Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Archaeomagnetic results from Cambodia in Southeast Asia: Evidence for possible low-latitude flux expulsion.

Extensive spatial and temporal distribution of high-quality data are essential for understanding regional and global behaviors of the geomagnetic field. We carried out chronological and archaeomagnetic studies at the Angkor-era iron-smelting site of Tonle Bak in Cambodia in Southeast Asia, an area with no data available to date. We recovered high-fidelity full-vector geomagnetic information from the 11th to 14th century for this region, which fill gaps in the global distribution of data and will significantly improve the global models. These results reveal a sharp directional change of the geomagnetic field between 1200 and 1300 CE, accompanied by an intensity dip between 1100 and 1300 CE. The fast geomagnetic variation recorded by our data provides evidence for the possible existence of low-latitude flux expulsion. Related discussions in this paper will inspire a new focus on detailed geomagnetic research in low-latitude areas around the equator, and exploration of related dynamic processes.

Equatorial auroral records reveal dynamics of the paleo-West Pacific geomagnetic anomaly.

Localized regions of low geomagnetic intensity such as the South Atlantic Anomaly allow energetic particles from the Van Allen radiation belt to precipitate into the atmosphere and have been linked to a signature in the form of red aurora-like airglow visible to the naked eye. Smoothed global geomagnetic models predict a low-intensity West Pacific Anomaly (WPA) during the sixteenth to nineteenth centuries characterized by a simple time dependence. Here, we link the WPA to an independent database of equatorial aurorae recorded in Seoul, South Korea. These records show a complex fluctuating behavior in auroral frequency, whose overall trend from 1500 to 1800 AD is consistent with the locally weak geomagnetic field of the WPA, with a minimum at 1650 AD. We propose that the fluctuations in auroral frequency are caused by corresponding and hitherto unknown fluctuations in the regional magnetic intensity with peaks at 1590 and 1720 AD, a time dependence that has been masked by the smoothing inherent in regularized global geomagnetic models. A physical core flow model demonstrates that such behavior requires localized time-dependent upwelling flows in the Earth's core, possibly driven by regional lower-mantle anomalies.

A Pure Shift-Based Nuclear Magnetic Resonance Method for In-Phase Three-Dimensional Diffusion-Ordered Spectroscopy.

Diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) plays a vital role in mixture studies. However, its applications to complex mixture samples are generally limited by spectral congestion along the chemical shift domain caused by extensive J coupling networks and abundant compounds. Herein, we develop the in-phase multidimensional DOSY strategy for complex mixture analyses by simultaneously revealing molecular self-diffusion behaviors and multiplet structures with optimal spectral resolution. As a proof of concept, two pure shift-based three-dimensional (3D) DOSY protocols are proposed to record high-resolution 3D spectroscopic view with separated mixture components and their resolved multiplet coupling structures, thus suitable for analyzing complex mixtures that contain abundant compounds and complicated molecular structures, even under adverse magnetic field conditions. Therefore, this study shows a promising tool for component analyses and multiplet structure studies on practical mixture samples.

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising. METHODS: DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods. RESULTS: Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes. CONCLUSIONS: DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

PURPOSE: To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping. METHODS: In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. RESULTS: The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI. CONCLUSION: GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

IMPULSED model based cytological feature estimation with U-Net: Application to human brain tumor at 3T.

PURPOSE: This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. METHODS: The U-Net was applied to rapidly quantify extracellular diffusion coefficient (Dex ), cell size (d), and intracellular volume fraction (vin ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. RESULTS: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). CONCLUSION: The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single-Shot Ultrafast MR T2 Mapping.

BACKGROUND: Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times. PURPOSE: To investigate the potential of T2 maps derived from multiple overlapping-echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki-67 index, and to compare the diagnostic efficiency of two different region-of-interest (ROI) placements (whole-tumor and contrast-enhanced, respectively). STUDY TYPE: Prospective. PHANTOM/SUBJECTS: A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma. FIELD STRENGTH/SEQUENCE: 3 T scanner. MOLED, T2-weighted spin-echo sequence, T2-dark-fluid sequence, and postcontrast T1-weighted gradient echo sequence. ASSESSMENT: Two ROIs were delineated: the whole-tumor area (ROI1) and contrast-enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki-67 index were reviewed by a neuropathologist according to the 2021 classification criteria. STATISTICAL TESTS: Linear regression, Bland-Altman analysis, Pearson's correlation analysis, independent t test, Mann-Whitney U test, Kruskal-Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P-value significance level of 0.05. RESULTS: The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = -1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki-67 index (r = 0.237-0.374). DATA CONCLUSION: MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki-67 index. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

A simultaneous multi-slice T2 mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction.

PURPOSE: Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition, a framework is proposed to realize reliable simultaneous multi-slice T2 mapping. METHODS: The simultaneous multi-slice T2 mapping framework is based on overlapping-echo detachment (OLED) planar imaging (dubbed SMS-OLED). Multi-slice overlapping-echo signals were generated by multiple excitation pulses together with echo-shifting gradients. The signals were excited and acquired with a single-channel coil. U-Net was used to reconstruct T2 maps from the acquired overlapping-echo image. RESULTS: Single-shot double-slice and two-shot triple-slice SMS-OLED scan schemes were designed according to the framework for evaluation. Simulations, water phantom, and in vivo rat brain experiments were carried out. Overlapping-echo signals were acquired, and T2 maps were reconstructed and compared with references. The results demonstrate the superior performance of our method. CONCLUSION: Two slices of T2 maps can be obtained in a single shot within hundreds of milliseconds. Higher quality multi-slice T2 maps can be obtained via multiple shots. SMS-OLED provides a lower specific absorption rate scheme compared with conventional SMS methods with a coil with only a single receiver channel. The new method is of potential in dynamic qMRI and functional qMRI where temporal resolution is vital.

Ultrafast water-fat separation using deep learning-based single-shot MRI.

PURPOSE: To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images. METHODS: Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods. RESULTS: With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results. CONCLUSION: Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-of-the-art methods.

Single-step calculation of susceptibility through multiple orientation sampling.

Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.

[Quality evaluation of fried Glycyrrhizae Radix et Rhizoma pieces by HPLC fingerprint and multicomponent quantitative analysis].

To establish the HPLC fingerprint and multi-component determination method of fried Glycyrrhizae Radix et Rhizoma pieces. HPLC analysis was performed on Thermo Acclaim ~(TM)120 C_(18) column(4.6 mm×250 mm, 5 µm). Acetonitrile-0.1% phosphoric acid aqueous solution was taken as the mobile phase for gradient elution. The flow rate was 1 mL·min~(-1),the column temperature was maintained at 30 ℃, and the detection wavelength was 237 nm and 360 nm. The similarity of 15 batches of fried Glycyrrhizae Radix et Rhizoma pieces was higher than 0.849, and 17 common peaks were identified. Liquiritin, isoliquiritin apioside, isoliquiritin, liquiritigenin, isoliquiritigenin and glycyrrhizic acid were identified; among them, the mass fractions of Liquiritin, isoliquiritin apioside, isoliquiritin, liquiritigenin, glycyrrhizic acid were were 0.519%-3.058%, 0.227%-0.389%, 0.070%-0.439%, 0.038%-0.173%, 1.381%-4.252%, respectively. According to the cluster analysis, the 15 batches of decoction pieces were classified into three categories; principal component analysis screened out four principal components, with the cumulative variance contribution rate of 86.630%, indicating that the principal components contained most information of original data. Partial least squares discriminant ana-lysis marked 6 differential components in the decoction pieces. The established fingerprint and multicomponent determination are stable and reliable, and can provide a reference for the quality control of Radix Glycyrrhizae Radix et Rhizomae and fried Glycyrrhizae Radix et Rhizoma pieces.

Fast chemical exchange saturation transfer imaging based on PROPELLER acquisition and deep neural network reconstruction.

PURPOSE: To develop a method for fast chemical exchange saturation transfer (CEST) imaging. METHODS: The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method. RESULTS: The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST-PROPELLER reconstructed images under an acceleration factor of 8. CONCLUSION: Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.

Archaeointensity results spanning the past 6 kiloyears from eastern China and implications for extreme behaviors of the geomagnetic field.

Variations of the Earth's geomagnetic field during the Holocene are important for understanding centennial to millennial-scale processes of the Earth's deep interior and have enormous potential implications for chronological correlations (e.g., comparisons between different sedimentary recording sequences, archaeomagnetic dating). Here, we present 21 robust archaeointensity data points from eastern China spanning the past ∼6 kyr. These results add significantly to the published data both regionally and globally. Taking together, we establish an archaeointensity reference curve for Eastern Asia, which can be used for archaeomagnetic dating in this region. Virtual axial dipole moments (VADMs) of the data range from a Holocene-wide low of ∼27 to "spike" values of ∼166 ZAm2 (Z: 1021). The results, in conjunction with our recently published data, confirm the existence of a decrease in paleointensity (DIP) in China around ∼2200 BCE. These low intensities are the lowest ever found for the Holocene and have not been reported outside of China. We also report a spike intensity of 165.8 ± 6.0 ZAm2 at ∼1300 BCE (±300 y), which is either a prelude to or the same event (within age uncertainties) as spikes first reported in the Levant.

Protein aggregation linked to Alzheimer's disease revealed by saturation transfer MRI.

The goal of this study was to develop a molecular biomarker for the detection of protein aggregation involved in Alzheimer's disease (AD) by exploiting the features of the water saturation transfer spectrum (Z-spectrum), the CEST signal of which is sensitive to the molecular configuration of proteins. A radial-sampling steady-state sequence based ultrashort echo time (UTE) readout was implemented to image the Z-spectrum in the mouse brain, especially the contributions from mobile proteins at the frequency offsets for the composite protein amide proton (+3.6 ppm) and aliphatic proton (-3.6 ppm) signals. Using a relatively weak radiofrequency (RF) saturation amplitude, contributions due to strong magnetization transfer contrast (MTC) from solid-like macromolecules and direct water saturation (DS) were minimized. For practical measure of the changes in the mobile protein configuration, we defined a saturation transfer difference (ΔST) by subtracting the Z-spectral signals at ±3.6 ppm from a control signal at 8 ppm. Phantom studies of glutamate solution, protein (egg white) and hair conditioner show the capability of the proposed scheme to minimize the contributions from amine protons, DS, and MTC, respectively. The ST signal at ±3.6 ppm of the cross-linked bovine serum albumin (BSA) solutions demonstrated that the ΔST signal can be used to monitor the aggregation process of the mobile proteins. High-resolution ΔST images of AD mouse brains at ±3.6 ppm of mouse brains showed significantly reduced ΔST (-3.6) signal compared to the age-matched wild-type (WT) mice. Thus, this signal has potential to serve as a molecular biomarker for monitoring protein aggregation in AD.

Altered brain iron content and deposition rate in Huntington's disease as indicated by quantitative susceptibility MRI.

Altered brain iron content in the striatum of premanifest and manifest Huntington's disease (HD) has been reported. However, its natural history remains unclear. This study aims to investigate altered brain iron content in premanifest and early HD, and the iron deposition rate in these patients through a longitudinal one-year follow-up test, with quantitative magnetic susceptibility as an iron imaging marker. Twenty-four gene mutation carriers divided into three groups (further-from-onset, closer-to-onset and early HD) and 16 age-matched healthy controls were recruited at baseline, and of these, 14 carriers and 7 controls completed the one-year follow-up. Quantitative magnetic susceptibility and effective transverse relaxation rate ( R2∗ ) were measured at 7.0 Tesla and correlated with atrophy and available clinical and cognitive measurements. Higher susceptibility values indicating higher iron content in the striatum and globus pallidus were only observed in closer-to-onset (N = 6, p < 0.05 in caudate and p < 0.01 in putamen) and early HD (N = 9, p < 0.05 in caudate and globus pallidus and p < 0.01 in putamen). Similar results were found by R2∗ measurement. Such increases directly correlated with HD CAG-age product score and brain atrophy, but not with motor or cognitive scores. More importantly, a significantly higher iron deposition rate (11.9%/years in caudate and 6.1%/years in globus pallidus) was firstly observed in closer-to-onset premanifest HD and early HD as compared to the controls. These results suggest that monitoring brain iron may provide further insights into the pathophysiology of HD disease progression, and may provide a biomarker for clinical trials.

Fast quantitative susceptibility reconstruction via total field inversion with improved weighted L0 norm approximation.

Quantitative susceptibility mapping (QSM) is a meaningful MRI technique owing to its unique relation to actual physical tissue magnetic properties. The reconstruction of QSM is usually decomposed into three sub-problems, which are solved independently. However, this decomposition does not conform to the causes of the problems, and may cause discontinuity of parameters and error accumulation. In this paper, a fast reconstruction method named fast TFI based on total field inversion was proposed. It can accelerate the total field inversion by using a specially selected preconditioner and advanced solution of the weighted L0 regularization. Due to the employment of an effective model, the proposed method can efficiently reconstruct the QSM of brains with lesions, where other methods may encounter problems. Experimental results from simulation and in vivo data verified that the new method has better reconstruction accuracy, faster convergence ability and excellent robustness, which may promote clinical application of QSM.

High-resolution creatine mapping of mouse brain at 11.7 T using non-steady-state chemical exchange saturation transfer.

The current study aims to optimize the acquisition scheme for the creatine chemical exchange saturation transfer weighted (CrCESTw) signal on mouse brain at 11.7 T, in which a strong magnetization transfer contrast (MTC) is present, and to further develop the polynomial and Lorentzian line-shape fitting (PLOF) method for quantifying CrCESTw signal with a non-steady-state (NSS) acquisition scheme. Studies on a Cr phantom with cross-linked bovine serum albumin (BSA) as well as on mouse brain demonstrated that the maximum CrCESTw signal was reached with a short saturation time determined by the rotating frame relaxation time of the MTC pool instead of the steady-state saturation. The saturation power for the maximal signal was around 1-1.5 µT for Cr with 20% cross-linked BSA and in vivo applications, but 2 µT was found to be most practical for signal stability. For the CrCEST acquisition with strong MTC interference, the optimal saturation power and length are completely different from those on Cr solution alone. This observation could be explained well using R1ρ theory by incorporating the strong MTC pool. Finally, a high-resolution Cr map was obtained on mouse brain using the PLOF method with the NSS CEST acquisition and a cryogenic coil. The Cr map obtained by CEST showed homogenous intensity across the mouse brain except for regions with cerebrospinal fluid.