Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising techniqu...Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.展开更多
Background: Rhinoplasty is a complex surgical procedure that requires critical analysis and precise design before surgery, making it a challenging operation for both the surgical team and medical educators. This study...Background: Rhinoplasty is a complex surgical procedure that requires critical analysis and precise design before surgery, making it a challenging operation for both the surgical team and medical educators. This study aimed to evaluate the impact of 3D design involvement on learning curves and to establish a more effective method for rhinoplasty education.Methods: Surgeons who participated in an educational program were divided into two groups. The experimental group was involved in the 3D design before the operation, and the control group was asked to review the rhinoplasty atlas. A self-assessment questionnaire was used to evaluate the learning curve of the eight rhinoplasty procedures for each surgeon, and the overall satisfaction rate data were also collected.Results: The self-assessment scores in both groups showed an increasing trend from the first to the eighth operation. The mean scores of the experimental group were significantly higher than those of the control group at the fifth operation(P=0.01). The satisfaction rate of the experimental group(91.7%) was higher than that of the control group(54.5%).Conclusion: The 3D imaging system can improve the learning curve and satisfaction rate of rhinoplasty education,proving that it is an easy and effective tool for medical education.展开更多
This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop ...This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.展开更多
Medical models, or ＂phantoms,＂ have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification a...Medical models, or ＂phantoms,＂ have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification and validation, and medical devices development. Such new applications demand high-fidelity, patient-specific, tissue-mimicking medical phantoms that can not only closely emulate the geometric structures of human organs, but also possess the properties and functions of the organ structure. With the rapid advancement of three-dimensional （3D） printing and 3D bioprinting technologies, many researchers have explored the use of these additive manufacturing techniques to fabricate functional medical phantoms for various applications. This paper reviews the applications of these 3D printing and 3D bioprinting technologies for the fabrication of functional medical phantoms and bio-structures. This review specifically discusses the state of the art along with new developments and trends in 3D printed functional medical phantoms （i.e., tissue-mimicking medical phantoms, radiologically relevant medical phantoms, and physiological medical phantoms） and 3D bio-printed structures （i.e., hybrid scaffolding materials, convertible scaffolds, and integrated sensors） for regenerated tissues and organs.展开更多
Medical devices are instruments and other tools that act on the human body to aid clinical diagnosis and disease treatment,playing an indispensable role in modern medicine.Nowadays,the increasing demand for personaliz...Medical devices are instruments and other tools that act on the human body to aid clinical diagnosis and disease treatment,playing an indispensable role in modern medicine.Nowadays,the increasing demand for personalized medical devices poses a significant challenge to traditional manufacturing methods.The emerging manufacturing technology of three-dimensional(3D)printing as an alternative has shown exciting applications in the medical field and is an ideal method for manufacturing such personalized medical devices with complex structures.However,the application of this new technology has also brought new risks to medical devices,making 3D-printed devices face severe challenges due to insufficient regulation and the lack of standards to provide guidance to the industry.This review aims to summarize the current regulatory landscape and existing research on the standardization of 3D-printed medical devices in China,and provide ideas to address these challenges.We focus on the aspects concerned by the regulatory authorities in 3D-printed medical devices,highlighting the quality system of such devices,and discuss the guidelines that manufacturers should follow,as well as the current limitations and the feasible path of regulation and standardization work based on this perspective.The key points of the whole process quality control,performance evaluation methods and the concept of whole life cycle management of 3D-printed medical devices are emphasized.Furthermore,the significance of regulation and standardization is pointed out.Finally,aspects worthy of attention and future perspectives in this field are discussed.展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
Different from reduction manufacturing and equal manufacturing, 3D printing is an additive manufacturing method, which transforms 3D model into 2D cross-section data to form entity layer by layer. This makes its proce...Different from reduction manufacturing and equal manufacturing, 3D printing is an additive manufacturing method, which transforms 3D model into 2D cross-section data to form entity layer by layer. This makes its processing not limited by complexity of the design model and number of the manufacturing products. It is very suitable for the medical field with high customization requirements. In fact, application of 3D printing technology in the medical field is particularly noticeable. In this paper, application and development </span><span style="font-family:Verdana;">of 3D printing technology are reviewed in medical model, rehabilitation equi</span><span style="font-family:Verdana;">pment, tissue engineering, medical hygiene materials, lab-on-chip. Its applications include medical education, surgical planning, prosthesis customization, tissue culture and biosensor manufacturing and so on. Its wide application is due to its digital model, which makes the whole manufacturing process easier to digitize, so it is more conductive to updating and customization of products via 3D printing.展开更多
Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shap...Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.展开更多
Background:Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes.The introduction of this technique into pediatric cardiac...Background:Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes.The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case.Methods:We performed the prospective cohort study which included 29 children with congenital heart defects.The hearts and the great vessels were modeled and printed out.Measurements of the same cardiac areas were taken in the same planes and points at multislice computed tomography images(group 1)and on printed 3D models of the hearts(group 2).Pre-printing treatment of the multislice computed tomography data and 3D model preparation were performed according to a newly developed algorithm.Results:The measurements taken on the 3D-printed cardiac models and the tomographic images did not differ significantly,which allowed us to conclude that the models were highly accurate and informative.The new algorithm greatly simplifies and speeds up the preparation of a 3D model for printing,while maintaining high accuracy and level of detail.Conclusions:The 3D-printed models provide an accurate preoperative assessment of the anatomy of a defect in each case.The new algorithm has several important advantages over other available programs.They enable the development of customized preliminary plans for surgical repair of each specific complex congenital heart disease,predict possible issues,determine the optimal surgical tactics,and significantly improve surgical outcomes.展开更多
Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of iden...Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.展开更多
Fractal theory offers a powerful tool for the precise description and quantification of the complex pore structures in reservoir rocks,crucial for understanding the storage and migration characteristics of media withi...Fractal theory offers a powerful tool for the precise description and quantification of the complex pore structures in reservoir rocks,crucial for understanding the storage and migration characteristics of media within these rocks.Faced with the challenge of calculating the three-dimensional fractal dimensions of rock porosity,this study proposes an innovative computational process that directly calculates the three-dimensional fractal dimensions from a geometric perspective.By employing a composite denoising approach that integrates Fourier transform(FT)and wavelet transform(WT),coupled with multimodal pore extraction techniques such as threshold segmentation,top-hat transformation,and membrane enhancement,we successfully crafted accurate digital rock models.The improved box-counting method was then applied to analyze the voxel data of these digital rocks,accurately calculating the fractal dimensions of the rock pore distribution.Further numerical simulations of permeability experiments were conducted to explore the physical correlations between the rock pore fractal dimensions,porosity,and absolute permeability.The results reveal that rocks with higher fractal dimensions exhibit more complex pore connectivity pathways and a wider,more uneven pore distribution,suggesting that the ideal rock samples should possess lower fractal dimensions and higher effective porosity rates to achieve optimal fluid transmission properties.The methodology and conclusions of this study provide new tools and insights for the quantitative analysis of complex pores in rocks and contribute to the exploration of the fractal transport properties of media within rocks.展开更多
Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions requir...Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.展开更多
This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) w...This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions: positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.展开更多
This paper proposes a practical algorithms of plane cutting, stereo clipping and arbitrary cutting for 3D surface model reconstructed from medical images. In plane cutting and stereo clipping algorithms, the 3D model ...This paper proposes a practical algorithms of plane cutting, stereo clipping and arbitrary cutting for 3D surface model reconstructed from medical images. In plane cutting and stereo clipping algorithms, the 3D model is cut by plane or polyhedron. Lists of edge and vertex in every cut plane are established. From these lists the boundary contours are created and their relationship of embrace is ascertained. The region closed by the contours is triangulated using Delaunay triangulation algorithm. Arbitrary cutting operation creates cutting curve interactively. The cut model still maintains its correct topology structure. With these operations, tissues inside can be observed easily and it can aid doctors to diagnose. The methods can also be used in surgery planning of radiotherapy.展开更多
目的验证三维多回波数据联合成像(three dimensional multi-echo data imagine combination with selective water excitation,3D MEDIC WE)和三维快速自旋回波成像(three dimensional sampling perfection with application optimized ...目的验证三维多回波数据联合成像(three dimensional multi-echo data imagine combination with selective water excitation,3D MEDIC WE)和三维快速自旋回波成像(three dimensional sampling perfection with application optimized contrasts by using different flip angle evolution,3D SPACE STIR)序列在腰骶丛神经根成像中的可行性和重复性。方法将55例受试者分为腰椎无异常表现的正常对照组(20例)、单纯性腰椎间盘突出症(lumbar disc herniation,LDH)组(20例)和慢性炎性脱髓鞘性多发性神经根神经病症(chronic inflammatory demyelinating polyradiculoneuropathy,CIDP)组(15例),分别应用两种腰骶丛神经根成像,评价图像质量参数信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和对比度(contrast ratio,CR),并验证正常对照组、CIDP组和LDH组测量神经根直径的一致性。结果两序列测得神经根直径的一致性较高(正常组r=0.95,CIDP组r=0.99,LDH组r=0.97,P<0.001),图像质量评价指标显示,3D SPACE STIR序列在SNR、CNR和CR三项指标中占优,3D MEDIC WE定性评估图像质量评分较高。两序列均能清晰显示正常腰骶丛神经根、病变所致的弥漫性形态增粗神经根以及间盘突出受挤压变形的神经根。结论3D MEDIC WE和3D SPACE STIR序列可应用于腰骶丛神经根成像,两序列对正常和异常形态、走行的神经根评估具备很高的可行性和重复性。综合考量临床图像的定性、定量评价,可择优选择恰当的序列为腰骶丛神经根成像提供影像支持。展开更多
目的探讨1.5T磁共振三维快速自旋回波成像(3D sampling perfection with application optimized contrasts by using different flip angle evolutions,3D-SPACE)与多回波融合成像(multiple echo data image combination,3D-MEDIC)序列...目的探讨1.5T磁共振三维快速自旋回波成像(3D sampling perfection with application optimized contrasts by using different flip angle evolutions,3D-SPACE)与多回波融合成像(multiple echo data image combination,3D-MEDIC)序列对正常人腰骶丛神经的显示。方法 31例无症状正常志愿者行MRI检查,包括常规腰椎MRI、3D-SPACE及MEDIC序列扫描,原始图像传入后处理工作站行多平面图像重组。结合原始及重组图像,比较3D-SPACE及MEDIC序列图像腰骶丛神经根的信噪比及对神经的显示评分。结果 3D-MEDIC序列显示神经根的信噪比高于3D-SPACE序列(分别为70.15±24.03及28.78±7.12,P=0.000)。3D-MEDIC序列对腰骶丛的显示评分高于3D-SPACE序列(P=0.000)。结论1.5T磁共振腰骶丛神经显像中,3D-MEDIC序列优于3D-SPACE序列,可清晰显示神经根走行,是常规腰椎MRI的重要补充。展开更多
文摘Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.
文摘Background: Rhinoplasty is a complex surgical procedure that requires critical analysis and precise design before surgery, making it a challenging operation for both the surgical team and medical educators. This study aimed to evaluate the impact of 3D design involvement on learning curves and to establish a more effective method for rhinoplasty education.Methods: Surgeons who participated in an educational program were divided into two groups. The experimental group was involved in the 3D design before the operation, and the control group was asked to review the rhinoplasty atlas. A self-assessment questionnaire was used to evaluate the learning curve of the eight rhinoplasty procedures for each surgeon, and the overall satisfaction rate data were also collected.Results: The self-assessment scores in both groups showed an increasing trend from the first to the eighth operation. The mean scores of the experimental group were significantly higher than those of the control group at the fifth operation(P=0.01). The satisfaction rate of the experimental group(91.7%) was higher than that of the control group(54.5%).Conclusion: The 3D imaging system can improve the learning curve and satisfaction rate of rhinoplasty education,proving that it is an easy and effective tool for medical education.
文摘This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.
文摘Medical models, or ＂phantoms,＂ have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification and validation, and medical devices development. Such new applications demand high-fidelity, patient-specific, tissue-mimicking medical phantoms that can not only closely emulate the geometric structures of human organs, but also possess the properties and functions of the organ structure. With the rapid advancement of three-dimensional （3D） printing and 3D bioprinting technologies, many researchers have explored the use of these additive manufacturing techniques to fabricate functional medical phantoms for various applications. This paper reviews the applications of these 3D printing and 3D bioprinting technologies for the fabrication of functional medical phantoms and bio-structures. This review specifically discusses the state of the art along with new developments and trends in 3D printed functional medical phantoms （i.e., tissue-mimicking medical phantoms, radiologically relevant medical phantoms, and physiological medical phantoms） and 3D bio-printed structures （i.e., hybrid scaffolding materials, convertible scaffolds, and integrated sensors） for regenerated tissues and organs.
基金the National Natural Science Foundation of China(No.81827804,U1909218)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(No.T2121004).
文摘Medical devices are instruments and other tools that act on the human body to aid clinical diagnosis and disease treatment,playing an indispensable role in modern medicine.Nowadays,the increasing demand for personalized medical devices poses a significant challenge to traditional manufacturing methods.The emerging manufacturing technology of three-dimensional(3D)printing as an alternative has shown exciting applications in the medical field and is an ideal method for manufacturing such personalized medical devices with complex structures.However,the application of this new technology has also brought new risks to medical devices,making 3D-printed devices face severe challenges due to insufficient regulation and the lack of standards to provide guidance to the industry.This review aims to summarize the current regulatory landscape and existing research on the standardization of 3D-printed medical devices in China,and provide ideas to address these challenges.We focus on the aspects concerned by the regulatory authorities in 3D-printed medical devices,highlighting the quality system of such devices,and discuss the guidelines that manufacturers should follow,as well as the current limitations and the feasible path of regulation and standardization work based on this perspective.The key points of the whole process quality control,performance evaluation methods and the concept of whole life cycle management of 3D-printed medical devices are emphasized.Furthermore,the significance of regulation and standardization is pointed out.Finally,aspects worthy of attention and future perspectives in this field are discussed.
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
文摘Different from reduction manufacturing and equal manufacturing, 3D printing is an additive manufacturing method, which transforms 3D model into 2D cross-section data to form entity layer by layer. This makes its processing not limited by complexity of the design model and number of the manufacturing products. It is very suitable for the medical field with high customization requirements. In fact, application of 3D printing technology in the medical field is particularly noticeable. In this paper, application and development </span><span style="font-family:Verdana;">of 3D printing technology are reviewed in medical model, rehabilitation equi</span><span style="font-family:Verdana;">pment, tissue engineering, medical hygiene materials, lab-on-chip. Its applications include medical education, surgical planning, prosthesis customization, tissue culture and biosensor manufacturing and so on. Its wide application is due to its digital model, which makes the whole manufacturing process easier to digitize, so it is more conductive to updating and customization of products via 3D printing.
基金supported by the National Key Research and Development Program of China under Grant No.2018YFE0206900the National Natural Science Foundation of China under Grant No.61871440 and CAAI‐Huawei Mind-Spore Open Fund.
文摘Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.
基金funded by the Ministry of Science and Higher Education of the Russian Federation as part of the World-Class Research Center Program:Advanced Digital Technologies(Contract No.075-15-2022-311,dated 20.04.2022).
文摘Background:Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes.The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case.Methods:We performed the prospective cohort study which included 29 children with congenital heart defects.The hearts and the great vessels were modeled and printed out.Measurements of the same cardiac areas were taken in the same planes and points at multislice computed tomography images(group 1)and on printed 3D models of the hearts(group 2).Pre-printing treatment of the multislice computed tomography data and 3D model preparation were performed according to a newly developed algorithm.Results:The measurements taken on the 3D-printed cardiac models and the tomographic images did not differ significantly,which allowed us to conclude that the models were highly accurate and informative.The new algorithm greatly simplifies and speeds up the preparation of a 3D model for printing,while maintaining high accuracy and level of detail.Conclusions:The 3D-printed models provide an accurate preoperative assessment of the anatomy of a defect in each case.The new algorithm has several important advantages over other available programs.They enable the development of customized preliminary plans for surgical repair of each specific complex congenital heart disease,predict possible issues,determine the optimal surgical tactics,and significantly improve surgical outcomes.
基金funded by the National Science Foundation of China,Nos.51674121 and 61702184the Returned Overseas Scholar Funding of Hebei Province,No.C2015005014the Hebei Key Laboratory of Science and Application,and Tangshan Innovation Team Project,No.18130209B.
文摘Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.
基金supported by the National Natural Science Foundation of China (Nos.52374078 and 52074043)the Fundamental Research Funds for the Central Universities (No.2023CDJKYJH021)。
文摘Fractal theory offers a powerful tool for the precise description and quantification of the complex pore structures in reservoir rocks,crucial for understanding the storage and migration characteristics of media within these rocks.Faced with the challenge of calculating the three-dimensional fractal dimensions of rock porosity,this study proposes an innovative computational process that directly calculates the three-dimensional fractal dimensions from a geometric perspective.By employing a composite denoising approach that integrates Fourier transform(FT)and wavelet transform(WT),coupled with multimodal pore extraction techniques such as threshold segmentation,top-hat transformation,and membrane enhancement,we successfully crafted accurate digital rock models.The improved box-counting method was then applied to analyze the voxel data of these digital rocks,accurately calculating the fractal dimensions of the rock pore distribution.Further numerical simulations of permeability experiments were conducted to explore the physical correlations between the rock pore fractal dimensions,porosity,and absolute permeability.The results reveal that rocks with higher fractal dimensions exhibit more complex pore connectivity pathways and a wider,more uneven pore distribution,suggesting that the ideal rock samples should possess lower fractal dimensions and higher effective porosity rates to achieve optimal fluid transmission properties.The methodology and conclusions of this study provide new tools and insights for the quantitative analysis of complex pores in rocks and contribute to the exploration of the fractal transport properties of media within rocks.
文摘Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.
基金PHD Site from Chinese Educational Department,Grant number:20040699015
文摘This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions: positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.
基金This research was supported by the National Nature Science Foundation of China under Grant No.60473024 the Nature Science Foundation of Zhejiang Province of China under Grant No.Y104341 and z105391.
文摘This paper proposes a practical algorithms of plane cutting, stereo clipping and arbitrary cutting for 3D surface model reconstructed from medical images. In plane cutting and stereo clipping algorithms, the 3D model is cut by plane or polyhedron. Lists of edge and vertex in every cut plane are established. From these lists the boundary contours are created and their relationship of embrace is ascertained. The region closed by the contours is triangulated using Delaunay triangulation algorithm. Arbitrary cutting operation creates cutting curve interactively. The cut model still maintains its correct topology structure. With these operations, tissues inside can be observed easily and it can aid doctors to diagnose. The methods can also be used in surgery planning of radiotherapy.
文摘目的验证三维多回波数据联合成像(three dimensional multi-echo data imagine combination with selective water excitation,3D MEDIC WE)和三维快速自旋回波成像(three dimensional sampling perfection with application optimized contrasts by using different flip angle evolution,3D SPACE STIR)序列在腰骶丛神经根成像中的可行性和重复性。方法将55例受试者分为腰椎无异常表现的正常对照组(20例)、单纯性腰椎间盘突出症(lumbar disc herniation,LDH)组(20例)和慢性炎性脱髓鞘性多发性神经根神经病症(chronic inflammatory demyelinating polyradiculoneuropathy,CIDP)组(15例),分别应用两种腰骶丛神经根成像,评价图像质量参数信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和对比度(contrast ratio,CR),并验证正常对照组、CIDP组和LDH组测量神经根直径的一致性。结果两序列测得神经根直径的一致性较高(正常组r=0.95,CIDP组r=0.99,LDH组r=0.97,P<0.001),图像质量评价指标显示,3D SPACE STIR序列在SNR、CNR和CR三项指标中占优,3D MEDIC WE定性评估图像质量评分较高。两序列均能清晰显示正常腰骶丛神经根、病变所致的弥漫性形态增粗神经根以及间盘突出受挤压变形的神经根。结论3D MEDIC WE和3D SPACE STIR序列可应用于腰骶丛神经根成像,两序列对正常和异常形态、走行的神经根评估具备很高的可行性和重复性。综合考量临床图像的定性、定量评价,可择优选择恰当的序列为腰骶丛神经根成像提供影像支持。
文摘目的探讨1.5T磁共振三维快速自旋回波成像(3D sampling perfection with application optimized contrasts by using different flip angle evolutions,3D-SPACE)与多回波融合成像(multiple echo data image combination,3D-MEDIC)序列对正常人腰骶丛神经的显示。方法 31例无症状正常志愿者行MRI检查,包括常规腰椎MRI、3D-SPACE及MEDIC序列扫描,原始图像传入后处理工作站行多平面图像重组。结合原始及重组图像,比较3D-SPACE及MEDIC序列图像腰骶丛神经根的信噪比及对神经的显示评分。结果 3D-MEDIC序列显示神经根的信噪比高于3D-SPACE序列(分别为70.15±24.03及28.78±7.12,P=0.000)。3D-MEDIC序列对腰骶丛的显示评分高于3D-SPACE序列(P=0.000)。结论1.5T磁共振腰骶丛神经显像中,3D-MEDIC序列优于3D-SPACE序列,可清晰显示神经根走行,是常规腰椎MRI的重要补充。
