Vector flow imaging(VFI)is an innovative ultrasound flow measurement technology.Compared with the traditional color Doppler and spectral Doppler,VFI has the advantages of independence of angle correction and direct acquisition of real-time amplitude and direction of flow.Transverse oscillation(TO)method is one of the effective methods for vector flow imaging.However,a complete and detailed algorithm validation process based on commercial ultrasound machines is still lacking due to more complex convex probes.This study starts with introducing the imaging process and principle of transverse oscillation vector flow technique,and calculates the error between the set velocity value and the measured velocity value through the simulation experiment,and verifies the error between the set velocity value and the measured velocity value through the Doppler flow phantom experiment.Among them,the velocity value measured by the TO vector flow technique in the simulation experiment is 0.48 m/s and the preset value is 0.50 m/s,the error between them is-4％.The velocity values are 8.33,11.14,14.44 and 16.67 cm/s measured by the Doppler flow phantom experiment,the actual velocity values are 7.97,10.78,14.06 and 17.34 cm/s,the errors between them are all within±5％.Both experiments verify the feasibility of using vector flow technique on abdominal convex probe.

Based on preprocessed MRI images of low back pain patients,this study extracted MRI image features that can reflect the dysfunction of low back pain patients,and proposed a stacking ensemble learning algorithm model based on algorithm diversity,which provided a reliable method and an implementation method for the accurate assessment of limb dysfunction in low back pain patients.

The development history of pMBRT,the biological role of minibeam,the mechanism of minibeam protection of tissues,the generation of minibeam(collimator method and magnetic focusing method),and the analysis of advantages and disadvantages of proton minibeam matrix arrays is introduced with advanced proton minibeam arrays(pMBRT).It is proposed that the combination of proton minibeam arrays and magnetic resonance fields can help to exploit the normal tissue protective function of pMBRT,and improve the precision of proton therapy.

Different porous structures were studied through finite element analysis and then optimal porous structure was selected for the orthopedic applications.The optimal Voronoi structure was designed and fabricated using 3D printing.The mechanical properties and osseointegration ability were both investigated.The mechanical tests showed that the tensile strength,compressive strength and bending strength of Voronoi structures were obviously higher than that of the human bone,and the modulus of Voronoi structures were similar to human bone.In addition,the animal experimental results exhibited that obvious bone ingrowth was found from Month 1 to Month 6.This study provides some theoretical references for the orthopedic application of porous structures.

Based on the original data of cervical spine,the models of cervical C6 and C7 segments were reconstructed through medical image processing and reverse modeling operations,then the models were assembled to obtain the basic data of interbody fusion cage.According to the basic data,the structures of rectangular porous,gradient porous and octahedral porous interbody fusion cages were established respectively.Maximum force on the adult male neck was applied to the fusion device,and the stress,strain and maximum deformation of the fusion device were solved by finite element analysis.The elastic modulus decrease of the design,and the rectangular porosity structures with different porosity were analyzed and optimized.The results showed that the elastic modulus of the fusion cage with three structures decreased in varying degrees,and the porosity of the interbody fusion cage with rectangular structure was about 60％,which was the most decreased elastic modulus.

In the past 20 years,near infrared spectrum technology has been widely used in human body monitoring due to its non-invasive and real-time characteristics.Oxygen,as the main metabolic substance of the human body,is consumed the most in brain tissue.In order to prevent complications caused by a decrease in brain tissue oxygen during treatment,the patient's brain tissue blood oxygen saturation needs to be monitored in real time.Currently,most of the clinically used non-invasive cerebral blood oxygen detection equipments use dual wavelengths.Other substances on the detection path will cause errors in the measurement results.Therefore,this article proposes a three-wavelength method based on the basic principle of non-invasive monitoring of cerebral blood oxygen using near-infrared spectrum.The brain tissue oxygen saturation monitoring method of detecting light sources was initially verified through the built system,laying the foundation for subsequent system engineering.

As an assistive device to restore lower limb athletic ability,human lower limb rehabilitation robots are becoming increasingly important in the field of rehabilitation and clinical applications.With the advancement of science and technology,both domestic and foreign research in this field has been developed.This study provides a detailed overview of the development of lower limb rehabilitation robots and reviews the current status of clinical applications,with a focus on mechanism research,from the perspectives of degrees of freedom,workspace,singularity,gait simulation,kinematic simulation and human-robot interaction,etc.The results show that domestic research on lower limb rehabilitation robots focus on the design and optimization of the mechanism configuration,while foreign research focus on the improvement and innovation of the control system and training mode based on human-computer interaction.Finally,the current state of research is combined with an outlook on future trends.

Naked-eye 3D display technology has excellent 3D visual effects and does not require wearable devices assistance.It can present the depth,position and complex structure information of 3D medical images,allowing viewers to obtain information about tissues and organs from different points,reducing cognitive load,contributing to medical teaching and opening up innovative methods for planning and diagnosis.Naked-eye 3D augmented reality display can display medical images in real 3D space,achieving virtual and real vision.It helps a lot to medical research.The applications of naked-eye 3D display technology in three major aspects of medical diagnosis,clinical surgery and rehabilitation training is reviewed in the study.It provides the direction for the subsequent research in medical field,thus assisting medical research and improving medical practice.

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony(PVA)during mechanical ventilation.In the early stages,the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms.While these methods were intuitive and easy to operate,they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status.Subsequently,machine learning and deep learning technologies began to emerge and develop.These technologies automatically extract and learn data characteristics through algorithms,making PVA detection more robust and universal.Among them,logistic regression,support vector machines,random forest,hidden Markov models,convolutional autoencoders,long short-term memory networks,one-dimensional convolutional neural networks,etc.,have all been successfully used for PVA recognition.Despite the significant advancements in feature extraction through deep learning methods,their demand for labelled data is high,potentially consuming significant medical resources.Therefore,the combination of reinforcement learning and self-supervised learning may be a viable solution.In addition,most algorithm validations are based on a single dataset,so the need for cross-dataset validation in the future will be an important and challenging direction for development.

Vascular cognitive impairment(VCI)is a group of syndromes ranging from mild cognitive impairment to dementia caused by cerebrovascular disease,due to the lack of sensitivity and specific biomarkers,it is difficult to identify and diagnose early.Abnormal connectivity is observed in brain regions of patients with vascular cognitive disorders,locates mainly in the default mode network(DMN),and changes in their abnormal functional connectivity correlated with the degree of patients' cognitive impairment.Resting-state functional magnetic resonance imaging(rs-fMRI)is a commonly used method to detect the internal activity of the brain at resting state.The use of various rs-fMRI to study abnormal changes in the DMN in patients with VCI is useful to further investigate the pathogenesis of VCI and provide an objective basis for imaging.This article mainly reviews the application of rs-fMRI in the DMN in patients with VCI,bringing new perspectives for the correct diagnosis and assessment of VCI.