105 research outputs found

    Preoperative evaluation of superficial cortical venous drainage

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    Objectives and methodology: The preoperative exact localization of superficial intracranial lesions and superficial cortical veins is often necessary for making craniotomy and evaluation of cortical veins. We developed a simple and cheap method for such localization using cod liver oil capsule during the preoperative MRI and MRV brain examination. With the help of MRV brain, 3DCEMRV and 2DTOF images were taken and superficial cortical veins studied in the marked area for comparison between both modalities of MRV and planning of surgery for avoiding venous injury. Results: Most of the cases were in the age group 16-60 years (91.6%). The most common clinical manifestation was headache (85.4%) and meningioma (60.4%) was found to be the most common pathology. Clear visualization (Grade 3) of the individual superficial cortical vein was observed in 48 cases (100%) in 3DCEMRV as compared to 2DTOF 22 cases (45.8%) P <0.001S. Clear visualization (Grade3) of superior sagittal sinus was observed in 48 cases (100%) in 3DCEMRV as compared to 2DTOF 33 cases (68.6%) P <0.001S.  In post-operative CT Head, we found 4 (8.3%) cases were having venous infarction.  5 patients (10.4%) developed motor weakness postoperatively. In 3 cases, postoperative MRV were done and found no venous injury. Conclusion: This study showed that preoperative localization and evaluation of the tumoral area and cortical veins with the help of cod liver oil in MRI and MRV brain was very helpful in planning the surgery, making craniotomy and to avoid injury of the veins. This technique is easy to perform and the capsule is easily constructed and inexpensive. 3DCEMRV was found to be better modality than 2DTOF for delineation of veins. Final neurosurgical outcomes were better

    Visual Perception and Cognition in Image-Guided Intervention

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    Surgical image visualization and interaction systems can dramatically affect the efficacy and efficiency of surgical training, planning, and interventions. This is even more profound in the case of minimally-invasive surgery where restricted access to the operative field in conjunction with limited field of view necessitate a visualization medium to provide patient-specific information at any given moment. Unfortunately, little research has been devoted to studying human factors associated with medical image displays and the need for a robust, intuitive visualization and interaction interfaces has remained largely unfulfilled to this day. Failure to engineer efficient medical solutions and design intuitive visualization interfaces is argued to be one of the major barriers to the meaningful transfer of innovative technology to the operating room. This thesis was, therefore, motivated by the need to study various cognitive and perceptual aspects of human factors in surgical image visualization systems, to increase the efficiency and effectiveness of medical interfaces, and ultimately to improve patient outcomes. To this end, we chose four different minimally-invasive interventions in the realm of surgical training, planning, training for planning, and navigation: The first chapter involves the use of stereoendoscopes to reduce morbidity in endoscopic third ventriculostomy. The results of this study suggest that, compared with conventional endoscopes, the detection of the basilar artery on the surface of the third ventricle can be facilitated with the use of stereoendoscopes, increasing the safety of targeting in third ventriculostomy procedures. In the second chapter, a contour enhancement technique is described to improve preoperative planning of arteriovenous malformation interventions. The proposed method, particularly when combined with stereopsis, is shown to increase the speed and accuracy of understanding the spatial relationship between vascular structures. In the third chapter, an augmented-reality system is proposed to facilitate the training of planning brain tumour resection. The results of our user study indicate that the proposed system improves subjects\u27 performance, particularly novices\u27, in formulating the optimal point of entry and surgical path independent of the sensorimotor tasks performed. In the last chapter, the role of fully-immersive simulation environments on the surgeons\u27 non-technical skills to perform vertebroplasty procedure is investigated. Our results suggest that while training surgeons may increase their technical skills, the introduction of crisis scenarios significantly disturbs the performance, emphasizing the need of realistic simulation environments as part of training curriculum

    Study of medical image data transformation techniques and compatibility analysis for 3D printing

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    Various applications exist for additive manufacturing (AM) and reverse engineering (RE) within the medical sector. One of the significant challenges identified in the literature is the accuracy of 3D printed medical models compared to their original CAD models. Some studies have reported that 3D printed models are accurate, while others claim the opposite. This thesis aims to highlight the medical applications of AM and RE, study medical image reconstruction techniques into a 3D printable file format, and the deviations of a 3D printed model using RE. A case study on a human femur bone was conducted through medical imaging, 3D printing, and RE for comparative deviation analysis. In addition, another medical application of RE has been presented, which is for solid modelling. Segmentation was done using opensource software for trial and training purposes, while the experiment was done using commercial software. The femur model was 3D printed using an industrial FDM printer. Three different non-contact 3D scanners were investigated for the RE process. Post-processing of the point cloud was done in the VX Elements software environment, while mesh analysis was conducted in MeshLab. The scanning performance was measured using the VX Inspect environment and MeshLab. Both relative and absolute metrics were used to determine the deviation of the scanned models from the reference mesh. The scanners' range of deviations was approximately from -0.375 mm to 0.388 mm (range of about 0.763mm) with an average RMS of about 0.22 mm. The results showed that the mean deviation of the 3D printed model (based on 3D scanning) has an average range of about 0.46mm, with an average mean value of about 0.16 mm

    Federated learning enables big data for rare cancer boundary detection.

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    Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (nā€‰=ā€‰6,ā€‰314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

    Federated learning enables big data for rare cancer boundary detection

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    Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (nā€‰=ā€‰6,ā€‰314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

    Brain and Human Body Modelling 2021

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    This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this bookā€™s coverage of the latest models and techniques available to assess a given technologyā€™s safety and efficacy in a timely and efficient manner. This is an Open Access book

    Brain and Human Body Modelling 2021

    Get PDF
    This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this bookā€™s coverage of the latest models and techniques available to assess a given technologyā€™s safety and efficacy in a timely and efficient manner. This is an Open Access book

    Federated learning enables big data for rare cancer boundary detection

    Get PDF
    Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.ope

    Cable-driven parallel mechanisms for minimally invasive robotic surgery

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    Minimally invasive surgery (MIS) has revolutionised surgery by providing faster recovery times, less post-operative complications, improved cosmesis and reduced pain for the patient. Surgical robotics are used to further decrease the invasiveness of procedures, by using yet smaller and fewer incisions or using natural orifices as entry point. However, many robotic systems still suffer from technical challenges such as sufficient instrument dexterity and payloads, leading to limited adoption in clinical practice. Cable-driven parallel mechanisms (CDPMs) have unique properties, which can be used to overcome existing challenges in surgical robotics. These beneficial properties include high end-effector payloads, efficient force transmission and a large configurable instrument workspace. However, the use of CDPMs in MIS is largely unexplored. This research presents the first structured exploration of CDPMs for MIS and demonstrates the potential of this type of mechanism through the development of multiple prototypes: the ESD CYCLOPS, CDAQS, SIMPLE, neuroCYCLOPS and microCYCLOPS. One key challenge for MIS is the access method used to introduce CDPMs into the body. Three different access methods are presented by the prototypes. By focusing on the minimally invasive access method in which CDPMs are introduced into the body, the thesis provides a framework, which can be used by researchers, engineers and clinicians to identify future opportunities of CDPMs in MIS. Additionally, through user studies and pre-clinical studies, these prototypes demonstrate that this type of mechanism has several key advantages for surgical applications in which haptic feedback, safe automation or a high payload are required. These advantages, combined with the different access methods, demonstrate that CDPMs can have a key role in the advancement of MIS technology.Open Acces
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