Search

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 [ABSTRACT FROM AUTHOR]

(1) Background: Functional magnetic resonance imaging (fMRI) utilizing multi-echo gradient echo-planar imaging (ME-GE-EPI) has demonstrated higher sensitivity and stability compared to utilizing single-echo gradient echo-planar imaging (SE-GE-EPI). The direct derivation of T2* maps from fitting multi-echo data enables accurate recording of dynamic functional changes in the brain, exhibiting higher sensitivity than echo combination maps. However, the widely employed voxel-wise log-linear fitting is susceptible to inevitable noise accumulation during image acquisition. (2) Methods: This work introduced a synthetic data-driven deep learning (SD-DL) method to obtain T2* maps for multi-echo (ME) fMRI analysis. (3) Results: The experimental results showed the efficient enhancement of the temporal signal-to-noise ratio (tSNR), improved task-based blood oxygen level-dependent (BOLD) percentage signal change, and enhanced performance in multi-echo independent component analysis (MEICA) using the proposed method. (4) Conclusion: T2* maps derived from ME-fMRI data using the proposed SD-DL method exhibit enhanced BOLD sensitivity in comparison to T2* maps derived from the LLF method. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a single‐shot quantitative MRI technique called GRE‐MOLED (gradient‐echo multiple overlapping‐echo detachment) for rapid T2*$$ {T}_2^{\ast } $$ 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 T2*$$ {T}_2^{\ast } $$ 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 T2*$$ {T}_2^{\ast } $$ 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 T2*$$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method. [ABSTRACT FROM AUTHOR]

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]

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. [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]