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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 MnCl 2 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 (Mean diff  = -1.29%, SD diff  = 1.25% and Mean diff  = 0.36%, SD diff  = 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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Rationale and Objectives: Stroke patients commonly face challenges during magnetic resonance imaging (MRI) examinations due to involuntary movements. This study aims to overcome these challenges by utilizing multiple overlapping-echo detachment (MOLED) quantitative technology. Through this technology, we also seek to detect microstructural changes of the normal-appearing corticospinal tract (NA-CST) in subacute-chronic stroke patients., Materials and Methods: 79 patients underwent 3.0 T MRI scans, including routine scans and MOLED technique. A deep learning network was utilized for image reconstruction, and the accuracy, reliability, and resistance to motion of the MOLED technique were validated on phantoms and volunteers. Subsequently, we assessed motor dysfunction severity, ischemic lesion volume, T 2 values of the bilateral NA-CST, and the T 2 ratio (rT 2 ) between the ipsilesional and contralesional NA-CST in patients., Results: The MOLED technique showed high accuracy (P < 0.001) and excellent repeatability, with a mean coefficient of variation (CoV) of 1.11%. It provided reliable quantitative results even under head movement, with a mean difference (Mean diff )= 0.28% and a standard deviation difference (SD diff )= 1.34%. Additionally, the T 2 value of the ipsilesional NA-CST was significantly higher than contralesional side (P < 0.001), and a positive correlation was observed between rT 2 and the severity of motor dysfunction (r s =0.575, P < 0.001). Furthermore, rT 2 successfully predicted post-stroke motor impairment, with an area under the curve (AUC) was 0.883., Conclusion: The MOLED technique offers significant advantages for quantitatively imaging stroke patients with involuntary movements. Additionally, T 2 mapping from MOLED can detect microstructural changes in the NA-CST, potentially aiding in monitoring stroke-induced motor impairment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective . The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time consuming. This work aims to accelerate the scan through a highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and to develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data. Approach . The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades per b -value are acquired and rotated along the b -value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) is proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficient D and the perfusion fraction f ) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used an unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along the b -value follow a bi-exponential decay. The residual between the realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for genuine DW-TSE-PROPELLER data. Main results . The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-free D and f maps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on a simulated human brain and realistic phantom/rat brain/human brain data. Significance . Our proposed method greatly accelerates IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurate D and f maps from six-fold under-sampled DW-TSE-PROPELLER data., (© 2023 Institute of Physics and Engineering in Medicine.)

Background and Objective: Compressed sensing (CS) has gained increased attention in magnetic resonance imaging (MRI), leveraging its efficacy to accelerate image acquisition. Incoherence measurement and non-linear reconstruction are the most crucial guarantees of accurate restoration. However, the loose link between measurement and reconstruction hinders the further improvement of reconstruction quality, i.e., the default sampling pattern is not adaptively tailored to the downstream reconstruction method. When single-contrast reconstruction (SCR) has been upgraded to its multi-contrast reconstruction (MCR) variant, the identical morphologic information as a priori source could be integrated into the reconstruction procedure. How to measure less and reconstruct effectively by using the shareable morphologic information of various contrast images is an attractive topic., Methods: An adaptive sampling (AS) based end-to-end framework (ASSCR or ASMCR) is proposed to address this issue, which simultaneously optimizes sampling patterns and reconstruction from under-sampled data in SCR or MCR scenarios. Several deep probabilistic subsampling (DPS) modules are used in AS network to construct a sampling pattern generator. In SCR and MCR, a convolution block and a data consistency layer are iteratively applied in the reconstruction network. Specifically, the learned optimal sampling pattern output from the trained AS sub-net is used for under-sampling. Incoherence measurement for single-contrast images and the combination of sampling patterns for multi-contrast data are guided by the SCR/MCR sub-net., Results: Experiments were conducted on two single-contrast and one multi-contrast public MRI datasets. Compared with several state-of-the-art reconstruction methods, SCR results show that a learned sampling pattern brings the quality of the reconstructed image closer to the fully-sampled reference. With the addition of different contrast images, under-sampled images with higher acceleration factors could be well recovered., Conclusion: The proposed method could improve the reconstruction quality of under-sampled images by using adaptive sampling patterns and learning-based reconstruction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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 T 2 , T 2 *, 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, T 2 , and T 1 -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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Recently, magnetic resonance imaging (MRI) reconstruction based on deep learning has become popular. Nevertheless, reconstruction of highly undersampled MR images is still challenging due to severe aliasing effects. In this study we built a deep fusion connection network (DFCN) to efficiently utilize the correlation information between adjacent slices. The proposed method was evaluated with online public IXI dataset and Calgary-Campinas-359 dataset. The results show that DFCN can generate the best reconstruction images in de-aliasing and restoring tissue structure compared with several state-of-the-art methods. The mean value of the peak signal-to-noise ratio could reach 34.16 dB, the mean value of the structural similarity is 0.9626, and the mean value of the normalized mean square error is 0.1144 on T 2 -weighted brain data of IXI dataset under 10× acceleration. Additionally, the mean value of the peak signal-to-noise ratio could reach 30.17 dB, the mean value of the structural similarity is 0.9259, and the mean value of the normalized mean square error is 0.1294 on T 1 -weighted brain data of Calgary-Campinas-359 dataset under 10× acceleration. With the correlation information between adjacent slices as prior knowledge, our method can dramatically eliminate aliasing effects and enhance the reconstruction quality of undersampled MR images., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Single-shot spatiotemporally encoded (SPEN) MRI has been validated to possess considerable performance in both spatial and temporal resolution. Water/fat separation is essential for MRI applications in which only water signal is needed. In this article, a super-resolved water/fat image reconstruction method (dubbed SWAF) combined prior knowledge was developed based on single-shot SPEN MRI. The point spread function of spatiotemporal encoding under multiple chemical shifts situation was derived and used for constructing an equation for SWAF image reconstruction. By processing the prior chemical shift information with filtering operation, an initial spin density profile of water/fat and a weighting matrix for water/fat residual artifacts suppression were obtained to guide the reconstruction process. A l 1 norm minimization problem with regularization was exploited to reconstruct separated water/fat images with high spatial resolution and less residual/aliasing artifacts. Numeric simulation and experiments on water-oil phantom and rat abdomen/neck imaging demonstrated the effectiveness and robustness of this new method. The SWAF method proposed herein would promote the application of SPEN MRI in the cases where water/fat separation is required., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often neglected during reconstruction. As a result, more densely sampled low frequency information tends to dominate penalization schemes for reconstructing MRI at the expense of high frequency details. In this paper, we propose a new framework for CS-MRI inversion in which we decompose the observed k-space data into "subspaces" via sets of filters in a lossless way, and reconstruct the images in these various spaces individually using off-the-shelf algorithms. We then fuse the results to obtain the final reconstruction. In this way, we are able to focus reconstruction on frequency information within the entire k-space more equally, preserving both high and low frequency details. We demonstrate that the proposed framework is competitive with state-of-the-art methods in CS-MRI in terms of quantitative performance, and often improves an algorithm's results qualitatively compared with its direct application to k-space., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T 2 mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T 2 mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T 2 mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based methods. The results of simulation, phantom, and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties.

When using aggressive undersampling, it is difficult to recover the high quality image with reliably fine features. In this paper, we propose an enhanced recursive residual network (ERRN) that improves the basic recursive residual network with a high-frequency feature guidance, an error-correction unit and dense connections. The feature guidance is designed to predict the underlying anatomy based on image a priori learned from the label data, playing a complementary role to the residual learning. The ERRN is adapted for two important applications: compressed sensing (CS) MRI and super resolution (SR) MRI, while an application-specific error-correction unit is added into the framework, i.e. data consistency for CS-MRI and back projection for SR-MRI due to their different sampling schemes. Our proposed network was evaluated using a real-valued brain dataset, a complex-valued knee dataset, pathological brain data and in vivo rat brain data with different undersampling masks and rates. Experimental results demonstrated that ERRN presented superior reconstructions at all cases with distinctly restored structural features and highest image quality metrics compared to both the state-of-the-art convolutional neural networks and the conventional optimization-based methods, particularly for the undersampling rate over 5-fold. Thus, an excellent framework design can endow the network with a flexible architecture, fewer parameters, outstanding performances for various undersampling schemes, and reduced overfitting in generalization, which will facilitate real-time reconstruction on MRI scanners., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Chemical exchange saturation transfer (CEST) imaging is an important magnetic resonance molecular imaging technology. However, long acquisition time limits its clinical application, especially when multi-slice CEST imaging is needed. Though single-shot EPI can be used to accelerate CEST imaging, images are often distorted under inhomogeneous magnetic fields. In this work, we propose a new method called CEST-SeSPEN for ultrafast multi-slice CEST imaging based on segmented spatiotemporally encoded (SeSPEN) MRI. Experiments were performed on creatine phantom and hen egg. The results show that CEST-SeSPEN can provide good CEST contrast images. Its acquisition time is much shorter than other multi-slice CEST methods currently available. It may be used in challenging situation where high temporal resolution and robustness to field inhomogeneity are vital., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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 L 0 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., (© 2019 John Wiley & Sons, Ltd.)

Single-shot spatiotemporally encoded (SPEN) ultrafast magnetic resonance imaging (MRI) is of great value to both scientific research and clinical application owing to its capability for delivering MR images with greater robustness to magnetic field inhomogeneity and chemical-shift displacement effects than conventional methods like EPI due to high effective phase-encoded bandwidth. Many SPEN MRI methods have been developed, among which multi-slice SPEN MRI arises as a promising supplement to ultrafast multi-slice sampling. In this work, we propose a new multi-slice SPEN MRI method, termed multi-echo segmented SPEN (ME-SeSPEN) method, which produces multiple images within a single train of echoes and successively samples widely separated slices. The resulting images were reconstructed using de-convolution super-resolved algorithm. The robustness and efficiency of the proposed method were demonstrated by phantom, lemon and in vivo experiments in comparison with spin-echo EPI, spin-echo simultaneous echo refocusing (SER), and segmented SPEN (SeSPEN) MRI. The results indicate that the new method effectively shortens the sampling time (20% reduction practically in comparison with SeSPEN when two slices are simultaneously sampled). ME-SeSPEN also reduces eddy current effects while maintaining the benefits of SPEN MRI, such as similar robustness to field inhomogeneity, spatial resolution and signal-to-noise ratio to SeSPEN MRI. The new method will promote the versatility of multi-slice MRI in practical applications., (Copyright © 2018 Elsevier Inc. All rights reserved.)

In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which can be grouped into two categories: single-contrast super-resolution and multi-contrast super-resolution, where the former has no reference information, and the latter applies a high-resolution image of another modality as a reference. In this paper, we propose a deep convolutional neural network model, which performs single- and multi-contrast super-resolution reconstructions simultaneously. Experimental results on synthetic and real brain MRI images show that our convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Purpose: To design a new approach that can not only keep the spatial and temporal resolution but also have better built-in immunity to magnetic field inhomogeneity and chemical shift effects than the single-shot echo planar imaging (EPI) for chemical exchange saturation transfer (CEST) MRI., Method: The single-shot spatiotemporally encoded (SPEN) MRI sequence was combined with a continuous wave saturation pulse for fast CEST MRI (CEST-SPEN MRI). The resulting images were super-resolved reconstructed by a hybrid method that solves the l 1 norm minimization together with total variation (TV) regularization. Partial Lorentzian fitting was used to analyze the subsequent Z-spectra., Results: Experimental results of a creatine phantom and in vivo tumor rat brains show that CEST-SPEN MRI has good capability in providing CEST-based and NOE-based contrast images., Conclusions: Compared with CEST-EPI, CEST-SPEN MRI has better immunity to magnetic field inhomogeneity and provides better contrast images within identical acquisition time, especially under an identical inhomogeneous field. Magn Reson Med 77:1786-1796, 2017. © 2016 International Society for Magnetic Resonance in Medicine., (© 2016 International Society for Magnetic Resonance in Medicine.)

Hypoxia can induce physiological changes. This study aims to explore effects of high-altitude (HA) hypoxia on cerebral iron concentration. Twenty-nine healthy sea-level participants were tested shortly before and after approximately 4-week adaptation to the HA environment at fQinghai-Tibet Plateau (4200m), and were re-investigated after re-adaptation to the sea-level environment one year later. Iron concentration was quantified with quantitative susceptibility mapping (QSM), and the results were compared with transverse relaxation rate (R * 2 ) measurements. The variations of magnetic susceptibility indicate that the iron concentration in gray matter regions, especially in basal ganglia, including caudate nucleus, putamen, globus pallidus and substantia nigra, increases significantly after HA exposure. This increase appears consistent with the conclusion from R * 2 value variations. However, unlike QSM, the R * 2 value fails to demonstrate the statistical difference of iron content in red nucleus. The re-investigation results show that most variations are recovered after sea-level re-adaptation for one year. Additionally, hemisphere- and gender-related differences in iron concentration changes were analyzed among cerebral regions. The results show greater possibilities in the right hemisphere and females. Further studies based on diffusion tensor imaging (DTI) suggest that the fractional anisotropy increases and the mean diffusivity decreases after HA exposure in six deep gray matter nuclei, with linear dependence on iron concentration only in putamen. In conclusion, the magnetic susceptibility value can serve as a quantitative marker of brain iron, and variations of regional susceptibility reported herein indicate that HA hypoxia can result in significant iron deposition in most deep gray matter regions. Additionally, the linear dependence of DTI metrics on iron concentration in putamen indicates a potential relationship between ferritin and water diffusion., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Quantitative susceptibility mapping (QSM) reconstruction is a well-known ill-posed problem. Various regularization techniques have been proposed for solving this problem. In this paper, a rapid method is proposed that uses ℓ 0 norm minimization in a gradient domain. Because ℓ 0 minimization is an NP-hard problem, a special alternating optimization strategy is employed to simplify the reconstruction algorithm. The proposed algorithm uses only simple point-wise multiplications and thresholding operations, and significantly speeds up the calculation. Both numerical simulations and in vivo experiments demonstrate that the proposed method can reconstruct susceptibility fast and accurately. Because morphology information weighted methods have achieved considerable success in QSM, we performed a quantitative comparison with some typical weighted methods, such as MEDI (morphology enabled dipole inversion), iLSQR (improved least squares algorithm), and wℓ 1 (weighted ℓ 1 norm minimization). The reconstructed results show that the proposed method can provide accurate results with a satisfactory speed., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

As a recently emerging method, spatiotemporally encoded (SPEN) magnetic resonance imaging (MRI) has a high robustness to field inhomogeneity and chemical shift effect. It has been broadened from single-slice scanning to multi-slice scanning. In this paper, a novel multi-slice SPEN MRI method was proposed. In this method, the slice-selective dimension was segmented to lower the specific absorption rate (SAR) and improve the image quality. This segmented method, dubbed SeSPEN method, was theoretically analyzed and demonstrated with phantom, lemon and in vivo rat brain experiments. The experimental results were compared with the results obtained from the spin-echo EPI, spin-echo SPEN method and multi-slice global SPEN method proposed by Frydman and coauthors (abbr. GlSPEN method). All the SPEN images were super-resolved reconstructed using deconvolution method. The results indicate that the SeSPEN method retains the advantage of SPEN MRI with respect to resistance to field inhomogeneity and can provide better signal-to-noise ratio than multi-slice GlSPEN MRI technique. The SeSPEN method has comparable SAR to the GlSPEN method while the T1 signal attenuation effect is alleviated. The proposed method will facilitate the multi-slice SPEN MRI to scan more slices within one scan with better image quality., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Spatiotemporally encoded (SPEN) single-shot MRI is an ultrafast MRI technique proposed recently, which utilizes quadratic rather than linear phase profile to extract the spatial information. Compared to the echo planar imaging (EPI), this technique has great advantages in resisting field inhomogeneity and chemical shift effects. Super-resolved (SR) reconstruction is adopted to compensate the inherent low resolution of SPEN images. Due to insufficient sampling rate, the SR image is challenged by aliasing artifacts and edge ghosts. The existing SR algorithms always compromise in spatial resolution to suppress these undesirable artifacts. In this paper, we proposed a novel SR algorithm termed super-resolved enhancing and edge deghosting (SEED). Different from artifacts suppression methods, our algorithm aims at exploiting the relationship between aliasing artifacts and real signal. Based on this relationship, the aliasing artifacts can be eliminated without spatial resolution loss. According to the trait of edge ghosts, finite differences and high-pass filter are employed to extract the prior knowledge of edge ghosts. By combining the prior knowledge with compressed sensing, our algorithm can efficiently reduce the edge ghosts. The robustness of SEED is demonstrated by experiments under various situations. The results indicate that the SEED can provide better spatial resolution compared to state-of-the-art SR reconstruction algorithms in SPEN MRI. Theoretical analysis and experimental results also show that the SR images reconstructed by SEED have better spatial resolution than the images obtained with conventional k-space encoding methods under similar experimental condition., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Purpose: We employ an amplitude-modulated chirp pulse to selectively excite spins in one or more regions of interest (ROIs) to realize reduced field-of-view (rFOV) imaging based on single-shot spatiotemporally encoded (SPEN) sequence and Fourier transform reconstruction., Materials and Methods: The proposed rFOV imaging method was theoretically analyzed and illustrated with numerical simulation and tested with phantom experiments and in vivo rat experiments. In addition, point spread function was applied to demonstrate the feasibility of the proposed method. To evaluate the proposed method, the rFOV results were compared with those obtained using the EPI method with orthogonal RF excitation., Results: The simulation and experimental results show that the proposed method can image one or two separated ROIs along the SPEN dimension in a single shot with higher spatial resolution, less sensitive to field inhomogeneity, and practically no aliasing artifacts. In addition, the proposed method may produce rFOV images with comparable signal-to-noise ratio to the rFOV EPI images., Conclusion: The proposed method is promising for the applications under severe susceptibility heterogeneities and for imaging separate ROIs simultaneously., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: To investigate the characteristics of nuclear Overhauser enhancement (NOE) imaging signals in the brain at 7T., Methods: Fresh hen eggs, as well as six healthy, and six C6 glioma-bearing Wistar rats were scanned using chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) and chemical exchange saturation transfer-magnetic resonance spectroscopy (CEST-MRS) sequences (saturation duration 3 s, power 1.47 µT) with and without lipid suppression. CEST data were acquired over an offset range of -6 to +6 ppm relative to the water resonance in 0.5 ppm steps., Results: The water signals were not disrupted by other protons during the CEST-MRS sequences, and true NOE signals could be observed. Using the CEST-MRI sequence without lipid suppression, pseudo NOE imaging signals were observed in the lipid-containing regions (egg yolk, scalp, and even white matter). These pseudo NOE signals were almost (but incompletely) removed with the lipid suppression. Egg yolk results indicated the presence of the NOE to olefinic protons overlapping with the water signal. In vivo experiments showed that the amide proton transfer signal was larger in the tumor, whereas the NOE signal was larger in the normal white matter., Conclusions: True NOE signals can be detected using MRS sequences, and considerable pseudo NOE imaging signals may be observed using MRI sequences., (© 2014 Wiley Periodicals, Inc.)

Purpose: To present a new high-resolution single-point water-fat separation algorithm based on the spatiotemporally encoded chemical shift imaging technique., Theory: Identifying water and fat peaks on the ensemble of the nominal k-space profiles of all spatiotemporally encoded lines enables evaluation of the mean off-resonance frequencies of the two components. With utilization of the spatial smoothness and filtering regularizations, the water/fat profiles can be discriminated with twice joint linear least squares estimations line-by-line., Methods: The effectiveness of the proposed algorithm was assessed by experiments on oil-water phantoms and in vivo in rats at 7T using a spatiotemporally encoded variant of the multishot spin-echo sequence. The results were compared with those obtained from previously proposed 1-point Dixon, 2-point Dixon, and 3-point IDEAL methods., Results: The results demonstrate that the new technique can achieve high-quality water-fat separations, comparable in signal-to-noise ratio and contrast to the multipoint methods and is more robust in cases when large areas of low signals or motion artifacts jeopardize the results from the 1-point Dixon method., Conclusions: The proposed technique is potentially a new viable alternative for single-point water-fat separation., (© 2014 Wiley Periodicals, Inc.)

Owing to its intrinsic characteristics, spatiotemporally encoded (SPEN) imaging is less sensitive to adverse effects due to field inhomogeneity in comparison with echo planar imaging, a feature highly desired for functional, diffusion, and real-time MRI. However, the quality of images obtained with SPEN MRI is still degraded by geometric distortions when field inhomogeneity exists. In this study, a single-shot biaxial SPEN (bi-SPEN) pulse sequence is implemented, utilizing a 90° and a 180° chirp pulse incorporated with two orthogonal gradients. A referenceless geometric-distortion correction based on the single-shot bi-SPEN sequence is then proposed. The distorted image acquired with the single-shot bi-SPEN sequence is corrected by iterative super-resolved reconstruction involving the field gradients estimated from a field map, which in turn is obtained from its own super-resolved data after a phase-unwrapping procedure without additional scans. In addition, the distortion correction method is applied to improve the quality of the multiple region-of-interest images obtained with single-shot bi-SPEN sequence., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Chemical exchange saturation transfer (CEST) provides a new type of image contrast in MRI. Due to the intrinsically low CEST effect, new and improved experimental techniques are required to achieve reliable and quantitative CEST images. In the present work, we proposed a novel and more sensitive CEST acquisition approach, based on the intermolecular double-quantum coherence with a module of multiple refocusing pulses (iDQC-MRP). Experiments were performed on creatine and egg white phantoms using a Varian 7T animal MRI scanner. The iDQC-MRP CEST technique showed a substantial enhancement in CEST and nuclear Overhauser enhancement (NOE) signal intensities, compared to the standard single-quantum coherence approach. In addition, the iDQC-MRP approach increased the signal-to-noise ratio of acquired saturation images, compared to the conventional iDQC approach. The new iDQC-MRP CEST sequence provides a promising way for exploiting in vivo CEST and NOE imaging applications., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Compared to the echo planar imaging (EPI), spatiotemporally encoded (SPEN) single-shot MRI holds better immunity to the field inhomogeneity, while retaining comparable spatial and temporal resolutions after the super-resolved reconstruction. Though various reconstruction methods have been proposed, the reconstructed SPEN images usually contain aliasing artifacts because of vast undersampling. A hybrid scheme based on random sampling, singular value decomposition (SVD) and compressed sensing (CS) was introduced to reduce these aliasing artifacts and improve the image quality. The efficiency of this hybrid scheme was demonstrated by numerical simulations and experiments on water phantom and in vivo rat brain. The hybrid scheme provided herein would benefit the SPEN approach in vast undersampling situation., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Brown adipose tissue (BAT) is a promising therapeutic target in obesity studies. Recently, MRI has been proposed for the mapping of BAT. However, because of the limitation of spatial resolution, similar to the existing positron emission tomography and computed tomography techniques for BAT detection, it fails to distinguish BAT cells when they are mixed with other cells. In this work, a new MRI method is proposed, combining intermolecular double-quantum coherence and the chemical shift-encoded Dixon method. Its contrast depends on the water to fat ratio at the cellular scale, which is smaller than the imaging voxel size. The feasibility of this MRI method was shown with computer simulations and phantoms, and preliminary imaging of BAT of rats at 7 T. Both computer simulations and experimental results are consistent with theoretical predictions. The method provides a novel contrast mechanism and can map BAT distribution exclusively. In particular, a mixture of BAT cells and white adipose tissue cells was detected in an older rat, which was undetectable by other noninvasive methods. This method may be applicable to a wide range of uses in BAT-related studies, including the formation and variation of BAT., (Copyright © 2013 John Wiley & Sons, Ltd.)

In the COSY Revamped with Asymmetric Z-gradient Echo Detection (CRAZED) experiments, magnetization is modulated by the distant dipolar field (DDF) generated by coherence selection gradient (CSG) commonly in sinusoidal wave-form and results in detectable intermolecular multiple-quantum coherence (iMQC) signal. IMQCs have some attractive features, but their intrinsic weak signal intensity prevents their widespread applications. In this paper, a new phase cycling scheme was applied to obtain intermolecular double-quantum coherence (iDQC) signal. It is found that DDF can arise from nonspherical sample geometry or background inhomogeneous field in the absence of CSGs, which is more efficient than that created from CSGs. The experimental results show that the resulting DDF can refocus the ±iDQC signals simultaneously and thus enhance the signal intensity to about two folds of that from the conventional CRAZED sequence. Theoretical prediction and experiments give coincident results., (Copyright © 2013 Elsevier Inc. All rights reserved.)

A novel image encoding approach based on linear frequency-swept excitation has been recently proposed to overcome artifacts induced by various field perturbations in single-shot echo planar imaging. In this article, we develop a new super-resolved reconstruction method for it using the concepts of local k-space and partial Fourier transform. This method is superior to the originally developed conjugate gradient algorithm in convenience, image quality, and stability of solution. Reduced field-of-view is applied to the phase encoding direction to further enhance the spatial resolution and field perturbation immunity of the image obtained. Effectiveness of this new combined reconstruction method is demonstrated with a series of experiments on biological samples. Two single-shot sequences with different encoding features are tested. The results show that this reconstruction method maintains excellent field perturbation immunity and improves fidelity of the images. In vivo experiments on rat indicate that this solution is favorable for ultrafast imaging applications in which severe susceptibility heterogeneities around the tissue-air or tissue-bone interfaces, motion and oblique plane effects usually compromise the echo planar imaging image quality., (Copyright © 2012 Wiley Periodicals, Inc.)

Spatiotemporal-encoding single-scan MRI method is relatively insensitive to field inhomogeneity compared to EPI method. Conjugate gradient (CG) method has been used to reconstruct super-resolved images from the original blurred ones based on coarse magnitude-calculation. In this article, a new de-convolution reconstruction method is proposed. Through removing the quadratic phase modulation from the signal acquired with spatiotemporal-encoding MRI, the signal can be described as a convolution of desired super-resolved image and a point spread function. The de-convolution method proposed herein not only is simpler than the CG method, but also provides super-resolved images with better quality. This new reconstruction method may make the spatiotemporal-encoding 2D MRI technique more valuable for clinic applications., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Intermolecular multiple quantum coherences generated by distant dipolar field (DDF) have some attractive properties, but the intrinsic weak signal intensity prevents their widespread applications. Recently, Branca et al. (J Chem Phys 2008;129:054502) suggested that square wave DDF was more efficient than conventional sinusoidal DDF because it could simultaneously produce intermolecular multiple quantum coherences signal with various major orders. In this article, instead of a series of adiabatic inversion pulses proposed previously, a more efficient composite adiabatic inversion pulse was applied to create square wave DDF. The square wave DDF was applied to in vivo MRI for the first time, and the corresponding simulations were performed. Both experimental and simulated results show that square wave DDF with composite adiabatic inversion pulse improves over the original Z-modulation enhanced to binary for self-refocused acquisition implementation and can enhance the signal intensity to about 2-fold of that from conventional correlation spectroscopy (COSY) revamped with asymmetric Z-gradient echo detection sequence for in vivo MRI, close to the theoretical prediction.

The tremendous potential of chemical exchange saturation transfer (CEST) agents as an emerging class of magnetic resonance imaging contrast media has been demonstrated in recent years. In a CEST experiment, a high CEST contrast is always welcome. However, when the exchange rate is low, which may happen in exchangeable solute protons of low concentration, it is usually hard to obtain an excellent CEST efficiency. Recently, we noted that the intermolecular multiple quantum coherence signal is more sensitive to the changes of the magnetization magnitude than a conventional single quantum coherence signal. Consequently, it may be easier when used in obtaining a CEST contrast. In this note, a modified COSY (two-dimensional correlated spectroscopy) revamped with an asymmetric Z-gradient echo detection (CRAZED) sequence combined with an off-resonance saturation pulse followed by a standard spin-echo imaging sequence was designed to obtain a better CEST contrast image based on the intermolecular double quantum coherence method. An analytical expression was derived from a dipolar field theory. Experiments were performed on an agar-glucose phantom, and the results demonstrate the feasibility of our method.

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T 2 mapping and comparable results to conventional methods were obtained in the human brain. Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods. [ABSTRACT FROM AUTHOR]

Background: Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single‐shot T2 mapping method based on multiple overlapping‐echo detachment (MOLED) planar imaging was presented, but the relatively small echo time range limits its accuracy, especially in tissues with large T2. Purpose: In this work we proposed a novel single‐shot method, Multi‐Echo‐Train Multiple OverLapping‐Echo Detachment (METMOLED) planar imaging, to accommodate a large range of T2 quantification without additional measurements to rectify signal degeneration arisen from refocusing pulse imperfection. Methods: Multiple echo‐train techniques were integrated into the MOLED sequence to capture larger TE information. Maps of T2, B1, and spin density were reconstructed synchronously from acquired METMOLED data via multitask deep learning. A typical U‐Net was trained with 3000/600 synthetic data with geometric/brain patterns to learn the mapping relationship between METMOLED signals and quantitative maps. The refocusing pulse imperfection was settled through the inherent information of METMOLED data and auxiliary tasks. Results: Experimental results on the digital brain (structural similarity (SSIM) index = 0.975/0.991/0.988 for MOLED/METMOLED‐2/METMOLED‐3, hyphenated number denotes the number of echo‐trains), physical phantom (the slope of linear fitting with reference T2 map = 1.047/1.017/1.006 for MOLED/METMOLED‐2/METMOLED‐3), and human brain (Pearson's correlation coefficient (PCC) = 0.9581/0.9760/0.9900 for MOLED/METMOLED‐2/METMOLED‐3) demonstrated that the METMOLED improved the quantitative accuracy and the tissue details in contrast to the MOLED. These improvements were more pronounced in tissues with large T2 and in application scenarios with high temporal resolution (PCC = 0.8692/0.9465/0.9743 for MOLED/METMOLED‐2/METMOLED‐3). Moreover, the METMOLED could rectify the signal deviations induced by the non‐ideal slice profiles of refocusing pulses without additional measurements. A preliminary measurement also demonstrated that the METMOLED is highly repeatable (mean coefficient of variation (CV) = 1.65%). Conclusions: METMOLED breaks the restriction of echo‐train length to TE and implements unbiased T2 estimates in an extensive range. Furthermore, it corrects the effect of refocusing pulse inaccuracy without additional measurements or signal post‐processing, thus retaining its single‐shot characteristic. This technique would be beneficial for accurate T2 quantification. [ABSTRACT FROM AUTHOR]

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust $\text{T}_{{2}}$ mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for $\text{T}_{{2}}$ mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in $\text{T}_{{2}}$ maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Intermolecular multiple-quantum coherences (iMQCs) originated from distant dipolar field (DDF) possess some appealing unique properties for magnetic resonance imaging (MRI). DDF is usually induced with continuous wave (i.e., sine- or square-wave) magnetization modulation in the whole sample. In this article, a spatially localized and enhanced DDF was optimally tailored in a thin slice with an adiabatic inversion pulse. Evidence was provided to show that careful tailoring of the spatially localized DDF can generate highly efficient iMQC signals, with more than two-fold enhancement compared to the conventional sine-wave magnetization modulation method, and 1.5 times of that with the square-wave modulation under the similar condition. Theoretical predictions, simulation results, and experimental verifications agree well with each other. Practical implementation of this approach for efficient iMQC MRI was explored. [ABSTRACT FROM AUTHOR]

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T2 mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T2 mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T2 mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based methods. The results of simulation, phantom, and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties. [ABSTRACT FROM AUTHOR]

Objective: Develop a reliable single-shot \rmT{2} mapping method with extra robustness to motion and the potential for real-time dynamic and quantitative MR imaging. Methods: A single-shot \rmT{2} mapping sequence was proposed based on spin-echo planar imaging acquisition scheme. Two overlapped echo signals with different \rmT{2} weighting were obtained simultaneously by using two small flip-angle excitation pulses and corresponding echo-shifting gradients. A detachment algorithm based on structure similarity constraint was proposed to separate the two echo signals. \rmT{2} mapping was obtained from the two separated echo signals. Results: The robustness and efficiency of the method were demonstrated through simulation, phantom experiments, and human brain measurements. Conclusion: Reliable \rmT{2} mapping can be obtained within milliseconds even under continuous head motion. Significance: Reliable \rmT{2} mapping was achieved with a single shot for the first time. The proposed method will facilitate real-time dynamic and quantitative MR imaging. [ABSTRACT FROM AUTHOR]

Abstract: Inhomogeneity in static field B 0 and/or RF field B 1 is inevitable under some circumstances. In this work, a method based on intermolecular double-quantum coherences is employed for high-resolution 1D MR spectroscopy via 2D acquisition under such a condition. High-resolution information on chemical shifts, multiplet patterns, J coupling constants and relative peak areas can be retained in the resulting 1D projected spectra, as shown with results from a narrow-bore NMR spectrometer and a whole-body clinical scanner. [Copyright &y& Elsevier]

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l1 norm optimization for CS-MRI. Considering the recovered information and image features, we introduce three objective criteria, which are the peak signal-to-noise ratio (PSNR), mutual information and transferred edge information, to evaluate the performance of different image transforms. Simulation results demonstrate that contourlet-based CS-MRI can better reconstruct the curves and edges than traditional wavelet-based methods, especially at low k-space sampling rate. [ABSTRACT FROM AUTHOR]

Abstract: A software package has been designed to simulate nuclear magnetic resonance spectra and images. Combining the product operator matrix with the non-linear Bloch equations, the software can efficiently simulate classical and quantum effects including scalar coupling, dipolar coupling, translational diffusion, chemical shift, radiation damping, transverse relaxation, and longitudinal relaxation. One of the most unique features of the software is its ability to incorporate effects of inter- and intra-molecular multiple quantum coherences in complex multiple-spin coupled systems, which are difficult with other existing software packages. The software, written in Visual C++, has a friendly graphical user interface and is easy to use. To cite this article: C. Cai et al., C. R. Physique 9 (2008). [Copyright &y& Elsevier]

Abstract: In this paper, we demonstrated that despite the insensitivity of intermolecular zero-quantum coherences (iZQCs) to B 0 variations, the influence of unstable fields on the observable single-quantum coherence signals causes strong t 1 noises in the high-resolution iZQC projection spectra. Stroboscopic acquisition was then proposed for noise suppression. The feasibility of the modified sequences with the proposed acquisition scheme was verified by computer simulations and experiments in different unstable fields generated by the Z0 and Z1 coil current oscillations, which mimic the unstable fields of NMR using externally powered magnets and MRS in the presence of physiological motions, respectively. [Copyright &y& Elsevier]

Compressed sensing is utilized with the aims of reconstructing an MRI using a fraction of measurements to accelerate magnetic resonance imaging called compressed sensing magnetic resonance imaging (CS-MRI). Conventional optimization-based CS-MRI methods use random under-sampling patterns and model the MRI data in the image domain as the classic CS-MRI paradigm. Instead, we design a uniform under-sampling strategy and explore the potential of modeling the MRI data directly in the measured Fourier domain. We propose a dual-domain deep lattice network (DD-DLN) for CS-MRI with variable density uniform under-sampling. We train the networks to learn the mapping between both image and frequency domains. We observe the dual networks have complementary advantages, which motivates their combination via a lattice structure. Experiments show that the proposed DD-DLN model provides promising performance in CS-MRI under the designed variable density uniform under-sampling. [ABSTRACT FROM AUTHOR]

Spatiotemporally encoded (SPEN) single-shot MRI is an emerging ultrafast technique, which is capable of spatially selective acquisition and reduced field-of-view imaging. Compared to uniform sampling, variable density sampling has great potential in reducing aliasing artifacts and improving sampling efficiency. In this study, variable density spiral trajectory and non-Cartesian super-resolved (SR) reconstruction method are developed for SPEN MRI. The gradient waveforms design of spiral trajectory is mathematically described as an optimization problem subjected to the limitations of hardware. Non-Cartesian SR reconstruction with specific gridding method is developed to retrieve a resolution enhanced image from raw SPEN data. The robustness and efficiency of the proposed methods are demonstrated by numerical simulation and various experiments. The results indicate that variable density SPEN MRI can provide better spatial resolution and fewer aliasing artifacts compared to Cartesian counterpart. The proposed methods will facilitate the development of variable density SPEN MRI. [ABSTRACT FROM AUTHOR]