Most kinematic structures in robot architectures for medical tasks are not optimal. Further, the workspace and payloads are often oversized which results in high product prices that are not suitable for a clinical technology transfer. To investigate optimal kinematic structures and configurations, we have developed an adaptive simulation framework with an associated workflow for requirement analyses, modelling and simulation of specific robot kinematics. The framework is used to build simple and cost effective medical robot designs and was evaluated in a tool manipulation task where medical instruments had to be positioned precisely and oriented on the patient's body. The model quality is measured based on the maximum workspace coverage according to a configurable scoring metric. The metric generalizes among different human body shapes that are based on anthropometric data from UMTRI Human Shape. This dexterity measure is used to analyze different kinematic structures in simulations using the open source simulation tool V-REP. Therefor we developed simulation and visualization procedures for medical tasks based on a patchwork of size-variant anatomical target regions that can be configured and selectively activated in a motion planning controller. In our evaluations we compared the dexterity scores of a commercial lightweight robot arm with 7 joints to optimized kinematic structures with 6, 7 and 8 joints. Compared to the commercial hardware, we achieved improvements of 59% when using an optimized 6- dimensional robot arm, 64% with the 7-dimensional arm and 96% with an 8-dimensional robot arm. Our results show that simpler robot designs can outperform the typically used commercial robot arms in medical applications where the maximum workspace coverage is essential. Our framework provides the basis for a fully automatic optimization tool of the robot parameters that can be applied to a large variety of problems.
In radiation therapy of abdominal targets, optimal tumor irradiation can be challenging due to intrafractional motion. Current target localization methods are mainly indirect, surrogate-based and the patient is exposed to additional radiation due to X-ray imaging. In contrast, 4D ultrasound (4DUS) imaging provides volumetric images of soft tissue tumors in real-time without ionizing radiation, facilitating a non-invasive, direct tracking method. In this study, the target was defined by features located in its local neighborhood. Features were extracted using the FAST detector and the BRISK descriptor, which were extended to 3D. To account for anatomical variability, a feature library was generated that contains manually annotated target information and relative locations of the features. During tracking, features were extracted from the current 4DUS volume and compared to the feature library. Recognized features are used to estimate feature position and shape. The developed method was evaluated in 4DUS sequences of the liver of three healthy subjects. For each dataset, a target was defined and manually contoured in a training and a test sequence. Training was used for library creation, the test sequence for target tracking. The target estimations are compared to the annotations to quantify a tracking error. The results show that binary feature libraries can be used for robust target localization in 4DUS data of the liver and could potentially serve as a tracking method less sensitive to target deformation.
4D ultrasound (4D US) is gaining relevance as a tracking method in radiation therapy (RT) with modern matrix array probes offering new possibilities for real-time target detection. However, for clinical implementation of USguided RT, image quality, volumetric framerate and artifacts caused by the probe’s presence during planning and / or setup computed tomography (CT) must be quantified. We compared three diagnostic 4D US systems with matrix array probes using a commercial wire phantom to measure spatial resolution as well as a calibration and a torso phantom to assess different image quality metrics. CT artifacts were quantified in the torso phantom by calculating the total variation and percentage of affected voxels between a reference CT scan and CT scans with probes in place. We found that state-of-the-art 4D US systems with small probes can fit inside the CT bore and cause fewer metal artifacts than larger probes. US image quality varies between systems and is task-dependent. Volume sizes and framerates are much higher than the commercial guidance solution for US-guided RT, warranting further investigation regarding clinical performance for image guidance.
This contribution introduces a computer- and robot-assisted framework for stereotactic neurosurgery on small animals. Two major elements of this framework are presented in detail: a robotic stereotactic assistant and the software framework for placement of probes into the brain. The latter integrates modules for registration, insertion control, and preoperative path planning. Two options for path planning are addressed: (a) atlas-based planning and (b) image-based planning based on computed tomography data. The framework is tested performing robot-assisted insertion of microelectrodes and acquisition of electrophysiological recordings in vivo. Concepts for data analysis pointing towards a mapping of position and neural structure to functional data are introduced. Results show that the presented framework allows precise small animal stereotaxy and therefore offers new options for brain research.
In this paper, we presented a deep convolutional neural network (CNN) approach for forehead tissue thickness estimation. We use down sampled NIR laser backscattering images acquired from a novel marker-less near-infrared laser-based head tracking system, combined with the beam’s incident angle parameter. These two-channel augmented images were constructed for the CNN input, while a single node output layer represents the estimated value of the forehead tissue thickness. The models were – separately for each subject – trained and tested on datasets acquired from 30 subjects (high resolution MRI data is used as ground truth). To speed up training, we used a pre-trained network from the first subject to bootstrap training for each of the other subjects. We could show a clear improvement for the tissue thickness estimation (mean RMSE of 0.096 mm). This proposed CNN model outperformed previous support vector regression (mean RMSE of 0.155 mm) or Gaussian processes learning approaches (mean RMSE of 0.114 mm) and eliminated their restrictions for future research.
Real-time target localization with ultrasound holds high potential for image guidance and motion compensation in radiosurgery due to its non-invasive image acquisition free from ionizing radiation. However, a two-step localization has to be performed when integrating ultrasound into the existing radiosurgery workflow. In addition to target localization inside the ultrasound volume, the probe itself has to be localized in order to transform the target position into treatment room coordinates. By adapting existing camera calibration tools, we have developed a method to extend the stereoscopic X-ray tracking system of a radiosurgery platform in order to locate objects such as marker geometries with six degrees of freedom. The calibration was performed with 0.1 mm reprojection error. By using the full area of the flat-panel detectors without pre-processing the extended software increased the tracking volume and resolution by up to 80%, substantially improving patient localization and marker detectability. Furthermore, marker-tracking showed sub-millimeter accuracy and rotational errors below 0.1°. This demonstrates that the developed extension framework can accurately localize marker geometries using an integrated X-ray system, establishing the link for the integration of real-time ultrasound image guidance into the existing system.
Transcranial alternating current stimulation (tACS) is an emerging non-invasive tool for modulating brain oscillations. There is evidence that weak oscillatory electrical stimulation during sleep can entrain cortical slow oscillations to improve the memory consolidation in rodents and humans. Using a novel method and a custom built stimulation device, automatic stimulation of slow oscillations in-phase with the endogenous activity in a real-time closed-loop setup is possible. Preliminary data from neuroplasticity experiments show a high detection performance of the proposed method, electrical measurements demonstrate the outstanding quality of the presented stimulation device.
Accuracy is essential for optical head-tracking in cranial radiotherapy. Recently, the exploitation of local patterns of tissue information was proposed to achieve a more robust registration. Here, we validate a ground truth for this information obtained from high resolution MRI scans. In five subjects we compared the segmentation accuracy of a semi-automatic algorithm with five human experts. While the algorithm segments the skin and bone surface with an average accuracy of less than 0.1 mm and 0.2 mm, respectively, the mean error on the tissue thickness was 0.17 mm. We conclude that this accuracy is a reasonable basis for extracting reliable cutaneous structures to support surface registration.