Diffusion Tensor Imaging Images Research Articles

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach

BackgroundThe role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2–4 gliomas.MethodsThis retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.ResultsWe comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients’ age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].ConclusionsImaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer's Disease.

Several studies have shown white matter (WM) abnormalities in Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Nonetheless, robust characterization of WM changes has been challenging due to the methodological limitations of DTI. We applied fixel-based analyses (FBA) to examine microscopic differences in fiber density (FD) and macroscopic changes in fiber cross-section (FC) in early stages of AD (N = 393, 212 females). FBA was also compared with DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). We further investigated the correlation of FBA and tensor-derived metrics with AD pathology and cognition. FBA metrics were decreased in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations in Aβ-positive patients with mild cognitive impairment compared to control groups. Metrics derived from DKI, and FW-DTI showed similar alterations whereas WM degeneration detected by DTI was more widespread. Tau-PET uptake in medial temporal regions was only correlated with reduced FC mainly in the parahippocampal cingulum in Aβ-positive individuals. This tau-related WM alteration was also associated with impaired memory. Despite the spatially extensive between-group differences in DTI-metrics, the link between WM and tau aggregation was only revealed using FBA metrics implying high sensitivity but low specificity of DTI-based measures in identifying subtle tau-related WM degeneration. No relationship was found between amyloid load and any diffusion-MRI measures. Our results indicate that early tau-related WM alterations in AD are due to macrostructural changes specifically captured by FBA metrics. Thus, future studies assessing the effects of AD pathology in WM tracts should consider using FBA metrics.

Validation of diffusion tensor imaging for diagnosis of traumatic brain injury

Background and PurposeWith an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the use of DTI with Statistical Parametric Mapping (SPM) for Traumatic Brain Injury (TBI) supporting its use as a diagnostic tool. Materials and MethodsThis study was retrospective and compared controls to patients clinically diagnosed with TBI. Forty-two controls (mean age = 34.1; range, 19 - 58; 28 Males and 13 Females) were screened (n = 41) for cognitive impairment and neurological abnormality. Two cohorts, each of eighteen patients (first cohort: mean age, 41.8; range, 23 - 70; 9 Males and 9 Females; second cohort: mean age, 45.7; range, 23 - 68; 9 Males and 9 Females) clinically diagnosed with TBI (n = 36) were pooled. DTI image acquisition was obtained using a 3 Tesla MRI scanner. DTI images were analyzed through voxel-based t-tests using SPM comparing each individual to the normative control group to generate z-maps for each individual control and each individual patient with a TBI. Test statistics were ranged for p-values (0.001-0.05) and cluster extent values (0, 30, 60, 65, 70, 75). Area Underneath A Receiver Operating Characteristic Curve (AUCROC) was used to validate diagnostic capability. AUCROC analysis was conducted on all sets of p-value and extent threshold values. Significance of results was determined by examining the AUCROC values. Results and ConclusionsA maximal AUCROC of 1.000 was obtained across the p-value range and cluster extent thresholding values specified across the two cohorts. The high AUCROC supports validation of the methodology presented and the use of diffusion tensor imaging and SPM as an adjunct diagnostic tool for TBI.

Feasibility of diffusion tensor imaging in cervical spondylotic myelopathy using MUSE sequence

The most frequent type of spinal cord injury is cervical spondylotic myelopathy (CSM). Conventional structural magnetic resonance imaging (MRI) is the gold diagnosis standard for CSM. Diffusion tensor imaging (DTI) could reflect microstructural changes in the spinal cord by tracing water molecular diffusion in early stages of CSM. However, due to the complex local anatomical structure and small field of view of the spinal cord, the imaging effect of traditional DTI imaging on the spinal cord is limited. MUSE (MUltiplexed Sensitivity-Encoding) -DTI is a novel diffusion-weighted imaging (DWI) sequence that achieves higher signal intensity through multiple excitation acquisition. MUSE sequence may improve the quality of spinal cord DTI imaging. Prospective study. This study aimed to investigate the clinical diagnosis value of a novel protocol of MUSE-DTI in patients with cervical spondylotic myelopathy (CSM). From August 2021 to March 2022, a total of 60 subjects (22-71 years) were enrolled, including 51 CSM patients (22 males, 29 females) and 9 healthy subjects (4 males and 5 females). Each subject underwent a MUSE-DTI examination and a clinical Japanese Orthopedic Association (JOA) scale. We measured values of FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), and RD (Radial Diffusivity), and collected the clinical JOA scores of each subject before the MR examination. A 3.0T MR scanner (Signa Architect, GE Healthcare) performed the MUSE-DTI sequence on each subject. The cervical canal stenosis of subjects was classified from grade 0 to grade Ⅲ according to the method of an MRI grading system. FA, MD, AD, and RD maps were generated by postprocessing MUSE-DTI data on the GE workstation. Regions of interest (ROIs) were manually drawn at the C2 vertebral body level and C2/3-C6/7 intervertebral disc levels by covering the whole spinal cord. The clinical severity of myelopathy of subjects was assessed by the clinical Japanese Orthopedic Association scale (JOA). MUSE-DTI can acquire a high-resolution diffusion image compared to traditional DTI. The FAMCL values showed a decreasing trend from grade 0 to grade Ⅲ, while the MDMCL, ADMCL, and RDMCL values showed an overall increasing trend. Significant differences in MDMCL, ADMCL, and RDMCL values were found between adjacent groups among grades Ⅰ-Ⅲ (p<.05). The ADC2 values in CSM patients (grade I-Ⅲ) were significantly lower than in healthy individuals (grade 0) (p=.019). The clinical JOA score has a significant correlation with FAMCL (p=.035), MDMCL (p<.001), ADMCL (p<.001), and RDMCL (p<.001) values. MUSE-DTI displayed a better image quality compared to traditional DTI. MUSE-DTI parameters displayed a grade-dependent trend. All the MUSE-DTI parameters at MCL were correlated with the clinical JOA scores. The ADC2 values can reflect the secondary damage of distal spinal cord. Therefore, MUSE-DTI could be a reliable biomarker for clinical auxiliary diagnosis of spinal cord injury severity in cervical spondylotic myelopathy.

Role of Diffusion Tensor Imaging in Early Diagnosis and Characterization of Movement Disorders.

Symptoms of movement disorders in early stages are similar, which makes definite diagnosis difficult. Hence this study was conducted to explore the role of diffusion tensor imaging (DTI) in enhancing the early diagnosis and characterization of movement disorders. A cross-sectional study was conducted including 60 subjects. All of them were reviewed using conventional magnetic resonance imaging (MRI) and movement disorder DTI protocol. Commercially available software was used to produce fractional anisotropy (FA) maps. Post-processing 3D reconstruction was done to obtain tractograms. Both single and multiple regions of interest (ROIs) were selected for tractography in the pons, midbrain, substantia nigra (SN) and cerebellum. MRIandDTI images were interpretedand correlated with confirmatory diagnosis. According to DTI diagnosis, out of the 30 cases, 28 had movement disorders. Among cases, 36.67% had Parkinson's disease (PD), 23.33% had progressive supranuclear palsy (PSP), 16.67% had essential tremor, 13.33% had multi-system atrophy (MSA) C, and 3.33% had MSA P. DTI correctly classified all cases with PD and PSP. All cases with long disease duration and 88.24% of cases with short disease duration were also correctly classified. A statistically significant difference was observed in the proportion of diagnosis between DTI and conventional MRI. DTI has high sensitivity and specificity for the diagnosis of movement disorders. It is capable of early diagnosis of movement disorders and also differentiating and subcategorizing them.

Diagnostic value of Video Electroencephalography combined with Magnetic Resonance Imaging-diffusion tensor imaging in epilepsy.

To explore the diagnostic value of video electroencephalography (VEEG) combined with magnetic resonance imaging diffusion tensor imaging (MRI-DTI) in epilepsy. In this retrospective observational study, clinical data of 60 patients who underwent both VEEG and MRI examinations at the Neurosurgery Department of Quzhou People's Hospital from December 2020 to March 2023 were retrospectively reviewed, and a total of 55 patients were finally included. We evaluated the diagnostic value of combining VEEG and MRI to determine seizure type, location of epileptic focus, and structural abnormalities of brain tissue, using surgical and pathological results as the gold standard. The accuracy of the combined approach for determining the seizure type was 98.18%, which was higher than the accuracy of MRI (85.45%, P<0.05) or VEEG (81.82%, P<0.05) alone. The accuracy of joint examination for lesion location was 100.00%, which was higher than those of MRI (85.45%, P<0.05) or VEEG (83.64%, P<0.05) alone. Similar abnormal brain tissue structure detection rates was found for both MRI and VEEG (P>0.05). The application of MRI-DTI combined with VEEG to diagnose patients with epilepsy allows for the identification of abnormal structural changes in brain and the location of lesions. Combining both approaches can improve the diagnostic accuracy of each technique alone and provide a reference for the formulation and adjustment of disease management plans.

Correlation between postoperative microstructural changes in cerebral white matter and early postoperative cognitive function in patients undergoing meningioma resection

Objective: To analyze the correlation between microstructure changes in cerebral white matter before and after surgery and early postoperative cognitive function in patients undergoing meningioma resection. Methods: A total of 17 patients who underwent their first meningioma resection at Xuanwu Hospital of Capital Medical University from April 2022 to April 2023 were prospectively included as observation group, with 5 males and 12 females, aged (56.4±7.3) years. Another 15 age- and education-matched patients with cerebral benign tumor were recruited as control group during the same period, with 5 males and 10 females, aged (55.2±8.0) years. Neuropsychological tests (NST), mainly including auditory verbal learning test of Huashan version (AVLT-H), the Montreal cognitive assessment-basic (MoCA-B), clock drawing task-30 (CDT-30), shape trails test-B (STT-B) and animal fluence test (AFT), were conducted at 1 day before surgery, 1 day and within 3-4 days after surgery in the observation group. Simultaneously, magnetic resonance imaging (MRI) scans were performed to collect diffusion tensor imaging (DTI) images at 1 day before surgery and within 3-4 days after surgery. The same NST were conducted at 1 day, 3 days and 6 days after admission in the control group to adjust for learning effects from repeated tests. The microstructure changes of the whole brain white matter were evaluated at the group level by using tract-based spatial statistics (TBSS) technology, including changes of fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD). Then, correlation was performed between DTI indicators with statistically significant and cognitive function. Results: After adjusting for the learning effects, the AVLT-H (R), MoCA-B, and CDT-30 scores decreased, and the evaluation time of STT-B prolonged after surgery in patients with meningioma. And their perioperative decreased values were -0.78 (95%CI:-3.28--0.28) points, -2.22 (95%CI:-4.22--0.72) points, -2.74 (95%CI:-5.29--0.19) points, and 61.49 (95%CI: 5.71-117.27) seconds, respectively, with statistically significant differences (all P<0.05). Group level analysis of TBSS based on DTI images showed decreased FA mainly in the right superior cerebellar peduncle, left posterior limb of internal capsule and genu of corpus callosum, and increased RD mainly in the left anterior corona radiata in patients undergoing meningioma resection, with statistically significant differences (all PFWE<0.05). Linear correlation showed that the perioperative decreased values of FA in genu of corpus callosum and right superior cerebellar peduncle were positively correlated with the perioperative decreased values of AVLT-H (L) after adjusting for learning effects (r=0.72, 0.52, all PFWE<0.05). Conclusions: Patients undergoing meningioma resection are at risk of postoperative cognitive decline. Perioperative decreased values of FA in genu of corpus callosum and right superior cerebellar peduncle based on DTI images are positively correlated with the perioperative decreased values of AVLT-H (L) after adjusting for learning effects.

Diffusion tensor imaging: survival analysis prediction in breast cancer patients.

We aimed to explore the performance of diffusion-tensor imaging (DTI) and apparent diffusion coefficient (ADC) parameters in evaluating disease-free survival (DFS) and overall survival (OS) in patients with invasive breast cancer. Atotal of 49women with invasive breast cancer who were diagnosed between 2017 and 2022 were included. All patients underwent breast magnetic resonance imaging (MRI) with DTI and diffusion-weighted imaging (DWI) features, with examiners blinded to the clinical data. Volume anisotropy (VA), fractional anisotropy (FA), and ADC values were measured to assess intratumoral measured heterogeneity. Correlations and differences in diffusion metrics according to OS and DFS status of the cases were analyzed. The discriminative ability of the quantitative findings was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort. We evaluated patients with metastases (n = 13, 36.5%) and those without metastases (n = 36, 73.5%). Differences in the ADC, FA, and VA values were observed. The results of Cox regression survival analysis for all the patients included in the survival analysis revealed that DTI metrics contributed to the prediction of overall survival (OS) in the emerging models (p < 0.05). Both FA and VA were associated with OS (p = 0.037 and p = 0.038, respectively). However, ADC was not associated with OS (p = 0.177) or DFS (p = 0.252). To the best of our knowledge, this is the first study to assess the prognostic value of DTI-MRI in breast cancer with statistical survival analysis techniques. We believe that DTI measurements can be used as abiomarker for OS analysis in breast cancer given the available data.

Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode

ObjectiveBecause of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI). MethodPatients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared. ResultsCommon abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus. ConclusionThe study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.

What Clinical Parameter Strongly Associates White Matter Tract Alterations in a Multiple Sclerosis Population With Voiding Dysfunction? A Prospective Exploratory Study

Purpose: To correlate clinical and urodynamics parameters in patients with multiple sclerosis (MS) presenting lower urinary tract symptoms (LUTS) with both Expanded Disability Status Scale (EDSS) and changes in white matter integrity as seen on diffusion tensor images (DTIs). LUTS worsen throughout MS, as does lesion burden. We investigated which symptoms correlated best with structural changes in the white matter structure. Materials and Methods: Ten adult women &gt;18 years were recruited with stable MS for ≥ 3 months and voiding dysfunction defined as % of post-void residual/bladder volume (%PVR/BV) &gt; 20%. Patients participated in a clinical Urodynamic Study and completed several questionnaires (ie, Hamilton Rating Scale, American Urologic Association Symptom Score, NBS-QoL). DTI images were acquired using a 7-Tesla Siemens MAGNETOM Terra MRI scanner. DTI maps were constructed, and individual patients were coregistered with the International Consortium of Brain Mapping-DTI-81 white matter atlas to extract fractional anisotropy and mean diffusivity (MD). Pearson's correlation test was performed between each white matter tracts (WMT) and clinical parameters and between clinical parameters and the EDSS score as well. P &lt; 0.05 was considered significant. Results: Of the clinical parameters, %PVR/BV obtained from the average of multiple un-instrumented uroflow assessments had significant correlations with the greatest number of WMTs. Furthermore, we observed that in all recorded clinical parameters, %PVR/BV was the only significant parameter correlated with the EDSS score. Conclusion: This study demonstrates that %PVR/BV can be used as an objective parameter to gauge WMT changes and disease progression in patients with MS. Future studies are needed to refine this model.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

BackgroundEarly detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease. ObjectivesTo investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods. MethodsWe included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance. ResultsA total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62–0.68; ACC range: 0.63–0.77; SEN range: 0.45–0.66; SPE range: 0.64–0.84). Models trained with structural connectivity (AUC range, 0.81–0.84; ACC range, 0.75–0.86; SEN range, 0.77–0.91; SPE range, 0.71–0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81–0.85; ACC range, 0.74–0.85; SEN range, 0.79–0.91; SPE range, 0.70–0.89). ConclusionsBaseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

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.

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.

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.