Polyurethane Vitrimers Engineered with Nitrogen-Coordinating Cyclic Boronic Diester Bonds for Sustainable Bioelectronics.

Flexible bioelectronic devices seamlessly interface with organs and tissues, offering unprecedented opportunity for timely prevention, early diagnosis, and medical therapies. However, the majority of flexible substrates utilized in bioelectronics still encounter significant challenges in terms of recyclability and reprocessing, leading to the accumulation of environmentally and biologically hazardous toxic waste. Here, the study reports the design of recyclable polyurethane (PU) vitrimers engineered with internal boron-nitrogen coordination bonds that can reversibly dissociate to boronic acids and hydroxyl, or undergo metathesis reaction following an associative pathway. The study demonstrates the capacity of these recyclable PU vitrimers as flexible substrates in various wearable and implantable bioelectronic applications, achieving high-quality electrophysiological recordings and stimulation. Furthermore, the study establishes a sustainable recycling process by reconstructing a range of bioelectronic devices from the recycled PU vitrimers without compromising the mechanical performance. This closed-loop approach not only addresses the critical challenge of the reclaiming medical electronic waste but also paves the way for the development of sustainable flexible bioelectronics for healthcare applications.

Self-Growing Hydrogel Bioadhesives for Chronic Wound Management.

Hydrogel bioadhesives have emerged as a promising alternative to wound dressings for chronic wound management. However, many existing bioadhesives do not meet the functional requirements for efficient wound management through dynamically mechanical modulation, due to the reduced wound contractibility, frequent wound recurrence, incapability to actively adapt to external microenvironment variation, especially for those gradually-expanded chronic wounds. Here, a self-growing hydrogel bioadhesive (sGHB) patch that exhibits instant adhesion to biological tissues but also a gradual increase in mechanical strength and interfacial adhesive strength within a 120-h application is presented. The gradually increased mechanics of the sGHB patch could effectively mitigate the stress concentration at the wound edge, and also resist the wound expansion at various stages, thus mechanically contracting the chronic wounds in a programmable manner. The self-growing hydrogel patch demonstrated enhanced wound healing efficacy in a mouse diabetic wound model, by regulating the inflammatory response, promoting the faster re-epithelialization and angiogenesis through mechanical modulation. Such kind of self-growing hydrogel bioadhesives have potential clinical utility for a variety of wound management where dynamic mechanical modulation is indispensable.

Wireless Bioelectronics for In Vivo Pressure Monitoring with Mechanically-Compliant Hydrogel Biointerfaces.

Recent electronics-tissues biointefacing technology has offered unprecedented opportunities for long-term disease diagnosis and treatment. It remains a grand challenge to robustly anchor the pressure sensing bioelectronics onto specific organs, since the periodically-varying stress generated by normal biological processes may pose high risk of interfacial failures. Here, a general yet reliable approach is reported to achieve the robust hydrogel interface between wireless pressure sensor and biological tissues/organs, featuring highly desirable mechanical compliance and swelling resistance, despite the direct contact with biofluids and dynamic conditions. The sensor is operated wirelessly through inductive coupling, characterizing minimal hysteresis, fast response times, excellent stability, and robustness, thus allowing for easy handling and eliminating the necessity for surgical extraction after a functional period. The operation of the wireless sensor has been demonstrated with a custom-made pressure sensing model and in vivo intracranial pressure monitoring in rats. This technology may be advantageous in real-time post-operative monitoring of various biological inner pressures after the reconstructive surgery, thus guaranteeing the timely treatment of lethal diseases.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer.

PURPOSE: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

Tough adhesion enhancing strategies for injectable hydrogel adhesives in biomedical applications.

Injectable hydrogel adhesives have gained widespread attention due to their ease of use, fast application time, and suitability for minimally invasive procedures. Several biomedical applications depend on tough adhesion between hydrogel adhesives and tissues, including wound closure and healing, hemostasis, tissue regeneration, drug delivery, and wearable electronic devices. Compared with bulk hydrogel adhesives formed ex situ, injectable hydrogel adhesives are more difficult to achieve strong adhesion strength due to a further balance of cohesion and adhesion while maintaining their flowability. In this review, the critical principles in designing tough adhesion of injectable hydrogel adhesives are summarized, including simultaneously enhancing their intrinsic interfacial toughness (Γ0inter) and mechanical dissipation (ΓDinter). Thereafter, various design strategies to enhance the Γ0inter and ΓDinter are discussed and evaluated respectively, involving multiple noncovalent/covalent interactions, topological connections, and polymer network structures. Furthermore, targeted biomedical applications of injectable hydrogel adhesives for specific tissue needs are systematically highlighted. In the end, this review outlines the challenges and trends in producing next-generation multifunctional injectable hydrogels for both practical and translational applications.

Barnacle-Inspired Wet Tissue Adhesive Hydrogels with Inherent Antibacterial Properties for Infected Wound Treatment.

Currently, antibiotics are the most common treatment for bacterial infections in clinical practice. However, with the abuse of antibiotics and the emergence of drug-resistant bacteria, the use of antibiotics has faced an unprecedented challenge. It is imminent to develop nonantibiotic antimicrobial agents. Based on the cation-π structure of barnacle cement protein, a polyphosphazene-based polymer poly[(N,N-dimethylethylenediamine)-g-(N,N,N,N-dimethylaminoethyl p-ammonium bromide (ammonium bromide)-g-(N,N,N,N-dimethylaminoethyl acetate ethylammonium bromide)] (PZBA) with potential adhesion and inherent antibacterial properties was synthesized, and a series of injectable antibacterial adhesive hydrogels (PZBA-PVA) were prepared by cross-linking with poly(vinyl alcohol) (PVA). PZBA-PVA hydrogels showed good biocompatibility, and the antibacterial rate of the best-performed hydrogel reached 99.81 ± 0.04% and 98.80 ± 2.16% against Staphylococcus aureus and Escherichia coli within 0.5 h in vitro, respectively. In the infected wound model, the healing rate of the PZBA-PVA-treated group was significantly higher than that of the Tegaderm film group due to the fact that the hydrogel suppressed inflammatory responses and modulated the infiltration of immune cells. Moreover, the wound healing mechanism of the PZBA-PVA hydrogel was further evaluated by real-time polymerase chain reaction and total RNA sequencing. The results indicated that the process of hemostasis and tissue development was prompted and the inflammatory and immune responses were suppressed to accelerate wound healing. Overall, the PZBA-PVA hydrogel is shown to have the potential for infected wound healing application.

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.

BACKGROUND: The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training. PURPOSE: The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-based methods. METHODS: We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imaging results. RESULTS: The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel, respectively. CONCLUSIONS: The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurate registration.

Multistage ROS-Responsive and Natural Polyphenol-Driven Prodrug Hydrogels for Diabetic Wound Healing.

The high level of reactive oxygen species (ROS) and bacterial infection impede wound healing of the diabetic wound. Here, benefiting from the antioxidation effects of tannic acid (TA) and ROS-responsive phenylborate ester (PBAE), a series of ROS-responsive anti-inflammatory TA-conjugated nanoparticle hydrogels (PPBA-TA-PVA) can be obtained by conveniently mixing TA, phenylboric acid modified polyphosphazene (PPBA), and poly(vinyl alcohol) (PVA). The obtained PPBA-TA-PVA hydrogels could effectively inhibit the growth of Escherichia coli (antibacterial rate = 93.1 ± 1.1%) within 4 h and effectively scavenge both 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals and •OH radicals in vitro. Besides, the cell migration rate of HDFa cells treated with PPBA-TA-PVA hydrogels (84.2 ± 4.6%) was twice the rate of normal cells (43.8 ± 8.1%) after 24 h of cocultivation. The clinical relevance was demonstrated further by assessing the PPBA-TA-PVA hydrogels in full-thickness excisional wounds in a streptozotocin (STZ)-induced diabetic rat model. The PPBA-TA-PVA hydrogels could act as effective ROS-scavenging agents to alleviate inflammation and accelerate wound closure by decreasing the proinflammatory cytokines (IL-6, IL-1ß) and increasing the gene expression of TGF-ß1, COL-1, and COL-3, which resulted in faster re-epithelialization and increased formation of granulation tissue.

Glucose-responsive nanoparticles designed via a molecular-docking-driven method for insulin delivery.

Nocturnal blood glucose regulation was one of the key challenges in diabetic treatments. However, development of the smart insulin complexes with mild and glucose-responsive delivering performances was mostly relied on experience of the senior researchers and numerous confirmation experiments. In this work, a series of bioinspired fatty-acid-modified glucose-responsive insulin-delivering polymeric nanoparticles were designed. The molecular docking technique was utilized to efficiently screen the fatty-acid-derived functional groups. The results provided the basis for polymer functionalization and simplified the optimization experiments. For the optimized formulation (C10MS), insulin-loaded C10MS successfully fulfilled the nocturnal-glycemic-controlling requirement of the diabetic rats with lower occurrence of hypoglycemia than the conventional insulin injection schemes. Such formulation also possessed good biocompatibility with the moderate elimination kinetics in vivo, which matched the demand of bio-safety in the daily treatments. Overall, this work opened up a new path for efficient design of functional polymeric materials.

Instant and Tough Adhesives for Rapid Gastric Perforation and Traumatic Pneumothorax Sealing.

Hydrogel adhesives are hot spots due to their ubiquity and practical relevance. However, achieving a robust wet adhesion is still a challenge due to the preferential formation of hydrogen bonds between interfacial fluids and bulk hydrogel, as well as targeted substrates. Herein, a half-dry adhesive consisting of a silk fibroin (SF) semi-interpenetrating network and poly(acrylic acid) covalent network, which can allow a rapid liquid adsorption and repulsion process encountering a wet tissue, is reported. The remaining water enables excellent hydrogel flexibility to a dynamic surface, while the ß-sheet fold endows its tough bulk strength under the peeling-off process. Notably, the wet adhesion energy versus porcine skin is 1440 J m-2 due to the combination of hydrogen bonds, electrostatic interactions, and chain entanglement derived from SF. In particular, both in vitro and in vivo outcomes indicate excellent hemostatic effects and result in incision closure of skin, artery, gastric perforation, and lung. After the first-stage closure, polyacrylic-silk fibroin adhesive (PSA) sealants can detach from the lung surface, fitting well to the healing period. By virtue of the reliable adhesion and good noncytotoxicity, PSA may be a prospective candidate for tissue sealant and drug carrier applications.

CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.

Accurate segmentation of kidney in ultrasound images is a vital procedure in clinical diagnosis and interventional operation. In recent years, deep learning technology has demonstrated promising prospects in medical image analysis. However, due to the inherent problems of ultrasound images, data with annotations are scarce and arduous to acquire, hampering the application of data-hungry deep learning methods. In this paper, we propose cross-modal transfer learning from computerized tomography (CT) to ultrasound (US) by leveraging annotated data in the CT modality. In particular, we adopt cycle generative adversarial network (CycleGAN) to synthesize US images from CT data and construct a transition dataset to mitigate the immense domain discrepancy between US and CT. Mainstream convolutional neural networks such as U-Net, U-Res, PSPNet, and DeepLab v3+ are pretrained on the transition dataset and then transferred to real US images. We first trained CNN models on a data set composed of 50 ultrasound images and validated them on a validation set composed of 30 ultrasound images. In addition, we selected 82 ultrasound images from another hospital to construct a cross-site data set to verify the generalization performance of the models. The experimental results show that with our proposed transfer learning strategy, the segmentation accuracy in dice similarity coefficient (DSC) reaches 0.853 for U-Net, 0.850 for U-Res, 0.826 for PSPNet and 0.827 for DeepLab v3+ on the cross-site test set. Compared with training from scratch, the accuracy improvement was 0.127, 0.097, 0.105 and 0.036 respectively. Our transfer learning strategy effectively improves the accuracy and generalization ability of ultrasound image segmentation model with limited training data.

Polyphosphazene and Non-Catechol-Based Antibacterial Injectable Hydrogel for Adhesion of Wet Tissues as Wound Dressing.

Wound dressings with excellent adhesiveness, antibacterial, self-healing, hemostasis properties, and therapeutic effects have great significance for the treatment of acute trauma. So far, numerous mussel-inspired catechol-based wet adhesives have been reported, opening a pathway for the treatment of acute trauma. However, catechol-based hydrogels are easily oxidized, which limits their applications. Here, the design of a polyphosphazene and non-catechol based antibacterial injectable hydrogel is reported as a multifunctional first aid bandage. Inspired by barnacle cement proteins, a series of dynamic phenylborate ester based adhesive hydrogels are prepared by combining the cation-π structure modified polyphosphazene with polyvinyl alcohol. The inherent antibacterial property (4 h antibacterial rate 99.6 ± 0.2%), anti-mechanical damage, and hemostatic behavior are investigated to confirm multi-functions of wound dressings. In water, the hydrogels firmly adhere to tissue surfaces through cation-π and π-π interactions as well as hydrogen bonding (adhesion strength = 45 kPa). Moreover, in vivo experiments indicate the hydrogels can shorten the bleeding time and reduce the amount of bleeding by 88%, and significantly accelerate the wound healing rate. These hydrogels have a promising application in the treatment of acute trauma, which is in urgent need of anti-infection and hemostasis.

Multiple-stimuli-responsiveness and conformational inversion of smart supramolecular nanoparticles assembled from spin labeled amphiphilic random copolymers.

HYPOTHESIS: Organic radical polymers with tailored pendant functionalities have emerged as exciting and promising materials for their application versatility. Moreover, eco-friendly polymer-based organic nanomaterials with redox-active pendant side groups can replace the harmful heavy metal-based inorganic materials. On the other hand, self-assembled nanomaterials are of great interest and attracted more attention recently for their promising application in different advanced fields, but it is yet challenging to predict suitable hydrophilic-lipophilic balance (HLB) for stimuli-responsive random copolymers assembly due to structural irregularity. Among several experimental techniques, electron paramagnetic resonance (EPR) spectroscopy plays a unique and promising role in revealing structural and dynamic information of nanostructured radical containing materials. EXPERIMENTS: In this study, a series of spin labeled amphiphilic random copolymers poly(methyl methacrylate-co-acrylic acid) have been synthesized and characterized by FT-IR, UV-Vis spectroscopies, TGA, DSC and water contact angle (CA) techniques. Their electrochemical properties have been determined by cyclic voltammetry (CV) in different organic solvents. EPR spectroscopy has been applied with other analytical techniques to elucidate the smart supramolecular nanoparticles (SNPs) formation, stimuli-responsiveness and structural changes through the dynamics of different molecular interactions. FINDINGS: The structural and dynamic information of self-assembled nanoparticles have been observed to be dependent on multiple-stimuli-responsiveness in different microenvironments by applying physiological and chemical parameters such as the different concentration of radicals, pH, temperature, nature of the solvent and reducing agent. The obtained results reveal the knowledge to understand insight into the mechanism for the formation of stimuli-responsive colloidal nanoparticles assembled from amphiphilic random copolymers with apt HLB value. The CV results reveal that the charge transfer process of the nanoparticles in solution was diffusion regulated and depended on the accessibility of radicals. The radical (spin labeled) polymers offer a broad way to develop stimuli-responsive materials in various colloidal nanostructures by changing the microenvironment, appreciating their potential advanced applications in electronic devices, catalysis, stimuli-triggered drug/gene delivery and reactive oxygen species (ROS) scavenger.

Recent research progress on polyphosphazene-based drug delivery systems.

In recent years, synthetic polymer materials have become a research hotspot in the field of drug delivery. Compared with natural polymer materials, synthetic polymer materials have more flexible structural adjustability, and can be designed to obtain clinically required delivery vehicles. Polyphosphazenes are one of the most promising biomedical materials in the future due to their controllable degradation properties and structural flexibility. These materials can be designed by controlling the hydrophilic and hydrophobic balance, introducing functional groups or drugs to form different forms of administration, such as nanoparticles, polyphosphazene-drug conjugates, injectable hydrogels, coatings, etc. In addition, the flexible backbone of polyphosphazenes and the flexibility of substitution enable them to meet researchers' design requirements in terms of stereochemistry, nanostructures, and topologies. At present, researchers have achieved a lot of successful practices in the field of targeted delivery of anticancer drugs/proteins/genes, bone tissue engineering repair, cell imaging tracking, photothermal therapy, and immunologic preparations. This review provides a summary of the progress of the recent 10 years of polyphosphazene-based drug delivery systems in terms of of chemical structure and functions.

Clinical Value of the Elastographic Q-Analysis Score in Assisting Real-Time Elastography-Guided Prostate Biopsy: A Retrospective Study of 125 Patients.

OBJECTIVES: This study aimed to evaluate the clinical value of the elastographic Q-analysis score (EQS) in assisting real-time elastography- and transrectal US-guided prostate biopsy. METHODS: A total of 125 patients with 301 lesions were enrolled in this study; all were confirmed by pathologic results. The patients underwent transrectal US and elastographic examinations before biopsy. Elastographic Q-analysis score analysis software was used for measuring the mean EQS of the elastic images. First, the suspicious regions on elastography underwent biopsy. Then 12-core systematic prostate biopsy was performed. An EQS curve was used to calculate the mean EQS, and a receiver operating characteristic curve was drawn to find the cutoff point for the EQS to predict prostate cancer. RESULTS: Of the 301 lesions in this study, 125 were malignant, and 176 were benign. The mean EQS values of benign and malignant lesions ± SD were 1.47 ± 0.75 and 2.98 ± 1.06, respectively. The difference was statistically significant (P < .05). The area under the receiver operating characteristic curve was 0.87. When the cutoff point was 1.95 for diagnosing malignant and benign lesions, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 83.5%, 84.4%, 76.8%, 89.2%, 5.35, and 0.20. CONCLUSIONS: The EQS could be used as a way to predict benign and malignant lesions and thus could serve as guidance for adding targeted biopsy.

Evaluation of the Performance of the Ultrasound (US) Elastographic Q-Analysis Score Combined With the Prostate Imaging Reporting and Data System for Malignancy Risk Stratification in Prostate Nodules Based on Transrectal US-Magnetic Resonance Imaging Fusion Imaging.

OBJECTIVES: This study retrospectively evaluated the prognostic performance of the ultrasound elastographic Q-analysis score (EQS) combined with the Prostate Imaging Reporting and Data System (PI-RADS) for malignancy risk stratification in prostate nodules based on transrectal ultrasound-magnetic resonance imaging fusion imaging. METHODS: Sixty-two patients who were suspected to have PCa between October 2017 and May 2018 in our hospital were retrospectively evaluated. The performance of the EQS and PI-RADS was evaluated by patients' receiver operating characteristic curves in differentiating malignant and benign prostate nodules. The combination of the EQS and PI-RADS methods for prostate imaging was evaluated. RESULTS: Sixty-two prostate nodules in 62 patients were included. All of the patients underwent biopsy; 29 cases were prostate cancer, and the rest were benign prostate lesions. Both the EQS and PI-RADS were significantly higher in malignant nodules than in benign nodules. The sensitivity, specificity, area under the curve, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and Youden index of an EQS cutoff of 2.05 were 86.2%, 81.8%, 85.9%, 4.73, 0.169, 80.6%, 87.1%, and 68%, respectively. The corresponding numbers for a PI-RADS cutoff of 4 were 82.7%, 69.7%, 84.2%, 2.72, 0.25, 70.6%, 82.1%, and 52.4%. The "tandem" method had a higher diagnostic specificity (87.9%), positive likelihood ratio (6.55), and positive predictive value (85.1%). The "parallel" method had a higher diagnostic sensitivity (96.5%), negative likelihood ratio (0.06), and negative predictive value (95.2%). CONCLUSIONS: both the EQS and PI-RADS had good diagnostic performance in differentiating between malignant and benign prostate lesions. The combination of the EQS and PI-RADS improved the diagnostic performance to a certain degree.

A novel passive shimming method for the correction of magnetic fields above the patient bed in MRI.

This paper presents a novel passive shimming method for the effective correction of static magnetic field (B0) inhomogeneities in Magnetic Resonance Imaging (MRI) systems. Passive shimming is used to find an optimum configuration for the placement of iron pieces applied to improve the B0 uniformity in the predefined imaging region referred to as the diameter of spherical volume (DSV). However, most passive shimming methods neglect to recognize that the space under the patient bed is not in use for imaging. In this work, we present a new algorithm that attempts to avoid the unnecessary shimming of the space under the patient bed. During implementation, the B0 field is still measured over the DSV surface and then mapped onto the effective imaging volume surface; a dedicated sensitivity matrix is generated only for the imaging area above the patient bed. A linear programming optimization procedure is performed for the determination of thicknesses and locations the shim pieces. Our experimental results showed that by revising the shimming target area, the new method provides superior optimization solutions. Compared to a conventional approach, the new method requires smaller amount of iron to correct the B0 inhomogeneities in the imaging area which has the effect of improving thermal stability to the B0 field. It also reduces the complexity of the optimization problem. Our new shimming strategy helps to improve the magnetic field homogeneity within the realistic imaging space, and ultimately improve image quality.

4-[Bis(3,4-dimethoxy-phen-yl)meth-yl]pyridine ethanol monosolvate.

In the title compound, C(22)H(23)NO(4)·C(2)H(6)O, the pyridyl ring is aligned at 89.39 (2) and 87.41 (2)° with respect to the benzene rings, and the three rings connected to the methine C atom are arranged in a propeller-like conformation. The heterocycle is linked to the solvent mol-ecule by an O-H⋯N hydrogen bond.