Compact In Situ Electrochemical NMR with Wireless and Anti-interference Strategy in Multiscenario Applications.

The integration of electrochemistry with nuclear magnetic resonance (NMR) spectroscopy recently offers a powerful approach to understanding oxidative metabolism, detecting reactive intermediates, and predicting biological activities. This combination is particularly effective as electrochemical methods provide excellent mimics of metabolic processes, while NMR spectroscopy offers precise chemical analysis. NMR is already widely utilized in the quality control of pharmaceuticals, foods, and additives and in metabolomic studies. However, the introduction of additional and external connections into the magnet has posed challenges, leading to signal deterioration and limitations in routine measurements. Herein, we report an anti-interference compact in situ electrochemical NMR system (AICISENS). Through a wireless strategy, the compact design allows for the independent and stable operation of electrochemical NMR components with effective interference isolation. Thus, it opens an avenue toward easy integration into in situ platforms, applicable not only to laboratory settings but also to fieldwork. The operability, reliability, and versatility were validated with a series of biomimetic assessments, including measurements of microbial electrochemical systems, functional foods, and simulated drug metabolisms. The robust performance of AICISENS demonstrates its high potential as a powerful analytical tool across diverse applications.

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

Antioxidant Activity and the Therapeutic Effect of Sinomenine Hydrochloride-Loaded Liposomes-in-Hydrogel on Atopic Dermatitis.

Sinomenine hydrochloride is an excellent drug with anti-inflammatory, antioxidant, immune-regulatory, and other functions. Atopic dermatitis is an inherited allergic inflammation that causes itchiness, redness, and swelling in the affected area, which can have a significant impact on the life of the patient. There are many therapeutic methods for atopic dermatitis, and sinomenine with immunomodulatory activity might be effective in the treatment of atopic dermatitis. In this study, the atopic dermatitis model was established in experimental mice, and physical experiments were carried out on the mice. In the experiment, sinomenine hydrochloride liposomes-in-hydrogel as a new preparation was selected for delivery. In this case, liposomes were dispersed in the colloidal hydrogel on a mesoscopic scale and could provide specific transfer properties. The results showed that the sinomenine hydrochloride-loaded liposomes-in-hydrogel system could effectively inhibit atopic dermatitis.

Evaluation of Antioxidant Activity and Treatment of Eczema by Berberine Hydrochloride-Loaded Liposomes-in-Gel.

In this paper, berberine hydrochloride-loaded liposomes-in-gel were designed and developed to investigate their antioxidant properties and therapeutic effects on the eczema model of the mouse. Berberine hydrochloride-liposomes (BBH-L) as the nanoparticles were prepared by the thin-film hydration method and then dispersed BBH-L evenly in the gel matrix to prepare the berberine hydrochloride liposomes-gel (BBH-L-Gel) by the natural swelling method. Their antioxidant capacity was investigated by the free radical scavenging ability on 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H2O2 and the inhibition of lipid peroxides malondialdehyde (MDA). An eczema model was established, and the efficacy of the eczema treatment was preliminarily evaluated using ear swelling, the spleen index, and pathological sections as indicators. The results indicate that the entrapment efficiency of BBH-L prepared by the thin-film hydration method was 78.56% ± 0.7%, with a particle size of 155.4 ± 9.3 nm. For BBH-L-Gel, the viscosity and pH were 18.16 ± 6.34 m Pas and 7.32 ± 0.08, respectively. The cumulative release in the unit area of the in vitro transdermal study was 85.01 ± 4.53 µg/cm2. BBH-L-Gel had a good scavenging capacity on DPPH and H2O2, and it could effectively inhibit the production of hepatic lipid peroxides MDA in the concentration range of 0.4-2.0 mg/mL. The topical application of BBH-L-Gel could effectively alleviate eczema symptoms and reduce oxidative stress injury in mice. This study demonstrates that BBH-L-Gel has good skin permeability, excellent sustained release, and antioxidant capabilities. They can effectively alleviate the itching, inflammation, and allergic symptoms caused by eczema, providing a new strategy for clinical applications in eczema treatment.

[Medicinal plant biodiversity in Jilin province based on big data of the fourth national survey of traditional Chinese medicine resources].

Using Origin2022Pro, PAST4.09, GraphPad, and ArcGIS, this study analyzed the big data of the fourth national survey of traditional Chinese medicine resources in Jilin province from five dimensions: differences in resource quantity, taxonomic group, family, and genus, regional distribution, and spatiotemporal distribution, aiming to fully elucidate the biodiversity of medicinal plants in Jilin province. The results indicated that 2 241 species of medicinal plants existed in Jilin province, belonging to 881 genera of 243 families, with 20 dominant families and 3 dominant genera. There were 1 901 species of medicinal plants(belonging to 778 genera of 227 families) in the eastern mountainous region, 1 503 species(belonging to 690 genera of 225 families) in the mid-mountainous areas of the central mountainous region, and 811 species(belonging to 436 genera of 136 families) in the western plain region. The biodiversity of medicinal plants in Jilin province was high and presented a trend of high in the east and low in the west. The medicinal plant resources were mainly concentrated in the eastern mountainous region, and the number of medicinal plant groups had significant diffe-rences between regions, following the trend of western region > central region > eastern region. The species richness was in the order of eastern region > western region > central region. The species diversity structure in the central region was similar to that in the eastern and western regions, while it was significantly different between the western and eastern regions. Compared with the third national survey of traditional Chinese medicine resources, the fourth survey showed an increase of 1 417 species, a decrease of 580 species, and 824 common species, indicating significant changes in the biodiversity of medicinal plants in Jilin province. The reasons for these changes need to be further explored. This article elucidates the background and biodiversity changes of medicinal plant resources in Jilin province, laying a foundation for the protection, utilization, and industrial development of traditional Chinese medicine resources in Jilin province.

Evaluation of the diagnostic utility on 1.5T and 3.0T 1H magnetic resonance spectroscopy for temporal lobe epilepsy.

BACKGROUND: 1H magnetic resonance spectroscopy (1H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of different field strengths for temporal lobe epilepsy (TLE) remains unclear. The purpose of this study is to make quantitative comparisons of metabolites of TLE at 1.5T and 3.0T and evaluate their efficacy. METHODS: Our retrospective collections included the single-voxel 1H-MRS of 23 TLE patients and 17 healthy control volunteers (HCs) with a 1.5T scanner, as well as 29 TLE patients and 17 HCs with a 3.0T scanner. Particularly, HCs were involved both the scans with 1.5T and 3.0T scanners, respectively. The metabolites, including the N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), were measured in the left or right temporal pole of brain. To analyze the ratio of brain metabolites, including NAA/Cr, NAA/Cho, NAA/(Cho + Cr) and Cho/Cr, four controlled experiments were designed to evaluate the diagnostic utility of TLE on 1.5T and 3.0T MRS, included: (1) 1.5T TLE group vs. 1.5T HCs by the Mann-Whitney U Test, (2) 3.0T TLE group vs. 3.0T HCs by the Mann-Whitney U Test, (3) the power analysis for the 1.5T and 3.0T scanner, and (4) 3.0T HCs vs. 1.5T HCs by Paired T-Test. RESULTS: Three metabolite ratios (NAA/Cr, NAA/Cho, and NAA/(Cho + Cr) showed the same statistical difference (p < 0.05) in distinguishing the TLE from HCs in the bilateral temporal poles when using 1.5T or 3.0T scanners. Similarly, the power analysis demonstrated that four metabolite ratios (NAA/Cr, NAA/Cho, NAA/(Cho + Cr), Cho/Cr) had similar distinction abilities between 1.5T and 3.0T scanner, denoting both 1.5T and 3.0T scanners were provided with similar sensitivities and reproducibilities for metabolites detection. Moreover, the metabolite ratios of the same healthy volunteers were not statistically different between 1.5T and 3.0T scanners, except for NAA/Cho (p < 0.05). CONCLUSIONS: 1.5T and 3.0T scanners may have comparable diagnostic potential when 1H-MRS was used to diagnose patients with TLE.

A review on deep learning MRI reconstruction without fully sampled k-space.

BACKGROUND: Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable. MAIN TEXT: In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information. CONCLUSION: Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

Coil Combination of Multichannel Single Voxel Magnetic Resonance Spectroscopy with Repeatedly Sampled In Vivo Data.

Magnetic resonance spectroscopy (MRS), as a noninvasive method for molecular structure determination and metabolite detection, has grown into a significant tool in clinical applications. However, the relatively low signal-to-noise ratio (SNR) limits its further development. Although the multichannel coil and repeated sampling are commonly used to alleviate this problem, there is still potential room for promotion. One possible improvement way is combining these two acquisition methods so that the complementary of them can be well utilized. In this paper, a novel coil-combination method, average smoothing singular value decomposition, is proposed to further improve the SNR by introducing repeatedly sampled signals into multichannel coil combination. Specifically, the sensitivity matrix of each sampling was pretreated by whitened singular value decomposition (WSVD), then the smoothing was performed along the repeated samplings' dimension. By comparing with three existing popular methods, Brown, WSVD, and generalized least squares, the proposed method showed better performance in one phantom and 20 in vivo spectra.

Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy.

Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.

Preliminary application of 3.0 T magnetic resonance chemical exchange saturation transfer imaging in brain metastasis of lung cancer.

BACKGROUND: Lung cancer brain metastases are very common and one of the common causes of treatment failure. We aimed to examine the clinical use of chemical exchange saturation transfer (CEST) technology in the evaluation of brain metastases for lung cancer diagnosis and prognosis. METHODS: We included26 cases of lung cancer brain metastases, 15 cases of gliomas, and 20 cases with normal tests. The magnetization transfer ratio (MTR;3.5 ppm) image from the GRE-EPI-CEST sequence was analyzed using the ASSET technique and APT technology. The MTR values were measured in the lesion-parenchymal, edema, and non-focus regions, and the MTR image was compared with the conventional MRI. ANOVA and t-test were used for statistical analysis. RESULTS: The lesion-parenchymal, edema, and non-focus areas in the metastatic-tumor-group were red-yellow, yellow-green, and green-blue, and the MTR values were 3.29 ± 1.14%,1.28 ± 0.36%,and 1.26 ± 0.31%, respectively. However, in the glioma-group, the corresponding areas were red, red-yellow, and green-blue, and the MTR values were 6.29 ± 1.58%, 2.87 ± 0.65%, and 1.03 ± 0.30%, respectively. The MTR values of the corresponding areas in the normal-group were 1.07 ± 0.22%,1.04 ± 0.23%, and 1.06 ± 0.24%, respectively. Traditional MR images are in black-white contrast and no metabolic information is displayed. The MTRvalues of the three regions were significantly different among the three groups. The values were also significantly different between the parenchymal and edema areas in the metastatic-tumor-group. There were significant differences in the MTR values between the non-lesion and edema regions, but there was no significant difference between the edema and non-focus areas. In the glioma-group, there were significant differences in the MTR values between the parenchymal and edema areas, between the parenchymal and non-focus areas, and between the edema and non-focus areas. CONCLUSIONS: CEST reflects the protein metabolism; therefore, early diagnosis of brain metastases and assessment of the prognosis can be achieved using molecular imaging.

Antiapoptotic effects of velvet antler polypeptides on damaged neurons through the hypothalamic-pituitary-adrenal axis.

We investigated the effects of velvet antler polypeptide on cognitive impairment and the underlying mechanisms. Hydrogen peroxide-induced cell injury was used to establish an in vitro model of SH-SY5Y cells. In addition, we established an in vivo mouse model of cognitive impairment using intraperitoneal injections of scopolamine hydrobromide in strain mice. We administered three different doses of velvet antler polypeptide in this mouse model and assessed the influence of velvet antler polypeptide on the morphology of hippocampal neurons, hippocampal neuronal apoptosis, adrenocorticotropic hormone, and corticosterone activities in brain tissue samples, and the molecular and biochemical regulation of B-cell lymphoma-2, B-cell lymphoma-2 Associated X-protein, Cysteine-aspartic acid protease-3, glucocorticoid receptor, mineralocorticoid receptor, and corticotropin-releasing hormone in murine hippocampal neurons. Our data suggest that velvet antler polypeptide decreases glucocorticoid receptor, mineralocorticoid receptor, and corticotropin-releasing hormone levels and regulates the hormones released by the hypothalamic-pituitary-adrenal axis, thus suppressing neuronal apoptosis.

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning.

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.

Ginsenoside compound K ameliorates Alzheimer's disease in HT22 cells by adjusting energy metabolism.

Energy metabolism disorders have been shown to exert detrimental effects on the pathology of Alzheimer's disease (AD). The ginsenoside compound K (CK), a major intestinal metabolite underlying the pharmacological actions of orally administered ginseng, has an ameliorating effect against AD, but the relevant molecular mechanism remains unclear. We hypothesized that the improvement of AD by CK is mediated by the energy metabolism signaling pathway induced by amyloid ß peptide (Aß) and tested this hypothesis in HT22 cells. HT22 cells were incubated with CK and exposed to Aß. Cell viability was analyzed using the MTT assay. Cell growth curves were derived from real-time cell analysis. Apoptosis was determined by flow cytometry, Aß localization and expression by immunofluorescence, and ATP content by a specific assay kit. The expression of proteins related to the energy metabolism signaling pathway was analyzed using Western blotting. CK treatment improved cell viability, cell growth, and apoptosis induced by Aß, and the cellular localization and expression of Aß. Moreover, CK increased ATP content by promoting the activity of glucose transporters (GLUTs). Therefore, the neuroprotective effect of CK against Aß injury was mainly realized through the activation of the energy metabolism signaling pathway. CK treatment inhibits neuronal damage caused by Aß through the activation of the energy metabolism signaling pathway, revealing that CK might be one of the key bioactive ingredients of ginseng in the treatment of Alzheimer's disease and may serve as a preventive or therapeutic agent for Alzheimer's disease.

Oviductus Ranae protein hydrolyzate prevents menopausal osteoporosis by regulating TGFß/BMP2 signaling.

PURPOSE: It is known that menopausal osteoporosis (MOP) is the most typical form of osteoporosis, which is characterized by low bone mass and microstructure damage of the bone tissue, leading to increased bone fragility and risk of fracture. This study aimed to evaluate the protective effects of Oviductus Ranae protein hydrolyzate (ORPH) on the MOP in vivo. METHODS: Osteoporosis model was induced by ovariectomy, treated with ORPH 150 or 75 mg kg-1. Body weight and bone mineral density (BMD) of rats were measured at the beginning and the end of the experiment, and femoral maximum load was determined immediately after killing. The expression levels of alkaline phosphatase (ALP), Smad4, tartrate acid phosphatase (TRAP), BMP2, Runx2, CPB, ColI and osteocalcin were examined by RT-PCR or western-blotting. HE staining was used to observe the pathological changes in the femurs. Immunohistochemistry was used to detect the expression of ALP and BMP2. All data were analyzed by SPSS 13.0. RESULTS: The results revealed that ORPH had no effect on the weight of normal and osteoporotic rats. ORPH could significantly improve the femur BMD and increase the maximum load of the osteoporotic rats. ORPH could significantly upregulate the expression level of bone formation makers, ALP, osteocalcin, ColI, and Runx2, and downregulate the expression level of bone resorption marker, TRAP. In the ORPH group, the expression levels of BMP2, Smad4, and CPB of key proteins in the TGFß/BMP2 signaling pathway were significantly upregulated. In addition, immunohistochemistry showed that ALP and BMP2 expression in femurs of the ORPH group was stranger. H&E staining showed that ORPH (150 mg kg-1) significantly increased the thickness of trabeculae and decreased fracture risk. CONCLUSION: Collectively, ORPH plays a role in the prevention and treatment of osteoporosis, which may be a potential anti-osteoporosis drug.

Error Measures for Trajectories Estimations with Geo-tagged Mobility Sample Data.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers' whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This study proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and people's activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns.

Metabonomics study of the effects of traditional Chinese medicine formula Ermiaowan on hyperuricemic rats.

To explore the global mechanism of Ermiaowan on hyperuricemia regulation, the holistic function of Ermiaowan for hyperuricemia in rats was firstly assessed by the urinary metabonomics method which was based on ultra-high performance liquid chromatography with electrospray ionization quadrupole time-of-flight mass spectrometry. The urinary targeted metabonomics approach combined with the serum biochemical analysis and histological assay was conducted to verify the research result. As a result, the significant differences in metabolic profiles were observed from Ermiaowan-treated group, model group, and healthy control group by using multivariate statistical approaches. Twenty therapeutic related metabolites were identified in response to the therapeutic effects of Ermiaowan, which were mainly associated with purine metabolism, pyrimidine metabolism, tryptophan metabolism, tricarboxylic acid cycle, and tyrosine metabolism. In addition, the urinary targeted metabonomics study showed that Ermiaowan can better restore the disturbed pathways than Phellodendri cortex and Atractylodis rhizome. The biochemical assay and histopathological assay confirmed that Ermiaowan could significantly reduce uric acid and fibrosis areas of kidney. These results provided new insights into the mechanism of Ermiaowan on hyperuricemia.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

BACKGROUND: Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). METHODS: First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. RESULTS: Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. CONCLUSIONS: The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity.

BACKGROUND: Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. METHODS: In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart. RESULTS: The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments. CONCLUSION: Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights.