Analysis of normal-appearing white matter of multiple sclerosis by tensor-based two-compartment model of water diffusion

Monte Carlo Simulator for Diffusion-weighted Imaging Sequences

We developed a Monte Carlo simulator for diffusion-weighted imaging sequences which displays the motion of water molecules and computes the dynamic phase dispersion due to the applied motion probing gradients. This simulator can be used to validate the analytical equations of diffusion models and understand their limitations due to their approximations. Here, we introduce the software and some specific use cases. The software can be downloaded from the following website: https://www.nirs.qst.go.jp/amr_diag.

The Utility of a Convolutional Neural Network for Generating a Myelin Volume Index Map from Rapid Simultaneous Relaxometry Imaging

Purpose:

A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolutional neural network (CNN) to overcome this problem.

Methods:

RSRI and magnetization transfer images from 20 healthy volunteers were included. A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the MVI map calculated from magnetization transfer images (MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by testing how well they correlated with the MTMVI map. The correlations were evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and (ii) pixel-based comparison for ROIs defined in four different tissue types (cortical and subcortical gray matter, white matter, and whole brain).

Results:

For atlas-based ROIs, the overall correlation with the MTMVI map was higher for the GenMVI map than for the SyMVF map. In the pixel-based comparison, correlation with the MTMVI map was stronger for the GenMVI map than for the SyMVF map, and the difference in the distribution for the volunteers was significant (Wilcoxon sign-rank test, P < 0.001) in all tissue types.

Conclusion:

The proposed method is useful, as it can incorporate more specific information about local tissue properties than the existing method. However, clinical validation is necessary.

The Utility of Applying Various Image Preprocessing Strategies to Reduce the Ambiguity in Deep Learning-based Clinical Image Diagnosis

PURPOSE

A general problem of machine-learning algorithms based on the convolutional neural network (CNN) technique is that the reason for the output judgement is unclear. The purpose of this study was to introduce a strategy that may facilitate better understanding of how and why a specific judgement was made by the algorithm. The strategy is to preprocess the input image data in different ways to highlight the most important aspects of the images for reaching the output judgement.

MATERIALS AND METHODS

T2-weighted brain image series falling into two age-ranges were used. Classifying each series into one of the two age-ranges was the given task for the CNN model. The images from each series were preprocessed in five different ways to generate five different image sets: (1) subimages from the inner area of the brain, (2) subimages from the periphery of the brain, (3-5) subimages of brain parenchyma, gray matter area, and white matter area, respectively, extracted from the subimages of (2). The CNN model was trained and tested in five different ways using one of these image sets. The network architecture and all the parameters for training and testing remained unchanged.

RESULTS

The judgement accuracy achieved by training was different when the image set used for training was different. Some of the differences was statistically significant. The judgement accuracy decreased significantly when either extra-parenchymal or gray matter area was removed from the periphery of the brain (P < 0.05).

CONCLUSION

The proposed strategy may help visualize what features of the images were important for the algorithm to reach correct judgement, helping humans to understand how and why a particular judgement was made by a CNN.

Comparison of Glass Capillary Plates and Polyethylene Fiber Bundles as Phantoms to Assess the Quality of Diffusion Tensor Imaging

Purpose:

The purpose of this study was to evaluate the suitability of two phantoms, one made of capillary plates and the other polyethylene fibers, for assessing the quality of diffusion tensor imaging (DTI).

Methods:

The first phantom was a stack of glass capillary plates with many parallel micropores (CP). The second phantom was a bundle of polyethylene fiber Dyneema held together with a thermal shrinkage tube (Dy). High resolution multi-shot echo planar imaging (EPI) DTI acquisitions were performed at b-values of 0 and 1000 s/mm² and diffusion-times (T_diff) of 37.7 and 97.7 ms on a preclinical 7T MRI scanner. Thirty diffusion-encoding directions were used. The data were used to calculate the fractional anisotropy (FA), mean diffusivity (MD), and angular dispersion (AD). Further acquisitions were performed at b-values from 0 to 8000 s/mm² in 14 steps with the diffusion gradient applied parallel (axial) and perpendicular (radial) to the Z direction. On the other hand, the data acquired with a 3T MRI scanner were used to confirm that measurements on a clinical machine are consistent with the 7T MRI results.

Results:

The dependence of FA, MD and AD on T_diff was smaller for the Dy than for the CPs. The b-value-dependent signal attenuations in the axial direction at T_diff = 37.7 and 97.7 ms were similar for both phantoms. In the radial direction, Dy demonstrated similar b-value attenuation to that of in vivo tissue for both T_diffs, but the attenuation for the CPs was affected by the change in T_diff. Parameter estimates were similar for 3T and 7T MRI.

Conclusion:

The characteristics of the CP indicate that it can be used as a restricted-diffusion dominant phantom, while the characteristics of the Dy suggest that it can be used as a hindered-diffusion dominant phantom. Dy may be more suitable than CP for DTI quality control.

Enlarged Virchow Robin spaces associate with cognitive decline in multiple sclerosis

The clinical significance of Virchow Robin spaces (VRS) in inflammatory brain disorders, especially in multiple sclerosis (MS), is still undefined. We analysed enlarged VRS (eVRS) by means of phase sensitive inversion recovery (PSIR) MRI sequence and investigated their association with inflammation or brain atrophy, and to clinical or physical disability. Forty-three MS patients (21 clinically isolated syndrome suggestive of MS [CIS], 15 RRMS, 7 progressive [PMS]) and 10 healthy controls (HC) were studied. 3DT1, 3DFLAIR and 2DPSIR images were obtained with a 3T MRI scanner. eVRS number and volume were calculated by manual segmentation (ITK-SNAP). Freesurfer was used to assess brain parenchymal fraction (BPF). All patients underwent clinical (EDSS) and cognitive (Rao’s BRB and DKEFS) evaluation.

eVRS number and volume resulted significantly higher on 2D-PSIR compared to both 3D-T1 (p<0.001) and 3D-FLAIR (p<0.001) and were significantly increased in CIS compared to HC (p<0.05), in PMS and RRMS compared to CIS (p<0.001) and in male versus female patients (p<0.05). eVRS volume increased significantly with disease duration (r = 0.6) but did not correlate with EDSS. eVRS significantly correlated with SPARTd (r = -0.47) and DKEFSfs (r = -0.46), especially when RRMS and PMS were merged in a single group (r = 0.89, p = 0.002 and r = 0.66, p = 0.009 respectively), while no correlation was found with BPF (r = 0.3), gadolinium-enhancing lesions (r = 0.2) and WMT2 lesion volume (r = 0.2). 2DPSIR allowed the detection of an impressive higher number of eVRS compared to 3DT1 and 3DFLAIR. eVRS associate with SPARTd and DKEFSfs failure in relapse-onset MS, suggesting they may contribute to cognitive decline in MS.

Dose-dependent white matter damage after brain radiotherapy

BACKGROUND AND PURPOSE

Brain radiotherapy is limited in part by damage to white matter, contributing to neurocognitive decline. We utilized diffusion tensor imaging (DTI) with multiple b-values (diffusion weightings) to model the dose-dependency and time course of radiation effects on white matter.

MATERIALS AND METHODS

Fifteen patients with high-grade gliomas treated with radiotherapy and chemotherapy underwent MRI with DTI prior to radiotherapy, and after months 1, 4-6, and 9-11. Diffusion tensors were calculated using three weightings (high, standard, and low b-values) and maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ∥), and radial diffusivity (λ⊥) were generated. The region of interest was all white matter.

RESULTS

MD, λ∥, and λ⊥ increased significantly with time and dose, with corresponding decrease in FA. Greater changes were seen at lower b-values, except for FA. Time-dose interactions were highly significant at 4-6months and beyond (p<.001), and the difference in dose response between high and low b-values reached statistical significance at 9-11months for MD, λ∥, and λ⊥ (p<.001, p<.001, p=.005 respectively) as well as at 4-6months for λ∥ (p=.04).

CONCLUSIONS

We detected dose-dependent changes across all doses, even <10Gy. Greater changes were observed at low b-values, suggesting prominent extracellular changes possibly due to vascular permeability and neuroinflammation.

Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis

Objectives

A new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.

Methods

Fourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm²) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).

Results

Error-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.

Conclusion

eDKI might be useful for assessing DKI in clinical settings.

Key Points

• A method to practically estimate axial/radial DKI from limited data was developed.

• The high robustness of the proposed method can greatly improve map images.

• The accuracy of the proposed method was high.

• Axial/radial K maps can be calculated from limited diffusion-encoding directions.

• The proposed method might be useful for assessing DKI in clinical settings.