, 20/24 and 19/24 scans, respectively. Whereas, in VR MRI, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 19/24, 19/24 and 21/24 scans, respectively. Interobserver agreement as measured by Fleiss κ was 0.7 for CT and 0.4 for MRI, respectively (p < 0.001). Scans were significantly assessed more accurately in VR CT than standard 2D PACS CT, with a median of 5.5 (IQR 4.75–6) and a median of 3 (IQR 2–3) correctly assessed out of 6 scans (p < 0.001).ConclusionVR enhanced visualisation of abdominal CT and MRI scan data provides intuitive handling and understanding of anatomy and might allow for more accurate staging of PDAC and could thus become a valuable adjunct in PDAC resectability assessment in the future.

A semi-automatic segmentation method for meningioma developed using a variational approach model.

Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma. A database of patients with a meningioma (2007-2015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the Sørensen-Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model. 49 meningioma cases were included. The most common meningioma location was convexity (n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0cm3 (IQR 4.9-31.2). The median meningioma volume using the mathematical model was 16.9cm3 (IQR 4.6-28.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively. Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses.

Structural and Functional Changes in Mild Cognitive Impairment in Parkinson's Disease.

Background and Objectives: The pathophysiology of mild cognitive impairment in Parkinson's disease (PD-MCI) is still not fully elucidated. It has been shown in a few studies in the literature that volume loss in the occipital, parietal and frontal cortices and atrophy in the hippocampus of PD-MCI patients can occur in the early stages of PD. The aim of this study was to evaluate the relationship between gray and white matter volumes and different neuropsychological tests and volumetric magnetic resonance imaging parameters in patients with mild cognitive impairment in Parkinson's disease (PD-MCI). Materials and Methods: Twenty-six PD-MCI and twenty-six healthy elderly (HC) were included in this study. Results: We found that Mini Mental State Examination, Trail Making Test Part A, Clock Drawing Test, Benton Line Judgment Orientation Test and pentagon figure-copying scores were impaired in PD-MCI patients due to the decrease in brain volumes. Conclusions: Our study revealed that among PD-MCI patients, there was a more noticeable decline in White matter volume (WMV) based on volumetric Magnetic Resonance Imaging (MRI) compared to the localized loss of GMV. We think that these abnormal neuropsychological tests in PD-MCI patients can be used as pretests in the evaluation of the stage of transition to dementia.

Neurofilament and Brain Atrophy and Their Association with Cognition in Multiple Sclerosis: A 10-Year Follow-Up Study

Introduction. Cognitive impairment is an important contributor to disability in multiple sclerosis (MS). Disconnection of neuronal circuits due to axonal injury is probably an important underlying mechanism for this disability. Neurofilament light chain (NfL) is a neuron-specific constituent of axons and has gained increasing attention as a biomarker of axonal injury. Objective. To assess the association between NfL in serum (sNfL) and cerebrospinal fluid (cNfL) and cognitive function over 10 years and compare these associations with volumetric brain magnetic resonance imaging (MRI) measurements. Methods. Newly diagnosed MS patients were followed prospectively with baseline NfL and MRI as well as with clinical and cognitive assessments for up to 10 years. Results. Forty-one patients were included. Baseline sNfL correlated negatively with symbol digit modalities test (SDMT) at baseline (r=−0.45, p=0.005), year 5 (r=−0.41, p=0.017), and at year 10 (r=−0.52, p=0.008). Baseline cNfL correlated with baseline SDMT (r=−0.34, p=0.030) and SDMT at year 10 (r=−0.44, p=0.037). Baseline volumes of whole brain (r=0.476, p=0.002), gray matter (r=0.467, p=0.002), T1 (r=−0.627, p&lt;0.001), and T2 lesion volumes (r=−0.475, p=0.002) correlated significantly with baseline SDMT. Longitudinal analyses showed that both MRI volumes and EDSS were associated with the rate of SDMT decline, whereas sNfL and cNfL were not. Conclusion. NfL levels measured in serum and cerebrospinal fluid were both associated with cognitive functioning in MS patients over a 10-year period from diagnosis. However, MRI volumes correlated strongly in addition to the rate of cognitive decline.

Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation

Objective. Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable. Approach. In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods. Significance. Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.

Volumetric changes in brain MRI of infants with hypoxic-ischemic encephalopathy and abnormal neurodevelopment who underwent therapeutic hypothermia

BackgroundHypoxic-ischemic encephalopathy (HIE) is a severe neonatal complication that can result in 40–60 % of long-term morbidity. Magnetic Resonance Imaging (MRI) is a noninvasive method which is usually performed before discharge to visually assess acquired cerebral lesions associated with HIE and severity of lesions possibly providing a guide for detecting adverse outcomes. This study aims to evaluate the impact of HIE on brain volume changes observed in MRI scans performed at a mean 10 days of life, which can serve as a prognostic indicator for abnormal neurodevelopmental (ND) outcomes at 18–24 months among HIE infants. MethodsWe retrospectively identified a cohort of HIE patients between June 2013 and March 2017. The inclusion criteria for therapeutic hypothermia (TH) were a gestational age ≥35 weeks, a birth weight ≥1800 g, and the presence of ≥ moderate HIE. Brain MRI was performed at a mean 10 days of life and brain volumes (total brain volume, cerebral volume, cerebellar volume, brain stem volume, and ventricle volume) were measured for quantitative assessment. At 18–24 months, the infants returned for follow-up evaluations, during which their cognitive, language, and motor skills were assessed using the Bayley Scales of Infant and Toddler Development III. ResultsThe study recruited a total of 240 infants between 2013 and 2017 for volumetric brain MRI evaluation. Among these, 83 were normal control infants, 107 were TH-treated HIE infants and 37 were HIE infants who did not receive TH due to contraindications. Clinical evaluation was further proceeded. We compared the brain volumes between the normal control infants (n = 83) with normal ND but TH-treated HIE infants (n = 76), abnormal ND TH-treated HIE infants (n = 31), and the severe HIE MRI group with no TH (n = 37). The abnormal ND TH-treated HIE infants demonstrated a significant decrease in brainstem volume and an increase in ventricle size (p < 0.001) (Table 4). Lastly, the severe brain MRI group who did not receive TH showed significantly smaller brain stem (p = 0.006), cerebellar (p = 0.006) and cerebrum volumes (p = 0.027), accompanied by larger ventricular size (p = 0.013) compared to the normal control group (Table 5). ConclusionIn addition to assessing the location of brain injuries in MRI scans, the reduction in brain stem volume coupled with an increase in ventricular volume in HIE infants may serve as a biomarker indicating severe HIE and adverse long-term ND outcomes among HIE infants who either received therapeutic hypothermia (TH) treatment or not.

Comparison of aortic blood flow rotational direction in healthy volunteers and patients with bicuspid aortic valves using volumetric velocity-sensitive cardiovascular magnetic resonance imaging.

The rotational direction (RD) of helical blood flow can be classified as either a clockwise (RD+) or counter-clockwise (RD-) flow. We hypothesized that this simple classification might not be sufficient for analysis in vivo and a simultaneous existence of RD+/- may occur. We utilized volumetric velocity-sensitive cardiovascular magnetic resonance imaging (4D flow MRI) to analyze rotational blood flow in the thoracic aorta. Forty volunteers (22 females; mean age, 41±16 years) and seventeen patients with bicuspid aortic valves (BAVs) (9 females; mean age, 42±14 years) were prospectively included. The RDs and the calculation of the rotating blood volumes (RBVs) in the thoracic aorta were performed using a pathline-projection strategy. We could confirm a mainly clockwise RD in the ascending, descending aorta and in the aortic arch. Furthermore, we found a simultaneous existence of RD+/RD-. The RD+/--volume in the ascending aorta was significantly higher in BAV patients, the mean RD+/RD- percentage was approximately 80%/20% vs. 60%/40% in volunteers (P<0.01). The maximum RBV always occurred during systole. There was significantly more clockwise than counter-clockwise rotational flow in the ascending aorta (P<0.01) and the aortic arch (P<0.01), but no significant differences in the descending aorta (P=0.48). A simultaneous occurrence of RD+/RD- indicates that a simple categorization in either of both is insufficient to describe blood flow in vivo. Rotational flow in the ascending aorta and in the aortic arch differs significantly from flow in the descending aorta. BAV patients show significantly more clockwise rotating volume in the ascending aorta compared to healthy volunteers.

GRADUATE I and II: Subgroup analysis and correlations of biomarkers

AbstractBackgroundGantenerumab is a subcutaneous fully human monoclonal antibody targeting aggregated forms of amyloid‐beta. Phase III GRADUATE I (NCT03444870) and II (NCT03443973) trials were designed based on previous key learnings to provide evidence on the benefit:risk profile of subcutaneous gantenerumab in early symptomatic AD. Although primary endpoints were not met, gantenerumab showed treatment effects on amyloid positron emission tomography (PET), volumetric magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) and plasma biomarkers. Correlations of biomarkers with clinical outcomes in the GRADUATE studies were assessed to further evaluate their utility.MethodParticipants aged 50‐90 years with mild cognitive impairment due to AD or mild AD dementia were randomised 1:1 to subcutaneous gantenerumab or placebo. Gantenerumab was up‐titrated over 36 weeks to target dose of 510 mg every 2 weeks. Changes in amyloid centiloid levels were obtained from [18F]Florbetaben or [18F]Flutemetamol PET. Regional changes in tau standardised uptake value ratio were obtained with [18F]GTP1 PET. Whole brain and regional atrophy were assessed via volumetric MRI. Fluid pharmacodynamics biomarkers were measured using Elecsys® immunoassays from Roche Diagnostics.ResultSignificant (but not corrected for multiple testing) treatment effects of gantenerumab on change from baseline to Week 116 were observed for CSF biomarkers (pooled GRADUATE I and II population) including Aβ40 (–10.42%, p = 0.0188), Aβ42 (26.06%, p&lt;0.0001), tTau (–16.49%, p&lt;0.0001), pTau181 (–23.35%; p &lt; 0.0001), NfL (10.76%; p = 0.0159), GFAP (–5.49%; p = 0.0003), and plasma biomarkers including Aβ42 (GRADUATE I: 65.1%, p&lt;0.0001; GRADUATE II: 54.9%, p&lt;0.0001) and pTau181 (GRADUATE I: –17.2%, p&lt;0.0001; GRADUATE II: –15.3%, p&lt;0.0001). Preliminary biomarker analyses showed changes in amyloid PET in gantenerumab‐treated participants had a numerically higher correlation with changes in plasma Aβ42 (R = –0.43), versus pTau181 (R = 0.34) and GFAP (R = 0.16). Biomarker changes in subgroups will also be presented.ConclusionPhase III GRADUATE I and II study results provide evidence of the effect of subcutaneous gantenerumab on biomarkers of AD pathology and neurodegeneration in subgroups.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings. To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy. A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02±2.60dB and the structure similarity index measure was 0.98±0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41±0.77mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1mm in all three orthogonal directions. A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

White Matter Organization and Cortical Thickness Differ Among Active Duty Service Members With Chronic Mild, Moderate, and Severe Traumatic Brain Injury.

Abstract This study compared findings from whole-brain diffusion tensor imaging (DTI) and volumetric magnetic resonance imaging (MRI) among 90 Active Duty Service Members with chronic mild traumatic brain injury (TBI; n = 52), chronic moderate-to-severe TBI (n = 17), and TBI-negative controls (n = 21). Data were collected on a Philips Ingenia 3T MRI with DTI in 32 directions. Results demonstrated that history of TBI was associated with differences in white matter microstructure, white matter volume, and cortical thickness in both mild TBI and moderate-to-severe TBI groups relative to controls. However, the presence, pattern, and distribution of these findings varied substantially depending on the injury severity. Spatially-defined forms of DTI fractional anisotropy (FA) analyses identified altered white matter organization within the chronic moderate-to-severe TBI group, but they did not provide clear evidence of abnormalities within the chronic mild TBI group. In contrast, DTI FA "pothole" analyses identified widely distributed areas of decreased FA throughout the white matter in both the chronic mild TBI and chronic moderate-to-severe TBI groups. Additionally, decreased white matter volume was found in several brain regions for the chronic moderate-to-severe TBI group compared with the other groups. Greater number of DTI FA potholes and reduced cortical thickness were also related to greater severity of self-reported symptoms. In sum, this study expands upon a growing body of literature using advanced imaging techniques to identify potential effects of brain injury in military Service Members. These findings may differ from work in other TBI populations due to varying mechanisms and frequency of injury, as well as a potentially higher level of functioning in the current sample related to the ability to maintain continued Active Duty status after injury. In conclusion, this study provides DTI and volumetric MRI findings across the spectrum of TBI severity. These results provide support for the use of DTI and volumetric MRI to identify differences in white matter microstructure and volume related to TBI. In particular, DTI FA pothole analysis may provide greater sensitivity for detecting subtle forms of white matter injury than conventional DTI FA analyses.

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.