Medical Imaging: Image-Guided Procedures最新文献

{"title":"Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers","authors":"Siddhartha Nanda, J. Antunes, K. Bera, Justin T. Brady, K. Friedman, J. Willis, R. Paspulati, S. Viswanath","doi":"10.1117/12.2552175","DOIUrl":"https://doi.org/10.1117/12.2552175","url":null,"abstract":"Dynamic contrast-enhanced (DCE) MRI is increasingly used to stage and evaluate rectal cancer extent in vivo in order to plan and target interventions for locally advanced tumors. The major clinical challenge faced with rectal cancers today is to personalize interventions through early identification of patients will benefit from neoadjuvant chemoradiation (nCRT) alone and who will benefit from aggressive surgery (with adjuvant radiation) instead; via baseline imaging. In this study, we evaluated texture kinetic features of rectal tumors using baseline DCE MRI scans, in order to predict pathologic tumor stage regression in response to nCRT. Our texture kinetics approach utilized a combination of texture features (from multiple DCE uptake phases) and polynomial curve fitting to uniquely quantify spatiotemporal patterns of lesion texture during contrast uptake and diffusion that were different between responders and non-responders to nCRT. We utilized a cohort of 48 rectal cancer patients for whom pre-nCRT 3 T DCE MRI was available, including pre-, early-, and delayed-enhancement phases. All DCE MRI phases were processed for motion and spatial alignment artifacts via rigid co-registration, and the tumor ROI on all 3 contrast phases was normalized with respect to non-enhancing muscle. 191 texture features were extracted from each of 3 contrast phases separately, following which each feature was plotted with respect to time to yield a feature enhancement curve. Polynomial fitting was applied to each feature enhancement curve to result in a vector of coefficients which was considered the texture kinetic representation of that feature. All 191 features were evaluated in terms of their texture kinetic representation as well as the raw feature enhancement, for predicting pathologically regressed tumor stages (ypT0-2) from non-regressed tumors (ypT3-4) via a cross-validated QDA classifier. Texture kinetics of gradient XY enhancement yielded the best overall AUC=0:762±0:053, which was significantly higher than any feature enhancement representation (best AUC=0:696±0:050). Texture kinetic representations also outperformed their corresponding raw feature enhancement representations in 54.5% of the features compared, and performed significantly worse in only 13% of the comparisons. Non-invasive guidance of interventions in rectal cancers could therefore be enhanced through the use of texture kinetic features from DCE MRI, which may better characterize spatiotemporal differences between responders and non-responders on baseline imaging.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"18 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":"123957773","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}

{"title":"Value based decision support to prioritize development of innovative technologies for image-guided vascular surgery in the hybrid operating theater","authors":"Friso G. Heslinga, H. Koffijberg, R. Geelkerken, R. Meerwaldt, T. G. T. Mors, C. Doggen, M. Hummel","doi":"10.1117/12.2549486","DOIUrl":"https://doi.org/10.1117/12.2549486","url":null,"abstract":"Innovative technologies for minimally invasive interventions have the potential to add value to vascular procedures in the hybrid operating theater (HOT). Restricted budgets require prioritization of the development of these technologies. We aim to provide vascular surgeons with a structured methodology to incorporate possibly conflicting criteria in prioritizing the development of new technologies. We propose a multi-criteria decision analysis framework to evaluate the value of innovative technologies for the HOT based on the MACBETH methodology. The framework is applied to a specific case: The new HOT in a large teaching hospital. Three upcoming innovations are scored for three different endovascular procedures. Two vascular surgeons scored the expected performance of these innovations for each of the procedures on six performance criteria and weighed the importance of these criteria. The overall value of the innovations was calculated as the weighted average of the performance scores. On a scale from 0-100 describing the overall value, the current HOT scored halfway the scale (49.9). A wound perfusion measurement tool scored highest (69.1) of the three innovations, mainly due to the relatively high score for crural revascularization procedures (72). The novel framework could be used to determine the relative value of innovative technologies for the HOT. When development costs are assumed to be similar, and a single budget holder decides on technology development, priority should be given to the development of a wound perfusion measurement tool.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"21 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":"130012626","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}

{"title":"Multi-slot extended view imaging on the O-Arm: image quality and application to intraoperative assessment of spinal morphology","authors":"Xiaoxuan Zhang, A. Uneri, P. Wu, M. Ketcha, Sophia A. Doerr, Craig K. Jones, P. Helm, J. Siewerdsen","doi":"10.1117/12.2549710","DOIUrl":"https://doi.org/10.1117/12.2549710","url":null,"abstract":"","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"202 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":"115963086","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}

{"title":"A mechatronic guidance system for positron emission mammography and ultrasound-guided breast biopsy","authors":"Claire Park, J. Bax, L. Gardi, A. Fenster","doi":"10.1117/12.2549731","DOIUrl":"https://doi.org/10.1117/12.2549731","url":null,"abstract":"Introduction: Image-guided biopsy is crucial for diagnosis and treatment planning for women with breast cancer. Positron emission mammography (PEM) is an emerging breast-specific functional imaging method, demonstrating high sensitivity and diagnostic accuracy compared to conventional breast imaging modalities. PEM shows potential to improve tumour detection for image-guided breast biopsy, but anatomical reference and visualization for needle guidance is not available. These limitations can be overcome by combining PEM with an established ultrasound (US) guided method to improve image-guided biopsy. Therefore, we aim to develop a mechatronic image-guidance system for combined PEM and US-guided breast biopsy. The specific aim of this work is to develop the mechatronic system and evaluate its needle-tracking accuracy for image-guided breast biopsy.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"120 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":"130399193","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}

{"title":"Ultrasound image simulation with generative adversarial network","authors":"Grace Pigeau, Lydia Elbatarny, V. Wu, Abigael Schonewille, G. Fichtinger, T. Ungi","doi":"10.1117/12.2549592","DOIUrl":"https://doi.org/10.1117/12.2549592","url":null,"abstract":"PURPOSE: It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network. METHODS: To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented with ten simulated images not included in the first part of the survey and asked to evaluate the realism of the images. RESULTS: The average number of correctly identified images was 28 of 50 (56%). On a scale of 1-5, where 5 is indistinguishable from real US, the generated images received an average score of 3.75 for realistic anatomy and 4.0 for realistic ultrasound effects. CONCLUSIONS: We evaluated the realism of kidney ultrasound images generated using adversarial networks. Generative adversarial networks appear to be a promising method of simulating realistic ultrasound images from crosssectional anatomical label-maps.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"56 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":"124426478","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}

{"title":"Rigid and deformable corrections in real-time using deep learning for prostate fusion biopsy","authors":"Adit Bhardwaj, Jun-Sung Park, Soumik Mukhopadhyay, Sikander Sharda, Yuri Son, B. Ajani, S. Kudavelly","doi":"10.1117/12.2548589","DOIUrl":"https://doi.org/10.1117/12.2548589","url":null,"abstract":"","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"8 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":"129178533","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}

{"title":"Feasibility of 3D motion-compensated needle guidance for TIPS procedures","authors":"M. Wagner, S. Periyasamy, M. Speidel, P. Laeseke","doi":"10.1117/12.2548874","DOIUrl":"https://doi.org/10.1117/12.2548874","url":null,"abstract":"X-ray fluoroscopy is commonly used to guide needles during transjugular intrahepatic portosystemic shunt (TIPS) procedures. Respiratory motion and the 2D nature of the x-ray projections, however, make it difficult to accurately guide the needle from a hepatic vein, through the liver parenchyma, and into the portal vein, which is not visible on x-ray images in the absence of continuous contrast-enhancement. Due to these challenges, multiple needle passes are often required, which increase the risk for perforation of the liver capsule and hemorrhage. To overcome these challenges, we propose a motion-compensated 3D needle guidance system, which generates a respiratory motion model of the portal venous system using a 3D DSA and a contrast enhanced 2D x-ray sequence acquired under free breathing conditions. The respiratory motion is tracked during the needle pass based on brightness variations above and below the diaphragm in the 2D images, which allows for creation of a motion-compensated surface model of the target vasculature. Additionally, a 3D needle reconstruction algorithm from two 2D x-ray images is presented, which allows for motion-compensated 3D device imaging. A preliminary pig study was performed to evaluate the feasibility of the proposed techniques. The biplane needle reconstruction was compared to conventional cone beam CT acquisitions, where a root mean squared distance of 0.98 mm and a tip localization error of 1.22 mm were measured. The maximum error of the estimated vascular motion per frame in the two pig studies was 0.63 mm and 1.63 mm respectively. If successfully translated to clinical TIPS procedures, the proposed needle guidance could result in fewer unnecessary needle passes and therefore shorter procedure times and lower risk to the patient.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"149 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":"117288365","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}

{"title":"Assessment of skill translation of intrathecal needle insertion using real-time needle tracking with an augmented reality display","authors":"Saleh Choueib, C. McGarry, M. Jaeger, T. Ungi, N. Janssen, G. Fichtinger, Lindsey Patterson","doi":"10.1117/12.2549663","DOIUrl":"https://doi.org/10.1117/12.2549663","url":null,"abstract":"PURPOSE: Current lumbar puncture simulators lack visual feedback of the needle path. We propose a lumbar puncture simulator that introduces a visual virtual reality feedback to enhance the learning experience. This method incorporates virtual reality and a position tracking system. We aim to assess the advantages of the stereoscopy of virtual reality (VR) on needle insertion skills learning. METHODS: We scanned and rendered spine models into three-dimensional (3D) virtual models to be used in the lumbar puncture simulator. The motion of the needle was tracked relative to the spine model in real-time using electromagnetic tracking, which allows accurate replay of the needle insertion path. Using 3D Slicer and SlicerVR, we created a virtual environment with the tracked needle and spine. In this study, 23 medical students performed a traditional lumbar puncture procedure using the augmented simulator. The participants’ insertions were tracked and recorded, allowing them to review their procedure afterwards. Twelve students were randomized into a VR group; they reviewed their procedure in VR, while the Control group reviewed their procedures on computer monitor. Students completed a standard System Usability Survey (SUS) about the system, and a self-reported confidence scale (1-5) in performing lumbar puncture. RESULTS: We integrated VR visual feedback in a traditional lumbar puncture simulator. The VR group gave an average 70.4 on the System Usability Survey (SUS) vs. 66.8 of the Control group. The only negative feedback on VR was that students felt they required technical assistance to set it up (SUS4). The results show a general affinity for VR and its easeof- use. Furthermore, the mean confidence level rose from 1.6 to 3.2 in the VR group, vs. 1.8 to 3.1 in the Control group (1.6 vs. 1.3 improvement). CONCLUSION: The VR-augmented lumbar puncture simulator workflow incorporates visual feedback capabilities and accurate tracking of the needle relative to the spine model. Moreover, VR feedback allow for a more comprehensive spatial awareness of the target anatomy for improved learning.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"115 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":"124817946","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}

{"title":"Error analysis for a navigation system using 3D abdominal ultrasound","authors":"David Iommi, A. Valladares, M. Figl, J. Hummel","doi":"10.1117/12.2548866","DOIUrl":"https://doi.org/10.1117/12.2548866","url":null,"abstract":"","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"28 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":"125178425","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}

{"title":"Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study","authors":"Laura Connolly, A. Jamzad, M. Kaufmann, Rachel Rubino, A. Sedghi, T. Ungi, Mark Asselin, S. Yam, J. Rudan, C. Nicol, G. Fichtinger, P. Mousavi","doi":"10.1117/12.2549343","DOIUrl":"https://doi.org/10.1117/12.2549343","url":null,"abstract":"PURPOSE: The iKnife is a new surgical tool designed to aid in tumor resection procedures by providing enriched chemical feedback about the tumor resection cavity from electrosurgical vapors. We build and compare machine learning classifiers that are capable of distinguishing primary cancer from surrounding tissue at different stages of tumor progression. In developing our classification framework, we implement feature reduction and recognition tools that will assist in the translation of xenograft studies to clinical application and compare these tools to standard linear methods that have been previously demonstrated. METHODS: Two cohorts (n=6 each) of 12 week old female immunocompromised (Rag2−/−;Il2rg−/−) mice were injected with the same human breast adenocarcinoma (MDA-MB-231) cell line. At 4 and 6 weeks after cell injection, mice in each cohort were respectively euthanized, followed by iKnife burns performed on tumors and tissues prior to sample collection for future studies. A feature reduction technique that uses a neural network is compared to traditional linear analysis. For each method, we fit a classifier to distinguish primary cancer from surrounding tissue. RESULTS: Both classifiers can distinguish primary cancer from metastasis and surrounding tissue. The classifier that uses a neural network achieves an accuracy of 96.8% and the classifier without the neural network achieves an accuracy of 96%. CONCLUSIONS: The performance of these classifiers indicate that this device has the potential to offer real-time, intraoperative classification of tissue. This technology may be used to assist in intraoperative margin detection and inform surgical decisions to offer a better standard of care for cancer patients.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"18 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":"124774856","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}