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.

To develop a set of artificial neural networks, collectively termed qMT-Net, 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 qMTNetacq 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 dictionarydriven 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 (qMT-Net-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.

While phase imaging with a gradient echo (GRE) sequence is popular, phase imaging with balanced steady-state free precession (bSSFP) has been underexplored. The purpose of this study was to investigate anatomical and functional phase imaging with multiple phase-cycled bSSFP, in expectation of increasing spatial coverage of steep phase-change regions of bSSFP. Eight different dynamic 2D pass-band bSSFP studies at four phase-cycling (PC) angles and two T /T values were performed on rat brains at 9.4 T with electrical forepaw stimulation, in comparison with dynamic 2D GRE. Anatomical and functional phase images were obtained by averaging the dynamic phase images and mapping correlation between the dynamic images and the stimulation paradigm, and were compared with their corresponding magnitude images. Phase imaging with 3D pass-band and 3D transition-band bSSFP was also performed for comparison with 3D GRE phase imaging. Two strategies of combining the multiple phase-cycled bSSFP phase images were also proposed. Contrast between white matter and gray matter in bSSFP phase images significantly varied with PC angle and became twice as high as that of GRE phase images at a specific PC angle. With the same total scan time, the combined bSSFP phase images provided stronger phase contrast and visualized neuronal fiber-like structures more clearly than the GRE phase images. The combined phase images of both 3D pass-band and 3D transition-band bSSFP showed phase contrasts stronger than those of the GRE phase images in overall brain regions, even at a longer T of 20 ms. In contrast, phase functional MRI (fMRI) signals were weak overall and mostly located in draining veins for both bSSFP and GRE. Multiple phase-cycled bSSFP phase imaging is a promising anatomical imaging technique, while its usage as fMRI does not seem desirable with the current approach.