Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editor

Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding

Purpose: Asymmetric gradient waveforms are attractive for diffusion encoding due to their superior efficiency, however, the asymmetry may cause a residual gradient moment at the end of the encoding. Depending on the experiment setup, this residual moment may cause significant signal bias and image artifacts. The purpose of this study was to develop an asymmetric gradient waveform design for tensor-valued diffusion encoding that is not affected by concomitant gradient. Methods: The Maxwell index was proposed as a scalar invariant that captures the effect of concomitant gradients and was constrained in the numerical optimization to 100 (mT/m)$^2$ms to yield Maxwell-compensated waveforms. The efficacy of this design was tested in an oil phantom, and in a healthy human brain. For reference, waveforms from literature were included in the analysis. Simulations were performed to investigate if the design was valid for a wide range of experiments and if it could predict the signal bias. Results: Maxwell-compensated waveforms showed no signal bias in oil or in the brain. By contrast, several waveforms from literature showed gross signal bias. In the brain, the bias was large enough to markedly affect both signal and parameter maps, and the bias could be accurately predicted by theory. Conclusion: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient tensor-valued encoding with concomitant gradients that have a negligible effect on the signal. This waveform design is especially relevant in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition and large/oblique FOVs

Glioma Microstructure Modeling from Diffusion MRI: A Self-Supervised Deep Learning Approach

Purpose: Gliomas are a highly heterogeneous group of primary brain tumors with poor prognosis, and treatment monitoring is challenging with its current diagnostic tool being invasive biopsy. In recent years, diffusion-weighted magnetic resonance imaging (dMRI) has become a widely used non-invasive technique that infers tissue microstructure in tumors. Such analysis presents opportunities in cancer diagnosis, tumor grading, and monitoring treatment effectiveness. A technique called vascular, extracellular and restricted diffusion for cytometry in tumors (VERDICT) has been demonstrated to be feasible for inferring glioma microstructure, but current methods are time-consuming in terms of acquisition and analysis and therefore not clinically applicable. In this thesis, the purpose was therefore to investigate whether VERDICT microstructure fitting in glioma tissue could be approached with a newly introduced self-supervised fully connected neural network method. Methods: A large-scale synthetic dataset was simulated using the VERDICT signal equation based on known ground-truth glioma tissue parameters, with a highly detailed acquisition protocol. A feed-forward neural network (FFNN) was constructed to predict four tissue parameters from dMRI signals: the cell radius, intracellular (IC) volume fraction, extracellular-extravascular (EES) volume fraction and vascular volume fraction. The network was trained and tuned on the large-scale simulated dataset before it was trained on clinically applicable sequences and tested on in vivo dMRI. Furthermore, experiments were conducted to test the network’s performance with different acquisition protocols, aiming to determine the potential improvement that could be achieved through protocol optimization. Lastly, the proposed ANN was compared to existing methods in terms of computational time and accuracy. Results: The model trained on the large-scale synthetic data managed to estimate the four parameters with high precision, and the prediction time in one voxel was less than 10−4 s when applying a trained network to an in vivo dMRI. Predictions on acquired in vivo data were compatible with tissue parameters from the literature for a longer dMRI scan, but the model showed lower accuracy in terms of fitting data with a shorter acquisition (i.e. less rich diffusion protocol). From experimenting with different acquisition protocols, the best performance was found on the longest protocol, but it was found that the accuracy of predictions was also dependent on the individual acquisition parameter ranges. Conclusion: This project successfully demonstrated the ability to use the FFNN approach to fit the VERDICT model to dMRI data. Findings indicated that the FFNN fitting is heavily influenced by the acquisition protocol, and the method is not suitable for MRI acquisitions with less than 20 measurements. However, the method showed good potential for use in acquisitions with 436 measurements, and for predicting a radius of up to 10 µm, 145 high b-value measurements can be sufficient. Optimization of the acquisition schemes suggested that alternative schemes could further enhance the effectiveness of the technique. The FFNN showed several advantages in terms of computational time and accuracy of predictions compared to existing methods.Masteroppgave i medisinsk teknologiMTEK39

Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI

Purpose: Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, the general Standard Model has recently shown that model parameter estimation from dMRI data is ill-posed unless very strong magnetic gradients are used. We analyse this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from Single Diffusion Encoding (SDE) to Double Diffusion Encoding (DDE) solves the ill-posedness and increases the accuracy of the parameter estimation. Methods: We analyse theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions. Results: We prove analytically that DDE provides invariant information non-accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space. Conclusions: DDE adds additional information for estimating the model parameters, unexplored by SDE, which is enough to solve the degeneracy in the NODDIDA model parameter estimation.Comment: 22 pages, 7 figure

Optimizing Filter-Probe Diffusion Weighting in the Rat Spinal Cord for Human Translation

Diffusion tensor imaging (DTI) is a promising biomarker of spinal cord injury (SCI). In the acute aftermath, DTI in SCI animal models consistently demonstrates high sensitivity and prognostic performance, yet translation of DTI to acute human SCI has been limited. In addition to technical challenges, interpretation of the resulting metrics is ambiguous, with contributions in the acute setting from both axonal injury and edema. Novel diffusion MRI acquisition strategies such as double diffusion encoding (DDE) have recently enabled detection of features not available with DTI or similar methods. In this work, we perform a systematic optimization of DDE using simulations and an in vivo rat model of SCI and subsequently implement the protocol to the healthy human spinal cord. First, two complementary DDE approaches were evaluated using an orientationally invariant or a filter-probe diffusion encoding approach. While the two methods were similar in their ability to detect acute SCI, the filter-probe DDE approach had greater predictive power for functional outcomes. Next, the filter-probe DDE was compared to an analogous single diffusion encoding (SDE) approach, with the results indicating that in the spinal cord, SDE provides similar contrast with improved signal to noise. In the SCI rat model, the filter-probe SDE scheme was coupled with a reduced field of view (rFOV) excitation, and the results demonstrate high quality maps of the spinal cord without contamination from edema and cerebrospinal fluid, thereby providing high sensitivity to injury severity. The optimized protocol was demonstrated in the healthy human spinal cord using the commercially-available diffusion MRI sequence with modifications only to the diffusion encoding directions. Maps of axial diffusivity devoid of CSF partial volume effects were obtained in a clinically feasible imaging time with a straightforward analysis and variability comparable to axial diffusivity derived from DTI. Overall, the results and optimizations describe a protocol that mitigates several difficulties with DTI of the spinal cord. Detection of acute axonal damage in the injured or diseased spinal cord will benefit the optimized filter-probe diffusion MRI protocol outlined here