scholarly journals Enabling complex fibre geometries using 3D printed axon-mimetic phantoms

2021 ◽  
Author(s):  
Tristan K. Kuehn ◽  
Farah N. Mushtaha ◽  
Ali R. Khan ◽  
Corey A. Baron
Keyword(s):  
Diffusion Mri ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Ground Truth ◽  
Diffusion Kurtosis Imaging ◽  
Strongly Correlated ◽  
3D Print ◽  
3D Printed ◽  
Stick Model

AbstractPurposeTo introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterize the performance of diffusion MRI models and representations in the presence of orientation dispersion.MethodsAn extension to an open source 3D printing package was created to 3D print a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A fiducial in each phantom was used to register a ground truth map of that phantom’s crossing angles and/or arc radius to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric.ResultsThe mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle, but increased with fibre curvature. Axial diffusivity (AD) decreased sharply with increasing crossing angle. DKI’s diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric, decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI’s intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was strongly correlated to crossing angle. Bingham-NODDI’s intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also strongly correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle.ConclusionsThis study demonstrates that inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. As a proof of concept, the dependence of several representations and models on fibre dispersion/crossing were investigated. As expected, Bingham-NODDI was best able to characterize planar fibre dispersion in the phantoms.

scholarly journals Assessment of IDH1 Genotype and Cell Proliferation in Gliomas Using Diffusion Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Yan Xie ◽  
Shihui Li ◽  
Nanxi Shen ◽  
Tongjia Gan ◽  
Shun Zhang ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Diffusion Coefficient ◽  
Correlation Coefficient ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Effective Diffusion ◽  
Mean Diffusivity ◽  
Diffusion Kurtosis Imaging ◽  
Wild Type ◽  
Type Group

Abstract Purpose To compare the efficacy of parameters from mono-, bi- and stretch-exponential diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) for prediction of IDH1 genotype and assessment of cell proliferation in gliomas. Methods 91 patients with pathologically confirmed gliomas underwent DWI, multi-b-value DWI and DKI/NODDI on 3.0T MRI. The ROIs were manually placed to obtain measurements including apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), orientation dispersion index (ODI) and intracellular volume fraction (ICVF). Results In LrGGs, IDH1 wild-type group showed significantly lower ADC, D, f, DDC, α, MD and higher D*, MK, ODI and ICVF values than IDH1-mutant group (P < 0.05). Among them, α has the highest AUC value (0.846). In GBMs, no difference between IDH1-mutant and IDH1 wild-type group was found. For IDH1-mutant group, all parameters, except for FA and D*, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC and DDC statistically discriminated LrGGs from GBMs (P = 0.039 and 0.046, respectively). In addition, MK has maximal correlation coefficient (r = 0.612, P < 0.001) and D* has the minimal correlation coefficient (r = 0.146, P = 0.176) with Ki-67 LI. Conclusion The α may be the most effective diffusion MRI marker for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

scholarly journals Perivascular space fluid contributes to diffusion tensor imaging changes in white matter

2018 ◽  
Author(s):  
Farshid Sepehrband ◽  
Ryan P Cabeen ◽  
Jeiran Choupan ◽  
Giuseppe Barisano ◽  
Meng Law ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Mean Diffusivity ◽  
Brain Parenchyma ◽  
Perivascular Space ◽  
List Type

AbstractDiffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.HighlightsPerivascular space (PVS) fluid significantly contributes to diffusion tensor imaging metricsIncreased PVS fluid results in increased mean diffusivity and decreased fractional anisotropyPVS contribution to diffusion signal is overlooked and demands further investigation

scholarly journals Diffusion Kurtosis Imaging Detects Microstructural Changes in the Brain after Acute Alcohol Intoxication in Rats

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Xi-ran Chen ◽  
Jie-ying Zeng ◽  
Zhi-Wei Shen ◽  
Ling-mei Kong ◽  
Wen-bin Zheng
Keyword(s):  
Frontal Lobe ◽  
Diffusion Tensor ◽  
Alcohol Intoxication ◽  
Mean Diffusivity ◽  
Diffusion Kurtosis Imaging ◽  
Acute Alcohol Intoxication ◽  
Additional Information ◽  
Diffusion Kurtosis ◽  
The Brain ◽  
Acute Intake

The aim of this study was to test the technical feasibility of diffusion kurtosis imaging (DKI) in the brain after acute alcohol intoxication. Diffusion tensor imaging (DTI) and DKI during 7.0 T MRI were performed in the frontal lobe and thalamus before and 30 min, 2 h, and 6 h after ethyl alcohol administration. Compared with controls, mean kurtosis values of the frontal lobe and thalamus first decreased by 44% and 38% within 30 min (p<0.01 all) and then increased by 14% and 46% at 2 h (frontal lobe, p>0.05; thalamus, p<0.01) and by 29% and 68% at 6 h (frontal lobe, p<0.05; thalamus, p<0.01) after acute intake. Mean diffusivity decreased significantly in both the frontal lobe and the thalamus at various stages. However, fractional anisotropy decreased only in the frontal lobe, with no detectable change in the thalamus. This demonstrates that DKI possesses sufficient sensitivity for tracking pathophysiological changes at various stages associated with acute alcohol intoxication and may provide additional information that may be missed by conventional DTI parameters.

scholarly journals Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jiaji Mao ◽  
Weike Zeng ◽  
Qinyuan Zhang ◽  
Zehong Yang ◽  
Xu Yan ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Fractional Anisotropy ◽  
Volume Fraction ◽  
Mean Diffusivity ◽  
Diffusion Kurtosis Imaging ◽  
High Grade ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
High Grade Gliomas ◽  
Solitary Brain Metastases

Abstract Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.

scholarly journals Diffusion MRI Captures White Matter Microstructure Alterations in PRKN Disease

2021 ◽  
pp. 1-15
Author(s):  
Takahiro Koinuma ◽  
Taku Hatano ◽  
Koji Kamagata ◽  
Christina Andica ◽  
Akio Mori ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
White Matter ◽  
Diffusion Mri ◽  
Disease Duration ◽  
Diffusion Tensor ◽  
Neuronal Degeneration ◽  
Mean Diffusivity ◽  
B Value ◽  
Oxidative Stress Marker ◽  
Systemic Oxidative Stress

Background: Although pathological studies usually indicate pure dopaminergic neuronal degeneration in patients with parkin (PRKN) mutations, there is no evidence to date regarding white matter (WM) pathology. A previous diffusion MRI study has revealed WM microstructural alterations caused by systemic oxidative stress in idiopathic Parkinson’s disease (PD), and we found that PRKN patients have systemic oxidative stress in serum biomarker studies. Thus, we hypothesized that PRKN mutations might lead to WM abnormalities. Objective: To investigate whether there are WM microstructural abnormalities in early-onset PD patients with PRKN mutations using diffusion tensor imaging (DTI). Methods: Nine PRKN patients and 19 age- and sex-matched healthy controls were recruited. DTI measures were acquired on a 3T MR scanner using a b value of 1,000 s/mm2 along 32 isotropic diffusion gradients. The DTI measures were compared between groups using tract-based spatial statistics (TBSS) analysis. Correlation analysis was also performed between the DTI parameters and several serum oxidative stress markers obtained in a previously conducted metabolomic analysis. Results: Although the WM volumes were not significantly different, the TBSS analysis revealed a corresponding decrease in fractional anisotropy and an increase in mean diffusivity and radial diffusivity in WM areas, such as the anterior and superior corona radiata and uncinate fasciculus, in PRKN patients compared with controls. Furthermore, 9-hydroxystearate, an oxidative stress marker, and disease duration were positively correlated with several parameters in PRKN patients. Conclusion: This pilot study suggests that WM microstructural impairments occur in PRKN patients and are associated with disease duration and oxidative stress.

scholarly journals Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zahra Riahi Samani ◽  
Drew Parker ◽  
Ronald Wolf ◽  
Wes Hodges ◽  
Steven Brem ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Free Water ◽  
Majority Voting ◽  
Water Volume ◽  
Extracellular Water ◽  
Peritumoral Tissue ◽  
Water Volume Fraction

AbstractTumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

scholarly journals Diffusion MRI Changes in the Healthy Aging Canine Brain

2020 ◽  
Author(s):  
Erica F. Barry ◽  
John P. Loftus ◽  
Wen-Ming Luh ◽  
Mony J. de Leon ◽  
Sumit N. Niogi ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Healthy Aging ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Human Aging ◽  
Age Related ◽  
Age Related Changes

AbstractWhite matter dysfunction and degeneration have been a topic of great interest in healthy and pathological aging. While ex vivo studies have investigated age-related changes in canines, little in vivo canine aging research exists. Quantitative diffusion MRI such as diffusion tensor imaging (DTI) has demonstrated aging and neurodegenerative white matter changes in humans. However, this method has not been applied and adapted in vivo to canine populations. This study aimed to test the hypothesis that white matter diffusion changes frequently reported in human aging are also found in aged canines. The study used Tract Based Spatial Statistics (TBSS) and a region of interest (ROI) approach to investigate age related changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD) and radial diffusivity (RD). The results show that, compared to younger animals, aged canines have significant decreases in FA in parietal and temporal regions as well as the corpus callosum and fornix. Additionally, AxD decreases were observed in parietal, frontal and midbrain regions. Similarly, an age-related increase in RD was observed in the right parietal lobe while MD decreases were found in the midbrain. These findings suggest that canine samples offer a model for healthy human aging as they exhibit similar white matter diffusion tensor changes with age.

scholarly journals Loss and Reorganisation of Superficial White Matter in Alzheimer's disease: A Diffusion MRI study

2021 ◽  
Author(s):  
Thomas Veale ◽  
Ian B Malone ◽  
Teresa Poole ◽  
Thomas D Parker ◽  
Catherine F Slattery ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Diffusion Mri ◽  
Grey Matter ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Young Onset ◽  
Neurite Density

Pathological involvement of cerebral white matter in Alzheimer's disease has been shown using diffusion tensor imaging. Superficial white matter (SWM) changes have been relatively understudied despite their importance in cortico-cortical connections. Measuring SWM degeneration using diffusion tensor imaging is challenging due to its complex structure and proximity to the cortex. To overcome this we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are due to degeneration (e.g. loss of myelinated fibres) and those due to reorganisation (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, the grey/white boundary, SWM (1mm below grey/white boundary) and SWM/deeper white matter (2mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The SWM of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P<0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P<0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the SWM of two regions (entorhinal and parahippocampus) (both P<0.05) and higher fractional anisotropy within the postcentral region (P<0.05). Mean diffusivity in SWM was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P<0.05) and lower in three regions (postcentral, precentral and superior temporal) (all P<0.05). In the overlying grey matter, disease-related changes were largely consistent with SWM findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity SWM changes. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P<0.001) but group differences reduced in magnitude and coverage when moving towards the SWM. These results show that microstructural changes occur within SWM and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying SWM fibres undergo neurodegeneration and reorganisation, two effects previously indiscernible using standard diffusion tensor metrics in SWM.

scholarly journals Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel Johnson ◽  
Antonio Ricciardi ◽  
Wallace Brownlee ◽  
Baris Kanber ◽  
Ferran Prados ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Diffusion Mri ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Healthy Controls ◽  
Age And Gender ◽  
Spherical Mean ◽  
White Matter Microstructure ◽  
And Gender

Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings.Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics.Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients.Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p &lt; 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p &lt; 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p &lt; 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42).Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.

scholarly journals Probabilistic Assessment of Nerve Regeneration with Diffusion MRI in Rat Models of Peripheral Nerve Trauma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Isaac V. Manzanera Esteve ◽  
Angel F. Farinas ◽  
Alonda C. Pollins ◽  
Marlieke E. Nussenbaum ◽  
Nancy L. Cardwell ◽  
...  
Keyword(s):  
Nerve Regeneration ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Behavioral Data ◽  
Diffusion Kurtosis Imaging ◽  
Partial Recovery ◽  
Limited Information ◽  
Sprague Dawley ◽  
Post Surgery

AbstractNerve regeneration after injury must occur in a timely fashion to restore function. Unfortunately, current methods (e.g., electrophysiology) provide limited information following trauma, resulting in delayed management and suboptimal outcomes. Herein, we evaluated the ability of diffusion MRI to monitor nerve regeneration after injury/repair. Sprague-Dawley rats were divided into three treatment groups (sham = 21, crush = 23, cut/repair = 19) and ex vivo diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was performed 1–12 weeks post-surgery. Behavioral data showed a distinction between crush and cut/repair nerves at 4 weeks. This was consistent with DTI, which found that thresholds based on the ratio of radial and axial diffusivities (RD/AD = 0.40 ± 0.02) and fractional anisotropy (FA = 0.53 ± 0.01) differentiated crush from cut/repair injuries. By the 12th week, cut/repair nerves whose behavioral data indicated a partial recovery were below the RD/AD threshold (and above the FA threshold), while nerves that did not recover were on the opposite side of each threshold. Additional morphometric analysis indicated that DTI-derived normalized scalar indices report on axon density (RD/AD: r = −0.54, p < 1e-3; FA: r = 0.56, p < 1e-3). Interestingly, higher-order DKI analyses did not improve our ability classify recovery. These findings suggest that DTI may provide promising biomarkers for distinguishing successful/unsuccessful nerve repairs and potentially identify cases that require reoperation.

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