Diffusion Tensor Imaging Model Research Articles

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network.

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

Spinal cord perfusion is associated with microstructural damage in cervical spondylotic myelopathy patients who underwent cervical laminoplasty.

To investigate the association between spinal cord perfusion and microstructural damage in CSM patients who underwent cervical laminoplasty using MR dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) techniques. A follow-up cohort study was conducted with 53 consecutively recruited CSM patients who had undergone cervical laminoplasty 12-14months after the surgery from April 2016 to December 2016. Twenty-one aged-matched healthy volunteers were recruited as controls. For each patient, decompressed spinal cord levels were imaged on a 3.0-T MRI scanner by diffusion and DSC sequences to quantify the degrees of microstructural damage and perfusion conditions, respectively. The diffusion data were analyzed by DTI and NODDI models to produce diffusion metrics. Classic indicator dilution model was used to quantify the DSC metrics. Mann-Whitney U test was performed for comparison of diffusion metrics between patients and healthy controls. Pearson correlation was used to explore the associations between the metrics of spinal cord perfusion and microstructural damage. DTI metrics, neurite density, and isotropic volume fraction had significant differences between postoperative patients and healthy controls. Pearson correlation test showed that SCBV was significantly positively correlated with RD, MD, and ODI, and negatively correlated with FA and NDI. SCBF was found to be significantly positively correlated with RD and MD, and negatively correlated with FA. Increased spinal cord perfusion quantified by DSC is associated with microstructural damage assessed by diffusion MRI in CSM patients who underwent cervical laminoplasty. This study found that the spinal cord perfusion is associated with microstructural damage in postoperative cervical spondylotic myelopathy patients, indicating that high perfusion may play a role in the pathophysiological process of cervical spondylotic myelopathy and deserves more attention. • Spinal cord microstructural damage can be persistent despite the compression had been relieved 12-14 months after the cervical laminoplasty in cervical spondylotic myelopathy (CSM) patients. • Spinal cord perfusion is associated with microstructural damage in CSM patients after the cervical laminoplasty. • Inflammation in the decompressed spinal cord may be a cause of increased perfusion and is associated with microstructural damage during the recovery period of CSM.

Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint

To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio. The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method. The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters. The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.

Neurite orientation dispersion and density imaging of white matter microstructure in sensory processing dysfunction with versus without comorbid ADHD.

Sensory Processing Dysfunction (SPD) is common yet understudied, affecting up to one in six children with 40% experiencing co-occurring challenges with attention. The neural architecture of SPD with Attention Deficit and Hyperactivity Disorder (ADHD) (SPD+ADHD) versus SPD without ADHD (SPD-ADHD) has yet to be explored in diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) has yet to be examined. The present study computed DTI and NODDI biophysical model parameter maps of one hundred children with SPD. Global, regional and voxel-level white matter tract measures were analyzed and compared between SPD+ADHD and SPD-ADHD groups. SPD+ADHD children had global WM Fractional Anisotropy (FA) and Neurite Density Index (NDI) that trended lower than SPD-ADHD children, primarily in boys only. Data-driven voxelwise and WM tract-based analysis revealed statistically significant decreases of NDI in boys with SPD+ADHD compared to those with SPD-ADHD, primarily in projection tracts of the internal capsule and commissural fibers of the splenium of the corpus callosum. We conclude that WM microstructure is more delayed/disrupted in boys with SPD+ADHD compared to SPD-ADHD, with NODDI showing a larger effect than DTI. This may represent the combined WM pathology of SPD and ADHD, or it may result from a greater degree of SPD WM pathology causing the development of ADHD.

Characterization of diffusion MRI using the mean apparent propagator model in hemodialysis patients: A pilot study

To better understand documented cognitive decline in hemodialysis (HD) patients, diffusion MRI (dMRI) has been used to characterize brain anatomical deficits relative to controls. Studies to this point have primarily used diffusion tensor imaging (DTI) to model the three-dimensional diffusion of water in HD patients, with DTI parameters reflecting underlying microstructural changes of brain tissue. Since DTI has some limitations in characterizing tissue microstructure, some of which may be complicated by HD, we explored the use of the mean apparent propagator (MAP) model to describe diffusion in HD patients. We collected anatomical T1 and T2 FLAIR MRIs as well as multi-shell dMRI in ten HD participants and ten age-matched controls. The T1 and T2 FLAIR MRIs were used for tissue segmentation and identification of white matter hyperintensity, respectively. Multi-shell dMRI data were used to estimate MAP and DTI diffusion models. Each model was then used to characterize the differences between the HD cohort and the age-matched controls in normal appearing white matter, subcortical gray matter, corpus callosum (CC) and bilateral radiata (Rad). As expected, parameters of both DTI and MAP models of dMRI were significantly different in HD participants compared to controls. However, some MAP parameters suggested additional tissue microstructural changes in HD participants, such as increased axonal diameter. Measurements of non-Gaussianity indicated that MAP provided better a diffusion estimate than DTI, and MAP appeared to provide a more accurate measure of anisotropy in Rad, based on measures of the Rad/CC ratio. In conclusion, parameters of the MAP and DTI models were both sensitive to changes in diffusivity in HD participants compared to controls; however, the MAP model appeared to provide additional detailed information about changes in brain tissue microstructure.

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging.

To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas. Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed. The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05). Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models. • Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats.

Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R > 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage.

Effects of Hippocampal Sparing Radiotherapy on Brain Microstructure-A Diffusion Tensor Imaging Analysis.

Hippocampal-sparing radiotherapy (HSR) is a promising approach to alleviate cognitive side effects following cranial radiotherapy. Microstructural brain changes after irradiation have been demonstrated using Diffusion Tensor Imaging (DTI). However, evidence is conflicting for certain parameters and anatomic structures. This study examines the effects of radiation on white matter and hippocampal microstructure using DTI and evaluates whether these may be mitigated using HSR. A total of 35 tumor patients undergoing a prospective randomized controlled trial receiving either conventional or HSR underwent DTI before as well as 6, 12, 18, 24, and 30 (±3) months after radiotherapy. Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were measured in the hippocampus (CA), temporal, and frontal lobe white matter (TL, FL), and corpus callosum (CC). Longitudinal analysis was performed using linear mixed models. Analysis of the entire patient collective demonstrated an overall FACC decrease and RDCC increase compared to baseline in all follow-ups; ADCC decreased after 6 months, and MDCC increased after 12 months (p ≤ 0.001, 0.001, 0.007, 0.018). ADTL decreased after 24 and 30 months (p ≤ 0.004, 0.009). Hippocampal FA increased after 6 and 12 months, driven by a distinct increase in ADCA and MDCA, with RDCA not increasing until 30 months after radiotherapy (p ≤ 0.011, 0.039, 0.005, 0.040, 0.019). Mean radiation dose correlated positively with hippocampal FA (p < 0.001). These findings may indicate complex pathophysiological changes in cerebral microstructures after radiation, insufficiently explained by conventional DTI models. Hippocampal microstructure differed between patients undergoing HSR and conventional cranial radiotherapy after 6 months with a higher ADCA in the HSR subgroup (p ≤ 0.034).

Magic angle effect on diffusion tensor imaging in ligament and brain

PurposeTo evaluate the magic angle effect on diffusion tensor imaging (DTI) measurements in rat ligaments and mouse brains. MethodsThree rat knee joints and three mouse brains were scanned at 9.4 T using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm. The b value was 1000 s/mm2 for rat knee and 4000 s/mm2 for mouse brain. DTI model was used to investigate the quantitative metrics at different orientations with respect to the main magnetic field. The collagen fiber structure of the ligament was validated with polarized light microscopy (PLM) imaging. ResultsThe signal intensity, signal-to-noise ratio (SNR), and DTI metrics in the ligament were strongly dependent on the collagen fiber orientation with respect to the main magnetic field from both simulation and actual MRI scans. The variation of fractional anisotropy (FA) was about ~32%, and the variation of mean diffusivity (MD) was ~11%. These findings were further validated with the numerical simulation at different SNRs (~10.0 to 86.0). Compared to the ligament, the DTI metrics showed little orientation dependence in mouse brains. ConclusionMagic angle effect plays an important role in DTI measurements in the highly ordered collagen-rich tissues, while MD showed less orientation dependence than FA.

A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches

Objective. Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain. Approach. Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively. Main results. Age predictions using FA features were more accurate than predictions using QA features for all the six machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with mean absolute error ranging from 7.74 to 10.54, mean square error (MSE) ranging from 87.79 to 150.86, and normalized MSE ranging from 0.05 to 0.14. Significance. FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping.

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on December 14, 2021.

Compartmental models for diffusion weighted MRI reveal widespread brain changes in HIV-infected patients.

Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/specificity. In this study we use both the NODDI and the DTI models to evaluate microstructural differences between 35 HIV-positive patients and 20 healthy controls. Diffusion-weighted imaging was acquired using three b-values (0, 1000 and 2500 s/mm2). Both DTI and NODDI models were fitted to the data, obtaining estimates for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), neurite density index (NDI) and orientation dispersion index (ODI), after which we performed group comparisons using Tract-based spatial statistics (TBSS). While significant group effects were found in in FA, MD, RD, AD and NDI, NDI analysis uncovered a much wider involvement of brain tissue in HIV infection as compared to DTI. In region-of interest (ROI)-based analysis, NDI estimates from the right corticospinal tract produced excellent performance in discriminating the two groups (AUC = 0.974, sensitivity = 90%; specificity =97%).

White Matter Integrity in a Rat Model of Epileptogenesis: Structural Connectomics and Fixel-Based Analysis.

Introduction: Electrophysiological and neuroimaging studies have demonstrated that large-scale brain networks are affected during the development of epilepsy. These networks can be investigated by using diffusion magnetic resonance imaging (dMRI). The most commonly used model to analyze dMRI is diffusion tensor imaging (DTI). However, DTI metrics are not specific to microstructure or pathology and the DTI model does not take into account crossing fibers, which may lead to erroneous results. To overcome these limitations, a more advanced model based on multi-shell multi-tissue constrained spherical deconvolution was used in this study to perform tractography with more precise fiber orientation estimates and to assess changes in intra-axonal volume by using fixel-based analysis. Methods: dMRI images were acquired before and at several time points after induction of status epilepticus in the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy. Tractography was performed, and fixel metrics were calculated in several white matter tracts. The tractogram was analyzed by using the graph theory. Results: Global degree, global and local efficiency were decreased in IPKA animals compared with controls during epileptogenesis. Nodal degree was decreased in the limbic system and default-mode network, mainly during early epileptogenesis. Further, fiber density (FD) and fiber-density-and-cross-section (FDC) were decreased in several white matter tracts. Discussion: These results indicate a decrease in overall structural connectivity, integration, and segregation and decreased structural connectivity in the limbic system and default-mode network. Decreased FD and FDC point to a decrease in intra-axonal volume fraction during epileptogenesis, which may be related to neuronal degeneration and gliosis. Impact statement To the best of our knowledge, this is the first longitudinal multi-shell diffusion magnetic resonance imaging study that combines whole-brain tractography and fixel-based analysis to investigate changes in structural brain connectivity and white matter integrity during epileptogenesis in a rat model of temporal lobe epilepsy. Our findings present better insights into how the topology of the structural brain network changes during epileptogenesis and how these changes are related to white matter integrity. This could improve the understanding of the basic mechanisms of epilepsy and aid the rational development of imaging biomarkers and epilepsy therapies.

Diffusion kurtosis imaging of white matter in bipolar disorder

White matter pathology likely contributes to the pathogenesis of bipolar disorder (BD). Most studies of white matter in BD have used diffusion tensor imaging (DTI), but the advent of more advanced multi-shell diffusion MRI imaging offers the possibility to investigate other aspects of white matter microstructure. Diffusion kurtosis imaging (DKI) extends the DTI model and provides additional measures related to diffusion restriction. Here, we investigated white matter in BD by applying whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution tractography to assess differences in DKI and DTI metrics between BD (n = 25) and controls (n = 24). The VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in DTI metrics following FDR-correction. The DKI findings indicate altered connectivity across cortical, subcortical and cerebellar areas in BD. DKI is sensitive to different microstructural properties and is a useful complementary technique to DTI to more fully investigate white matter in BD.

Probing white matter microstructure in youth with chronic pain

Alterations in white matter (WM) have been reported in individuals with chronic pain, although findings are inconsistent, and mostly derived using the limited Diffusion Tensor Imaging (DTI) model. Going beyond DTI, one study reported lower WM complexity in corpus callosum, internal capsule and a hippocampus tract. In youth with chronic pain, no studies have examined WM changes, except in headache. The current study therefore examined potential WM alterations in youth with chronic pain using DTI as well as Neurite Orientation Dispersion and Density Imaging (NODDI), allowing more specific interrogation of WM microstructure. Multi-shell diffusion-weighted MR images were analyzed in 45 youth with chronic pain (age M=15.8±2.5, 41 females) and 25 controls (age M=16.0±3.2, 18 females). Preprocessing included estimation and correction of susceptibility-, eddy current-, and motion-induced distortions (FSL). DTI and NODDI models were fit, deriving indices of fractional anisotropy (FA/DTI), neurite density (NDI/NODDI) and orientation dispersion (ODI/NODDI). Data was spatially aligned using tensor-based alignment (DTI-TK), followed by tract-based spatial statistics (TBSS; TFCE-corrected). Group contrasts showed that while there were no group differences in FA and NDI, patients showed increased ODI in several tracts, including the corticospinal tract, corpus callosum and forceps minor (connecting medial and lateral frontal lobe), uncinate fasciculus (connecting frontal and temporal lobe), internal capsule (connecting subcortical structures) and fornix (a hippocampus tract). Our findings reveal specific WM alterations in several tracts that are implicated in (chronic) pain. In particular, the dispersion in fiber orientation was increased, reflective of less coherently organized fibers. This may indicate possible delayed pruning in these tracts. Neurite density indices were not different, reflective of similar amounts of fibers and myelin. Taken together, youth with chronic pain showed specific alterations in fiber dispersion of several WM tracts in cortico-limbic circuitry. Future work will examine associations with individual difference factors. NIH R01HD083270 to LE Simons.

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer\u2019s disease

Magnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

Orthogonal moment diffusion tensor decomposition reveals age-related degeneration patterns in complex fiber architecture

Diffusion tensor imaging (DTI) consistently detects increased mean diffusivity and decreased fractional anisotropy with advancing age in regions of primarily single white matter (WM) fiber populations, but findings have been inconsistent in regions of more complex fiber architecture. Given that DTI remains more common for characterizing aging WM than advanced diffusion MRI models due to DTI's simplicity, robustness, and efficiency, it is critical to strive to maximize the information extracted from DTI across the entire WM. The present study uses an orthogonal diffusion tensor decomposition based on the 3 eigenvalue moments (mean diffusivity, norm of anisotropy, and mode of anisotropy), yielding clear voxelwise degeneration patterns across the WM, including regions of complex fiber architecture. This indicates that the previous challenges of DTI in these regions were due to the choice of tensor decomposition rather than the DTI model itself. This study therefore presents a revised view of DTI of aging WM and indicates how age-related degeneration in complex fiber architecture can manifest in forms other than decreased fractional anisotropy.

In vivo assessment of neurodegeneration in Spinocerebellar Ataxia type 7

Spinocerebellar Ataxia type 7 (SCA7) is a neurodegenerative disease characterized by progressive cerebellar ataxia and retinal degeneration. Increasing loss of visual function complicates the use of clinical scales to track the progression of motor symptoms, hampering our ability to develop accurate biomarkers of disease progression, and thus test the efficacy of potential treatments. We aimed to identify imaging measures of neurodegeneration, which may more accurately reflect SCA7 severity and progression. While common structural MRI techniques have been previously used for this purpose, they can be biased by neurodegeneration-driven increases in extracellular CSF-like water. In a cross-sectional study, we analyzed diffusion tensor imaging (DTI) data collected from a cohort of 13 SCA7 patients and 14 healthy volunteers using: 1) a diffusion tensor-based image registration technique, and 2) a dual-compartment DTI model to control for the potential increase in extracellular CSF-like water. These methodologies allowed us to assess both volumetric and microstructural abnormalities in both white and gray matter brain-wide in SCA7 patients for the first time. To measure tissue volume, we performed diffusion tensor-based morphometry (DTBM) using the tensor-based registration. To assess tissue microstructure, we computed the parenchymal mean diffusivity (pMD) and parenchymal fractional anisotropy (pFA) using the dual compartment model. This model also enabled us to estimate the parenchymal volume fraction (pVF), a measure of parenchymal tissue volume within a given voxel. While DTBM and pVF revealed tissue loss primarily in the brainstem, cerebellum, thalamus, and major motor white matter tracts in patients (p < 0.05, FWE corrected; Hedge’s g > 1), pMD and pFA detected microstructural abnormalities in virtually all tissues brain-wide (p < 0.05, FWE corrected; Hedge’s g > 1). The Scale for the Assessment and Rating of Ataxia trended towards correlation with cerebellar pVF (r = −0.66, p = 0.104, FDR corrected) and global white matter pFA (r = −0.64, p = 0.104, FDR corrected). These results advance our understanding of neurodegeneration in living SCA7 patients by providing the first voxel-wise characterization of white matter volume loss and gray matter microstructural abnormalities. Moving forward, this comprehensive approach could be applied to characterize the full spatiotemporal pattern of neurodegeneration in SCA7, and potentially develop an accurate imaging biomarker of disease progression.

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

BackgroundTo 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).MethodsPatients 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.ResultsNODDI-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).ConclusionNODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.

PurposeThe aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters.Materials and methodsIn this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps.ResultsThe T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features.ConclusionsCombination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.