Search

International audience; Skin is an attractive target tissue for gene transfer due to its size, accessibility and its immune competence. One of the promising delivery methods is gene delivery by means of electroporation (EP), i.e. gene electrotransfer (GET). To assess the importance of different effects of electroporation for successful GET we investigated: i) stress response and ii) transfection efficacy upon different pulse protocols. Moreover, numerical modelling was used to explain experimental results and to test the agreement of experimental results with current knowledge about GET. Methods: Double transgenic mice Hspa1b-LucF (+/+) Hspa1b-mPlum (+/+) were used to determine the level of stress sensed by the cell in the tissue in vivo that was exposed to EP. The effect of five different pulse protocols on stress levels sensed by the exposed cells and their efficacy for gene electrotransfer for two plasmids pEGFP-C1 (EGFP) and pCMV-tdTomato was tested. Results: Quantification of the bioluminescence signal intensity shows that EP, regardless of the electric pulse parameters used, increased mean bioluminescence compared to the baseline bioluminescence signal of the non-exposed skin. The results of numerical modelling indicate that thermal stress alone is not sufficient to explain the measured bioluminescence signal. Of the tested pulse protocols, the highest expression of EGFP and tdTomato was achieved with HV-MV (high voltagemedium voltage) protocols, which agrees also with numerical model. Significance: Although EP is widely used as a method for gene delivery, we show that the field could benefit from the use of mathematical modelling by introducing additional parameters such as EP induced stress and electrophoretic movement of plasmids.

Objective: We propose a novel method to obtainmap patient-specific blood velocity profiles (obtained from imaging data such as 2D flow MRI or 3D colour Doppler ultrasound) and map them to geometric vascular models suitable to perform CFD simulations of haemodynamics. We describe the implementation and utilisation of the method within an open-source computational hemodynamics simulation software (CRIMSON). Methods: tThe proposed method establishes point-wise correspondences between the contour of a fixed geometric model and time-varying contours containing the velocity image data, from which a continuous, smooth and cyclic deformation field is calculated. Our methodology is validated using synthetic data, and demonstrated using two different in-vivo aortic velocity datasets: a healthy subject with normal tricuspid valve and a patient with bicuspid aortic valve. Results: We compare the performance of our method with results obtained with the state-of-the-art Schwarz-Christoffel method, in terms of preservation of velocities and execution time. Our method is as accurate as the Schwarz-Christoffel method, while being over 8 times faster. The proposed method can preserve either the flow rate or the velocity field through the surface, and can cope with inconsistencies in motion and contour shape. Conclusions: Our results show that the method is as accurate as the Schwarz-Christoffel method in terms of maintaining the velocity distributions, while being more computationally efficient.Our mapping method can accurately preserve either the flow rate or the velocity field through the surface, and can cope with inconsistencies in motion and contour shape. Significance: The proposed method and its integration into the CRIMSON software enable a streamlined approach towards incorporating more patient-specific data in blood flow simulations.

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization., Author summary We provide the first full presentation in the literature of CRIMSON, the Cardiovascular Integrated Modelling and Simulation Package. CRIMSON consists of a graphical user interface desktop computer program for creating geometric models of blood vessels from medical imaging scans, specifying parameters such as the stiffness of the artery walls, the resistance of connected vessels which are not visible on the scans, and determining the appropriate parameters for all aspects of the model. CRIMSON additionally consists of the Flowsolver, a high-performance computing package which simulates the flow of blood through the models created in the graphical user interface. Whilst several packages which can simulate blood flow exist, most target general fluid simulations, and this lack of specialisation means that blood flow simulation is harder to perform, and can require ad hoc (and potentially scientifically-limiting) workflow decisions. CRIMSON’s specialisation deals with these problems, as well as presenting a number of unique features which are unavailable elsewhere.

Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (i) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (ii) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high-risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15, and 60-120 min, respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803, respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave an accuracy of 0.9997 and 0.9991, and DSC of 0.9726 and 0.9354, respectively. We have proposed a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI sequences. This enables accurate anatomical and biochemical measurements to be obtained quickly and reproducibly. This is the first such method that shows clinical applicability, and could have large ramifications for the use of compositional MRI in research and clinically. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it can make use of careful analysis of patient-based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint — the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node-based region growing. So far we have evaluated our method on 15 MRI knee images. Our unsupervised segmentation compared against a hand-segmented gold standard has achieved an average Dice similarity coefficient of 0.95 for femur and 0.93 for tibia, and an average symmetric surface distance of 0.98 mm for femur and 0.73 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process.

Treatments based on electroporation are new and promising approach to treating tumors, especially non-resectable ones. The success of the treatment is, however, heavily dependent on coverage of the entire tumor volume with a sufficiently high electric field. Ensuring complete coverage in the case of deep-seated tumors is not trivial and can in best way be ensured by patient-specific treatment planning. The basis of the treatment planning process consists of two complex tasks: medical image segmentation and numerical optimization. In addition to earlier developing segmentation algorithms for several tissues and the algorithms for numerical optimization of treatment parameters we developed a web-based tool to support the translation of the algorithms and their application in the clinic. The developed web-based tool automatically builds a 3D model of target tissue from the medical images uploaded by the user and then uses this 3D model to optimize treatment parameters. The tool enables the user to validate the results of the automatic segmentation and make corrections if necessary before delivering the final treatment plan.

Electrochemotherapy is now in development for treatment of deep-seated tumors, like in bones and internal organs, such as liver. The technology is available with a newly developed electric pulse generator and long needle electrodes; however the procedures for the treatment are not standardized yet. In order to describe the treatment procedure, including treatment planning, within the ongoing clinical study, a case of successful treatment of a solitary metastasis in the liver of colorectal cancer is presented. The procedure was performed intraoperatively by inserting long needle electrodes, two in the center of the tumor and four around the tumor into the normal tissue. The insertion of electrodes proved to be feasible and was done according to the treatment plan, prepared by numerical modeling. After intravenous bolus injection of bleomycin the tumor was exposed to electric pulses. The delivery of the electric pulses did not interfere with functioning of the heart, since the pulses were synchronized with electrocardiogram in order to be delivered outside the vulnerable period of the ventricles. Also the post treatment period was uneventful without side effects. Re-operation of the treated metastasis demonstrated feasibility of the reoperation, without secondary effects of electrochemotherapy on normal tissue. Good antitumor effectiveness with complete tumor destruction was confirmed with histological analysis. The patient is disease-free 16 months after the procedure. In conclusion, treatment procedure for electrochemotherapy proved to be a feasible technological approach for treatment of liver metastasis. Due to the absence of the side effects and the first complete destruction of the treated tumor, treatment procedure for electrochemotherapy seems to be a safe method for treatment of liver metastases with good treatment effectiveness even in difficult-to-reach locations.

Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm) and medium-sized (30 mm) tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. Materials and methods. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results. Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. Conclusions. The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation- based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

Electroporation is a term describing an increase of cell membrane permeability due to exposure of a cell to a sufficiently high electric field. The described effect is used in treatment of tumors, whether in combination with chemotherapeutic drugs (electrochemotherapy) or as a non-thermal ablation method (non-thermal irreversible electroporation). In order for the treatment to be successful it is necessary to achieve complete coverage of the tumor volume by a sufficiently high electric field. In cases of applying electroporation-based treatments on deep-seated solid tumors the complete tumor coverage is predicted through treatment planning. In the core of treatment planning are numerical calculations based on real patient geometry acquired through segmentation of medical images. Our aim is to create an integrated treatment planning procedure to be used for electroporation-based treatments. The procedure includes image segmentation methods for images of various modalities and imaging parameters. Therefore, we aim towards creating image segmentation methods that would be as robust as possible with least user interaction. Our first task was performing image segmentation in order to facilitate treatment planning for electrochemotherapy of colorectal metastases in the liver. The aim was to segment hepatic vessels with a diameter size > 3 mm which should not be damaged during insertion of electrodes. We implemented two methods: the first based on region growing and the second combining multi-scale filtering with automatic thresholding. We also performed the validation of implemented methods on phantoms regarding the sizes of objects which are of interest in the treatment planning procedure. The mean errors of the first and second method in the worst case were 62, 67% and 0, 14%, respectively. These results indicate that the second method might be accurate enough to be used in the treatment planning. Still, additional validation based on radiologists’ segmentation needs to be performed.

Electroporation-based treatment combining high-voltage electric pulses and poorly permanent cytotoxic drugs, i.e., electrochemotherapy (ECT), is currently used for treating superficial tumor nodules by following standard operating procedures. Besides ECT, another electroporation-based treatment, nonthermal irreversible electroporation (N-TIRE), is also efficient at ablating deep-seated tumors. To perform ECT or N-TIRE of deep-seated tumors, following standard operating procedures is not sufficient and patient-specific treatment planning is required for successful treatment. Treatment planning is required because of the use of individual long-needle electrodes and the diverse shape, size and location of deep-seated tumors. Many institutions that already perform ECT of superficial metastases could benefit from treatment-planning software that would enable the preparation of patient-specific treatment plans. To this end, we have developed a Web-based treatment-planning software for planning electroporation-based treatments that does not require prior engineering knowledge from the user (e.g., the clinician). The software includes algorithms for automatic tissue segmentation and, after segmentation, generation of a 3D model of the tissue. The procedure allows the user to define how the electrodes will be inserted. Finally, electric field distribution is computed, the position of electrodes and the voltage to be applied are optimized using the 3D model and a downloadable treatment plan is made available to the user.

Electrochemotherapy uses electroporation for enhancing chemotherapy. Electrochemotherapy can be performed using standard operating procedures with predefined electrode geometries, or using patient-specific treatment planning to predict electroporation. The latter relies on realistic computer models to provide optimal results (i.e. electric field distribution as well as electrodes' position and number) and is suitable for treatment of deep-seated tumors. Since treatment planning for deep-seated tumors has been used in radiotherapy, we expose parallelisms with radiotherapy in order to establish the procedure for electrochemotherapy of deep-seated tumors. We partitioned electrochemotherapy in the following phases: the mathematical model of electroporation, treatment planning, set-up verification, treatment delivery and monitoring, and response assessment. We developed a conceptual treatment planning software that incorporates mathematical models of electroporation. Preprocessing and segmentation of the patient's medical images are performed, and a 3D model is constructed which allows placement of electrodes and implementation of the mathematical model of electroporation. We demonstrated the feasibility of electrochemotherapy of deep-seated tumors treatment planning within a clinical study where treatment planning contributed to the effective electrochemotherapy treatment of deep-seated colorectal metastases in the liver. The described procedure can provide medical practitioners with information on using electrochemotherapy in the clinical setting. The main aims of this paper are: 1) to present the procedure for treating deep-seated tumors by electrochemotherapy based on patient-specific treatment planning, and 2) to identify gaps in knowledge and possible pitfalls of such procedure.

In past years electrochemotherapy (ECT) has been successfully used as a method for treating small subcutaneous and cutaneous tumors. With such tumor nodules, it is enough to follow generally accepted standard operating procedures and general guidance on placement of electrodes for ECT to be successful. Results of the ESOPE study are encouraging, since they determined ECT is highly-effective with an 85% objective response rate. Currently, additional progress is being made by extending the reach of ECT for treating deep-seated solid tumors. Nevertheless, such tumors are very diverse in location, shape and size, and vary from patient to patient. Therefore, individual approach to each patient is needed. Patient-based numerical treatment planning involves interdisciplinary tasks from several fields (i.e. biomedical engineering, oncology, computer science, electrical engineering). To simplify the treatment planning procedure it should be feasible to develop integrated computer software that would allow the attending physician to safely generate a treatment plan. We developed new software that embeds all the procedures that are necessary for treatment planning: import of medical images (CT or MRI), preprocessing, segmentation (i.e. tissue marking), postprocessing, three-dimensional reconstruction, insertion of electrodes, and generation of optimized treatment plan. In order to develop a robust and safe solution, we separated the engine (core of treatment planning software) from the Graphical User Interface and, at this stage, developed two separate applications that will be merged together at the end of development. A clinical study has been started at Institute of Oncology (Ljubljana, Slovenia) where ECT was performed on five patients with colorectal liver metastases. ECT was performed by inserting long needle electrodes. Moreover, the delivery of ECT electric pulses was safe, and no side effects have been noticed in the post treatment period. The results demonstrate that ECT is feasible in treatment of deep-seated metastases ; treatment planning proved helpful for successful ECT treatment. Therefore, development of integrated treatment planning software is required and will assist to a more widespread application of ECT.

Background Treatments based on electroporation are a new and promising approach to treating tumors, especially non-resectable ones. The success of the treatment is, however, heavily dependent on coverage of the entire tumor volume with a sufficiently high electric field. Ensuring complete coverage in the case of deep-seated tumors is not trivial and can in best way be ensured by patient-specific treatment planning. The basis of the treatment planning process consists of two complex tasks: medical image segmentation, and numerical modeling and optimization. Methods In addition to previously developed segmentation algorithms for several tissues (human liver, hepatic vessels, bone tissue and canine brain) and the algorithms for numerical modeling and optimization of treatment parameters, we developed a web-based tool to facilitate the translation of the algorithms and their application in the clinic. The developed web-based tool automatically builds a 3D model of the target tissue from the medical images uploaded by the user and then uses this 3D model to optimize treatment parameters. The tool enables the user to validate the results of the automatic segmentation and make corrections if necessary before delivering the final treatment plan. Results Evaluation of the tool was performed by five independent experts from four different institutions. During the evaluation, we gathered data concerning user experience and measured performance times for different components of the tool. Both user reports and performance times show significant reduction in treatment-planning complexity and time-consumption from 1-2 days to a few hours. Conclusions The presented web-based tool is intended to facilitate the treatment planning process and reduce the time needed for it. It is crucial for facilitating expansion of electroporation-based treatments in the clinic and ensuring reliable treatment for the patients. The additional value of the tool is the possibility of easy upgrade and integration of modules with new functionalities as they are developed.