International Journal of Computer Assisted Radiology and Surgery最新文献

Purpose

Surgical robotics have demonstrated their significance in assisting physicians during minimally invasive surgery. Especially, the integration of haptic and tactile feedback technologies can enhance the surgeon’s performance and overall patient outcomes. However, the current state-of-the-art lacks such interaction feedback opportunities, especially in robotic-assisted interventional magnetic resonance imaging (iMRI), which is gaining importance in clinical practice, specifically for percutaneous needle punctures.

Methods

The cable-driven ‘Micropositioning Robotics for Image-Guided Surgery’ (µRIGS) system utilized the back-electromotive force effect of the stepper motor load to measure cable tensile forces without external sensors, employing the TMC5160 motor driver. The aim was to generate a sensorless haptic feedback (SHF) for remote needle advancement, incorporating collision detection and homing capabilities for internal automation processes. Three different phantoms capable of mimicking soft tissue were used to evaluate the difference in force feedback between manual needle puncture and the SHF, both technically and in terms of user experience.

Results

The SHF achieved a sampling rate of 800 Hz and a mean force resolution of 0.26 ± 0.22 N, primarily dependent on motor current and rotation speed, with a mean maximum force of 15 N. In most cases, the SHF data aligned with the intended phantom-related force progression. The evaluation of the user study demonstrated no significant differences between the SHF technology and manual puncturing.

Conclusion

The presented SHF of the µRIGS system introduced a novel MR-compatible technique to bridge the gap between medical robotics and interaction during real-time needle-based interventions.

Purpose

Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network that incorporates skeleton information to enhance segmentation accuracy in these tubular structures.

Methods

Our approach employs a 3D convolutional network to extract 3D tubular structures from medical images such as CT volumetric images. We introduce a skeleton-guided module that operates on extracted features to capture and preserve the skeleton information in the segmentation results. Additionally, to effectively train our deep model in leveraging skeleton information, we propose a sigmoid-adaptive Tversky loss function which is specifically designed for skeleton segmentation.

Results

We conducted experiments on two distinct 3D medical image datasets. The first dataset consisted of 90 cases of chest CT volumetric images, while the second dataset comprised 35 cases of abdominal CT volumetric images. Comparative analysis with previous segmentation approaches demonstrated the superior performance of our method. For the airway segmentation task, our method achieved an average tree length rate of 93.0%, a branch detection rate of 91.5%, and a precision rate of 90.0%. In the case of abdominal artery segmentation, our method attained an average precision rate of 97.7%, a recall rate of 91.7%, and an F-measure of 94.6%.

Conclusion

We present a skeleton-guided 3D convolutional network to segment tubular structures from 3D medical images. Our skeleton-guided 3D convolutional network could effectively segment small tubular structures, outperforming previous methods.

Purpose

Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts.

Methods

We propose a simple and robust hand–eye calibration process for the depth camera of the Microsoft HoloLens 2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool.

Results

Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65 mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67 mm in an average time of 42 s.

Conclusion

This work enables real-time hand–eye calibration for the Microsoft HoloLens 2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.

Purpose

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival.

Methods

We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians.

Results

The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information.

Conclusion

The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

Purpose: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards.

Methods: We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).

Results: The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics.

Conclusion: This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Purpose: Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance of renal procedures, but it remains an open research challenge. We propose a novel method that doesn't require an initial registration estimate (a global method) and also handles registration ambiguity caused by the organ's natural symmetry. Combined with a registration refinement algorithm, this method achieves robust and accurate kidney registration while avoiding manual initialization.

Methods: We propose solving global registration in a three-step approach: (1) Automatic anatomical landmark localization, where 2 deep neural networks (DNNs) localize a set of landmarks in each modality. (2) Registration hypothesis generation, where potential registrations are computed from the landmarks with a deterministic variant of RANSAC. Due to the Kidney's strong bilateral symmetry, there are usually 2 compatible solutions. Finally, in Step (3), the correct solution is determined automatically, using a DNN classifier that resolves the geometric ambiguity. The registration may then be iteratively improved with a registration refinement method. Results are presented with state-of-the-art surface-based refinement-Bayesian coherent point drift (BCPD).

Results: This automatic global registration approach gives better results than various competitive state-of-the-art methods, which, additionally, require organ segmentation. The results obtained on 59 pairs of 3D US/CT kidney images show that the proposed method, combined with BCPD refinement, achieves a target registration error (TRE) of an internal kidney landmark (the renal pelvis) of 5.78 mm and an average nearest neighbor surface distance (nndist) of 2.42 mm.

Conclusion: This work presents the first approach for automatic kidney registration in US and CT images, which doesn't require an initial manual registration estimate to be known a priori. The results show a fully automatic registration approach with performances comparable to manual methods is feasible.

Purpose: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.

Materials and methods: The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard.

Results: AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation.

Conclusion: The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Purpose: Virtual reality (VR) allows for an immersive and interactive analysis of imaging data such as computed tomography (CT) and magnetic resonance imaging (MRI). The aim of this study is to assess the comprehensibility of VR anatomy and its value in assessing resectability of pancreatic ductal adenocarcinoma (PDAC).

Methods: This study assesses exposure to VR anatomy and evaluates the potential role of VR in assessing resectability of PDAC. Firstly, volumetric abdominal CT and MRI data were displayed in an immersive VR environment. Volunteering physicians were asked to identify anatomical landmarks in VR. In the second stage, experienced clinicians were asked to identify vascular involvement in a total of 12 CT and MRI scans displaying PDAC (2 resectable, 2 borderline resectable, and 2 locally advanced tumours per modality). Results were compared to 2D standard PACS viewing.

Results: In VR visualisation of CT and MRI, the abdominal anatomical landmarks were recognised by all participants except the pancreas (30/34) in VR CT and the splenic (31/34) and common hepatic artery (18/34) in VR MRI, respectively. In VR CT, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 22/24, 20/24 and 19/24 scans, respectively. Whereas, in VR MRI, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 19/24, 19/24 and 21/24 scans, respectively. Interobserver agreement as measured by Fleiss κ was 0.7 for CT and 0.4 for MRI, respectively (p < 0.001). Scans were significantly assessed more accurately in VR CT than standard 2D PACS CT, with a median of 5.5 (IQR 4.75-6) and a median of 3 (IQR 2-3) correctly assessed out of 6 scans (p < 0.001).

Conclusion: VR enhanced visualisation of abdominal CT and MRI scan data provides intuitive handling and understanding of anatomy and might allow for more accurate staging of PDAC and could thus become a valuable adjunct in PDAC resectability assessment in the future.

Purpose: The trajectory along which the cochlear implant electrode array is inserted influences the insertion forces and the probability for intracochlear trauma. Controlling the trajectory is especially relevant for reproducible conditions in electrode insertion tests. Using ex vivo cochlear specimens, manual alignment of the invisibly embedded cochlea is imprecise and hardly reproducible. The aim of this study was to develop a method for creating a 3D printable pose setting adapter to align a specimen along a desired trajectory toward an insertion axis.

Methods: Planning points of the desired trajectory into the cochlea were set using CBCT images. A new custom-made algorithm processed these points for automated calculation of a pose setting adapter. Its shape ensures coaxial positioning of the planned trajectory to both the force sensor measuring direction and the insertion axis. The performance of the approach was evaluated by dissecting and aligning 15 porcine cochlear specimens of which four were subsequently used for automated electrode insertions.

Results: The pose setting adapter could easily be integrated into an insertion force test setup. Its calculation and 3D printing was possible in all 15 cases. Compared to planning data, a mean positioning accuracy of 0.21 ± 0.10 mm at the level of the round window and a mean angular accuracy of 0.43° ± 0.21° were measured. After alignment, four specimens were used for electrode insertions, demonstrating the practical applicability of our method.

Conclusion: In this work, we present a new method, which enables automated calculation and creation of a ready-to-print pose setting adapter for alignment of cochlear specimens in insertion test setups. The approach is characterized by a high level of accuracy and reproducibility in controlling the insertion trajectory. Therefore, it enables a higher degree of standardization in force measurement when performing ex vivo insertion tests and thereby improves reliability in electrode testing.

Purpose: Surgical robots effectively improve the accuracy and safety of surgical procedures. Current optical-navigated oral surgical robots are typically developed based on binocular vision positioning systems, which are susceptible to factors including obscured visibility, limited workplace, and ambient light interference. Hence, the purpose of this study was to develop a lightweight robotic platform based on monocular vision for oral surgery that enhances the precision and efficiency of surgical procedures.

Methods: A monocular optical positioning system (MOPS) was applied to oral surgical robots, and a semi-autonomous robotic platform was developed utilizing monocular vision. A series of vitro experiments were designed to simulate dental implant procedures to evaluate the performance of optical positioning systems and assess the robotic system accuracy. The singular configuration detection and avoidance test, the collision detection and processing test, and the drilling test under slight movement were conducted to validate the safety of the robotic system.

Results: The position error and rotation error of MOPS were 0.0906 ± 0.0762 mm and 0.0158 ± 0.0069 degrees, respectively. The attitude angle of robotic arms calculated by the forward and inverse solutions was accurate. Additionally, the robot's surgical calibration point exhibited an average error of 0.42 mm, with a maximum error of 0.57 mm. Meanwhile, the robot system was capable of effectively avoiding singularities and demonstrating robust safety measures in the presence of minor patient movements and collisions during vitro experiment procedures.

Conclusion: The results of this in vitro study demonstrate that the accuracy of MOPS meets clinical requirements, making it a promising alternative in the field of oral surgical robots. Further studies will be planned to make the monocular vision oral robot suitable for clinical application.