Density Imaging Research Articles

A novel and practical magneto-optical imaging of the current density of coated conductors within liquid nitrogen

A novel and practical magneto-optical imaging of the current density of coated conductors within liquid nitrogen

Bone Mineral Density and First Line Imaging with [18F]fluorocholine PET/CT in Normocalcemic and Hypercalcemic Primary Hyperparathyroidism: Results from a Single Center

Objectives: Primary hyperparathyroidism (PHPT) is associated with normal or elevated calcium levels and affects bone mineral density. The proportion of cases predisposed to metabolic bone disease is unknown in patients with PHPT. The aim of this study was to assess bone mineral density and bone quality in patients with normo- or hypercalcemic primary hyperparathyroidism undergoing baseline parathyroid gland assessment with [18F]fluorocholine PET/CT imaging. Methods: A total of 140 consecutive patients were enrolled in this observational study. All patients with normo- or hypercalcemic primary hyperparathyroidism underwent dual-energy X-ray absorptiometry (DXA) for assessment of bone mineral density (BMD) and trabecular bone score (TBS). [18F]fluorocholine PET/CT was performed in all patients for the detection and localization of parathyroid adenoma. Hyper- and normocalcemic patients were compared with regard to the proportion of osteoporosis and osteopenia, T-Score, TBS, serum calcium, phosphorus and parathyroid hormone levels, the maximum standardized uptake value (SUVmax) in PET/CT imaging, and laboratory results. Results: The majority of patients was female (88.57%) and had a pathologic bone mineral density (52.86%). Overall, 33 patients had osteoporosis and 41 osteopenia. The mean lumbar T-Score was −1.48 (SD 1.37) and the T-Score of the femoral neck was −1.21 (SD 0.92). Mean TBS was also decreased (−2.13). No difference was found between normo- or hypercalcemic patients regarding bone metabolism and imaging parameters. Conclusions: More than half of patients with normo- or hypercalcemic PHPT showed abnormal BMD. First-line [18F]fluorocholine PET/CT identified parathyroid adenoma in a high proportion of patients, even in patients with normocalcemic PHPT. The early evaluation of metabolic bone disease seems desirable in clinical management of females with PHPT.

An MRI-Based Method for the Morphologic Assessment of the Anterior Tibial Tuberosity

Background: A prominent anterior tibial tuberosity (or tibial tubercle) can be seen in ongoing Osgood–Schlatter disease (OSD) in teenagers or as a sequela of OSD in adults. Current radiological methods do not provide a true anatomical assessment of the tibial tuberosity; therefore, we proposed and developed a Magnetic Resonance Imaging (MRI)-based method for measuring the anterior tibial tuberosity index, aiming to deal with the current lack of effective techniques for accurately assessing these particular morphologic features. Methods: A retrospective study included 47 knees with tibial tuberosity measurements on both true sagittal MPR images of 3D proton density (PD)-weighted MRI sequences and lateral knee radiographs. The same landmarks were followed and the anterior tibial tuberosity index (ATTI) was measured. Results: The comparison of the results obtained by the two methods demonstrates that our method is reliable and reproducible with substantial inter- and intra-observer agreement. The intraclass correlation coefficient was 0.9250 (95% CI: 0.8654 to 0.9582), indicating excellent reliability between the two methods. A strong positive correlation was also identified, with a correlation coefficient of r = 0.8746 (95% CI: 0.7845 to 0.9286, p < 0.0001) between the two methods. No significant deviation from linearity was observed by analyzing the linear model validity using the cusum test (p = 0.62). Conclusions: Based on these results, we encourage the use of 3D PD-weighted MRI sequences for the measurement of the anterior tibial tuberosity on MRI in order to avoid unnecessary exposure to ionizing radiation and potentially obtain a more accurate measurement. Future larger studies should also explore the benefit of utilizing 3D sequences over 2D lateral projections to minimize measuring bias.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer’s disease neuropathology

Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD). Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n=291), resilient in both (n=260) or advanced in one/resilient in the other (n=163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.

Equilibrium dynamical models in the inner region of the Large Magellanic Cloud based on Gaia DR3 kinematics

The Large Magellanic Cloud (LMC) contains complex dynamics driven by both internal and external processes. The external forces are due to tidal interactions with the Small Magellanic Cloud and the Milky Way, while internally its dynamics mainly depend on the stellar, gas, and dark matter mass distributions. Despite this complexity, simple physical models often provide valuable insights into the primary driving factors. We used Gaia Data Release 3 (DR3) to explore how well equilibrium dynamical models based on the Jeans equations and the Schwarzschild orbit superposition method are able to describe the LMC's five-dimensional phase-space distribution and line-of-sight (LOS) velocity distribution, respectively. In the Schwarzschild model, we incorporated a triaxial bar component for the first time and derived the LMC's bar pattern speed. We fit comprehensive Jeans dynamical models to all Gaia DR3 stars with proper motion and LOS velocity measurements found in the footprint of the VISTA near-infrared survey of the Magellanic System using a discrete maximum likelihood approach. These models are very efficient at discriminating genuine LMC member stars from Milky Way foreground stars and background galaxies. They constrain the shape, orientation, and enclosed mass of the galaxy under the assumption of axisymmetry. We used the Jeans model results as a stepping stone to more complex two-component Schwarzschild models, which include an axisymmetric disc and a co-centric triaxial bar, which we fit to the LMC Gaia DR3 LOS velocity field using a $ minimisation approach. The Jeans models describe the rotation and velocity dispersion of the LMC disc well, and we find an inclination angle of $ circ circ $, line of nodes orientation of $ circ circ $, and an intrinsic thickness of the disc of $q_0^d = b a (minor to major axis ratio). However, bound to axisymmetry, these models fail to properly describe the kinematics in the central region of the galaxy dominated by the LMC bar. We used the derived disc orientation and the Gaia DR3 density image of the LMC to obtain the intrinsic shape of the bar. Using these two components as input to our Schwarzschild models, we performed orbit integration and weighting in a rotating reference frame fixed to the bar, deriving an independent measurement of the LMC bar pattern speed of $ Both the Jeans and Schwarzschild models predict the same enclosed mass distribution within a radius of $6.2$\,kpc of $ Msun.

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

Association of concussion history with psychiatric symptoms, limbic system structure, and kynurenine pathway metabolites in healthy, collegiate-aged athletes

Psychiatric outcomes are commonly observed in individuals with repeated concussions, though their underlying mechanism is unknown. One potential mechanism linking concussion with psychiatric symptoms is inflammation-induced activation of the kynurenine pathway, which is thought to play a role in the pathogenesis of mood disorders. Here, we investigated the association of prior concussion with multiple psychiatric-related outcomes in otherwise healthy male and female collegiate-aged athletes (N = 212) with varying histories of concussion recruited from the community. Specially, we tested the hypotheses that concussion history is associated with worse psychiatric symptoms, limbic system structural abnormalities (hippocampal volume, white matter microstructure assessed using neurite orientation dispersion and density imaging; NODDI), and elevations in kynurenine pathway (KP) metabolites (e.g., Quinolinic acid; QuinA). Given known sex-effects on concussion risk and recovery, psychiatric outcomes, and the kynurenine pathway, the moderating effect of sex was considered for all analyses. More concussions were associated with greater depression, anxiety, and anhedonia symptoms in female athletes (ps ≤ 0.005) and greater depression symptoms in male athletes (p = 0.011). More concussions were associated with smaller bilateral hippocampal tail (ps < 0.010) and left hippocampal body (p < 0.001) volumes across male and female athletes. Prior concussion was also associated with elevations in the orientation dispersion index (ODI) and lower intracellular volume fraction in several white matter tracts including the in uncinate fasciculus, cingulum-gyrus, and forceps major and minor, with evidence of female-specific associations in select regions. Regarding serum KP metabolites, more concussions were associated with elevated QuinA in females and lower tryptophan in males (ps ≤ 0.010). Finally, serum levels of QuinA were associated with elevated ODI (male and female athletes) and worse anxiety symptoms (females only), while higher ODI in female athletes and smaller hippocampal volumes in male athletes were associated with more severe anxiety and depression symptoms (ps ≤ 0.05). These data suggest that cumulative concussion is associated with psychiatric symptoms and limbic system structure in healthy athletes, with increased susceptibility to these effects in female athletes. Moreover, the associations of outcomes with serum KP metabolites highlight the KP as one potential molecular pathway underlying these observations.

Application of preoperative advanced diffusion magnetic resonance imaging in evaluating the postoperative recurrence of lower grade gliomas

BackgroundRecurrence of lower grade glioma (LrGG) appeared to be unavoidable despite considerable research performed in last decades. Thus, we evaluated the postoperative recurrence within two years after the surgery in patients with LrGG by preoperative advanced diffusion magnetic resonance imaging (dMRI).Materials and methods48 patients with lower-grade gliomas (23 recurrence, 25 nonrecurrence) were recruited into this study. Different models of dMRI were reconstructed, including apparent fiber density (AFD), white matter tract integrity (WMTI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), Bingham NODDI and standard model imaging (SMI). Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to construct a multiparametric prediction model for the diagnosis of postoperative recurrence.ResultsThe parameters derived from each dMRI model, including AFD, axon water fraction (AWF), mean diffusivity (MD), mean kurtosis (MK), fractional anisotropy (FA), intracellular volume fraction (ICVF), extra-axonal perpendicular diffusivity (De⊥), extra-axonal parallel diffusivity (De∥) and free water fraction (fw), showed significant differences between nonrecurrence group and recurrence group. The extra-axonal perpendicular diffusivity (De⊥) had the highest area under curve (AUC = 0.885), which was significantly higher than others. The variable importance for the projection (VIP) value of De⊥ was also the highest. The AUC value of the multiparametric prediction model merging AFD, WMTI, DTI, DKI, NODDI, Bingham NODDI and SMI was up to 0.96.ConclusionPreoperative advanced dMRI showed great efficacy in evaluating postoperative recurrence of LrGG and De⊥ of SMI might be a valuable marker.

Examining relationships among NODDI indices of white matter structure in prefrontal cortical-thalamic-striatal circuitry and OCD symptomatology

Obsessive-compulsive disorder is a psychiatric disorder characterized by intrusive thoughts and repetitive behaviors. There are two prominent features: Harm Avoidance (HA) and Incompleteness (INC). Previous resting-state studies reported abnormally elevated connectivity between prefrontal cortical (PFC) and subcortical regions (thalamus, striatum) in OCD participants. Yet, little is known about the white matter (WM) structural abnormalities in these connections. Using brain parcellation and segmentation, whole brain tractography, and Neurite Orientation Dispersion and Density Imaging (NODDI), we aimed to characterize WM structural abnormalities in OCD vs. healthy controls and determine the extent to which NODDI indices of these connections were associated with subthreshold-threshold HA, INC and overall OCD symptom severity across all participants. Four PFC regions were segmented: ventral medial (vmPFC), ventrolateral (vlPFC), dorsomedial (dmPFC), and dorsolateral (dlPFC). NODDI Neurite Density (NDI) and Orientation Dispersion (ODI) indices of WM structure were extracted from connections between these PFC regions and the thalamus (42 OCD, 44 healthy controls, mean age[SD] = 23.65[4.25]y, 63.9% female) and striatum (38 OCD, 41 healthy controls, mean age[SD] = 23.59[4.27]y, 64.5% female). Multivariate analyses of covariance revealed no between-group differences in these indices. Multivariate regression models revealed that greater NDI in vmPFC-thalamus, greater NDI and ODI in vmPFC-striatum, and greater NDI in dmPFC-thalamus connections were associated with greater INC severity (Q ≤ 0.032). These findings highlight the utility of NODDI in the examination of WM structure in OCD, provide valuable insights into specific WM alterations underlying dimensional INC, and can facilitate the development of customized treatments for OCD individuals with treatment-resistant symptoms.

A case study of the “21.7” Henan extremely rainfall event: From the perspective of water vapor monitored with GNSS tomography

Water vapor plays a vital role in the development of heavy rainfall system. The “21.7” Henan rainfall event, a record-breaking event, was investigated from the perspective of water vapor based on in-situ measurements, namely the 220 continuously operating GNSS ground stations in Henan Province, with GNSS tomography technique. Results showed that during and after the heavy rainfall, the error RMS of water vapor density from tomography was 21 % lower than that of GFS prediction data at the height of about 1450 m by comparing with radiosonde data. Taking ERA5 as the reference, the average relative error of tomographic water vapor density at the height below 4 km was within 10 % before and during the rainfall. The analyses of spatiotemporal variations of water vapor illustrated that tomographic products can, compared to ERA5 reanalysis data, more distinctly reflect the formation and convergence process of low-level water vapor bands before the occurrence of the extreme rainfall, as well as the fracture of the water vapor bands before the end of the rainfall. By combining the tomographic water vapor and GFS wind field data, the water vapor flux divergence can be calculated to further elucidate the movement of water vapor where significant variations of water vapor at the height of 10 to12 km (near the tropopause) and 1 to 4 km in both the vertical and horizontal directions were found before the extreme precipitation.

Cortical microstructural alterations in different stages of Parkinson's disease.

To explore the cortical microstructural alterations in Parkinson's disease (PD) at different stages.149 PD patients and 76 healthy controls were included. PD patients were divided into early stage PD (EPD) (Hoehn-Yahr stage ≤ 2) and moderate-to-late stage PD (MLPD) (Hoehn-Yahr stage ≥ 2.5) according to their Hoehn-Yahr stages. All participants underwent two-shell diffusion MRI and the images were fitted to Neurite Orientation Dispersion and Density Imaging (NODDI) model to obtain the neurite density index (NDI) and orientation dispersion index (ODI) to reflect the cortical microstructure. We used gray matter-based spatial statistics method to compare the voxel-wise cortical NODDI metrics between groups. Partial correlation was used to correlate the NODDI metrics and global composite outcome in PD patients.Compared with healthy controls, EPD patients showed lower ODI in widespread regions, covering bilateral frontal, temporal, parietal and occipital cortices, as well as regional lower NDI in bilateral cingulate and frontal lobes. Compared with healthy controls, MLPD patients showed lower ODI and NDI in more widespread regions. Compared with EPD patients, MLPD patients showed lower ODI in bilateral temporal, parietal and occipital cortices, where the ODI values were negatively correlated with global composite outcome in PD patients.PD patients showed widespread cortical microstructural degeneration, characterized by reduced neurite density and orientation dispersion, and the cortical neuritic microstructure exhibit progressive degeneration during the progression of PD.

Visualization of Injected Fuel Vaporization Using Background-Oriented Schlieren Method

In this experimental study, ethanol, an eco-friendly fuel used to reduce harmful exhaust emissions from internal combustion engines, was blended with gasoline. To optimize the combustion and the shape of the combustion chamber, the spray development and spray behavior of ethanol and gasoline were visualized and compared. Droplets of injected fuel were visualized using a high-speed camera. Because it is difficult to experimentally observe fuel vaporization using only high-speed cameras, the vaporization characteristics of the spray were compared and analyzed by using the background-oriented schlieren (BOS) method with density variation and image displacement in the spray flow field to visualize the vaporized fuel. The experimental results indicate that the fuel vaporization phenomenon could be observed during the spray development and that more fuel vaporization occurred at higher ambient temperatures and lower ambient pressures. Additionally, the dependence of the differences in the vaporization characteristics of the fuel and the wall-wetting phenomenon caused by the vaporized fuel was analyzed.

Photon-counting detector computed tomography: iodine density versus virtual monoenergetic imaging of pancreatic ductal adenocarcinoma.

Photon-counting detector computed tomography: iodine density versus virtual monoenergetic imaging of pancreatic ductal adenocarcinoma.

Quantitative MRI distinguishes different leukodystrophies and correlates with clinical measures.

The leukodystrophy "vanishing white matter" (VWM) and "metachromatic leukodystrophy" (MLD) affect the brain's white matter, but have very different underlying pathology. We aim to determine whether quantitative MRI reflects known neuropathological differences and correlates with clinical scores in these leukodystrophies. VWM and MLD patients and controls were prospectively included between 2020 and 2023. Clinical scores were recorded. MRI at 3 T included multi-compartment relaxometry diffusion-informed myelin water imaging (MCR-DIMWI) and multi-echo T2-relaxation imaging with compressed sensing (METRICS) to determine myelin water fractions (MWF). Multi-shell diffusion-weighted data were used for diffusion tensor imaging measures and neurite orientation dispersion and density imaging (NODDI) analysis, which estimates neurite density index, orientation dispersion index, and free water fraction. As quantitative MRI measures are age-dependent, ratios between actual and age-expected MRI measures were calculated. We performed the multilevel analysis with subsequent post-hoc and correlation tests to assess differences between groups and clinico-radiological correlations. Sixteen control (age range: 2.3-61.3 years, 8 male), 37 VWM (2.4-56.5 years, 20 male), and 14 MLD (2.2-41.7 years, 6 male) subjects were included. Neurite density index and MWF were lower in patients than in controls (p < 0.001). Free water fraction was highest in VWM (p = 0.01), but similar to controls in MLD (p = 0.99). Changes in diffusion tensor imaging measures relative to controls were generally more pronounced in VWM than in MLD. In both patient groups, MCR-DIMWI MWF correlated strongest with clinical measures. Quantitative MRI correlates to clinical measures and yields differential profiles in VWM and MLD, in line with differences in neuropathology. Question Can quantitative MRI reflect known neuropathological differences and correlate with clinical scores for these leukodystrophies? Finding Quantitative MRI measures, e.g., MWF, neurite density index, and free water fraction differ between leukodystrophies and controls, in correspondence to known histological differences. Clinical relevance MRI techniques producing quantitative, biologically-specific, measures regarding the health of myelin and axons deliver more comprehensive information regarding pathological changes in leukodystrophies than current approaches, and are thus viable tools for monitoring patients and providing clinical trial outcome measures.

Gray matter based spatial statistics framework in the 1-month brain: insights into gray matter microstructure in infancy.

The neurodevelopmental epoch from fetal stages to early life embodies a critical window of peak growth and plasticity in which differences believed to be associated with many neurodevelopmental and psychiatric disorders first emerge. Obtaining a detailed understanding of the developmental trajectories of the cortical gray matter microstructure is necessary to characterize differential patterns of neurodevelopment that may subserve future intellectual, behavioral, and psychiatric challenges. The neurite orientation dispersion density imaging (NODDI) Gray-Matter Based Spatial Statistics (GBSS) framework leverages information from the NODDI model to enable sensitive characterization of the gray matter microstructure while limiting partial volume contamination and misregistration errors between images collected in different spaces. However, limited contrast of the underdeveloped brain poses challenges for implementing this framework with infant diffusion MRI (dMRI) data. In this work, we aim to examine the development of cortical microstructure in infants. We utilize the NODDI GBSS framework and propose refinements to the original framework that aim to improve the delineation and characterization of gray matter in the infant brain. Taking this approach, we cross-sectionally investigate age relationships in the developing gray matter microstructural organization in infants within the first month of life and reveal widespread relationships with the gray matter architecture.

Assessing Visual Pathway White Matter Degeneration in Primary Open‐Angle Glaucoma Using Multiple MRI Morphology and Diffusion Metrics

BackgroundPrimary open‐angle glaucoma (POAG), a leading cause of irreversible blindness, is associated with neurodegeneration in the visual pathway, but the underlying pathophysiology remains incompletely resolved.PurposeTo characterize macro‐ and microstructural white matter abnormalities in optic tract (OT) and optic radiation (OR) of POAG.Study typeProspective.PopulationsA total of 34 POAG patients (21 males, 13 females) and 25 healthy controls (HCs) (16 males, nine females).Field Strength/Sequence3 T; multiband spin‐echo echo planar diffusion spectrum imaging (DSI).AssessmentWe compared multiple morphology metrics, including volume, area, length, and shape metrics, as well as diffusion metrics such as diffusion tensor imaging (fractional anisotropy [FA], mean diffusivity, radial diffusivity, and axial diffusivity), mean apparent propagator (mean squared displacement, q‐space inverse variance, return‐to‐origin probability, return‐to‐axis probabilities [RTAP] and return‐to‐plane probabilities, non‐Gaussianity, perpendicular non‐Gaussianity, parallel non‐Gaussianity), and neurite orientation dispersion and density imaging (intracellular volume fraction, orientation dispersion index [ODI], and isotropic volume fraction of the OT and OR).Statistical TestsStatistical comparisons and classifications employed linear mixed model and logistic regression. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). P‐value &lt;0.05 was statistically significant.ResultsMorphology analysis in POAG revealed a lower span in the OR (29.43 ± 2.30 vs. 30.59 ± 2.01, 3.8%) and OT (19.73 ± 2.21 vs. 20.68 ± 1.37, 4.6%), and a higher curl (3.03 ± 0.22 vs. 2.90 ± 0.16, 4.5%) in OT. Diffusion metrics revealed lower mean FA (OR: 0.328 ± 0.03 vs. 0.340 ± 0.018, 3.5%; OT: 0.255 ± 0.022 vs. 0.268 ± 0.018, 4.9%) and lower mean RTAP (OR: 5.919 ± 0.529 vs. 6.216 ± 0.489, 4.8%; OT: 4.089 ± 0.402 vs. 4.280 ± 0.353, 4.5%), with higher mean ODI in the OT (0.448 ± 0.029 vs. 0.433 ± 0.025, 3.5%). Combined models, incorporating these MRI metrics, effectively discriminated POAG from HCs, achieving AUCs of 0.84 for OR and 0.83 for OT.Data ConclusionsDSI‐derived morphology and diffusion metrics demonstrated macro‐ and micro abnormalities in the visual pathway, providing insights into POAG‐related neurodegeneration.Level of Evidence2Technical EfficacyStage 3

Supervised machine learning on Galactic filaments. II. Encoding the position to optimize the detection of filaments over a wide range of column density and contrast

Filaments host star formation and are fundamental structures of galaxies. Their diversity, as observed in the interstellar medium, from very low-density structures to very dense hubs, and their complex life cycles make their complete detection challenging over this large diversity range. Using 2D H$_2$ column density images obtained as part of the Herschel Hi-GAL survey of the Galactic plane (Gp), we want to detect, simultaneously and using a single model, filaments over a large range of column density and contrast over the whole Gp. In particular, we target low-contrast and low-density structures that are particularly difficult to detect with classical algorithms. The whole H$_2$ column density image of the Gp was subdivided into individual patches of 32times 32 pixels. Following our proof of concept study aimed at exploring the potential of supervised learning for the detection of filaments, we propose an innovative supervised learning method based on adding information by encoding the position of these patches in the Gp. To allow the segmentation of the whole Gp, we introduced a random procedure that preserves the balance within the model training and testing datasets over the Gp plane. Four architectures and six models were tested and compared using different metrics. For the first time, a segmentation of the whole Gp has been obtained using supervised deep learning. A comparison of the models based on metrics and astrophysical results shows that one of the architectures (PE-UNet-Latent), where the position encoding was done in the latent space gives the best performance to detect filaments over the whole range of density and contrast observed in the Gp. A normalized map of the whole Gp was also produced and reveals the highly filamentary structure of the Gp in all density regimes. We successfully tested the generalization of our best model by applying it to the 2D 12CO COHRS molecular data obtained on a 52 portion (in longitude) of the plane. We demonstrate the interest of position encoding to allow the detection of filaments over the wide range of density and contrast observed in the Gp. The produced maps (both normalized and segmented) offer a unique opportunity for follow-up studies of the life cycle of Galactic filaments. The promising generalization possibility tested on a molecular dataset of the Gp opens new opportunities for systematic detection of filamentary structures in the big data context available for the Gp.

Validation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET.

Simplified methods of acquisition and quantification would facilitate the use of synaptic density imaging in multicenter and longitudinal studies of Alzheimer disease (AD). We validated a simplified tissue-to-reference ratio method using SUV ratios (SUVRs) for estimating synaptic density with [11C]UCB-J PET. Methods: Participants included 31 older adults with AD and 16 with normal cognition. The distribution volume ratio (DVR) using simplified reference tissue model 2 was compared with SUVR at short scan windows using a whole-cerebellum reference region. Results: Synaptic density was reduced in AD participants using DVR or SUVR. SUVR using later scan windows (60-90 or 70-90 min) was minimally biased, with the strongest correlation with DVR. Effect sizes using SUVR at these late time windows were minimally reduced compared with effect sizes with DVR. Conclusion: A simplified tissue-to-reference method may be useful for multicenter and longitudinal studies seeking to measure synaptic density in AD.

Neurite Orientation Dispersion and Density Imaging (NODDI) reveals white matter microstructural changes in Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) patients with cognitive impairment

PurposeThis study aimed to assess changes in white matter microstructure among patients undergoing obstructive sleep apnea hypopnea syndrome (OSAHS) complicated by cognitive impairment through neurite orientation dispersion and density imaging (NODDI), and evaluate the relationship to cognitive impairment as well as the diagnostic performance in early intervention. MethodsTotally 23 OSAHS patients, 43 OSAHS patients complicated by cognitive impairment, and 15 healthy controls were enrolled in OSA, OSACI and HC groups of this work. NODDI toolbox and FMRIB's Software Library (FSL) were used to calculate neurite density index (NDI), Fractional anisotropy (FA), volume fraction of isotropic water molecules (Viso), and orientation dispersion index (ODI). Tract-based spatial statistics (TBSS) were carried out to examine the above metrics with one-way ANOVA. This study explored the correlations of the above metrics with mini-mental state examination (MMSE), and montreal cognitive assessment (MoCA) scores. Furthermore, receiver operating characteristic (ROC) curves were plotted. Meanwhile, area under curve (AUC) values were calculated to evaluate the diagnostic performance of the above metrics. ResultsNDI, ODI, Viso, and FA were significantly different among different brain white matter regions, among which, difference in NDI showed the greatest statistical significance. In comparison with HC group, OSA group had reduced NDI and ODI, whereas elevated Viso levels. Conversely, compared to the OSA group, the OSACI group displayed a slight increase in NDI and ODI values, which remained lower than HC group, viso values continued to rise. Post-hoc analysis highlighted significant differences in these metrics, except for FA, which showed no notable changes or correlations with neuropsychological tests. ROC analysis confirmed the diagnostic efficacy of NDI, ODI, and Viso with AUCs of 0.6908, 0.6626, and 0.6363, respectively, whereas FA's AUC of 0.5042, indicating insufficient diagnostic efficacy. ConclusionsThis study confirmed that NODDI effectively reveals microstructural changes in white matter of OSAHS patients with cognitive impairment, providing neuroimaging evidence for early clinical diagnosis and intervention.