Enlarged perivascular spaces in alcohol-related brain damage induced by dyslipidemia.

Perivascular spaces (PVSs) as the anatomical basis of the glymphatic system, are increasingly recognized as potential imaging biomarkers of neurological conditions. However, it is not clear whether enlarged PVSs are associated with alcohol-related brain damage (ARBD). We aimed to investigate the effect of long-term alcohol exposure on dyslipidemia and the glymphatic system in ARBD. We found that patients with ARBD exhibited significantly enlargement of PVSs in the frontal cortex and basal ganglia, as well as a notable increased levels of total cholesterol (TC) and triglycerides (TG). The anatomical changes of the glymphatic drainage system mentioned above were positively associated with TC and TG. To further explore whether enlarged PVSs affects the function of the glymphatic system in ARBD, we constructed long alcohol exposure and high fat diet mice models. The mouse model of long alcohol exposure exhibited increased levels of TC and TG, enlarged PVSs, the loss of aquaporin-4 polarity caused by reactive astrocytes and impaired glymphatic drainage function which ultimately caused cognitive deficits, in a similar way as high fat diet leading to impairment in glymphatic drainage. Our study highlights the contribution of dyslipidemia due to long-term alcohol abuse in the impairment of the glymphatic drainage system.

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.

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.

Cognitive Impairment in Chronic Kidney Disease Is Associated with Glymphatic System Dysfunction.

Introduction: This study was designed to explore the associations between impaired cognition in chronic kidney disease (CKD) patients and the dysfunction of the glymphatic system. Method: Data were obtained from 77 CKD patients and 50 age-matched healthy control individuals from the First Affiliated Hospital of Zhengzhou University. CKD patients were stratified into with and without impaired cognitive function. T2-weighted magnetic resonance imaging results were used to assess area ratios for the perivascular space and ventricles in participants, while the Montreal Cognitive Assessment and the Mini-Mental State Examination were employed to measure cognitive function. Correlations between the perivascular space or ventricle area ratios and cognitive impairment were assessed in CKD patients. Results: Significant increases in the burden of enlarged perivascular spaces in the frontal cortex and basal ganglia were observed in CKD patients with cognitive impairment relative to those without such impairment, with a concomitant increase in analyzed ventricle area ratios. Enlarged perivascular spaces in the frontal cortex, basal ganglia and increased area ratios of lateral ventricles and 4th ventricle exhibited relatively high sensitivity and specificity as means of differing between the CKD patients with and without cognitive impairment. Conclusion: These results indicate that the burden of enlarged perivascular spaces in the frontal cortex and basal ganglia and increases in ventricle area ratio values may offer utility as biomarkers that can aid in detection of even mild cognitive decline in individuals with CKD. The dysfunction of the glymphatic system may play a key role in the pathogenesis of CKD-related cognitive impairment.

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.

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.

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.

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.

Mutations in ARHGEF15 cause autosomal dominant hereditary cerebral small vessel disease and osteoporotic fracture.

Cerebral small vessel disease (CSVD) is a prominent cause of ischemic and hemorrhagic stroke and a leading cause of vascular dementia, affecting small penetrating vessels of the brain. Despite current advances in genetic susceptibility studies, challenges remain in defining the causative genes and the underlying pathophysiological mechanisms. Here, we reported that the ARHGEF15 gene was a causal gene linked to autosomal dominant inherited CSVD. We identified one heterozygous nonsynonymous mutation of the ARHGEF15 gene that cosegregated completely in two families with CSVD, and a heterozygous nonsynonymous mutation and a stop-gain mutation in two individuals with sporadic CSVD, respectively. Intriguingly, clinical imaging and pathological findings displayed severe osteoporosis and even osteoporotic fractures in all the ARHGEF15 mutation carriers. In vitro experiments indicated that ARHGEF15 mutations resulted in RhoA/ROCK2 inactivation-induced F-actin cytoskeleton disorganization in vascular smooth muscle cells and endothelial cells and osteoblast dysfunction by inhibiting the Wnt/ß-catenin signaling pathway in osteoblast cells. Furthermore, Arhgef15-e(V368M)1 transgenic mice developed CSVD-like pathological and behavioral phenotypes, accompanied by severe osteoporosis. Taken together, our findings provide strong evidence that loss-of-function mutations of the ARHGEF15 gene cause CSVD accompanied by osteoporotic fracture.

Microbiota-ear-brain interaction is associated with generalized anxiety disorder through activation of inflammatory cytokine responses.

Introduction: Generalized anxiety disorder (GAD) is one of the most enduring anxiety disorders, being associated with increased systemic inflammation. However, the trigger and mechanisms underlying the activation of inflammatory cytokine responses in GAD remain poorly understood. Materials and methods: We characterized the ear canal microbiome in GAD patients through 16S rRNA gene sequencing and metagenomic sequencing and identified the serum inflammatory markers in GAD patients. Spearman correlations were applied to test the relationship between the microbiota changes and systemic inflammation. Results: Our findings showed the higher microbial diversity, accompanied with the significantly increased abundance of Proteobacteria, and decreased abundance of Firmicutes in the ear canal of GAD participants compared to that of the age- and sex-matched healthy controls (HC). Metagenomic sequencing showed that Pseudomonas aeruginosa were significantly increased at species-level in GAD patients. Furthermore, we observed the relative abundance of Pseudomonas aeruginosa was positively associated with elevated systemic inflammatory markers and the severity of disease, suggesting that these ear canal microbiota alterations might be correlated with GAD by activating the inflammatory response. Conclusions: These findings indicate that microbiota-ear-brain interaction via upregulating inflammatory reaction involve in the development of GAD, as well as suggest that ear canal bacterial communities may be a target for therapeutic intervention.

Efficacy of faecal microbiota transplantation in patients with progressive supranuclear palsy-Richardson's syndrome: a phase 2, single centre, randomised clinical trial.

Background: Faecal microbiota transplantation (FMT) has demonstrated efficacy in treating gastrointestinal (GI) diseases, such as Clostridium difficile infection (CDI) and inflammatory bowel disease (IBD). GI dysfunction is a frequent and occasionally dominating symptom of progressive supranuclear palsy-Richardson's syndrome (PSP-RS). However, it is not known whether FMT has clinical efficacy for PSP-RS. Methods: This 36-week, randomised, placebo-controlled, parallel-group, phase 2 clinical trial was performed at a university tertiary referral hospital in China. From August 15 2021 to December 31 2021, a total of 68 newly diagnosed patients with PSP-RS (male 40 [59%], female 28 [41%]) who had never received any antiparkinsonian medications were enrolled and randomly assigned to receive either healthy donor FMT (n = 34, FMT group) or a mixture of 0.9% saline and food colouring (E150c) as sham transplantation (n = 34, placebo group) through transendoscopic enteral tubing (TET). Two days after oral antibiotics, participants received 1 week of transplantation. After an interval of 4 weeks, retransplantation was performed. Then, the last transplantation was given after another interval of 4 weeks, and the participants were followed up for 24 weeks (week 36). Clinicaltrials.gov identifier: ChiCTR-2100045397. Findings: Among 68 patients who were randomised (mean age, 67.2 (SD 5.1); 40 [59%] were male, 28 [41%] were female), 63 participants completed the trial. Efficacy analyses were performed on the intention-to-treat (ITT) analysis set. At week 16, the mean PSP Rating Scale (PSPRS) scores (the primary outcome) improved from 40.1 (SD 7.6) to 36.9 (SD 5.9) in the FMT group, whereas the scores changed from 40.1 (SD 6.9) to 41.7 (SD 6.2) in the placebo group, for a treatment benefit of 4.3 (95% CI, 3.2-5.4) (P < 0.0001). After 3-cycle intervention, symptoms of constipation, depression, and anxiety (the secondary outcome) improved significantly at week 16 in the FMT group compared with the placebo group, the majority of which were maintained at the 24-week follow-up (week 36). Interpretation: Our findings suggest that, compared with placebo, FMT treatment significantly improved motor and nonmotor symptoms in patients with PSP-RS, as well as reduced intestinal inflammation and enhanced the intestinal barrier by regulating the intestinal microbiota composition. Funding: The National Natural Science Foundation of China (No. 82122022, 82171248, 81873791, and 82230084), Natural Science Foundation of Henan Province for Excellent Young Scholars (no. 202300410357), and Henan Province Young and Middle-Aged Health Science and Technology Innovation Talent Project (YXKC2020033).

Impaired glymphatic drainage underlying obstructive sleep apnea is associated with cognitive dysfunction.

Obstructive sleep apnea (OSA) is highly prevalent but easily undiagnosed and is an independent risk factor for cognitive impairment. However, it remains unclear how OSA is linked to cognitive impairment. In the present study, we found the correlation between morphological changes of perivascular spaces (PVSs) and cognitive impairment in OSA patients. Moreover, we developed a novel set of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods to evaluate the fluid dynamics of glymphatic drainage system. We found that the inflow and outflow parameters of the glymphatic drainage system in patients with OSA were obviously changed, indicating impairment of glymphatic drainage due to excessive perfusion accompanied with deficient drainage in OSA patients. Moreover, parameters of the outflow were associated with the degree of cognitive impairment, as well as the hypoxia level. In addition, continuous positive airway pressure (CPAP) enhances performance of the glymphatic drainage system after 1 month treatment in OSA patients. We proposed that ventilation improvement might be a new strategy to ameliorate the impaired drainage of glymphatic drainage system due to OSA-induced chronic intermittent hypoxia, and consequently improved the cognitive decline.

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.

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.

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.

Impaired Meningeal Lymphatic Flow in NMOSD Patients With Acute Attack.

The meningeal lymphatic vessels (mLVs) in central nervous system (CNS) have been validated by rodent and human studies. The mLVs play a vital role in draining soluble molecules and trafficking lymphocytes, antigens and antibodies from CNS into cervical lymph nodes (CLNs). This indicates that mLVs may serve as a link between the CNS and peripheral immune system, perhaps involving in the neuroinflammatory disease. However, the morphology and drainage function of mLVs in patients with neuroinflammatory disease, such as neuromyelitis optica spectrum disorders (NMOSD), remains unexplored. Using the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), we found that slower flow through mLVs along superior sagittal sinus in NMOSD patients with acute attack instead of NMOSD patients in chronic phase. The reduced flow in mLVs correlated with the disease severity evaluated by expanded disability status scale (EDSS). The receiver operating characteristic curve (ROC) indicated DCE-MRI might provide objective evidence to predict the acute relapse of NMOSD through evaluating the function of mLVs. Promoting or restoring the function of mLVs might be a new target for the treatment of NMOSD relapse.

Incorporating reference guided priors into calibrationless parallel imaging reconstruction.

PURPOSE: To propose and evaluate a new calibrationless parallel imaging method aimed at further improving the reconstruction accuracy of the accelerated multi-channel MR images. METHOD: We introduce a new calibrationless parallel imaging method. On top of exploiting joint sparsity cross channels of the target image to be reconstructed, it incorporates similar priors on the grey-level intensity and edge orientation, which both come from a high-spatial resolution reference image that can be easily obtained in many clinical MRI scenarios. The mixed l2-l1 norm is used to enforce joint sparsity and a multi-scale gradient operator is applied to extract fine edges from the reference image. Additionally, this optimization problem can be solved via a non-linear conjugate gradient algorithm with line search in this work. RESULTS: The proposed method is compared with the existing state-of-the-art auto-calibration and calibrationless parallel imaging techniques. The experiments on different in-vivo brain MR datasets show that the proposed method has the superior performance in terms of both artifact suppression and detail preservation. CONCLUSION: The reference guided calibrationless parallel imaging method can significantly improve the performance of joint reconstruction of target channel images. Even when the reduction factor is high, it can keep edge structures well.