Pub Date : 2019-07-24DOI: 10.1080/24699322.2018.1557891
Jinao Zhang, J. Hills, Y. Zhong, B. Shirinzadeh, Julian Smith, Chengfan Gu
Abstract Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes’ bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.
{"title":"Modeling of soft tissue thermal damage based on GPU acceleration","authors":"Jinao Zhang, J. Hills, Y. Zhong, B. Shirinzadeh, Julian Smith, Chengfan Gu","doi":"10.1080/24699322.2018.1557891","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557891","url":null,"abstract":"Abstract Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes’ bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"12 - 5"},"PeriodicalIF":2.1,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1557891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43835146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1080/24699322.2018.1557906
Fang Zhang,Yue Wu,Zhitao Xiao,Lei Geng,Jun Wu,Jia Wen,Wen Wang,Ping Liu
To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.
{"title":"Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity.","authors":"Fang Zhang,Yue Wu,Zhitao Xiao,Lei Geng,Jun Wu,Jia Wen,Wen Wang,Ping Liu","doi":"10.1080/24699322.2018.1557906","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557906","url":null,"abstract":"To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"21 2","pages":"1-8"},"PeriodicalIF":2.1,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDMinimally invasive procedures are rapidly growing in popularity thanks to advancements in medical robots, visual navigation and space registration techniques. This paper presents a precise and efficient targeting method for robot-assisted percutaneous needle placement under C-arm fluoroscopy.METHODA special end-effector was constructed to perform fluoroscopy calibration and robot to image-space registration simultaneously and automatically. In addition, formulations were given to compute the movement of robot targeting and evaluate targeting accuracy using only one X-ray image.RESULTA pre-clinical experiment showed that the maximum angle error was 0.94° and the maximum position error of a target located 80 mm below the end-effector was 1.31 mm. And evaluation of the system in a robot-assisted pedicle screws placement surgery has justified the accuracy and reliability of proposed method in clinical applications.CONCLUSIONThe positioning accuracy was increased by using present method. Moreover, radiation exposure and operation time were reduced significantly compared to other commonly used methods.
{"title":"A targeting method for robot-assisted percutaneous needle placement under fluoroscopy guidance.","authors":"Zhonghao Han,Keyi Yu,Lei Hu,Weishi Li,Huilin Yang,Minfeng Gan,Na Guo,Biao Yang,Hongsheng Liu,Yuhan Wang","doi":"10.1080/24699322.2018.1560085","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560085","url":null,"abstract":"BACKGROUNDMinimally invasive procedures are rapidly growing in popularity thanks to advancements in medical robots, visual navigation and space registration techniques. This paper presents a precise and efficient targeting method for robot-assisted percutaneous needle placement under C-arm fluoroscopy.METHODA special end-effector was constructed to perform fluoroscopy calibration and robot to image-space registration simultaneously and automatically. In addition, formulations were given to compute the movement of robot targeting and evaluate targeting accuracy using only one X-ray image.RESULTA pre-clinical experiment showed that the maximum angle error was 0.94° and the maximum position error of a target located 80 mm below the end-effector was 1.31 mm. And evaluation of the system in a robot-assisted pedicle screws placement surgery has justified the accuracy and reliability of proposed method in clinical applications.CONCLUSIONThe positioning accuracy was increased by using present method. Moreover, radiation exposure and operation time were reduced significantly compared to other commonly used methods.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"22 1","pages":"1-9"},"PeriodicalIF":2.1,"publicationDate":"2019-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-28DOI: 10.1080/24699322.2018.1560098
Zijian Wang,Fei Liu,Yaoru Sun,Jie Li,Fang Wang,Zheng Lu
Neural substrates of action to the object or this specific direct route, however, remain unclear, especially for the connection from the visual pathway to the motor cortex. The study examined this issue by conducting an fMRI experiment, in which two action generation tasks involving pictures of real objects (PA) and the object's nouns (NA) were used, with pictures naming (PN) and covert noun reading (NR) being the control tasks. The result showed that the model predefined for the PCC and precuneus connecting IPL to the posterior-medial frontal cortex dominated over the others (with 0.45 probability), suggesting that the PCC and the precuneus locate at the neural substrates of action to the object. Furthermore, a feasibility study suggests that the neural pathway composed of the V3/MT, precuneus, PCC, and PM (premotor cortex) forms the direct route from perception to action, which also links to the dorsal pathway so that the perception of objects bypasses the semantic ventral pathway and then directly cues actions via the affordance.
{"title":"The role of the precuneus and posterior cingulate cortex in the neural routes to action.","authors":"Zijian Wang,Fei Liu,Yaoru Sun,Jie Li,Fang Wang,Zheng Lu","doi":"10.1080/24699322.2018.1560098","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560098","url":null,"abstract":"Neural substrates of action to the object or this specific direct route, however, remain unclear, especially for the connection from the visual pathway to the motor cortex. The study examined this issue by conducting an fMRI experiment, in which two action generation tasks involving pictures of real objects (PA) and the object's nouns (NA) were used, with pictures naming (PN) and covert noun reading (NR) being the control tasks. The result showed that the model predefined for the PCC and precuneus connecting IPL to the posterior-medial frontal cortex dominated over the others (with 0.45 probability), suggesting that the PCC and the precuneus locate at the neural substrates of action to the object. Furthermore, a feasibility study suggests that the neural pathway composed of the V3/MT, precuneus, PCC, and PM (premotor cortex) forms the direct route from perception to action, which also links to the dorsal pathway so that the perception of objects bypasses the semantic ventral pathway and then directly cues actions via the affordance.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"2 1","pages":"1-8"},"PeriodicalIF":2.1,"publicationDate":"2019-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-24DOI: 10.1080/24699322.2018.1557890
Ji Zhang,Jun Qian,Tao Yang,Hai-Yan Dong,Rui-Juan Wang
Simple fractal dimensions have been proposed for use in the analysis of the characteristics of digitized tongue pictures and tongue coating texture, which could further the establishment of objectified classification criteria under the conditions of expanding sample size. However, detailed descriptions on simple fractal dimensions have been limited. Therefore, BP (back propagation) neural network model classifiers could be designed by further calculation of the multiple fractal spectrum characteristics of digitized tongue pictures in order to classify and recognize the thin/thick or greasy characteristics of tongue coating.The fractal dimensions of sample data of 587 digitized tongue pictures were collected in a standard environment. A statistical analysis was conducted on the calculation results of the sample data, and the sensitivity of the fractal dimensions to the thin/thick and greasy characteristics of digitized tongue pictures was observed. As the overlap region resulted from a range of values of a single parameter, another eight characteristic parameters of the multiple fractal spectra of the digitized tongue pictures were further proposed as the elements in the input layer of the three-layers BP neural network. Automatic recognition classifiers were designed and trained for the characteristics of digitized tongue pictures and tongue coating textures.The simple fractal dimension was sensitive to the thin/thick and greasy characteristics of digitized tongue pictures and could better judge the characteristics of the thickness of the tongue coating. A classifier with characteristic parameters of multiple fractal spectra as the input vectors identified by the BP neural network models could effectively increase the accuracy rate judged by the characteristics of the tongue coating texture.
{"title":"Analysis and recognition of characteristics of digitized tongue pictures and tongue coating texture based on fractal theory in traditional Chinese medicine.","authors":"Ji Zhang,Jun Qian,Tao Yang,Hai-Yan Dong,Rui-Juan Wang","doi":"10.1080/24699322.2018.1557890","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557890","url":null,"abstract":"Simple fractal dimensions have been proposed for use in the analysis of the characteristics of digitized tongue pictures and tongue coating texture, which could further the establishment of objectified classification criteria under the conditions of expanding sample size. However, detailed descriptions on simple fractal dimensions have been limited. Therefore, BP (back propagation) neural network model classifiers could be designed by further calculation of the multiple fractal spectrum characteristics of digitized tongue pictures in order to classify and recognize the thin/thick or greasy characteristics of tongue coating.The fractal dimensions of sample data of 587 digitized tongue pictures were collected in a standard environment. A statistical analysis was conducted on the calculation results of the sample data, and the sensitivity of the fractal dimensions to the thin/thick and greasy characteristics of digitized tongue pictures was observed. As the overlap region resulted from a range of values of a single parameter, another eight characteristic parameters of the multiple fractal spectra of the digitized tongue pictures were further proposed as the elements in the input layer of the three-layers BP neural network. Automatic recognition classifiers were designed and trained for the characteristics of digitized tongue pictures and tongue coating textures.The simple fractal dimension was sensitive to the thin/thick and greasy characteristics of digitized tongue pictures and could better judge the characteristics of the thickness of the tongue coating. A classifier with characteristic parameters of multiple fractal spectra as the input vectors identified by the BP neural network models could effectively increase the accuracy rate judged by the characteristics of the tongue coating texture.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"207 1","pages":"1-11"},"PeriodicalIF":2.1,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24699322.2018.1560095
Xin Liu, Yan Zhang, Dan Liu, Qisong Wang, Ou Bai, Jinwei Sun, P. Rolfe
Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.
{"title":"Physiological interference reduction for near infrared spectroscopy brain activity measurement based on recursive least squares adaptive filtering and least squares support vector machines.","authors":"Xin Liu, Yan Zhang, Dan Liu, Qisong Wang, Ou Bai, Jinwei Sun, P. Rolfe","doi":"10.1080/24699322.2018.1560095","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560095","url":null,"abstract":"Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"1 1","pages":"1-7"},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1560095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60126834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study aimed to compare the accuracy of navigation-assisted percutaneous pedicle screw insertions between traditional posterior superior iliac spine (PSIS) fixed and cutaneously fixed dynamic reference frame (DRF) in minimally invasive surgery of transforaminal lumbar interbody fusion (MIS TLIF). This is a prospective randomized clinical study. Between May 2016 and Nov 2017, 100 patients who underwent MIS TLIF were randomly divided into bone fixed group (with PSIS fixed DRF) and skin fixed group (with cutaneously fixed DRF). The pedicel screws were inserted under navigational guidance using computed tomography (CT) data acquired intraoperatively with a Ziehm 3-dimensional fluoroscopy-based navigation system. Screw positions were immediately checked by a final intraoperative scan. The accuracy of screw placement was evaluated by a sophisticated computed tomography protocol. Both groups had similar patient demographics. Totally Five-hundred Twelve pedicle screws were placed in the lumbar spine. There were 2 moderate (2–4 mm) pedicle perforations in each group. The accuracy showed no significant difference between bone fixed and skin fixed DRF. There were no significant procedure-related complications. The skin fixed DRF provides similar accuracy in pedicle screw insertions with bone fixed DRF using intraoperative 3D image guided navigation in MIS TLIF. Skin fixed DRF not only serves as an alternative method but also saves a separate incision wound for bony attachment.
{"title":"Is bony attachment necessary for dynamic reference frame in navigation-assisted minimally invasive lumbar spine fusion surgery?","authors":"Hsi-Hsien Lin, Yueh-Hsiu Lu, Po-Hsin Chou, Ming-Chau Chang, Shih-Tien Wang, Chien-Lin Liu","doi":"10.1080/24699322.2018.1542028","DOIUrl":"https://doi.org/10.1080/24699322.2018.1542028","url":null,"abstract":"Abstract This study aimed to compare the accuracy of navigation-assisted percutaneous pedicle screw insertions between traditional posterior superior iliac spine (PSIS) fixed and cutaneously fixed dynamic reference frame (DRF) in minimally invasive surgery of transforaminal lumbar interbody fusion (MIS TLIF). This is a prospective randomized clinical study. Between May 2016 and Nov 2017, 100 patients who underwent MIS TLIF were randomly divided into bone fixed group (with PSIS fixed DRF) and skin fixed group (with cutaneously fixed DRF). The pedicel screws were inserted under navigational guidance using computed tomography (CT) data acquired intraoperatively with a Ziehm 3-dimensional fluoroscopy-based navigation system. Screw positions were immediately checked by a final intraoperative scan. The accuracy of screw placement was evaluated by a sophisticated computed tomography protocol. Both groups had similar patient demographics. Totally Five-hundred Twelve pedicle screws were placed in the lumbar spine. There were 2 moderate (2–4 mm) pedicle perforations in each group. The accuracy showed no significant difference between bone fixed and skin fixed DRF. There were no significant procedure-related complications. The skin fixed DRF provides similar accuracy in pedicle screw insertions with bone fixed DRF using intraoperative 3D image guided navigation in MIS TLIF. Skin fixed DRF not only serves as an alternative method but also saves a separate incision wound for bony attachment.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"12 - 7"},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1542028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47149134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24699322.2018.1560090
Yan Zhang, Xin Liu, Qisong Wang, Dan Liu, Chunling Yang, Jinwei Sun, P. Rolfe
Continuous wave near-infrared spectroscopy (CW-NIRS) can be used to measure cerebral activity because it is noninvasive, simple and portable. However, the performance of the CW-NIRS is distorted by the presence of extracerebral layer. Change of optical parameters in gray matter layer will then be inappropriately converted into the brain activity response. In the current study, a five-layer structure model constitute of scalp, skull, cerebrospinal fluid, gray matter and white matter is adopted and the mixture of the Intralipid, India ink and agar is applied to fabricate human brain tissue. To simulate optical properties in deep layer due to the brain activity, the absorption coefficients of gray matter are increased by 5%, 10%, 15%, 20%, and 25% relative to the baseline. The NIRS measurement system was designed to detect the changes in the absorption coefficients of the gray matter and quantitatively analyse the influence of the extracerebral layers. Monte Carlo technique is performed to compensate partial volume effect (PVE) introduced by the extracerebral layers. The results of the in-vitro experiments show that the measured absorption coefficients are about 9% of the standard value and the relative error is about 91% due to the extracerebral layers. The influence of the extracerebral layers is suppressed by correcting PVE with Monte Carlo simulations and the average relative error is improved to only about 6% for the whole data set. Therefore, the measurement and analysis of the brain activity could be further strengthened if the anatomic structure of the head could be predicted with Monte Carlo method or other technologies.
{"title":"Influence of extracerebral layers on estimates of optical properties with continuous wave near infrared spectroscopy: analysis based on multi-layered brain tissue architecture and Monte Carlo simulation.","authors":"Yan Zhang, Xin Liu, Qisong Wang, Dan Liu, Chunling Yang, Jinwei Sun, P. Rolfe","doi":"10.1080/24699322.2018.1560090","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560090","url":null,"abstract":"Continuous wave near-infrared spectroscopy (CW-NIRS) can be used to measure cerebral activity because it is noninvasive, simple and portable. However, the performance of the CW-NIRS is distorted by the presence of extracerebral layer. Change of optical parameters in gray matter layer will then be inappropriately converted into the brain activity response. In the current study, a five-layer structure model constitute of scalp, skull, cerebrospinal fluid, gray matter and white matter is adopted and the mixture of the Intralipid, India ink and agar is applied to fabricate human brain tissue. To simulate optical properties in deep layer due to the brain activity, the absorption coefficients of gray matter are increased by 5%, 10%, 15%, 20%, and 25% relative to the baseline. The NIRS measurement system was designed to detect the changes in the absorption coefficients of the gray matter and quantitatively analyse the influence of the extracerebral layers. Monte Carlo technique is performed to compensate partial volume effect (PVE) introduced by the extracerebral layers. The results of the in-vitro experiments show that the measured absorption coefficients are about 9% of the standard value and the relative error is about 91% due to the extracerebral layers. The influence of the extracerebral layers is suppressed by correcting PVE with Monte Carlo simulations and the average relative error is improved to only about 6% for the whole data set. Therefore, the measurement and analysis of the brain activity could be further strengthened if the anatomic structure of the head could be predicted with Monte Carlo method or other technologies.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"1 1","pages":"1-7"},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1560090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60126824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24699322.2018.1560084
Baoliang Zhao, C. Nelson
The existing surgical robots for laparoscopic surgery offer no or limited force feedback, and there are many problems for the traditional sensor-based solutions. This paper builds a teleoperation surgical system and validates the effectiveness of sensorless force feedback. The tool-tissue interaction force at the surgical grasper tip is estimated using the driving motor's current, and fed back to the master robot with a position-force bilateral control algorithm. The stiffness differentiation experiment and tumor detection experiment were conducted. In the stiffness differentiation experiment, 43 out of 45 pairs of ranking relationships were identified correctly, yielding a success rate of 96%. In the tumor detection experiment, 4 out of 5 participants identified the correct tumor location with force feedback, yielding a success rate of 80%. The proposed sensorless force-feedback system for robot-assisted laparoscopic surgery can help surgeons regain tactile information and distinguish between the healthy and cancerous tissue.
{"title":"A sensorless force-feedback system for robot-assisted laparoscopic surgery.","authors":"Baoliang Zhao, C. Nelson","doi":"10.1080/24699322.2018.1560084","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560084","url":null,"abstract":"The existing surgical robots for laparoscopic surgery offer no or limited force feedback, and there are many problems for the traditional sensor-based solutions. This paper builds a teleoperation surgical system and validates the effectiveness of sensorless force feedback. The tool-tissue interaction force at the surgical grasper tip is estimated using the driving motor's current, and fed back to the master robot with a position-force bilateral control algorithm. The stiffness differentiation experiment and tumor detection experiment were conducted. In the stiffness differentiation experiment, 43 out of 45 pairs of ranking relationships were identified correctly, yielding a success rate of 96%. In the tumor detection experiment, 4 out of 5 participants identified the correct tumor location with force feedback, yielding a success rate of 80%. The proposed sensorless force-feedback system for robot-assisted laparoscopic surgery can help surgeons regain tactile information and distinguish between the healthy and cancerous tissue.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"1 1","pages":"1-10"},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1560084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60126790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1080/24699322.2018.1560082
Ke Xu, Zhiyong Chen, F. Jia
Minimally invasive surgery (MIS) is characterized by less trauma, shorter recovery time, and lower postoperative infection rate. The two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting precise and complex surgical operations. Three-dimensional (3D) laparoscopic imaging provides surgeons depth perception. This study aims to 3D reconstruction of the surgical scene based on the disparity map generated by the depth estimation algorithm. An unsupervised learning autoencoder method was proposed to calculate the accurate disparity with a 101-layer residual convolutional network. The loss function included three parts: left-right consistency loss, structure similarity loss, and reconstruction error loss, the combination can improve reconstruction accuracy and robustness. The method was validated on a Hamlyn Center Laparoscopic/Endoscopic Video Dataset. The structural similarity index (SSIM) is 0.8349 ± 0.0523 and the peak signal-to-noise ratio (PSNR) is 14.4957 ± 1.9676. The depth prediction network has high accuracy and robustness. The average time to produce each disparity map is about 16 ms. The experimental result shows that the proposed depth estimation method can offer dense disparity map, and can meet surgical real-time requirement. Future work will focus on network structure optimization and loss function design, transfer learning to improve the robustness and accuracy further.
{"title":"Unsupervised binocular depth prediction network for laparoscopic surgery.","authors":"Ke Xu, Zhiyong Chen, F. Jia","doi":"10.1080/24699322.2018.1560082","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560082","url":null,"abstract":"Minimally invasive surgery (MIS) is characterized by less trauma, shorter recovery time, and lower postoperative infection rate. The two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting precise and complex surgical operations. Three-dimensional (3D) laparoscopic imaging provides surgeons depth perception. This study aims to 3D reconstruction of the surgical scene based on the disparity map generated by the depth estimation algorithm. An unsupervised learning autoencoder method was proposed to calculate the accurate disparity with a 101-layer residual convolutional network. The loss function included three parts: left-right consistency loss, structure similarity loss, and reconstruction error loss, the combination can improve reconstruction accuracy and robustness. The method was validated on a Hamlyn Center Laparoscopic/Endoscopic Video Dataset. The structural similarity index (SSIM) is 0.8349 ± 0.0523 and the peak signal-to-noise ratio (PSNR) is 14.4957 ± 1.9676. The depth prediction network has high accuracy and robustness. The average time to produce each disparity map is about 16 ms. The experimental result shows that the proposed depth estimation method can offer dense disparity map, and can meet surgical real-time requirement. Future work will focus on network structure optimization and loss function design, transfer learning to improve the robustness and accuracy further.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"1 1","pages":"1-7"},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60126723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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