Keywords: Diffusion Analysis & Visualization, Motion Correction, Diffusion Gradient Cycling

Motivation: Diffusion gradient cycling (DGC) enables more efficient diffusion-weighted imaging (DWI) scanning, but it is not compatible with typical preprocssing pipelines.

Goal(s): Our goal is to propose two approaches for correcting motion in DWI data acquired using DGC: slice-to-volume registration and volume-to-volume registration with the assistance of slice reordering.

Approach: Intentional motion was introduced during the DWI acquisition, and the proposed approaches were implemented and applied to remove artifacts caused by this motion.

Results: Both approaches effectively eliminated motion-induced artifacts, and the intentional motion was estimated correctly.

Impact: Motion-induced artifacts can be eliminated, and correct motion estimation can be achieved in DWI acquired with DGC. Our proposed approaches are publicly available and can be easily integrated into preprocessing pipelines.  
 

Keywords: Diffusion Analysis & Visualization, Diffusion Tensor Imaging, Spectroscopy, Morphology

Motivation: Clarification of tissue destruction and reconstruction process of holometabolous insects might lead to innovative technology for regenerative medicine.

Goal(s): Observation of Morpholochemical changes and water molecule dynamics in the pupal body.

Approach: We applied T2W, DTI and PRESS to the pupal body of Trypoxylus dichotomus.

Results: From the pupation, the digestive tract swelled and became a liquid reservoir. It has high ADC and FA in the Head-Foot direction. After the pupation, the structure was narrower and elongated. The results suggest that destructed tissues were stored temporarily in the form of reservoir, and through the liquid channel around the reservoir to reconstruction tissues.

Impact: In the pupal body, the tissue absorbed into the digestive tract while some of the nervous system and muscles are retained formed a liquid reservoir, used to form adult tissues such as flight muscles through certain liquid channel.

Keywords: Diffusion Analysis & Visualization, Diffusion/other diffusion imaging techniques, Harmonization

Motivation: The Human Connectome Project (HCP) is a multi-site neuroimaging initiative that studies brain connections across the lifespan and in diseases. Scanner variability, especially in diffusion MRI (dMRI) data, can introduce bias and prevent reliable pooling of data.

Goal(s): We present our harmonization efforts on the dMRI data from 11 HCP datasets using a well-validated harmonization algorithm based on rotation-invariant spherical harmonics.

Approach: Using several diffusion measures, we demonstrate that harmonization removes significant statistical differences between datasets.

Results: Harmonized HCP dMRI data will be shared in the NIMH Data Archive and facilitate large-scale analysis and potentially enhance our understanding of neurological and psychiatric disorders.

Impact: Harmonizing diffusion MRI data from 11 Human Connectome Project scanners enables more reliable cross-site brain connectivity analyses. Leveraging large-scale harmonized diffusion MRI data can enhance statistical power, paving the way for advanced neurological and psychiatric insights within the HCP study.

Keywords: Diffusion Analysis & Visualization, Diffusion/other diffusion imaging techniques, Statistical Analysis, Normalization Techniques, Zscore, Pscore, Human Connectome Project

Motivation: Conventional methods like Zscores are often used to evaluate individual patients against a normative distribution. This can lead to biased estimations when distributions are skewed. We showed this in a pilot study using 48 controls and proposed a novel metric - 'Pscore', to address this bias. We aimed to reproduce these results systematically using a larger dataset.

Goal(s): Validate the 'Pscore' approach on a large-scale high resolution neuroimaging dataset.

Approach: Diffusion MRI data from the Human Connectome Project (HCP) was used to test various normalization methods

Results: Pscores demonstrate symmetric distributions and no systematic biases observed in Zscores of all diffusion MRI derived metrics. 

Impact: The non-Gaussian nature of neuroimaging data has implications for building normative databases and their use to assess abnormalities in individual subjects. The proposed 'Pscore' approach reliably addresses this, which implies its usefulness for individual assessments even in smaller neuroimaging datasets. 

Keywords: Diffusion Analysis & Visualization, Motion Correction

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion.

Goal(s): To develop a semi-automatic post-processing pipeline for robust DT-CMR registration and automatic frame selection. 

Approach: We used low intrinsic rank averaged frames as the reference to register other low-ranked frames. A myocardium-guided frame selection rejected the frames with signal loss, through-plane motion and poor registration. 

Results: The proposed method outperformed our previous noise-robust rigid registration on helix angle data quality and reduced negative eigenvalues in healthy volunteers. 

Impact: This improved image registration and frame selection algorithm may enable groupwise deformable registration on DT-CMR, paving the way towards clinical translation. 

Keywords: Diffusion Analysis & Visualization, Visualization, Computer Graphics, Ray Marching, Signed Distance Functions

Motivation: Traditional rendering engines represent surfaces using simpler polygons (triangles or quads). This discretization can compromise real-time performance as more polygons are required to build smoother surfaces.

Goal(s): Develop a new efficient method for building and displaying DTI-related glyphs without compromising their visual quality using continuous geometric representations. 

Approach: Ray marching is a robust technique for tracing implicit surfaces using signed distance functions (SDFs). Our method uses these techniques to create representations of commonly used DTI-related glyphs.

Results: Our SDFs glyphs make it possible to define smooth surfaces in a more efficient way that allows us to visualize more objects than traditional polygon-based renderers.

Impact: By using SOTA efficient rendering algorithms for complex elements, such as DTI-related glyphs, we improved the performance of scientific visualization systems, enabling the visualization of more extensive datasets and/or multiple types of data in real-time without sacrificing visual quality. 

Keywords: Diffusion Analysis & Visualization, High-Field MRI, diffusion-relaxation correlation spectroscopic imaging，brain

Motivation: Motivated by the promise of higher SNR and gradient performance at ultra-high magnetic fields (5T), we conducted in-vivo experiments of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) to tap into its prospects at 5T.

Goal(s): This study aims to assess the feasibility and performance of DR-CSI at 5T for brain tissue measurement and to explore its 5T applications compared to 3T.

Approach: We have acquired two-dimensional diffusion-relaxation data and resolved the 2D spectra and spatial maps at both field strengths for comparative analysis.

Results: Results at 5T showed clearer differentiation of brain tissues and improved sensitivity to deep brain nuclei.

Impact: At 5T, diffusion-relaxation correlation spectroscopic imaging excels in estimating deep brain nuclei, promising for future applications in infarcts and neurodegenerative conditions.

Keywords: Diffusion Analysis & Visualization, Diffusion/other diffusion imaging techniques, brain tumor

Motivation: Imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) based on time-dependent diffusion MRI (dMRI) could be utilized to quantify cell size, cellularity, and extracellular space, but its ability to distinguish common brain tumors remains unknown.

Goal(s): To determine the capacity of IMPULSED to distinguish between glioma, brain metastases, and meningioma.

Approach: Time-dependent dMRI was performed on patients with untreated glioma (n = 24), brain metastases (n = 67), and meningioma (n = 38). These patients’ IMPULSED-based parameters were evaluated and compared.

Results: Glioma, brain metastasis, and meningioma have distinctive IMPULSED-based parameters.

Impact: IMPULSED-based parameters could distinguish glioma, brain metastases, and meningioma. Time-dependent diffusion MRI is a potential approach for assessing the microstructures of brain tumors.

Keywords: Diffusion Analysis & Visualization, Data Analysis

Motivation: Atlas-based diffusion imaging of whole brain white matter analysis might be helpful in evaluating obscure motor function recovery in chronic stroke.

Goal(s): Our goal was to evaluate the potential of diffusion imaging atlas-based in predicting motor function recovery in stroke rats with constraint-induced movement therapy.

Approach: We used atlas-based analysis of diffusion imaging to conduct multi-parameter measurements of white matter fibers in the entire brain, combined with catwalk-automated gait analysis to test quantificationally evaluated the motor function.

Results: Atlas-based analysis of diffusion observations indicated that treatment with constraint-induced movement therapy improved microstructural integrity of axon and myelin.

Impact: Atlas-based analysis of diffusion imaging were intended to advance non-invasive imaging approaches for understanding microstructural changes of axon and myelin in chronic stroke.

Keywords: Diffusion Analysis & Visualization, Diffusion Tensor Imaging, structural volumes

Motivation: The Periodic Table of DTI Elements organizes neural tract patterns by their diffusivity and neural properties but mainly describes white matter injury.

Goal(s): Our goal was to expand the utility of this methodology by interrogating other subcortical and cortical structures of interest and comparing them to brain metrics in controls derived from an open-access dataset.

Approach: We examined brain volumes and DTI profiles in Complex Normal Pressure Hydrocephalus (CoNPH) vs. healthy controls from ADNI.

Results: Lateral ventricular volumes were significantly higher but most other volumes significantly lower in CoNPH vs. controls. Most DTI metrics (supratentorial and infratentorial) were significantly higher in CoNPH.

Impact: The Periodic Table of DTI Elements can demonstrate white matter and cortical injury and alterations of grey matter structures. The strategy can be used to compare cohorts with controls-in-common from an open-access dataset for wider community use.

Keywords: Diffusion Analysis & Visualization, Diffusion/other diffusion imaging techniques

Motivation: Diffusion MRI (dMRI) is a very noisy MRI technique. The low signal-to-noise ratio introduces uncertainties in diffusion metrics and structural connectivity measures.

Goal(s): We searched for a method to investigate the uncertainty in dMRI measurements and to compare them across different magnetic field strengths.

Approach: We assessed uncertainties using wild and residual bootstrapping techniques in a group of  7 healthy subjects undergoing optimized acquisition protocols at 1.5T, 3T, and 7T.

Results: 7T diffusion metrics and uncertainties are comparable with 3T and exhibited significant differences with 1.5T. The enhanced 7T spatial resolution demonstrated capabilities in representing structural connectivity with greater complexity and reduced uncertainty.

Impact: The findings support the hypothesis that 7T dMRI can offer new insights into structural connectivity and may be particularly valuable for single-subject studies, thanks to its ability to detect more connections and reduce uncertainty compared to clinical fields.

Keywords: Diffusion Analysis & Visualization, Diffusion Tensor Imaging, Diffusion Denoising

Motivation: DTI suffers from intrinsic low SNR compared to conventional MRI, making denoising crucial.

Goal(s): Our study aims to introduce a novel filtration method based on an estimated pattern of areas most directionally affected in the spatial frequency domain.

Approach: A pattern was suggested through DTI theory and observations of simulation data. This was then used to propose a filter for noise reduction. The application of this filter was quantitatively and qualitatively evaluated on simulated MR and DTI images, considering added noise.

Results: The results showed not only the method’s increased robustness to noise but also a clearer representation of white matter tracts.

Impact: By integrating the fundamental concepts of diffusion within the white matter into our filter design, we present a promising approach to denoising DTI data, potentially yielding more reliable and biologically meaningful results, and benefiting researchers investigating brain connectivity.

Keywords: Diffusion Analysis & Visualization, Multi-Contrast

Motivation:  It is very important to suppress CSF partial-volume effects in tissue-specific quantitative parameters of T2, T1, PD, ADC, and FA etc. in brain MRI.
 

Goal(s): To assess and confirm the optimal method to improve the accuracy and precision of quantitative parameters in our  proposed method of T2wsup-dMRI. 

Approach: Evaluated the tissue SNRs of quantitative parameters for in-vivo brain MRI data with the several combinations of data sampling pattern in (TE, b) space and analysis algorithm.

Results: The combination of Triangle-pattern and 2d-single and bi-exponential combined LSQ fitting (2dSi&BiExpLSQ) was the best from the views of SNR, hardware, and computing costs.

Impact:  The combination of Triangle sampling pattern and 2dSi&BiExpLSQ fitting algorithm in T2wsup-dMRI provides high quality maps with minimum  hardware and computing costs for obtaining multi-quantitative parameter mapping especially in clinical brain diffusion MRI.

Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques, DTI, DKI, robust modeling, outliers, motion correction

Motivation: Clinical research with infants, subject motion can cause many subjects being excluded from analyses due to large parts of their data is corrupted by outliers. While robust modelling methods can mitigate this problem, how they affect dMRI model estimate precision is not well known.

Goal(s): We demonstrate how dMRI model precision can be evaluated with two robust modelling strategies.

Approach: We used white-matter simulation to compare multi-tensor model precision between 1) Gaussian Process outlier replacements and ordinary model estimator to 2) robustly weighted model estimation.

Results: Model precision estimation is possible with both robust approaches, but outlier replacement can cause inflated precision estimates.

Impact: Our aim is to enable larger sample sizes for clinical dMRI research by decreasing the need to exclude subject due to subject motion. Additionally, we provide new robust tools to evaluate the precision of dMRI model estimates.

Keywords: DWI/DTI/DKI, Diffusion Tensor Imaging, Sport-related concussion, Subject-specific analysis

Motivation: Unlike groupwise approaches, subject-specific analysis has the potential to pinpoint highly individualized abnormalities in white matter structure caused by sport-related concussion (SRC).

Goal(s): Develop a subject-specific analysis approach to investigate the heterogeneity and longitudinal changes in white matter microstructures after SRC.

Approach: The DTI metrics were voxel-by-voxel Z-transformed using a normal distributed template created from non-contact sport controls. The extreme Z maps were obtained, and averaged extreme Z-scores were compared across three study time points.

Results: The existence of heterogeneity in the concussed brains can be appreciated in the projected extreme Z maps and their longitudinal trajectories.

Impact: We developed a subject-specific analysis pipeline to demonstrate heterogeneity in sport-related concussion with respect to anatomical locations and recovery trajectories.

Keywords: Simulation/Validation, Data Analysis, Reliability

Motivation: DTI-MRI reliability studies are performed to validate findings, improve clinical applications, assure scientific precision, and advance methodology.

Goal(s): The aim was to assess the within-participant reliability of diffusion tensor imaging (DTI) measurements, specifically fractional anisotropy (FA) determinations, within different regions of the  (CSC). 

Approach: In total, 20 healthy controls were recruited over two months. Each participant was scanned twice. The within-participants coefficients of variation (CV%) was used to evaluate the reliability of FA metric between (C2-C5) for the WM, and WM sub-regions.
 

Results: In general, the CV%s were low for the WM, DC, VC and LC regions demonstrating higher reproducibility

Impact: Results from this  study indicated high within-participants reliability and demonstrated that FA may be highly effective in assessing CSC changes. Previous studies have used semi-automated or manual segmentation methods, while this study used automatic-segmentation by SCT, which provides tract-based analysis.