Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis. Higher resolution helps to capture more detailed content, but typically induces to lower signal-to-noise ratio and longer scanning time. To this end, MR image super-resolution has become a widely-interested topic in recent times. Existing works establish extensive deep models with the conventional architectures based on convolutional neural networks (CNN). In this work, to further advance this research field, we make an early effort to build a Transformer-based MR image super-resolution framework, with careful designs on exploring valuable domain prior knowledge. Specifically, we consider two-fold domain priors including the high-frequency structure prior and the inter-modality context prior, and establish a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution (LR) MR images. Comprehensive experiments on two datasets indicate that Cohf-T achieves new state-of-the-art performance

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging (cMRI) is crucial for improved cardiovascular disease diagnosis and understanding of the heart's motion. However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks, we propose a morphology-guided diffusion model for 3D cardiac volume reconstruction, DMCVR, that synthesizes high-resolution 2D images and corresponding 3D reconstructed volumes. Our method outperforms previous approaches by conditioning the cardiac morphology on the generative model, eliminating the time-consuming iterative optimization process of the latent code, and improving generation quality. The learned latent spaces provide global semantics, local cardiac morphology and details of each 2D cMRI slice with highly interpretable value to reconstruct 3D cardiac shape. Our experiments show that DMCVR is highly effective in several aspects, such as 2D generation and 3D reconstruction performance. With DMCVR, we can produce high-resolution 3D cardiac MRI reconstructions, surpassing current techniques. Our proposed framework has great potential for improving the accuracy of cardiac disease diagnosis and treatment planning. Code can be accessed at https://github.com/hexiaoxiao-cs/DMCVR.Comment: Accepted in MICCAI 202

Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study

IntroductionDynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated.MethodForty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well.ResultsThe pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas.DiscussionRetrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer

Unimolecular micelles composed of inner coil-like blocks and outer rod-like blocks crafted by combination of living polymerization with click chemistry

Air Force Office of Scientific Research [FA9550-13-1-0101]; Key Laboratory of Computational Physical Sciences at Fudan UniversityA new class of star-shaped coil-rod diblock copolymer polystyrene-block-poly(3-hexylthiophene) (PS-b-P3HT) with well-defined structures and the ratio of coil to rod blocks were synthesized by a combination of atom transfer radical polymerization, the Grignard metathesis method, and click reaction. The star-shaped PS-b-P3HT diblock copolymers covalently connected to a beta-cyclodextrin (beta-CD) core were composed of 21-arm coil-like PS inner blocks and rod-like conjugated polymer P3HT outer blocks with narrow molecular weight distribution. The intermediate and final products were systematically characterized and confirmed by gel permeation chromatography (GPC), H-1 nuclear magnetic resonance (H-1-NMR), and Fourier transfer infrared (FTIR) spectroscopy. The optical properties of star-shaped PS-b-P3HT were examined by UV-Vis absorption and photoluminescence (PL) measurements. In comparison to the linear P3HT, due to their compact structure and the introduction of PS blocks, the optical properties of 21-arm, star-shaped PS-b-P3HT were altered. The star-shaped PS-b-P3HT formed unimolecular micelles in good solvent as revealed by dynamic light scattering (DLS) and atomic force microscopy (AFM) studies

Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-\delta}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications

Bandwidth Optimization Design of a Multi Degree of Freedom MEMS Gyroscope

A new robust multi-degree of freedom (multi-DOF) MEMS gyroscope is presented in this paper. The designed gyroscope has its bandwidth and amplification factor of the sense mode adjusted more easily than the previous reported multi-DOF MEMS gyroscopes. Besides, a novel spring system with very small coupling stiffness is proposed, which helps achieve a narrow bandwidth and a high amplification factor for a 2-DOF vibration system. A multi-DOF gyroscope with the proposed weak spring system is designed, and simulations indicate that when the operating frequency is set at 12.59 kHz, the flat frequency response region of the sense mode can be designed as narrow as 80 Hz, and the amplification factor of the sense mode at the operating frequency is up to 91, which not only protects the amplification factor from instability against process and temperature variations, but also sacrifices less performance. An experiment is also carried out to demonstrate the validity of the design. The multi-DOF gyroscope with the proposed weak coupling spring system is capable of achieving a good tradeoff between robustness and the performance

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Network virtualization is considered as a key technology for the future network. The emergence of software-defined network (SDN) provides a platform for the research and development of network virtualization. One of the key challenges in network virtualization is virtual network embedding (VNE). Some of the previous VNE algorithms perform virtual node embedding, which combines the nodes’ resource attributes and local topology attributes by arithmetic operations. On the one hand, it is not easy to distinguish the topological differences between SN and VN only by simple topology metrics. On the other hand, it is easy to ignore the different weight impacts of different metrics using only arithmetic operations, which will lead to an unbalanced embedding solution. To deal with these issues, we propose a novel node-ranking approach based on topology-differentiating (VNE-NRTD) for SDN-based virtual network embedding. Owing to the topological difference between SN and VN, different node metrics are used to quantify the substrate nodes and virtual nodes, respectively. Then, the nodes are ranked using the modified set pair analysis (SPA) method to avoid the unbalanced embedding solution. On this basis, we introduce the global bandwidth of the network topology into node-ranking to further improve the efficiency of node embedding. The simulation results show that the VNE-NRTD algorithm proposed in this paper outperforms other latest heuristic algorithms in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate node utilization, and substrate link utilization

Bandwidth Optimization Design of a Multi Degree of Freedom MEMS Gyroscope

A new robust multi-degree of freedom (multi-DOF) MEMS gyroscope is presented in this paper. The designed gyroscope has its bandwidth and amplification factor of the sense mode adjusted more easily than the previous reported multi-DOF MEMS gyroscopes. Besides, a novel spring system with very small coupling stiffness is proposed, which helps achieve a narrow bandwidth and a high amplification factor for a 2-DOF vibration system. A multi-DOF gyroscope with the proposed weak spring system is designed, and simulations indicate that when the operating frequency is set at 12.59 kHz, the flat frequency response region of the sense mode can be designed as narrow as 80 Hz, and the amplification factor of the sense mode at the operating frequency is up to 91, which not only protects the amplification factor from instability against process and temperature variations, but also sacrifices less performance. An experiment is also carried out to demonstrate the validity of the design. The multi-DOF gyroscope with the proposed weak coupling spring system is capable of achieving a good tradeoff between robustness and the performance