Investigation of inter-slice magnetization transfer effects as a new method for MTR imaging of the human brain

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

PURPOSE

To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.

METHODS

Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach.

RESULTS

The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications.

CONCLUSION

The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.

Magnetic Resonance Imaging as a Biomarker in Rodent Peripheral Nerve Injury Models Reveals an Age-Related Impairment of Nerve Regeneration

Assessment of myelin integrity in peripheral nerve injuries and pathologies has largely been limited to post-mortem analysis owing to the difficulty in obtaining biopsies without affecting nerve function. This is further encumbered  by the small size of the tissue and its location. Therefore, the development of robust, non-invasive methods is highly attractive. In this study, we used magnetic resonance imaging (MRI) techniques, including magnetization transfer ratio (MTR), to longitudinally and non-invasively characterize both the sciatic nerve crush and lysolecithin (LCP) demyelination models of peripheral nerve injury in rodents. Electrophysiological, gene expression and histological assessments complemented the extensive MRI analyses in young and aged animals. In the nerve crush model, MTR analysis indicated a slower recovery in regions distal to the site of injury in aged animals, as well as incomplete recovery at six weeks post-crush when analyzing across the entire nerve surface. Similar regional impairments were also found in the LCP demyelination model. This research underlines the power of MTR for the study of peripheral nerve injury in small tissues such as the sciatic nerve of rodents and contributes new knowledge to the effect of aging on recovery after injury. A particular advantage of the approach is the translational potential to human neuropathies.

Advanced neuroimaging applied to veterans and service personnel with traumatic brain injury: state of the art and potential benefits

Traumatic brain injury (TBI) remains one of the most prevalent forms of morbidity among Veterans and Service Members, particularly for those engaged in the conflicts in Iraq and Afghanistan. Neuroimaging has been considered a potentially useful diagnostic and prognostic tool across the spectrum of TBI generally, but may have particular importance in military populations where the diagnosis of mild TBI is particularly challenging, given the frequent lack of documentation on the nature of the injuries and mixed etiologies, and highly comorbid with other disorders such as post-traumatic stress disorder, depression, and substance misuse. Imaging has also been employed in attempts to understand better the potential late effects of trauma and to evaluate the effects of promising therapeutic interventions. This review surveys the use of structural and functional neuroimaging techniques utilized in military studies published to date, including the utilization of quantitative fluid attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), volumetric analysis, diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), positron emission tomography (PET), magnetoencephalography (MEG), task-based and resting state functional MRI (fMRI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). The importance of quality assurance testing in current and future research is also highlighted. Current challenges and limitations of each technique are outlined, and future directions are discussed.

Inter-Slice Blood Flow and Magnetization Transfer Effects as A New Simultaneous Imaging Strategy

The recent blood flow and magnetization transfer (MT) technique termed alternate ascending/descending directional navigation (ALADDIN) achieves the contrast using interslice blood flow and MT effects with no separate preparation RF pulse, thereby potentially overcoming limitations of conventional methods. In this study, we examined the signal characteristics of ALADDIN as a simultaneous blood flow and MT imaging strategy, by comparing it with pseudo-continuous ASL (pCASL) and conventional MT asymmetry (MTA) methods, all of which had the same bSSFP readout. Bloch-equation simulations and experiments showed ALADDIN perfusion signals increased with flip angle, whereas MTA signals peaked at flip angle around 45°−60°. ALADDIN provided signals comparable to those of pCASL and conventional MTA methods emulating the first, second, and third prior slices of ALADDIN under the same scan conditions, suggesting ALADDIN signals to be superposition of signals from multiple labeling planes. The quantitative cerebral blood flow signals from a modified continuous ASL model overestimated the perfusion signals compared to those measured with a pulsed ASL method. Simultaneous mapping of blood flow, MTA, and MT ratio in the whole brain is feasible with ALADDIN within a clinically reasonable time, which can potentially help diagnosis of various diseases.

Preliminary Observations on Sensitivity and Specificity of Magnetization Transfer Asymmetry for Imaging Myelin of Rat Brain at High Field

Magnetization transfer ratio (MTR) has been often used for imaging myelination. Despite its high sensitivity, the specificity of MTR to myelination is not high because tissues with no myelin such as muscle can also show high MTR. In this study, we propose a new magnetization transfer (MT) indicator, MT asymmetry (MTA), as a new method of myelin imaging. The experiments were performed on rat brain at 9.4 T. MTA revealed high signals in white matter and significantly low signals in gray matter and muscle, indicating that MTA has higher specificity than MTR. Demyelination and remyelination studies demonstrated that the sensitivity of MTA to myelination was as high as that of MTR. These experimental results indicate that MTA can be a good biomarker for imaging myelination. In addition, MTA images can be efficiently acquired with an interslice MTA method, which may accelerate clinical application of myelin imaging.