Medical Imaging: Image-Guided Procedures最新文献

Assessment of proton beam ablation in myocardial infarct tissue using delayed contrast-enhanced magnetic resonance imaging (Erratum) 利用延迟对比增强磁共振成像评估心肌梗死组织中的质子束消融（勘误表）

Medical Imaging: Image-Guided Procedures Pub Date : 2020-04-28 DOI: 10.1117/12.2572836

M. Rettmann, S. Hohmann, A. Deisher, H. Konishi, J. J. Kruse, L. Newman, K. D. Parker, M. G. Herman, D. Packer

引用次数: 0

Intraoperative thermographic perfusion mapping in neurosurgery using regularized semiparametric regression (Conference Presentation) 应用正则化半参数回归的神经外科术中热成像灌注定位(会议报告)

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-17 DOI: 10.1117/12.2549641

Juliane Müller, Nico Hoffmann, M. Oelschlägel, C. Schnabel, G. Steiner, E. Koch, S. Sobottka, G. Schackert, M. Kirsch

引用次数: 0

Comparison of head pose tracking methods for mixed-reality neuronavigation for transcranial magnetic stimulation 经颅磁刺激下混合现实神经导航头部姿态跟踪方法的比较

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2547917

Supriya Sathyanarayana, Christoph Leuze, B. Hargreaves, B. Daniel, Gordon Wetzstein, A. Etkin, M. Bhati, J. McNab

引用次数: 8

A personalized approach for microwave ablation treatment planning fusing radiomics and bioheat transfer modeling 融合放射组学和生物传热模型的个性化微波消融治疗计划方法

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2549790

Pinyo Taeprasartsit, Chanok Pathompatai, Kasidit Jusomjai, H. Wibowo, J. Sebek, P. Prakash

引用次数: 3

Automated segmentation of computed tomography colonography images using a 3D U-Net 使用3D U-Net的计算机断层扫描结肠镜图像的自动分割

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2549749

Keira L. Barr, J. Laframboise, T. Ungi, L. Hookey, G. Fichtinger

{"title":"Automated segmentation of computed tomography colonography images using a 3D U-Net","authors":"Keira L. Barr, J. Laframboise, T. Ungi, L. Hookey, G. Fichtinger","doi":"10.1117/12.2549749","DOIUrl":"https://doi.org/10.1117/12.2549749","url":null,"abstract":"PURPOSE: The segmentation of Computed Tomography (CT) colonography images is important to both colorectal research and diagnosis. This process often relies on manual interaction, and therefore depends on the user. Consequently, there is unavoidable interrater variability. An accurate method which eliminates this variability would be preferable. Current barriers to automated segmentation include discontinuities of the colon, liquid pooling, and that all air will appear the same intensity on the scan. This study proposes an automated approach to segmentation which employs a 3D implementation of U-Net. METHODS: This research is conducted on 76 CT scans. The U-Net comprises an analysis and synthesis path, both with 7 convolutional layers. By nature of the U-Net, output segmentation resolution matches the input resolution of the CT volumes. K-fold cross-validation is applied to ensure no evaluative bias, and accuracy is assessed by the Sorensen-Dice coefficient. Binary cross-entropy is employed as a loss metric. RESULTS: Average network accuracy is 98.81%, with maximum and minimum accuracies of 99.48% and 97.03% respectively. Standard deviation of K accuracies is 0.5%. CONCLUSION: The network performs with considerable accuracy, and can reliably distinguish between colon, small intestine, lungs, and ambient air. A low standard deviation is indicative of high consistency. This method for automatic segmentation could prove a supplemental or alternative tool for threshold-based segmentation. Future studies will include an expanded dataset and a further optimized network.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MRI-compatible needle guidance toolkit to streamline arthrography procedures: phantom accuracy study mri兼容的针引导工具包，以简化关节造影程序:幻影准确性研究

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2550654

R. Monfaredi, P. Yarmolenko, Eung-Joo Lee, Viktoriya Beskin, K. Cleary, Karun Sharma

引用次数: 3

Patient-specific, dynamic models of hypoplastic left heart syndrome tricuspid valves for simulation and planning 患者特异性，左心发育不全综合征三尖瓣模拟和规划的动态模型

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2549745

N. Boone, Hannah H. Nam, John Moore, Patrick Carnahan, Olivia K. Ginty, Christian Herz, A. Lasso, M. Jolley, E. Chen, T. Peters

{"title":"Patient-specific, dynamic models of hypoplastic left heart syndrome tricuspid valves for simulation and planning","authors":"N. Boone, Hannah H. Nam, John Moore, Patrick Carnahan, Olivia K. Ginty, Christian Herz, A. Lasso, M. Jolley, E. Chen, T. Peters","doi":"10.1117/12.2549745","DOIUrl":"https://doi.org/10.1117/12.2549745","url":null,"abstract":"Physical replicas of patient specific heart valve pathologies may improve clinicians’ ability to plan the optimal treatment for patients with complex valvular heart disease. Our previous work has demonstrated the ability to replicate patient pathology of the adult mitral valve (MV) in a dynamic environment [13]. Infant congenital heart defects present possibly the most challenging form of valvular disease, given the range of pathologies, the relative size of these valves compared to adult anatomy, and the rarity of congenital heart disease. Patient specific valve models could be particularly valuable for pediatric cardiologists and surgeons, as a means to both plan for and practice interventions. Our current goal is to assess our ability to apply our workflow to the more challenging case of the tricuspid valve (TV) presented in cases of hypoplastic left heart syndrome (HLHS). We explore the feasibility of adapting our previous workflow for creating dynamic silicone MV models for pre-surgical planning and simulation training, to developing 3D echocardiogram derived, patient specific TV models for use in a physical heart simulator. These models are intended for characterization of the TV, and exploration of the relationship between specific anatomical features and tricuspid regurgitation (TR) severity. The simulations may be relevant to pre-surgical planning of repair of the particularly complex and unique anatomical pathologies presented in children with HLHS.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128447100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Flexible piezoelectric sensor for real-time image-guided colonoscopies: a solution to endoscopic looping challenges in clinic 用于实时图像引导结肠镜检查的柔性压电传感器:解决临床内镜回路挑战的解决方案

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2548873

Adam L. Kenet, Eashwar Mahadevan, S. Elangovan, Justin Yan, Kamran Siddiq, Simon Liu, Amrita Ladwa, R. Narayanan, J. Dakkak, T. Benassi, K. Ng, A. Manbachi

引用次数: 4

Robotic tissue scanning with biophotonic probe 机器人组织扫描与生物光子探针

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2549635

Lauren Yates, Laura Connolly, A. Jamzad, Mark Asselin, Rachel Rubino, S. Yam, T. Ungi, A. Lasso, C. Nicol, P. Mousavi, G. Fichtinger

{"title":"Robotic tissue scanning with biophotonic probe","authors":"Lauren Yates, Laura Connolly, A. Jamzad, Mark Asselin, Rachel Rubino, S. Yam, T. Ungi, A. Lasso, C. Nicol, P. Mousavi, G. Fichtinger","doi":"10.1117/12.2549635","DOIUrl":"https://doi.org/10.1117/12.2549635","url":null,"abstract":"PURPOSE: Raman spectroscopy is an optical imaging technique used to characterize tissue via molecular analysis. The use of Raman spectroscopy for real-time intraoperative tissue classification requires fast analysis with minimal human intervention. In order to have accurate predictions and classifications, a large and reliable database of tissue classifications with spectra results is required. We have developed a system that can be used to generate an efficient scanning path for robotic scanning of tissues using Raman spectroscopy. METHODS: A camera mounted to a robotic controller is used to take an image of a tissue slide. The corners of the tissue slides within the sample image are identified, and the size of the slide is calculated. The image is cropped to fit the size of the slide and the image is manipulated to identify the tissue contour. A grid set to fit around the size of the tissue is calculated and a grid scanning pattern is generated. A masked image of the tissue contour is used to create a scanning pattern containing only the tissue. The tissue scanning pattern points are transformed to the robot controller coordinate system and used for robotic tissue scanning. The pattern is validated using spectroscopic scans of the tissue sample. The run time of the tissue scan pattern is compared to a region of interest scanning pattern encapsulating the tissue using the robotic controller. RESULTS: The average scanning time for the tissue scanning pattern compared to region of interest scanning reduced by 4 minutes and 58 seconds. CONCLUSION: This method reduced the number of points used for automated robotic scanning, and can be used to reduce scanning time and unusable data points to improve data collection efficiency.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126689403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions 联合放射组学和囊肿液炎症标志物模型预测胰腺囊性病变术前风险

Medical Imaging: Image-Guided Procedures Pub Date : 2020-03-16 DOI: 10.1117/12.2566425

T. Williams, K. Harrington, Sharon A. Lawrence, Jayasree Chakraborty, M. A. Efishat, M. Attiyeh, G. Askan, Yuting Chou, A. Pulvirenti, C. McIntyre, M. Gonen, O. Basturk, V. Balachandran, T. Kingham, M. D'Angelica, W. Jarnagin, J. Drebin, R. Do, P. Allen, Amber L. Simpson

{"title":"A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions","authors":"T. Williams, K. Harrington, Sharon A. Lawrence, Jayasree Chakraborty, M. A. Efishat, M. Attiyeh, G. Askan, Yuting Chou, A. Pulvirenti, C. McIntyre, M. Gonen, O. Basturk, V. Balachandran, T. Kingham, M. D'Angelica, W. Jarnagin, J. Drebin, R. Do, P. Allen, Amber L. Simpson","doi":"10.1117/12.2566425","DOIUrl":"https://doi.org/10.1117/12.2566425","url":null,"abstract":"This paper contributes to the burgeoning field of surgical data science. Specifically, multi-modal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. We extracted radiomic features from CT scans and combined this with cyst-fluid markers. The cyst fluid model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Radiomic analysis of routinely acquired CT scans combined with cyst fluid inflammatory markers provides accurate prediction of risk of pancreatic cancer progression.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126603158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1