Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.Comment: Accepted to MICCAI 201

Continuous roadmapping in liver TACE procedures using 2D–3D catheter-based registration

PURPOSE: Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention. METHODS: In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data. RESULTS: The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7–5.4 mm when using the brute force optimizer and 5.2–6.6 mm when using the Powell optimizer. CONCLUSION: We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity

Rare event simulation for dynamic fault trees

Fault trees (FT) are a popular industrial method for reliability engineering, for which Monte Carlo simulation is an important technique to estimate common dependability metrics, such as the system reliability and availability. A severe drawback of Monte Carlo simulation is that the number of simulations required to obtain accurate estimations grows extremely large in the presence of rare events, i.e., events whose probability of occurrence is very low, which typically holds for failures in highly reliable systems. This paper presents a novel method for rare event simulation of dynamic fault trees with complex repairs that requires only a modest number of simulations, while retaining statistically justified confidence intervals. Our method exploits the importance sampling technique for rare event simulation, together with a compositional state space generation method for dynamic fault trees. We demonstrate our approach using two parameterized sets of case studies, showing that our method can handle fault trees that could not be evaluated with either existing analytical techniques, nor with standard simulation techniques

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

This article presents uniform B-spline interpolation, completely contained on the graphics processing unit (GPU). This implies that the CPU does not need to compute any lookup tables or B-spline basis functions. The cubic interpolation can be decomposed into several linear interpolations [Sigg and Hadwiger 05], which are hard-wired on the GPU and therefore very fast. Here it is demonstrated that the cubic B-spline basis function can be evaluated in a short piece of GPU code without any conditional statements. Source code is available online

Modelling Smart Buildings Using Fault Maintenance Trees

Increasingly many industrial spheres are enforced by law to satisfy strict RAMS requirements—reliability, availability, maintain-ability, and safety. Applied to Fault Maintenance Trees (FMTs), formal methods offer flexible and trustworthy techniques to quantify the resilience of (abstract models of) systems. However, the estimated metrics are relevant only as far as the model reflects the actual system:Refining an abstract model to reduce the gap with reality is crucial for the usefulness of the results. In this work, we take a practical approach at the challenge by studying a Heating, Ventilation and Air-Conditioning unit (HVAC), ubiquitous in smart buildings. Using probabilistic and statistical model checking, we assess RAMS metrics of a basic fault maintenance tree HVAC model. We then implement four modifications augmenting the expressivity of the FMT model, and show that reliability,availability, expected number of failures, and costs, can vary by orders of magnitude depending on involved modelling detail

Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components' failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.Comment: This paper is an extended version of the SETTA 2019 paper, Springer-Verla

One Net Fits All: A unifying semantics of Dynamic Fault Trees using GSPNs

Dynamic Fault Trees (DFTs) are a prominent model in reliability engineering. They are strictly more expressive than static fault trees, but this comes at a price: their interpretation is non-trivial and leaves quite some freedom. This paper presents a GSPN semantics for DFTs. This semantics is rather simple and compositional. The key feature is that this GSPN semantics unifies all existing DFT semantics from the literature. All semantic variants can be obtained by choosing appropriate priorities and treatment of non-determinism.Comment: Accepted at Petri Nets 201

[¹⁸F]FET PET-Guided management of pseudoprogression in glioblastoma (FET POPPING):the study protocol for a diagnostic randomized clinical trial

Background: During follow-up of glioblastoma patients after chemoradiation, expert teams often observe abnormalities on MRI with difficulty in distinguishing between tumor growth and pseudoprogression. Although advanced MRI techniques such as perfusion weighted imaging provide additional information, diagnostic uncertainty often remains, leading to incorrect or delayed diagnosis, and inappropriate treatment, such as unnecessary surgery. [18F]Fluoro-ethyl-tyrosine positron emission tomography (FET PET) has good discriminating power for this setting. Still, this diagnostic tool is not used frequently in The Netherlands due to costs, logistics and uncertainty about clinical benefit. In the FET POPPING study, we aim to determine the added value of [18F]FET PET for clinical management of glioblastoma patients. Methods: A multicenter diagnostic randomized clinical trial will be performed, from August 2024 until December 2027. Adult patients (n=144) with isocitrate dehydrogenase (IDH)-wildtype glioblastoma will be included, who, at least ≥3 months after the concomitant phase of standard temozolomide-based chemoradiation, have new or increased contrast enhancement on MRI, causing doubt between tumor growth or pseudoprogression. In this trial, pseudoprogression will be used as an encompassing term that includes radionecrosis and other treatment-related changes after (chemo-)radiotherapy. Included patients will be randomized 1:1 in two arms. The investigational arm receives an additional [18F]FET PET scan, and clinical management is based on the index MRI and [18F]FET PET together. Clinical management of the control arm is based on the index MRI alone. Exact clinical management, as based on the available imaging, is chosen at the discretion of the local multidisciplinary board. The primary study endpoints are (a) the percentage of patients undergoing unnecessary interventions and (b) health-related quality of life after 12 weeks. Secondary endpoints include time-to-diagnosis, overall survival, and cost-effectiveness. Discussion: We hypothesize that the clinical management guided by an additional [18F]FET PET scan leads to fewer unnecessary interventions, better health-related quality of life after 12 weeks and among others reduced net healthcare costs, compared with management based on MRI only. Trial registration: The trial is registered on ClinicalTrials.gov on the 24th of June 2024, with registration number NCT06480721.</p