STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

The Impact of Simulation Education on Self-Efficacy in Pre-Registration Nursing Students

This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-efficacy of undergraduate pre-registration nursing students. The second aim of this study is to explore additional outcome variables that were examined in the previous studies. Five electronic databases were searched systematically. These databases were MEDLINE, CINAHL Plus, Scopus, Embase and PsycINFO. The PICO model was employed to identify the search terms, with a thesaurus being used to provide synonyms. Reference lists of relevant articles were examined and hand searches of journals were also undertaken. The quality of each study was assessed using the Simulation Research Rubric (SRR). A total of 11 studies were included. All studies explored the impact of simulation education on undergraduate pre-registration nursing. Six studies explored nursing students’ competence and performance and two papers examined their critical thinking. Problem solving, learning motivation, communication skills and knowledge acquisition were examined once. The majority of studies indicated that simulation training has a positive impact on pre-registration nursing students’ self-efficacy and other outcome variables. Furthermore, the study results indicate that simulation training is more dependable than traditional training, and students were extremely satisfied with the simulation training. However, most of the studies included in this review had several gaps, including study design, sample size and dissimilarities between the scales used. Further research with large samples, reliable and valid instruments, and outcomes measures (such as critical thinking and transferability of skills) is required to provide better insight into the effectiveness of simulation in undergraduate nursing education. .

Hierarchical Non-linear Image Registration Integrating Deformable Segmentation

A hierarchical non-linear method for image registration was presented, which integrates image segmentation and registration under a variational framework. An improved deformable model is used to simultaneously segment and register feature from multiple images. The objects in the image pair are segmented by evolving a single contour and meanwhile the parameters of affine registration transformation are found out. After that, a contour-constrained elastic registration is applied to register the images correctly. The experimental results indicate that the proposed approach is effective to segment and register medical images.

Disordered Multi-view Registration Method Based on the Soft Trimmed Deep Network

Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

One-step Eco-friendly Fabrication of Antibacterial Polyester Via On-line Amination Reaction by Melt Coextrusion

The work is dedicated to develop a one-step eco-friendly method to prepare antibacterial polyethylene terephthalate(PET).We report a one-step eco-friendly method to manufacture antibacterial PET via on-line amination reaction by melt coextrusion.Beside evenly mixing of poly(hexamethylene guanidine)(PHMG)and PET in the melt coextrusion procedure,the amination reaction also occurred between PHMG and PET under high temperature(230-270℃).The antibacterial ability of composite PET showed obvious PHMG concentration dependence,and antibacterial activity reached more than 99%when PHMG content was 2.5 wt%.Moreover,LIVE/DEAD fluorescence test further confirmed that the composite PET could kill bacteria quickly and efiectively(within 30 min);while negligible cytotoxicity was observed to HSF and HUVEC cells.Onestep eco-friendly fabrication of composite antibacterial PET was accomplished by on-line melt coextrusion.The composite antibacterial PET has potential use in multiple fields to combat with pathogenic including textiles,packaging materials,decoration materials and biomedical devices,etc.

Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

BACKGROUND It has been confirmed that three-dimensional(3D)imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography(ERCP),which reduces the radiation dose and procedure time with improved safety.However,current 3D biliary imaging does not have good real-time fusion with intraoperative imaging,a process meant to overcome the influence of intraoperative respiratory motion and guide navigation.The present study explored the feasibility of real-time continuous image-guided ERCP.AIM To explore the feasibility of real-time continuous image-guided ERCP.METHODS We selected 23D-printed abdominal biliary tract models with different structures to simulate different patients.The ERCP environment was simulated for the biliary phantom experiment to create a navigation system,which was further tested in patients.In addition,based on the estimation of the patient’s respiratory motion,preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP.RESULTS Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm±0.13 mm and a tracking error of 0.64 mm±0.24mm.After estimating the respiratory motion,3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients,with an average fusion rate of 88%.CONCLUSION Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

Natural forest ALS-TLS point cloud data registration without control points

Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.

Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

On-line血液透析滤过对移植肾功能延迟恢复的影响

目的 研究On -line血液透析滤过 (HDF)在移植肾功能延迟恢复患者中的应用。方法 我院2 0 0 2年 1月至 2 0 0 3年 7月肾移植术后发生移植肾功能延迟恢复患者 38例 ,2 9例采用Freseniuson -lineHDF透析机 ,F6 0透析器 ,透析滤过时间 3～ 4h ,血流量在 180～ 2 5 0ml/min ,置换液采取前稀释法 ,9例行HD治疗 ,采用GambroAK2 0 0透析机 ,F7透析器。结果 38例移植肾功能延迟恢复患者 ,2 9例用联机血液透析滤过 ,平均肾功能恢复时间为 ( 11.2± 5 .6 )天 ,治疗期间收缩压变化在 110～ 170mmHg,透析期间低血压发生率为 2 .6 % ,9例HD治疗者平均肾功能恢复时间为 ( 2 2 .3± 15 .7)天 ,透析期间低血压发生率为 13.2 %。结论 与HD比较 ,On -lineHDF对血压影响较小 ,可缩短移植肾功能恢复的时间。

平行机的实时到达on-line算法下界的改进

本文考虑了平行机实时到达的在线问题．模型中，工件是陆续到达的，工件的个数、到达时间是事先未知的，而且只有当工件到达，才知其加工时间，目标是使所有工件都加工完的时间达到最小．Ｃｈｅｎ与Ｖｅｓｔｊｅｎｓ（１９９６年）证明了该在线问题不存在性能比小于１．３４７３的ｏｎ－ｌｉｎｅ算法．本文将此界改进为（５－５）／２．

On-line血液透析滤过对维持性血液透析患者心率变异性的影响

目的探讨On-line血液透析滤过技术对维持性血液透析患者心率变异性(HRV)的影响。方法 50例维持性血液透析(MHD)患者随机分为常规血液透析治疗组(HD组)和On-line血液透析滤过治疗组(OL-HDF组),每组各25例。患者每周行3次、每次4 h的血液透析治疗或OL-HDF,治疗24个月。比较两组常规的实验室检查和24h动态心电图监测结果。结果治疗24个月后,OL-HDF组的β2-微球蛋白(β2-MG)由基线值30.1±7.2 mg/dl降至21.7±6.1 mg/dl(P<0.05),HD组的β2-MG则无明显下降。OL-HDF组HRV的时域性和频域性指标均值高于基线值(P<0.05),HD组的HRV指标则无显著性变化(P>0.05)。结论与普通血液透析比较,OL-HDF能更好地改善MHD患者的HRV,改善自主神经功能。

On-line置换液与生理盐水在血液透析滤过回血中的应用效果比较

[目的]探讨On-line置换液与生理盐水在血液透析滤过(HDF)病人回血中的应用效果,以期为临床护理工作提供理论依据。[方法]选取30例规律HDF病人为研究对象,采用自身对照方法,每例病人每周依次采用On-line置换液和生理盐水回血,比较回血过程中残余血量、静脉壶液体温度、护士的工作量及经济效益。[结果]采用On-line置换液回血法残余血量及回血后静脉壶温度分别为(1.89±0.91)mL,(33.36±0.15)℃,而采用生理盐水法为(2.88±0.23)mL、(27.13±0.14)℃,差异均有统计学意义(P<0.01);而透析器凝血状况比较差异无统计学意义(P=0.08);采用On-line置换液回血时护士工作量减少,回血液体成本降低。[结论]应用接近生理温度的On-line置换液回血能稀释血液、降低血液黏稠度,减少血路管残余血量,增加病人舒适感,提高工作效率。

On-lineHDF的临床观察及护理

目的 探讨应用On -lineHDF治疗慢性肾衰尿毒症患者的临床效果及护理对策。方法 回顾性分析 15例维持性HD患者应用On -lineHDF的治疗及护理过程。结果 15例患者经每 1～ 2周 1次的On -lineHDF治疗后 ,病情稳定 ,部分患者心功能、血压、食欲、体力及睡眠不同成度得到改善 ,在促红细胞生成素使用量不变的情况下 ,4例病人的血红蛋白有所增加。结论 On -lineHDF是当今最佳的肾脏替代疗法技术之一。

On-line HDF治疗维持性透析患者并发症的护理

对20例维持性透析并发难治性高血压、顽固性瘙痒、不安腿综合征等患者,采用每周2次血液透析+1次on-line HDF,加强观察和护理。患者血压得到良好的控制,皮肤瘙痒得到明显改善,不安腿症状有所好转,贫血、食欲减退、精神和神经症状得到改善,生活质量明显提高。

透析机自产回输液行血液透析滤过On-line HDF的临床研究

目的探讨On-lineHDF的安全性、病人耐受性及临床疗效。方法设置On-lineHDF治疗组(I组)和常规HD对照组(II组),观察治疗前、中、后病人状况,溶质变化和溶质下降率,包括Cr、BUN、β2-MG、PTH、KT/V。结果HDF治疗中、治疗后病人无发热,6例顽固性血压易于控制,5例在HD中易出现低血压症状好转,3例皮肤瘙痒严重者明显好转,HDF组治疗前后Cr,β2-MG,PTH显著下降;HD组治疗前后β2-MG、PTH相差不显著。治疗前及2月后溶质变化,两组Cr,β2-MG相差不显著;HDF组PTH前后有显著性差异;KT/V值比较,HDF为1.68±0.34,HD为1.21±0.23,两组有显著性差异(P<0.05)。结论On-lineHDF较传统HDF操作简单,自产回输液安全,病人有较好的耐受性和心血管稳定性,其对小分子、中分子物质的清除能力优于常规HD。