Diffusion Tensor Imaging Images Research Articles

VSTNet: Robust watermarking scheme based on voxel space transformation for diffusion tensor imaging images

Deep watermarking has been successfully applied to real-value domain (e.g., CT, MRI, DWI) medical images for copyright protection; however, manifold-value domain images, such as diffusion tensor imaging (DTI), have received less academic attention. The non-Euclidean nature of DTI images hinders deep models from generating plausible DTI images because it is difficult to guarantee that each generated DTI image voxel lies on a 3 × 3 symmetric positive definite manifold. In this paper, we propose a robust watermarking scheme based on voxel space transformation for DTI images. First, a voxel space transformation is proposed, which effectively solves the problem of fiber orientation estimation in Riemann networks for DTI images and obtains the eigenvalues and eigenvectors of each voxel using singular value decomposition (SVD) to transform the DTI voxels from the 3 × 3 symmetric positive definite manifold space to Euclidean space. Second, SVD is combined with a deep neural network to extract the high-level features of each voxel eigenvalue in the DTI images and embedding watermarking messages, avoiding the false positive errors problem in traditional watermarking algorithms based on an SVD transform. Finally, using a deep neural network combining multiscale dilated convolution, dense residual connection and channel attention, the algorithm demonstrates high robustness against various intentional or unintentional attacks on DTI images while ensuring the good visual quality and diffusion characteristics of the DTI images embedded with watermarking messages. Experiments show that the watermarking message extraction accuracy reaches 97.8% for Crop (p = 0.7) attacks and 95.4% for Gaussian Blur (k = 7) attacks.

Diffusion tensor imaging identifies atrial regional changes associated with atrial fibrillation remodelling

Abstract Background Structural remodeling in the context of persistent atrial fibrillation (PAF) has been mainly characterized by imaging techniques focused on fibrosis detection. Diffusion tensor imaging (DTI) has demonstrated its direct relationship with histological properties and myocyte organization, which may provide a more complete structural information. However, the potential of DTI in the characterization of the AF-related structural substrate remains unknown. Purposes To evaluate the performance of DTI-derived parameters to characterise the structural remodelling derived from persistent AF and its relationship with specific atrial regions associated with AF maintenance (i.e., driver regions). Methods Two experimental groups of pigs were studied: control (n=7) and PAF (n=10). The PAF swine model was developed using rapid atrial pacing until the animals reached a stage of self-sustained PAF. The AV node was ablated to prevent heart failure due to fast ventricular activation rates. A second lead in the right ventricular was used for ventricular pacing at 60-110 bpm. Animals with PAF were studied after ≥ 6 months in self-sustained persistent AF. AF animals underwent invasive electroanatomical to identify driver regions based on instantaneous frequency modulation maps. After euthanasia, explanted hearts from AF animals and controls underwent DTI including 15 diffusion weighted images with a b-value of 600 s/mm² plus a non-DWI volume (isotropic resolution, 0.6 mm³). DTI images were segmented and divided into 13 atrial anatomical regions. Driver regions in AF animals were also segmented and classified as functional regions of interest (ROIs) (Figure 2B). Afterwards, diffusion tensors were estimated and scalar features were analyzed (Figures 1A and 2A) according to both anatomical and functional ROIs. Results DTI analysis in AF animals revealed region-specific structural remodelling (Figure 1B). Compared to controls, PAF animals showed an increased fractional anisotropy (FA) in both right and left atrial free walls (Figure 1C). Other anatomical regions, such as the superior vena cava (SVC), the coronary sinus (CS) or the left atrium roof developed higher axial diffusivities in AF compared to controls (Figure 1D), without significant alterations in FA (Figure 1C). AF driver regions were frequently located in the CS, the SVC and the LAA (Figure 2C). AF driver ROIs into the CS and the SVC showed intracase increased diffusivities and decreased linear indexes compared to non-driver regions. However, AF driver ROIs in the LAA showed increased intervoxel main eigenvector dispersion in contrast to non-driver ROIs (Figure 2C). Conclusion DTI sequences provide atrial structural information associated with functional tissue properties relevant for AF maintenance.

Metal artifact reduction around cervical spine implant using diffusion tensor imaging at 3T: A phantom study

PurposeDiffusion MRI continues to play a key role in non-invasively assessing spinal cord integrity and pre-operative injury evaluation. However, post-operative Diffusion Tensor Imaging (DTI) acquisition of patients with metal implants results in severe geometric distortion. We propose and demonstrate a method to alleviate the technical challenges facing the acquisition of DTI on post-operative cases and longitudinal evaluation of therapeutics. Material and methodsThe described technique is based on the combination of the reduced Field-Of-View (rFOV) strategy and the phase segmented EPI, termed rFOV-PS-EPI. A custom-built phantom based on a cervical spine model with metal implants was used to collect DTI data at 3 Tesla scanner using: rFOV-PS-EPI, reduced Field-Of-View single-shot EPI (rFOV-SS-EPI), and conventional full FOV techniques including SS-EPI, PS-EPI, and readout-segmented EPI (RS-EPI). Geometric distortion, SNR, and signal void were assessed to evaluate images and compare the sequences. A two-sample t-test was performed with p-value of 0.05 or less to indicate statistical significance. ResultsThe reduced FOV techniques showed better capability to reduce distortions compared to the Full FOV techniques. The rFOV-PS-EPI method provided DTI images of the phantom at the level of the hardware whereas the conventional rFOV-SS-EPI is useful only when the metal is approximately 20 mm away. In addition, compared to the rFOV-SS-EPI technique, the suggested approach produced smaller signal voids area as well as significantly reduced geometric distortion in Circularity (p < 0.005) and Eccentricity (p < 0.005) measurements. No statistically significant differences were found for these geometric distortion measurements between the rFOV-PS-EPI DTI sequence and conventional structural T2 images (p > 0.05). ConclusionThe combination of rFOV and a phase-segmented acquisition approach is effective for reducing metal-induced distortions in DTI scan on spinal cord with metal hardware at 3 T.

Fibre-specific white matter changes in anorexia nervosa

A large body of literature exists on white matter (WM) abnormalities in individuals with anorexia nervosa (AN). However, these studies have primarily relied on the diffusion tensor imaging (DTI), a technique known for its limited ability to resolve complex WM fibre arrangements. To overcome limitations of DTI, this study employed fixel-based analysis (FBA) to investigate fibre-specific WM abnormalities in AN. FBA, which incorporates information on connectivity and crossing-fibres, offers greater anatomical specificity and interpretability than DTI. Twenty-six acute AN patients and thirty-one healthy controls underwent diffusion-weighted imaging scanning, and FBA and DTI metrics were compared between groups. Results revealed significant reductions in fibre-bundle cross-section within the anterior corona radiata and genu of corpus callosum, indicating morphological atrophy in AN patients. These specific tracts, identified by FBA, connect brain regions involved in the symptomatology of AN. DTI showed increased axial diffusivity in the cerebellar peduncles of AN patients but did not show significant differences in other metrics. Notably, there was no overlap between findings of FBA and DTI analyses. Overall, this study highlights the potential of FBA to detect WM abnormalities not accounted for by DTI and suggests that myelin loss contributes to the observed clinical picture in AN patients.

Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.

A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). Retrospective. Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. 3 TECHNICAL EFFICACY: Stage 2.

An intrinsic anisotropic feature of DTI images derived by geometric properties on the Riemannian manifold

Diffusion tensor imaging (DTI) is a promising technique for characterizing organic microstructural information by mapping a spatial point as a 3 × 3 symmetric matrix (called diffusion tensor). Analyzing DTI images directly is challenging since each element of the DTI image is a 3-order matrix rather than a scalar. It is meaningful to extract the scalar-valued feature for DTI image processing and analysis. In this paper, we propose an intrinsic anisotropic feature of DTI images using global geometric attributes of diffusion tensors on the Riemannian manifold. To measure the intrinsic similarity between diffusion tensors as symmetric matrices, we convert them into symmetric positive definite (SPD) matrices and use the normalized geodesic distance in the tensor manifold to define the visible intrinsic feature. Moreover, we extend the geodesic distance to make sure our feature can measure the intrinsic properties of diffusion tensors, and the neighborhood attributes and even the global information of a DTI image are utilized to define the proposed feature. To measure the anisotropy of DTI images, we define the average diffusion sphere at each point to measure the isotropic attributes and use the difference between the diffusion tensor and its average diffusion tensor to describe the anisotropic properties. It is experimentally demonstrated that the proposed feature performs adequately in characterizing various attributes of DTI images, including but not limited anisotropy, intrinsic geometric property, and average diffusion degree, etc. Compared with the most popular features, our anisotropy feature can represent DTI image properties more robustly and effectively since the contrast of various details in the feature is improved. Moreover, the results of fiber tracking defined by the anisotropy feature indicate that our feature performs better on representing substantial anisotropic attributes of DTI images and is potentially used in DTI image processing and analysis.

Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke.

The progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease. Participants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FATissue) and mean diffusivity (MDTissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors. In the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants. Baseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis.

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging.

Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.

Functional MRI and Diffusion Tensor Imaging in Migraine: A Review of Migraine Functional and White Matter Microstructural Changes.

Migraine is a complex and heterogenous disorder whose disease mechanisms remain disputed. This narrative review summarizes functional MRI (fMRI) and diffusion tensor imaging (DTI) findings and interprets their association with migraine symptoms and subtype to support and expand our current understanding of migraine pathophysiology. Our PubMed search evaluated and included fMRI and DTI studies involving comparisons between migraineurs vs healthy controls, migraineurs with vs without aura, and episodic vs chronic migraineurs. Migraineurs demonstrate changes in functional connectivity (FC) and regional activation in numerous pain-related networks depending on migraine phase, presence of aura, and chronicity. Changes to diffusion indices are observed in major cortical white matter tracts extending to the brainstem and cerebellum, more prominent in chronic migraine and associated with FC changes. Reported changes in FC and regional activation likely relate to pain processing and sensory hypersensitivities. Diffuse white matter microstructural changes in dysfunctional cortical pain and sensory pathways complement these functional differences. Interpretations of reported fMRI and DTI measure trends have not achieved a clear consensus due to inconsistencies in the migraine neuroimaging literature. Future fMRI and DTI studies should establish and implement a uniform methodology that reproduces existing results and directly compares migraineurs with different subtypes. Combined fMRI and DTI imaging may provide better pathophysiological explanations for nonspecific FC and white matter microstructural differences.

Predicting Isocitrate Dehydrogenase Mutation Status of Grade 2–4 Gliomas with Diffusion Tensor Imaging (DTI) Parameters Derived from Model-Based DTI and Model-Free Q-Sampling Imaging Reconstructions

To determine whether diffusion tensor imaging (DTI) parameters acquired with model-based DTI and model-free generalized Q-sampling imaging (GQI) reconstructions may noninvasively predict isocitrate dehydrogenase (IDH) mutational status in patients with grade 2-4 gliomas. Forty patients with known IDH genotype (28 IDH wild-type; 12 IDH mutant) who underwent preoperative DTI evaluation on a 3-Tesla magnetic resonance imaging scanner were analyzed retrospectively. Absolute values obtained from model-based and model-free reconstructions were compared. Using the intraclass correlation coefficient, interobserver agreement was assessed for various sampling techniques. Variables having statistically significant distributions between IDH groups were subjected to a receiver operating characteristic (ROC) analysis. Using multivariable logistic regression analysis, independent predictors, if present, were identified and a model was developed. Six imaging parameters (3 from model-based DTI and 3 from model-free GQI reconstructions) showed statistically significant differences between groups (P < 0.001, power >0.97), with very high correlation to each other (P < 0.001). Age difference between the groups was statistically significant (P < 0.001). The optimal logistic regression model comprised a GQI-based parameter and age, which were independent predictors as well, producing an area under the ROC curve, accuracy, sensitivity, and specificity of 0.926, 85%, 75%, and 89.3%, respectively. Using the GQI reconstruction feature alone with a cut-off of 1.60, an 85% of accuracy was also achieved with ROC analysis. The imaging parameters acquired from model-based DTI and model-free GQI reconstructions, combined with the clinical variable age, may have the ability to noninvasively predict the IDH genotype in gliomas, either alone or in particular combinations.

Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas

BackgroundDetermining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, which is a predictor of response to standard radiotherapy treatment in patients with glioblastoma (GBM), remains an invasive and time-consuming process. Magnetic resonance imaging (MRI)-based radiomics studies have used machine learning models as a non-invasive and time-saving approach to predict MGMT methylation status. However, more studies need to use radiomics features extracted from Diffusion-Tensor Imaging (DTI) and Dynamic Susceptibility Contrast (DSC) scans that have shown benefits in GBM patients. Therefore, we aimed to use the radiomics features from conventional and advanced MRI scans, including DSC and DTI, to build an effective model for predicting MGMT methylation status. Materials and MethodsWe enrolled 182 GBM patients whose data were obtained from The Cancer Imaging Archive (TCIA). From the initial 4514 radiomics features (1,644 conventional MRI-based and 2,870 advanced MRI-based), we used a two-stage feature selection method to select the top conventional and advanced features to build Conventional and Advanced predictive models, respectively. Besides, our Full model was developed from combining all these features. We evaluated the performances of our models and compared them with that of a “state-of-the-art” model reconstructed by re-running a flowchart from a previous study, which demonstrated the highest performance in predicting MGMT methylation status, using our dataset for the rebuilding process. ResultsThe Conventional model yielded 0.71 sensitivity, 0.78 specificity, and 0.75 accuracy, while the Advanced model scored 0.67, 0.74, and 0.71, respectively. The prior state-of-the-art model achieved 0.65, 0.57, and 0.62. Our Full model outperformed them with 0.78, 0.84, and 0.80 and had the highest AUC value (0.81) compared to Conventional (0.78) and previous (0.68) models. ConclusionOur predictive model, which incorporates radiomics features from conventional MRI scans and advanced DSC and DTI imaging, offers an improved approach to predicting the MGMT methylation status in GBM patients.

The role of MR diffusion kurtosis and neurite orientation dispersion and density imaging in evaluating gliomas.

Conventional MRI sequences in neuro-oncology are insufficient for glioma grading. However, newly developed diffusion-weighted imaging techniques have been shown to have a great potential for glioma grading. This study examined the diagnostic performance of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and their combinations in glioma grading. Multishell diffusion tensor images were obtained with 3T MRI in 38 glioma patients (22 high-grade glioma [HGG], 16 low-grade glioma [LGG]). DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (Axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), and NODDI (intracellular volume fraction [ICVF], orientation distribution index, isotropic water fraction [ISO]) images were obtained after preprocessing. The average value of these parameters was calculated in the solid components of the tumors. The receiver operating characteristic curve analyses were performed to investigate the diagnostic performance and the curves were compared with the Delong test. FA shows an increase in HGG, while MD, RD, and AD exhibit a decrease. AK, MK, and RK were higher in HGG than LGG. ICVF increased in HGG, while ISO decreased. AK demonstrated the best diagnostic performance among all parameters, and kurtosis outperformed NODDI but not DTI. Combining these parameters did not yield a statistically significant improvement in diagnostic performance. DTI, DKI, and NODDI approaches can differentiate between HGG and LGG; however, kurtosis parameters perform better and adding NODDI parameters does not improve diagnostic performance. Using multishell b-value has not led to an increase in diagnostic performance.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

Diffusion Tensor Imaging as a Tool to Evaluate the Cognitive Function of Patients With Vascular Dementia: A Meta-Analysis.

Vascular dementia (VaD) is the most common type of dementia secondary to Alzheimer's disease. The pathologic mechanism of VaD is complex, and VaD still lacks a more objective diagnosis and evaluation method. Diffusion tensor imaging (DTI) can better detect the organizational structure and functional characteristics compared with any other diagnosis methods. Therefore, DTI has broad application in evaluating the severity and prognosis of VaD. This study aimed to assess the value of DTI in evaluating the cognitive function of patients with VaD. Authors searched Pubmed, Embase, and Cochrane Library, using the search terms, such as "diffusion tensor imaging," "DTI," "Vascular Dementia," "Arteriosclerotic Dementia," "Cognition," and "Cognitive." A voxel-based meta-analysis combined with quality statistics was performed, using the anisotropic effect-size version of the signed differential mapping method. A total of 8 case-control studies were included in this meta-analysis. The sample size of patients ranged from 35 to 60, including 166 patients in the VaD group and 177 healthy individuals. The DTI imaging of the brain tissue of VaD patients was significantly different from that of healthy individuals. DTI imaging of the brain tissue of VaD patients was clearly different from that of healthy controls. Therefore it may be feasible to use DTI imaging as a diagnostic method for VaD.

Altered amide proton transfer weighted and diffusion signals in patients with multiple sclerosis: correlation with neurofilament light chain and disease duration.

To compare the signal alterations of amide proton transfer (APT), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) in white matter (WM) lesions in multiple sclerosis (MS), compared with healthy controls (HCs), and to investigate the relationships between these changes and clinical measurements such as serum neurofilament light chain (sNfL). Twenty-nine patients with relapsing-remitting MS (21 females and 8 males) and 30 HCs (23 females and 7 males) were recruited. APT-weighted (APTw) and diffusion tensor imaging (DTI) data were acquired using a 3.0-T magnetic resonance system. APTw and DTI images were registered to FLAIR-SPIR images and assessed by two neuroradiologists. MTRasym (3.5 ppm), ADC, FA values for MS and HC are calculated using mean values from all regions of interest (ROI). The ROI criteria were: (1) for MS patients, ROI were defined as MS lesions, and each lesion was identified. (2) The WM around each HC's lateral ventricle (frontal lobe, parietal lobe, and centrum semiovale) was assessed bilaterally. The diagnostic efficacy of MTRasym (3.5 ppm), ADC, and FA in the lesions of MS patients was compared using receiver operating characteristic (ROC) curve analysis. The associations between MTRasym (3.5 ppm), ADC, and FA values and the clinical measurements were investigated further. The MTRasym (3.5 ppm) and ADC values of brain lesions were increased, while FA values were decreased in patients with MS. The diagnostic area under curve (AUC) of MTRasym (3.5 ppm), ADC, and FA value was 0.891 (95% CI: 0.813, 0.970), 0.761 (95% CI: 0.647, 0.875) and 0.970 (95% CI: 0.924, 1.0), respectively. sNfL was considerably positively correlated with MTRasym (3.5 ppm) (P = 0.043, R = 0.38) and disease durations were significantly negatively correlated with FA (P = 0.046, R = -0.37). Amide proton transfer-weighted (APTw) and DTI are potential imaging methods for assessing brain lesions in patients with MS at the molecular and microscopic levels, respectively. The association between APTw, DTI parameters and clinical factors implies that they may play a role in disease damage monitoring.

Resistance Training Increases White Matter Density in Frail Elderly Women.

We aimed to investigate the effects of maternal obesity on brain structure and metabolism in frail women, and their reversibility in response to exercise. We recruited 37 frail elderly women (20 offspring of lean/normal-weight mothers (OLM) and 17 offspring of obese/overweight mothers (OOM)) and nine non-frail controls to undergo magnetic resonance and diffusion tensor imaging (DTI), positron emission tomography with Fluorine-18-fluorodeoxyglucose (PET), and cognitive function tests (CERAD). Frail women were studied before and after a 4-month resistance training, and controls were studied once. White matter (WM) density (voxel-based morphometry) was higher in OLM than in OOM subjects. Exercise increased WM density in both OLM and OOM in the cerebellum in superior parietal regions in OLM and in cuneal and precuneal regions in OOM. OLM gained more WM density than OOM in response to intervention. No significant results were found from the Freesurfer analysis, nor from PET or DTI images. Exercise has an impact on brain morphology and cognition in elderly frail women.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts.

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.

Predicting the efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine leiomyomas based on DTI indicators and imaging features.

To predict the efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine leiomyomas based on diffusion tensor imaging (DTI) indicators and imaging features. Sixty-two patients with 85 uterine leiomyomas were consecutively enrolled in this retrospective study and underwent DTI scanning before HIFU treatment. Based on whether the non-perfused volume ratio (NPVR) was greater than 70%, all patients were assigned to sufficient ablation (NPVR ≥ 70%) or insufficient ablation (NPVR < 70%) groups. The selected DTI indicators and imaging features were incorporated to construct a combined model. The predictive performance of DTI indicators and the combined model were assessed using receiver operating characteristic (ROC) curves. There were 42 leiomyomas in the sufficient ablation group (NPVR ≥ 70%) and 43 leiomyomas in the insufficient ablation group (NPVR < 70%). The fractional anisotropy (FA) and relative anisotropy (RA) values were higher in the sufficient ablation group than in the insufficient ablation group (p < 0.05). Conversely, the volume ratio (VR) and mean diffusivity (MD) values were lower in the sufficient ablation group than those in the insufficient ablation group (p < 0.05). Notably, the combined model composed of the RA and enhancement degree values had high predictive efficiency, with an AUC of 0.915. The combined model demonstrated higher predictive performance than FA and MD alone (p = 0.032 and p < 0.001, respectively) but showed no significant improvement compared with RA and VR (p > 0.05). DTI indicators, especially the combined model incorporating DTI indicators and imaging features, can be a promising imaging tool to assist clinicians in predicting HIFU efficacy for uterine leiomyomas.

Altered White Matter Connectivity in Children with Prenatal Marijuana Exposure

&#x0D; &#x0D; &#x0D; Background:&#x0D; The potential health outcomes of cannabis use during pregnancy are currently not well-known. Several studies have shown an association between prenatal marijuana exposure (PME) and adverse neonatal outcomes. Specifically, cannabis use has been associated with an increased risk of cognitive deficits and neuropsychiatric disease. The aim of this project is to examine alterations in cerebral white matter connectivity of children with PME compared to unexposed controls based on diffusion tensor imaging (DTI). Identifying such microstructural alterations may help us better understand neurodevelopmental and cognitive effects of PME.&#x0D; Hypothesis:&#x0D; We hypothesize that there will be differences in white matter connectivity in children with PME compared to controls.&#x0D; Methods:&#x0D; DTI images for children with (n=88) and without (n=90) PME were obtained from the Adolescent Brain Cognitive Development (ABCD) Study database. Image quality assurance, preprocessing, and tractography were completed using DSI Studio. Fiber counts between 94 regions of interest were assessed. A groupwise comparison was performed to assess differences in structural connectivity in children with and without PME.&#x0D; Results:&#x0D; The PME group and control group had 35 and 37 males, respectively. The mean age for both groups was 9.9 years. First level comparative analysis revealed alterations in microstructural connectivity between PME and control groups. Increased connectivity was found in the PME group compared to the control group in 51 tracts, while decreased connectivity was indicated in 22 tracts. However, significance was not maintained when corrected for multiple comparisons.&#x0D; Conclusion/Impact:&#x0D; Although there were small alterations in brain structural connectivity in children with PME, these were not significant after correcting for multiple comparisons. It is possible that microstructural neuroplasticity in the developing brain lowers the impact of potential long-term adverse effects of marijuana exposure in utero. Further studies are required to understand longitudinal brain development and adverse neurodevelopmental and cognitive outcomes associated with PME.&#x0D; &#x0D; &#x0D;