DPP: deep phase prior for parallel imaging with wave encoding.

Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection.

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

Integrating data distribution prior via Langevin dynamics for end-to-end MR reconstruction.

PURPOSE: To develop a novel deep learning-based method inheriting the advantages of data distribution prior and end-to-end training for accelerating MRI. METHODS: Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end-to-end adversarial training to mitigate the hyper-parameter selection and shorten the testing time compared to traditional probabilistic reconstruction. By seamlessly integrating the deep equilibrium model, the iteration of Langevin dynamics culminates in convergence to a fix-point, ensuring the stability of the learned distribution. RESULTS: The feasibility of the proposed method is evaluated on the brain and knee datasets. Retrospective results with uniform and random masks show that the proposed method demonstrates superior performance both quantitatively and qualitatively than the state-of-the-art. CONCLUSION: The proposed method incorporating Langevin dynamics with end-to-end adversarial training facilitates efficient and robust reconstruction for MRI. Empirical evaluations conducted on brain and knee datasets compellingly demonstrate the superior performance of the proposed method in terms of artifact removing and detail preserving.

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering.

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

High-Frequency Space Diffusion Model for Accelerated MRI.

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of k -space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or k -space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.

Annihilation-Net: Learned annihilation relation for dynamic MR imaging.

BACKGROUND: Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability. PURPOSE: This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation-Net and use it for accelerating dynamic MRI. METHODS: Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low-rankness. We employ low-rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi-quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation-Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation-Net. RESULTS: Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively. CONCLUSIONS: The proposed Annihilation-Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability.

PEARL: Cascaded Self-supervised Cross-fusion Learning For Parallel MRI Acceleration.

Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, eliminating the explicit training procedure but often struggling to remove noise and artifacts under significant degradation. This work introduces a novel self-supervised accelerated parallel MRI approach called PEARL, leveraging a multiple-stream joint deep decoder with two cross-fusion schemes to accurately reconstruct one or more target images from compressively sampled k-space. Each stream comprises cascaded cross-fusion sub-block networks (SBNs) that sequentially perform combined upsampling, 2D convolution, joint attention, ReLU activation and batch normalization (BN). Among them, combined upsampling and joint attention facilitate mutual learning between multiple-stream networks by integrating multi-parameter priors in both additive and multiplicative manners. Long-range unified skip connections within SBNs ensure effective information propagation between distant cross-fusion layers. Additionally, incorporating dual-normalized edge-orientation similarity regularization into the training loss enhances detail reconstruction and prevents overfitting. Experimental results consistently demonstrate that PEARL outperforms the existing state-of-the-art (SOTA) self-supervised AMRI technologies in various MRI cases. Notably, 5-fold  âˆ¼ 6-fold accelerated acquisition yields a 1 %  âˆ¼  2 % improvement in SSIM ROI and a 3 %  âˆ¼  6 % improvement in PSNR ROI, along with a significant 15 %  âˆ¼  20 % reduction in RLNE ROI.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

K-UNN: k-space interpolation with untrained neural network.

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module. This implies that the unrolled network output may not align with the regularization models. Furthermore, there are few established theories that guarantee global convergence and robustness (regularity) of unrolled networks under practical assumptions. To address this gap, we propose a safeguarded methodology for network unrolling. Specifically, for parallel MR imaging, we unroll a zeroth-order algorithm, where the network module serves as a regularizer itself, allowing the network output to be covered by a regularization model. Additionally, inspired by deep equilibrium models, we conduct the unrolled network before backpropagation to converge to a fixed point and then demonstrate that it can tightly approximate the actual MR image. We also prove that the proposed network is robust against noisy interferences if the measurement data contain noise. Finally, numerical experiments indicate that the proposed network consistently outperforms state-of-the-art MRI reconstruction methods, including traditional regularization and unrolled deep learning techniques.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

BACKGROUND: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data. PURPOSE: To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free. METHODS: Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively. RESULTS: In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. CONCLUSIONS: The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

Accelerating Magnetic Resonance T1ρ Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART).

Quantitative magnetic resonance (MR) [Formula: see text] mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor models have been employed and demonstrated exemplary performance in accelerating MR [Formula: see text] mapping. This study proposes a novel method that uses spatial patch-based and parametric group-based low-rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in [Formula: see text] mapping. The parametric group-based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR [Formula: see text] imaging.

Scatter correction for cone-beam CT via scatter kernel superposition-inspired convolutional neural network.

Objective.X-ray scatter leads to signal bias and degrades the image quality in Computed Tomography imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate x-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter x-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels.Approach.To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process.Main Results.By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects.Significance.The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

Characteristic-constrained accelerating MR T1rho mapping with blockwise infimal convolution of matrix elastic-net regularization.

BACKGROUND: Magnetic resonance parameter mapping (MRPM) plays an important role in clinical applications and biomedical researches. However, the acceleration of MRPM remains a major challenge for achieving further improvements. PURPOSE: In this work, a new undersampled k-space based joint multi-contrast image reconstruction approach named CC-IC-LMEN is proposed for accelerating MR T1rho mapping. METHODS: The reconstruction formulation of the proposed CC-IC-LMEN method imposes a blockwise low-rank assumption on the characteristic-image series (c-p space) and utilizes infimal convolution (IC) to exploit and balance the generalized low-rank properties in low-and high-order c-p spaces, thereby improving the accuracy. In addition, matrix elastic-net (MEN) regularization based on the nuclear and Frobenius norms is incorporated to obtain stable and exact solutions in cases with large accelerations and noisy observations. This formulation results in a minimization problem, that can be effectively solved using a numerical algorithm based on the alternating direction method of multipliers (ADMM). Finally, T1rho maps are then generated according to the reconstructed images using nonlinear least-squares (NLSQ) curve fitting with an established relaxometry model. RESULTS: The relative l2 -norm error (RLNE) and structural similarity (SSIM) in the regions of interest (ROI) show that the CC-IC-LMEN approach is more accurate than other competing methods even in situations with heavy undersampling or noisy observation. CONCLUSIONS: Our proposed CC-IC-LMEN method provides accurate and robust solutions for accelerated MR T1rho mapping.

Protective effects of three kinds of borneol on different brain regions in acute cerebral ischemia/reperfusion model rats / 中国中药杂志

This study compared the ameliorating effects of L-borneol, natural borneol, and synthetic borneol on the injury of different brain regions in the rat model of acute phase of cerebral ischemia/reperfusion(I/R) for the first time, which provides a reference for guiding the rational application of borneol in the early treatment of ischemic stroke and has important academic and application values. Healthy specific pathogen-free(SPF)-grade SD male rats were randomly assigned into 13 groups: a sham-operation group, a model group, a Tween model group, a positive drug(nimodipine) group, and high-, medium-, and low-dose(0.2, 0.1, and 0.05 g·kg~(-1), respectively) groups of L-borneol, natural borneol, and synthetic borneol according to body weight. After 3 days of pre-administration, the rat model of I/R was established by suture-occluded method and confirmed by laser speckle imaging. The corresponding agents in different groups were then administered for 1 day. The body temperature was monitored regularly before pre-administration, days 1, 2, and 3 of pre-administration, 2 h after model awakening, and 1 d after model establishment. Neurological function was evaluated based on Zea-Longa score and modified neurological severity score(mNSS) 2 h and next day after awakening. The rats were anesthetized 30 min after the last administration, and blood was collected from the abdominal aorta. Enzyme-linked immunoassay assay(ELISA) was employed to determine the serum levels of tumor necrosis factor-alpha(TNF-α), interleukin-6(IL-6), IL-4, and transforming growth factor-beta1(TGF-β1). The brain tissues were stained with triphenyltetrazolium chloride(TTC) for the calculation of cerebral infarction rate, and hematoxylin-eosin(HE) staining was used for observing and semi-quantitatively evaluating the pathological damage in different brain regions. Immunohistochemistry was employed to detect the expression of ionized calcium binding adapter molecule 1(IBA1) in microglia. q-PCR was carried out to determine the mRNA levels of iNOS and arginase 1(Arg1), markers of polarization phenotype M1 and M2 in microglia. Compared with the sham-operation group, the model group and the Tween model group showed significantly elevated body temperature, Zea-Longa score, mNSS, and cerebral infarction rate, severely damaged cortex, hippocampus, and striatum, increased serum levels of IL-6 and TNF-α, and decreased serum levels of IL-4 and TGF-β1. The three borneol products had a tendency to reduce the body temperature of rats 1 day after modeling. Synthetic borneol at the doses of 0.2 and 0.05 g·kg~(-1), as well as L-borneol of 0.1 g·kg~(-1), significantly reduced Zea-Longa score and mNSS. The three borneol products at the dose of 0.2 g·kg~(-1) significantly reduced the cerebral infarction rate. L-borneol at the doses of 0.2 and 0.1 g·kg~(-1) and natural borneol at the dose of 0.1 g·kg~(-1) significantly reduced the pathological damage of the cortex. L-borneol and natural borneol at the dose of 0.1 g·kg~(-1) attenuated the pathological damage of hippocampus, and 0.2 g·kg~(-1) L-borneol attenuated the damage of striatum. The 0.2 g·kg~(-1) L-borneol and the three doses of natural borneol and synthetic borneol significantly reduced the serum level of TNF-α, and the 0.1 g·kg~(-1) synthetic borneol reduced the level of IL-6. L-borneol and synthetic borneol at the dose of 0.2 g·kg~(-1) significantly inhibited the activation of cortical microglia, and 0.2 g·kg~(-1) L-borneol up-regulated the expression of Arg1 and down-regulated the expression level of iNOS. In conclusion, the three borneol products may alleviate inflammation to ameliorate the pathological damage of brain regions of rats in the acute phase of I/R by inhibiting the activation of microglia and promoting the polarization of microglia from M1 type to M2 type. The protective effect on brain followed a trend of L-borneol > synthetic borneol > natural borneol. We suggest L-borneol the first choice for the treatment of I/R in the acute phase.

Effect and mechanism of Bovis Calculus on ulcerative colitis by inhibiting IL-17/IL-17RA/Act1 signaling pathway / 中国中药杂志

This study aimed to elucidate the effect and underlying mechanism of Bovis Calculus in the treatment of ulcerative colitis(UC) through network pharmacological prediction and animal experimental verification. Databases such as BATMAN-TCM were used to mine the potential targets of Bovis Calculus against UC, and the pathway enrichment analysis was conducted. Seventy healthy C57BL/6J mice were randomly divided into a blank group, a model group, a solvent model(2% polysorbate 80) group, a salazosulfapyridine(SASP, 0.40 g·kg~(-1)) group, and high-, medium-, and low-dose Bovis Calculus Sativus(BCS, 0.20, 0.10, and 0.05 g·kg~(-1)) groups according to the body weight. The UC model was established in mice by drinking 3% dextran sulfate sodium(DSS) solution for 7 days. The mice in the groups with drug intervention received corresponding drugs for 3 days before modeling by gavage, and continued to take drugs for 7 days while modeling(continuous administration for 10 days). During the experiment, the body weight of mice was observed, and the disease activity index(DAI) score was recorded. After 7 days of modeling, the colon length was mea-sured, and the pathological changes in colon tissues were observed by hematoxylin-eosin(HE) staining. The levels of tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), interleukin-6(IL-6), and interleukin-17(IL-17) in colon tissues of mice were detected by enzyme-linked immunosorbent assay(ELISA). The mRNA expression of IL-17, IL-17RA, Act1, TRAF2, TRAF5, TNF-α, IL-6, IL-1β, CXCL1, CXCL2, and CXCL10 was evaluated by real-time polymerase chain reaction(RT-PCR). The protein expression of IL-17, IL-17RA, Act1, p-p38 MAPK, and p-ERK1/2 was investigated by Western blot. The results of network pharmacological prediction showed that Bovis Calculus might play a therapeutic role through the IL-17 signaling pathway and the TNF signaling pathway. As revealed by the results of animal experiments, on the 10th day of drug administration, compared with the solvent model group, all the BCS groups showed significantly increased body weight, decreased DAI score, increased colon length, improved pathological damage of colon mucosa, and significantly inhibited expression of TNF-α,IL-6,IL-1β, and IL-17 in colon tissues. The high-dose BCS(0.20 g·kg~(-1)) could significantly reduce the mRNA expression levels of IL-17, Act1, TRAF2, TRAF5, TNF-α, IL-6, IL-1β, CXCL1, and CXCL2 in colon tissues of UC model mice, tend to down-regulate mRNA expression levels of IL-17RA and CXCL10, significantly inhibit the protein expression of IL-17RA,Act1,and p-ERK1/2, and tend to decrease the protein expression of IL-17 and p-p38 MAPK. This study, for the first time from the whole-organ-tissue-molecular level, reveals that BCS may reduce the expression of pro-inflammatory cytokines and chemokines by inhibiting the IL-17/IL-17RA/Act1 signaling pathway, thereby improving the inflammatory injury of colon tissues in DSS-induced UC mice and exerting the effect of clearing heat and removing toxins.

Accelerated cardiac cine MRI using spatiotemporal correlation-based hybrid plug-and-play priors (SEABUS).

Objective. The plug-and-play prior (P3) can be flexibly coupled with multiple iterative optimizations, which has been successfully applied to the inverse problems of medical imaging. In this work, for accelerated cardiac cine magnetic resonance imaging (CC-MRI), the Spatiotemporal corrElAtion-based hyBrid plUg-and-play priorS (SEABUS) integrating a local P3and a nonlocal P3are introduced.Approach. Specifically, the local P3enforces pixelwise edge-orientation consistency by conducting reference frame guided multiscale orientation projection on a subset containing a few adjacent frames; the nonlocal P3constrains the cubewise anatomic-structure similarity by performing cube matching and 4D filtering (CM4D) on all frames. By using effectively a composite splitting algorithm (CSA), SEABUS is incorporated into a fast iterative shrinkage-thresholding algorithm and a new accelerated CC-MRI approach named SEABUS-FCSA is proposed.Main results. The experiment and algorithm analysis demonstrate the efficiency and potential of the proposed SEABUS-FCSA approach, which has the best performance in terms of reducing aliasing artifacts and capturing dynamic features in comparison with several state-of-the-art accelerated CC-MRI technologies.Significance. Our approach aims to propose a new hybrid P3based iterative algorithm, which is not only used to improve the quality of accelerated cardiac cine imaging but also extend the FCSA methodology.

Detection of RAS gene mutation and its clinical significance in children with acute lymphoblastic leukemia / 中国当代儿科杂志

OBJECTIVES@#To investigate the mutation rate of the RAS gene and its clinical significance in children with acute lymphoblastic leukemia.@*METHODS@#A retrospective analysis was performed on the medical data of 120 children with newly diagnosed acute lymphoblastic leukemia, who were admitted to the Third Affiliated Hospital of Zhengzhou University from January 2015 to January 2020 and underwent next-generation sequencing. The clinical and molecular features were analyzed. The impact of RAS gene mutation on the overall survival rate was evaluated in these children.@*RESULTS@#Among the 120 children, 35 (29.2%) had RAS gene mutation, 30 (25.0%) had KRAS gene mutation, and 5 (4.2%) had both NRAS and KRAS gene mutations. All NRAS mutations and 71% (25/35) of KRAS mutations were located at the 12th and 13th codons. RAS gene mutation was detected in 35 (33.3%) out of 105 children with B-lineage acute lymphoblastic leukemia, but it was not detected in those with acute T lymphocyte leukemia. Of all the children, 11 (9.2%) were lost to follow-up, and among the 109 children followed up, 16 (14.7%) died. The children with RAS gene mutation had a significantly lower 2-year overall survival rate than those without RAS gene mutation (P<0.05). The prognosis of children with RAS gene mutation combined with WT1 overexpression and WBC>50×109/L at diagnosis was worse (P<0.05).@*CONCLUSIONS@#RAS gene mutation is commonly observed in children with B-lineage acute lymphoblastic leukemia and may have an adverse effect on prognosis.

Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model.

BACKGROUND: Magnetic resonance (MR) quantitative T1ρ imaging has been increasingly used to detect the early stages of osteoarthritis. The small volume and curved surface of articular cartilage necessitate imaging with high in-plane resolution and thin slices for accurate T1ρ measurement. Compared with 2D T1ρ mapping, 3D T1ρ mapping is free from artifacts caused by slice cross-talk and has a thinner slice thickness and full volume coverage. However, this technique needs to acquire multiple T1ρ-weighted images with different spin-lock times, which results in a very long scan duration. It is highly expected that the scan time can be reduced in 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision. METHODS: To accelerate the acquisition of 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T1ρ-weighted images compensated at different spin-lock times were decomposed as a low-rank high-order tensor plus a sparse component. Poisson-disk random undersampling patterns were applied to k-space data in the phase- and partition-encoding directions in both retrospective and prospective experiments. Five volunteers were involved in this study. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled at R=5.2, 7.7, and 9.7, respectively. Reference values were obtained from the fully sampled data. Prospectively undersampled data for R=5 and R=7 were acquired from 2 volunteers. Bland-Altman analyses were used to assess the agreement between the accelerated and reference T1ρ measurements. The reconstruction performance was evaluated using the normalized root mean square error and the median of the normalized absolute deviation (MNAD) of the reconstructed T1ρ-weighted images and the corresponding T1ρ maps. RESULTS: T1ρ parameter maps were successfully estimated from T1ρ-weighted images reconstructed using the proposed method for all accelerations. The accelerated T1ρ measurements and reference values were in good agreement for R=5.2 (T1ρ: 40.4±1.4 ms), R=7.7 (T1ρ: 40.4±2.1 ms), and R=9.7 (T1ρ: 40.9±2.2 ms) in the Bland-Altman analyses. The T1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method. CONCLUSIONS: The proposed method achieves the 3D T1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.

Deep low-Rank plus sparse network for dynamic MR imaging.

In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods. Many deep learning approaches have been proposed to address these issues, but few of them use a low-rank prior. In this paper, a model-based low-rank plus sparse network, dubbed L+S-Net, is proposed for dynamic MR reconstruction. In particular, we use an alternating linearized minimization method to solve the optimization problem with low-rank and sparse regularization. Learned soft singular value thresholding is introduced to ensure the clear separation of the L component and S component. Then, the iterative steps are unrolled into a network in which the regularization parameters are learnable. We prove that the proposed L+S-Net achieves global convergence under two standard assumptions. Experiments on retrospective and prospective cardiac cine datasets show that the proposed model outperforms state-of-the-art CS and existing deep learning methods and has great potential for extremely high acceleration factors (up to 24×).