An Affordable Portable Obstetric Ultrasound Simulator for Synchronous and Asynchronous Scan Training

The increasing use of Point of Care (POC) ultrasound presents a challenge in providing efficient training to new POC ultrasound users. In response to this need, we have developed an affordable, compact, laptop-based obstetric ultrasound training simulator. It offers freehand ultrasound scan on an abdomen-sized scan surface with a 5 degrees of freedom sham transducer and utilizes 3D ultrasound image volumes as training material. On the simulator user interface is rendered a virtual torso, whose body surface models the abdomen of a particular pregnant scan subject. A virtual transducer scans the virtual torso, by following the sham transducer movements on the scan surface. The obstetric ultrasound training is self-paced and guided by the simulator using a set of tasks, which are focused on three broad areas, referred to as modules: 1) medical ultrasound basics, 2) orientation to obstetric space, and 3) fetal biometry. A learner completes the scan training through the following three steps: (i) watching demonstration videos, (ii) practicing scan skills by sequentially completing the tasks in Modules 2 and 3, with scan evaluation feedback and help functions available, and (iii) a final scan exercise on new image volumes for assessing the acquired competency. After each training task has been completed, the simulator evaluates whether the task has been carried out correctly or not, by comparing anatomical landmarks identified and/or measured by the learner to reference landmark bounds created by algorithms, or pre-inserted by experienced sonographers. Based on the simulator, an ultrasound E-training system has been developed for the medical practitioners for whom ultrasound training is not accessible at local level. The system, composed of a dedicated server and multiple networked simulators, provides synchronous and asynchronous training modes, and is able to operate with a very low bit rate. The synchronous (or group-learning) mode allows all training participants to observe the same 2D image in real-time, such as a demonstration by an instructor or scan ability of a chosen learner. The synchronization of 2D images on the different simulators is achieved by directly transmitting the position and orientation of the sham transducer, rather than the ultrasound image, and results in a system performance independent of network bandwidth. The asynchronous (or self-learning) mode is described in the previous paragraph. However, the E-training system allows all training participants to stay networked to communicate with each other via text channel. To verify the simulator performance and training efficacy, we conducted several performance experiments and clinical evaluations. The performance experiment results indicated that the simulator was able to generate greater than 30 2D ultrasound images per second with acceptable image quality on medium-priced computers. In our initial experiment investigating the simulator training capability and feasibility, three experienced sonographers individually scanned two image volumes on the simulator. They agreed that the simulated images and the scan experience were adequately realistic for ultrasound training; the training procedure followed standard obstetric ultrasound protocol. They further noted that the simulator had the potential for becoming a good supplemental training tool for medical students and resident doctors. A clinic study investigating the simulator training efficacy was integrated into the clerkship program of the Department of Obstetrics and Gynecology, University of Massachusetts Memorial Medical Center. A total of 24 3rd year medical students were recruited and each of them was directed to scan six image volumes on the simulator in two 2.5-hour sessions. The study results showed that the successful scan times for the training tasks significantly decreased as the training progressed. A post-training survey answered by the students found that they considered the simulator-based training useful and suitable for medical students and resident doctors. The experiment to validate the performance of the E-training system showed that the average transmission bit rate was approximately 3-4 kB/s; the data loss was less than 1% and no loss of 2D images was visually detected. The results also showed that the 2D images on all networked simulators could be considered to be synchronous even though inter-continental communication existed

نقش شبیه سازها در رژیونال آنستزی واقدامات مداخله ای درد

اين مقاله به معرفي شبيه سازهاي پزشكي در آموزش رژيونال آنستزي و اقدامات مداخلهاي درد و روش استفاده از آن درمحي طهاي مجازي مي پردازد. ساختمانهاي طراحي شده به گونهاي پردازش شدهاند كه بازتابي واقعي از آناتومي بدن انسان باشند. سهم اصلي اين شبيهسازها، تقليد انتقال ايمپالس الكتريكي از طريق بافت نرم و طناب نخاعي است. استفاده از شبيهسازها سبب تعاملي قابل قبول، تجسم و احساسي اطمينان بخش به پزشك ميگردد. استفاده از شبيهسازها توسط دستياران تخصصي ارزيابي و نتايج رضايتبخشي داشته است. بعنوان مثال، سناريوي بلوك اندام تحتاني و شبكه سلياك توضيح داده ميشود

Virtual and Augmented Reality Techniques for Minimally Invasive Cardiac Interventions: Concept, Design, Evaluation and Pre-clinical Implementation

While less invasive techniques have been employed for some procedures, most intracardiac interventions are still performed under cardiopulmonary bypass, on the drained, arrested heart. The progress toward off-pump intracardiac interventions has been hampered by the lack of adequate visualization inside the beating heart. This thesis describes the development, assessment, and pre-clinical implementation of a mixed reality environment that integrates pre-operative imaging and modeling with surgical tracking technologies and real-time ultrasound imaging. The intra-operative echo images are augmented with pre-operative representations of the cardiac anatomy and virtual models of the delivery instruments tracked in real time using magnetic tracking technologies. As a result, the otherwise context-less images can now be interpreted within the anatomical context provided by the anatomical models. The virtual models assist the user with the tool-to-target navigation, while real-time ultrasound ensures accurate positioning of the tool on target, providing the surgeon with sufficient information to ``see\u27\u27 and manipulate instruments in absence of direct vision. Several pre-clinical acute evaluation studies have been conducted in vivo on swine models to assess the feasibility of the proposed environment in a clinical context. Following direct access inside the beating heart using the UCI, the proposed mixed reality environment was used to provide the necessary visualization and navigation to position a prosthetic mitral valve on the the native annulus, or to place a repair patch on a created septal defect in vivo in porcine models. Following further development and seamless integration into the clinical workflow, we hope that the proposed mixed reality guidance environment may become a significant milestone toward enabling minimally invasive therapy on the beating heart

Medical image registration and soft tissue deformation for image guided surgery system

In parallel with the developments in imaging modalities, image-guided surgery (IGS) can now provide the surgeon with high quality three-dimensional images depicting human anatomy. Although IGS is now in widely use in neurosurgery, there remain some limitations that must be overcome before it can be employed in more general minimally invasive procedures. In this thesis, we have developed several contributions to the field of medical image registration and brain tissue deformation modeling. From the methodology point of view, medical image registration algorithms can be classified into feature-based and intensity-based methods. One of the challenges faced by feature-based registration would be to determine which specific type of feature is desired for a given task and imaging type. For this reason, a point set registration using points and curves feature is proposed, which has the accuracy of registration based on points and the robustness of registration based on lines or curves. We have also tackled the problem on rigid registration of multimodal images using intensity-based similarity measures. Mutual information (MI) has emerged in recent years as a popular similarity metric and widely being recognized in the field of medical image registration. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. We introduce a new similarity metric, called Adaptive Mutual Information (AMI) measure which incorporates the gradient spatial information. Salient pixels in the regions with high gradient value will contribute more in the estimation of mutual information of image pairs being registered. Experimental results showed that our proposed method improves registration accuracy and it is more robust to noise images which have large deviation from the reference image. Along with this direction, we further improve the technique to simultaneously use all information obtained from multiple features. Using multiple spatial features, the proposed algorithm is less sensitive to the effect of noise and some inherent variations, giving more accurate registration. Brain shift is a complex phenomenon and there are many different reasons causing brain deformation. We have investigated the pattern of brain deformation with respect to location and magnitude and to consider the implications of this pattern for correcting brain deformation in IGS systems. A computational finite element analysis was carried out to analyze the deformation and stress tensor experienced by the brain tissue during surgical operations. Finally, we have developed a prototype visualization display and navigation platform for interpretation of IGS. The system is based upon Qt (cross-platform GUI toolkit) and it integrates VTK (an object-oriented visualization library) as the rendering kernel. Based on the construction of a visualization software platform, we have laid a foundation on the future research to be extended to implement brain tissue deformation into the system

Medical image registration and soft tissue deformation for image guided surgery system

In parallel with the developments in imaging modalities, image-guided surgery (IGS) can now provide the surgeon with high quality three-dimensional images depicting human anatomy. Although IGS is now in widely use in neurosurgery, there remain some limitations that must be overcome before it can be employed in more general minimally invasive procedures. In this thesis, we have developed several contributions to the field of medical image registration and brain tissue deformation modeling. From the methodology point of view, medical image registration algorithms can be classified into feature-based and intensity-based methods. One of the challenges faced by feature-based registration would be to determine which specific type of feature is desired for a given task and imaging type. For this reason, a point set registration using points and curves feature is proposed, which has the accuracy of registration based on points and the robustness of registration based on lines or curves. We have also tackled the problem on rigid registration of multimodal images using intensity-based similarity measures. Mutual information (MI) has emerged in recent years as a popular similarity metric and widely being recognized in the field of medical image registration. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. We introduce a new similarity metric, called Adaptive Mutual Information (AMI) measure which incorporates the gradient spatial information. Salient pixels in the regions with high gradient value will contribute more in the estimation of mutual information of image pairs being registered. Experimental results showed that our proposed method improves registration accuracy and it is more robust to noise images which have large deviation from the reference image. Along with this direction, we further improve the technique to simultaneously use all information obtained from multiple features. Using multiple spatial features, the proposed algorithm is less sensitive to the effect of noise and some inherent variations, giving more accurate registration. Brain shift is a complex phenomenon and there are many different reasons causing brain deformation. We have investigated the pattern of brain deformation with respect to location and magnitude and to consider the implications of this pattern for correcting brain deformation in IGS systems. A computational finite element analysis was carried out to analyze the deformation and stress tensor experienced by the brain tissue during surgical operations. Finally, we have developed a prototype visualization display and navigation platform for interpretation of IGS. The system is based upon Qt (cross-platform GUI toolkit) and it integrates VTK (an object-oriented visualization library) as the rendering kernel. Based on the construction of a visualization software platform, we have laid a foundation on the future research to be extended to implement brain tissue deformation into the system

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance

Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations. Purpose. In this work, we particularly look at mitral valve reconstruction (MVR) surgery, which is to re-establish the functionality of an incompetent mitral valve (MV) through implantation of an artificial ring that reshapes the valvular morphology. We aim at supporting MVR by providing surgeons with biomechanical FEM-based MVR surgery simulations that enable them to assess the simulated behavior of the MV after an MVR. However, according to the above requirements, such surgery simulation is really beneficial to surgeons only if it is patient-specific, surgical expert knowledge-based, comprehensive in terms of the underlying model and the patient’s data, and if its setup and execution is fully automated and integrated into the surgical treatment workflow. Methods. This PhD work conducts research on simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. First, we derive a biomechanical MV/MVR model and develop an FEM-based MVR surgery simulation using the FEM software toolkit HiFlow3. Following, we outline the functionality and features of the Medical Simulation Markup Language (MSML) and how it simplifies the biomechanical modeling workflow. It is then detailed, how, by means of the MSML and a set of dedicated MVR simulation reprocessing operators, patient-individual medical data can comprehensively be analyzed and processed in order for the fully automated setup of MVR simulation scenarios. Finally, the presented work is integrated into the cognitive system architecture of the joint research project Cognition-Guided Surgery. We particularly look at its semantic knowledge and data infrastructure as well as at the setup of its cognitive software components, which eventually facilitate cognition-guidance and patient-specifity for the overall simulation-enhanced MVR assistance pipeline. Results and Discussion. We have proposed and implemented, for the first time, a prototypic system for simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. The overall system was evaluated in terms of functionality and performance. Through its cognitive, data-driven pipeline setup, medical patient data and surgical information is analyzed and processed comprehensively, efficiently and fully automatically, and the hence set-up simulation scenarios yield reliable, patient-specific MVR surgery simulation results. This indicates the system’s usability and applicability. The proposed work thus presents an important step towards a simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance, and can – once operative – be expected to significantly enhance MVR surgery. Concluding, we discuss possible further research contents and promising applications to build upon the presented work

Preclinical MRI of the Kidney

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Preclinical MRI of the kidney : methods and protocols

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers