Multi-Exponential Relaxometry Using l1 -Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging

T2* quantification using multi-echo gradient echo sequences: a comparative study of different readout gradients

To quantify T2*, multiple echoes are typically acquired with a multi-echo gradient echo sequence using either monopolar or bipolar readout gradients. The use of bipolar readout gradients achieves a shorter echo spacing time, enabling the acquisition of a larger number of echoes in the same scan time. However, despite their relative time efficiency and the potential for more accurate quantification, a comparative investigation of these readout gradients has not yet been addressed. This work aims to compare the performance of monopolar and bipolar readout gradients for T2* quantification. The differences in readout gradients were theoretically investigated with a Cramér-Rao lower bound and validated with computer simulations with respect to the various imaging parameters (e.g., flip angle, TR, TE, TE range, and BW). The readout gradients were then compared at 3 T using phantom and in vivo experiments. The bipolar readout gradients provided higher precision than monopolar readout gradients in both computer simulations and experimental results. The difference between the two readout gradients increased for a lower SNR and smaller TE range, consistent with the prediction made using Cramér-Rao lower bound. The use of bipolar readout gradients is advantageous for regions or situations where a lower SNR is expected or a shorter acquisition time is required.

Repeated Sub-Concussive Impacts and the Negative Effects of Contact Sports on Cognition and Brain Integrity

Sports are yielding a wealth of benefits for cardiovascular fitness, for psychological resilience, and for cognition. The amount of practice, and the type of practiced sports, are of importance to obtain these benefits and avoid any side effects. This is especially important in the context of contact sports. Contact sports are not only known to be a major source of injuries of the musculoskeletal apparatus, they are also significantly related to concussion and sub-concussion. Sub-concussive head impacts accumulate throughout the active sports career, and thus can cause measurable deficits and changes to brain health. Emerging research in the area of cumulative sub-concussions in contact sports has revealed several associated markers of brain injury. For example, recent studies discovered that repeated headers in soccer not only cause measurable signs of cognitive impairment but are also related to a prolonged cortical silent period in transcranial magnetic stimulation measurements. Other cognitive and neuroimaging biomarkers are also pointing to adverse effects of heading. A range of fluid biomarkers completes the picture of cumulating effects of sub-concussive impacts. Those accumulating effects can cause significant cognitive impairment later in life of active contact sportswomen and men. The aim of this review is to highlight the current scientific evidence on the effects of repeated sub-concussive head impacts on contact sports athletes’ brains, identify the areas in need of further investigation, highlight the potential of advanced neuroscientific methods, and comment on the steps governing bodies have made to address this issue. We conclude that there are indeed neural and biofluid markers that can help better understand the effects of repeated sub-concussive head impacts and that some aspects of contact sports should be redefined, especially in situations where sub-concussive impacts and concussions can be minimized.

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction

PURPOSE

To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL).

METHODS

We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 min for 2 mm × 2 mm × 2 mm $2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage.

RESULTS

The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI.

CONCLUSION

Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI.

Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares

Multi-echo T₂ magnetic resonance images contain information about the distribution of T₂ relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T₂ spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X²) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X², and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T₂ spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T₂ distributions and MWF maps.

Whole Brain Myelin Water Mapping in One Minute Using Tensor Dictionary Learning With Low-Rank Plus Sparse Regularization

The quantification of myelin water content in the brain can be obtained by the multi-echo [Formula: see text] weighted images ( [Formula: see text]WIs). To accelerate the long acquisition, a novel tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) is proposed to reconstruct the [Formula: see text]WIs from the undersampled data. The proposed algorithm explores the local and nonlocal similarity and the global temporal redundancy in the real and imaginary parts of the complex relaxation signals. The joint application of the low-rank constraints on the dictionaries and the sparse constraints on the core coefficient tensors improves the performance of the tensor-based recovery. Parallel imaging is incorporated into the TDLLS algorithm (pTDLLS) for further acceleration. A pulse sequence is proposed to prospectively undersample the Ky-t space to obtain the whole brain high-quality myelin water fraction (MWF) maps within 1 minute at an undersampling rate (R) of 6.

Artificial neural network for multi-echo gradient echo-based myelin water fraction estimation

PURPOSE

To demonstrate robust myelin water fraction (MWF) mapping using an artificial neural network (ANN) with multi-echo gradient-echo (GRE) signal.

METHODS

Multi-echo gradient-echo signals simulated with a three-pool exponential model were used to generate the training data set for the ANN, which was designed to yield the MWF. We investigated the performance of our proposed ANN for various conditions using both numerical simulations and in vivo data. Simulations were conducted with various SNRs to investigate the performance of the ANN. In vivo data with high spatial resolutions were applied in the analyses, and results were compared with MWFs derived by the nonlinear least-squares algorithm using a complex three-pool exponential model.

RESULTS

The network results for the simulations show high accuracies against noise compared with nonlinear least-squares MWFs: RMS-error value of 5.46 for the nonlinear least-squares MWF and 3.56 for the ANN MWF at an SNR of 150 (relative gain = 34.80%). These effects were also found in the in vivo data, with reduced SDs in the region-of-interest analyses. These effects of the ANN demonstrate the feasibility of acquiring high-resolution myelin water images.

CONCLUSION

The simulation results and in vivo data suggest that the ANN facilitates more robust MWF mapping in multi-echo gradient-echo sequences compared with the conventional nonlinear least-squares method.