Journal: International Journal of Computer Assisted Radiology and Surgery

Abbreviation

Int J CARS

Publisher

Springer

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ISSN

1861-6410
1861-6429

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Publications 1 - 10 of 43
  • Investigating workload and usability of remote magnetic navigation for catheter ablation
    Item type: Journal Article
    Heemeyer, Florian; Lopez, Leonardo E. Guido; Jáuregui Abularach, Miguel E.; et al. (2025)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose Robotic systems for catheter ablation have been in clinical use for many years. While their impact on the clinical outcome and procedure times is well studied, aspects like usability and operator workload have received limited attention in the literature. Reduced workload and stress levels benefit the operator’s mental and physical health, and can also lower the risk of errors and ultimately improve patient safety. The aim of this study is to investigate the workload and usability of remote magnetic navigation compared to conventional manual navigation. Methods We performed a user study with eight electrophysiologists. Each participant performed identical in-vitro navigation tasks replicating those found in pulmonary vein isolation using both manual and magnetic navigation. Magnetic navigation experiments were performed using the Navion, a mobile electromagnetic navigation system. Results Magnetic navigation significantly improved usability ( p < 0.02) and workload ( p < 0.01) compared to manual navigation, measured using the System Usability Scale (magnetic: 85.6 ± 9.3 vs. manual: 75.0 ± 17.8) and NASA Task Load Index (magnetic: 72.4 ± 13.5 vs. manual: 45.8 ± 16.7). Additionally, task completion times were shorter ( p < 0.01) with magnetic navigation (284.6 ± 80.7 s) compared to manual navigation (411.0 ± 123.7 s). Conclusion The findings of this study suggest that remote magnetic navigation using the Navion significantly improves operator experiences in terms of workload and usability, reinforcing the case for wider adoption of well-designed robotic systems in cardiac electrophysiology labs.
  • Interactive editing of virtual chordae tendineae for the simulation of the mitral valve in a decision support system
    Item type: Journal Article
    Walczak, Lars; Tautz, Lennart; Neugebauer, Mathias; et al. (2021)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose Decision support systems for mitral valve disease are an important step toward personalized surgery planning. A simulation of the mitral valve apparatus is required for decision support. Building a model of the chordae tendineae is an essential component of a mitral valve simulation. Due to image quality and artifacts, the chordae tendineae cannot be reliably detected in medical imaging. Methods Using the position-based dynamics framework, we are able to realistically simulate the opening and closing of the mitral valve. Here, we present a heuristic method for building an initial chordae model needed for a successful simulation. In addition to the heuristic, we present an interactive editor to refine the chordae model and to further improve pathology reproduction as well as geometric approximation of the closed valve. Results For evaluation, five mitral valves were reconstructed based on image sequences of patients scheduled for mitral valve surgery. We evaluated the approximation of the closed valves using either just the heuristic chordae model or a manually refined model. Using the manually refined models, prolapse was correctly reproduced in four of the five cases compared to two of the five cases when using the heuristic. In addition, using the editor improved the approximation in four cases. Conclusions Our approach is suitable to create realistically parameterized mitral valve apparatus reconstructions for the simulation of normally and abnormally closing valves in a decision support system.
  • Applicability of DICOM structured reporting for the standardized exchange of implantation plans
    Item type: Journal Article
    Treichel, Thomas; Liebmann, Philipp; Burgert, Oliver; et al. (2010)
    International Journal of Computer Assisted Radiology and Surgery
  • Automated and data-driven plate computation for presurgical cleft lip and palate treatment
    Item type: Journal Article
    Schnabel, Till N.; Gözcü, Baran; Gotardo, Paulo; et al. (2023)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose Presurgical orthopedic plates are widely used for the treatment of cleft lip and palate, which is the most common craniofacial birth defect. For the traditional plate fabrication, an impression is taken under airway-endangering conditions, which recent digital alternatives overcome via intraoral scanners. However, these alternatives demand proficiency in 3D modeling software in addition to the generally required clinical knowledge of plate design. Methods We address these limitations with a data-driven and fully automated digital pipeline, endowed with a graphical user interface. The pipeline adopts a deep learning model to landmark raw intraoral scans of arbitrary mesh topology and orientation, which guides the nonrigid surface registration subsequently employed to segment the scans. The plates that are individually fit to these segmented scans are 3D-printable and offer optional customization. Results With the distance to the alveolar ridges closely centered around the targeted 0.1 mm, our pipeline computes tightly fitting plates in less than 3 min. The plates were approved in 12 out of 12 cases by two cleft care professionals in a printed-model-based evaluation. Moreover, since the pipeline was implemented in clinical routine in two hospitals, 19 patients have been undergoing treatment utilizing our automated designs. Conclusion The results demonstrate that our automated pipeline meets the high precision requirements of the medical setting employed in cleft lip and palate care while substantially reducing the design time and required clinical expertise, which could facilitate access to this presurgical treatment, especially in low-income countries.
  • Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark
    Item type: Journal Article
    Ovinnikov, Ivan; Beuret, Ami; Cavaliere, Flavia; et al. (2024)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice. Methods Simulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees. A skill performance assessment protocol is presented based on the trained virtual agents. Results The proposed benchmark suite presents an API for training reinforcement learning agents in the context of arthroscopic skill training. The evaluation scheme based on both heuristic and learned reward functions robustly recovers the ground truth ranking on a diverse test set of human trajectories. Conclusion The presented benchmark enables the exploration of a novel reinforcement learning-based approach to skill performance assessment and in-procedure assistance for simulated surgical training scenarios. The evaluation protocol based on the learned reward model demonstrates potential for evaluating the performance of surgical trainees in simulation.
  • Uncertainty estimation for trust attribution to speed-of-sound reconstruction with variational networks
    Item type: Journal Article
    Laguna, Sonia; Zhang, Lin; Bezek, Can Deniz; et al. (2025)
    International Journal of Computer Assisted Radiology and Surgery
    PurposeSpeed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-angle computed-tomography problem, with variational networks being a promising model-based deep learning solution. Some acquired data frames may, however, get corrupted by noise due to, e.g., motion, lack of contact, and acoustic shadows, which in turn negatively affects the resulting SoS reconstructions.MethodsWe propose to use the uncertainty in SoS reconstructions to attribute trust to each individual acquired frame. Given multiple acquisitions, we then use an uncertainty-based automatic selection among these retrospectively, to improve diagnostic decisions. We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference.ResultsWe assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma. We evaluate 21 lesions classified as BI-RADS 4, which represents suspicious cases for probable malignancy. The most trustworthy frame among four acquisitions of each lesion was identified using uncertainty-based criteria. Selecting a frame informed by uncertainty achieved an area under curve of 76% and 80% for Monte Carlo Dropout and Bayesian Variational Inference, respectively, superior to any uncertainty-uninformed baselines with the best one achieving 64%.ConclusionA novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.
  • Self-supervised representation learning for surgical activity recognition
    Item type: Journal Article
    Paysan, Daniel; Haug, Luis; Bajka, Michael; et al. (2021)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose: Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way. Methods: We use self-supervised training of deep encoder-decoder architectures to learn representations of surgical trajectories from video data. These representations allow for semi-automatic extraction of features that capture information about semantically important events in the trajectories. Such features are processed as inputs of an unsupervised surgical activity recognition pipeline. Results: Our experiments document that the performance of hidden semi-Markov models used for recognizing activities in a simulated myomectomy scenario benefits from using features extracted from representations learned while training a deep encoder-decoder network on the task of predicting the remaining surgery progress. Conclusion: Our work is an important first step in the direction of making efficient use of features obtained from deep representation learning for surgical activity recognition in settings where only a small fraction of the existing data is annotated by human domain experts and where those annotations are potentially incomplete.
  • Precise proximal femur fracture classification for interactive training and surgical planning
    Item type: Journal Article
    Jiménez-Sánchez, Amelia; Kazi, Anees; Albarqouni, Shadi; et al. (2020)
    International Journal of Computer Assisted Radiology and Surgery
  • Geometric Meshes in Medical Applications - Steps towards a Specification of Geometric Models in DICOM
    Item type: Conference Paper
    Gessat, Michael; Zachow, Stefan; Lemke, Heinz U.; et al. (2007)
    International Journal of Computer Assisted Radiology and Surgery
  • Consistent reconstruction of 4D fetal heart ultrasound images to cope with fetal motion
    Item type: Journal Article
    Tanner, Christine; Flach, Barbara; Eggenberger, Céline; et al. (2017)
    International Journal of Computer Assisted Radiology and Surgery
    Purpose 4D ultrasound imaging of the fetal heart relies on reconstructions from B-mode images. In the presence of fetal motion, current approaches suffer from artifacts, which are unrecoverable for single sweeps. Methods We propose to use many sweeps and exploit the resulting redundancy to automatically recover from motion by reconstructing a 4D image which is consistent in phase, space, and time. An interactive visualization framework to view animated ultrasound slices from 4D reconstructions on arbitrary planes was developed using a magnetically tracked mock probe. Results We first quantified the performance of 10 4D reconstruction formulations on simulated data. Reconstructions of 14 in vivo sequences by a baseline, the current state-of-the-art, and the proposed approach were then visually ranked with respect to temporal quality on orthogonal views. Rankings from 5 observers showed that the proposed 4D reconstruction approach significantly improves temporal image quality in comparison with the baseline. The 4D reconstructions of the baseline and the proposed methods were then inspected interactively for accessibility to clinically important views and rated for their clinical usefulness by an ultrasound specialist in obstetrics and gynecology. The reconstructions by the proposed method were rated as ‘very useful’ in 71% and were statistically significantly more useful than the baseline reconstructions. Conclusions Multi-sweep fetal heart ultrasound acquisitions in combination with consistent 4D image reconstruction improves quality as well as clinical usefulness of the resulting 4D images in the presence of fetal motion.
Publications 1 - 10 of 43