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Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform. Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals. Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation. Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows., Comment: 6 pages, 5 figure, conference abstract

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms., Comment: 13pages, 9figures

Purpose: Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed sensing (cs)MP2RAGE T1 mapping for accelerating MTsat imaging. Methods: VFA, MP2RAGE and csMP2RAGE were compared against inversion recovery (IR) T1 in a phantom at 3 Tesla. The same 1 mm VFA, MP2RAGE and csMP2RAGE protocols were acquired in four healthy subjects to compare the resulting T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Finally, ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability. Results: The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. All approaches provided accurate T1 values (<5% difference) in the phantom, but the accuracy of the T1 times was more impacted by differences in T2 for VFA than for MP2RAGE. In vivo, VFA generated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the bias in the T1 values between VFA and IR-based approaches (MP2RAGE and IR) could be explained by the MT-effects from the inversion pulse. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 3% for T1 mapping and 7.9% for MTsat imaging. Conclusions: We demonstrated that csMP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence., Comment: 23 pages, 7 figures, 2 tables

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows., Comment: 6 pages, 5 figures

We present a novel dual-encoded magnetization transfer (MT) and diffusion-weighted sequence and demonstrate its potential to resolve distinct properties of white matter fiber tracts at the sub-voxel level. The sequence was designed and optimized for maximal MT contrast efficiency. The resulting whole brain 2.6 mm isotropic protocol to measure tract-specific MT ratio (MTR) has a scan time under 7 minutes. Ten healthy subjects were scanned twice to assess repeatability. Two different analysis methods were contrasted: a technique to extract tract-specific MTR using Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), a global optimization technique; and conventional MTR tractometry. The results demonstrate that the tract-specific method can reliably resolve the MT ratios of major white matter fiber pathways and is less affected by partial volume effects than conventional multi-modal tractometry. Dual-encoded MT and diffusion is expected to both increase the sensitivity to microstructure alterations of specific tracts due to disease, ageing or learning, as well as lead to weighted structural connectomes with more anatomical specificity., Comment: 26 pages, 7 figures

Purpose: Imaging biomarkers with increased myelin specificity are needed to better understand the complex progression of neurological disorders. Inhomogeneous magnetization transfer (ihMT) imaging is an emergent technique that has a high degree of specificity for myelin content but suffers from low signal-to-noise ratio (SNR). This study used simulations to determine optimal sequence parameters for ihMT imaging for use in high-resolution cortical mapping. Methods: MT-weighted cortical image intensity and ihMT SNR were simulated using modified Bloch equations for a range of sequence parameters. The acquisition time was limited to 4.5 min/volume. A custom MT-weighted RAGE sequence with center-out k-space encoding was used to enhance SNR at 3 Tesla. Pulsed MT imaging was studied over a range of saturation parameters and the impact of the turbo-factor on effective ihMT was investigated. 1 mm isotropic ihMTsat maps were generated in 25 healthy adults using an optimized protocol. Results: Greater SNR was observed for larger number of bursts consisting of 6-8 saturation pulses each, combined with a high readout turbo-factor. However, that protocol suffered from a point spread function that was more than twice the nominal resolution. For high-resolution cortical imaging, we selected a protocol with a higher effective resolution at the cost of a lower SNR. We present the first group-average ihMTsat whole-brain map at 1 mm isotropic resolution. Conclusion: This study presents the impact of saturation and excitation parameters on ihMTsat SNR and resolution. We demonstrate the feasibility of high-resolution cortical myelin imaging using ihMTsat in less than 20 minutes.

Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but amplify noise and errors. This study develops a large-stencil Laplacian preprocessed deep learning-based neural network for near instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MR phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks. The proposed iQFM and iQSM methods yielded comparable results to multi-step methods in healthy subjects while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. The reconstruction time was also substantially shortened from minutes using multi-step methods to only 30 milliseconds using the trained iQFM and iQSM neural networks.

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM) and region-of-interest susceptibility measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey matter susceptibility than some existing methods, when the acquisition was accelerated four times. The proposed DCRNet also dramatically shortened the reconstruction time by nearly 10 thousand times for each scan, from around 80 hours using conventional approaches to only 30 seconds., Comment: 10 figures

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), structural similarity (SSIM), and region-of-interest measurements. The results from a numerical phantom, a simulated human brain, four in vivo healthy human subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a healthy mouse brain showed that the xQSM led to suppressed artifacts than the conventional methods, and enhanced susceptibility contrast, particularly in the ironrich deep grey matter region, than the original U-net, consistently. The xQSM method also substantially shortened the reconstruction time from minutes using conventional iterative methods to only a few seconds., Comment: 37 pages, 10 figures, 3 table

The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so., Comment: This is the accepted version, accepted by NeuroImage. It contains minor revisions suggested by reviewers