Body-mounted robotic assistant for MRI-guided low back pain injection Abstract Purpose This paper presents the development of a body-mounted robotic assistant for magnetic resonance imaging (MRI)-guided low back pain injection. Our goal was to eliminate the radiation exposure of traditional X-ray guided procedures while enabling the exquisite image quality available under MRI. The robot is designed with a compact and lightweight profile that can be mounted directly on the patient’s lower back via straps, thus minimizing the effect of patient motion by moving along with the patient. The robot was built with MR-conditional materials and actuated with piezoelectric motors so it can operate inside the MRI scanner bore during imaging and therefore streamline the clinical workflow by utilizing intraoperative MR images. Methods The robot is designed with a four degrees of freedom parallel mechanism, stacking two identical Cartesian stages, to align the needle under intraoperative MRI-guidance. The system targeting accuracy was first evaluated in free space with an optical tracking system, and further assessed with a phantom study under live MRI-guidance. Qualitative imaging quality evaluation was performed on a human volunteer to assess the image quality degradation caused by the robotic assistant. Results Free space positioning accuracy study demonstrated that the mean error of the tip position to be \(0.51\pm 0.27\) mm and needle angle to be \(0.70^\circ \pm 0.38^\circ \) . MRI-guided phantom study indicated the mean errors of the target to be \(1.70\pm 0.21\) mm, entry point to be \(1.53\pm 0.19\) mm, and needle angle to be \(0.66^\circ \pm 0.43^\circ \) . Qualitative imaging quality evaluation validated that the image degradation caused by the robotic assistant in the lumbar spine anatomy is negligible. Conclusions The study demonstrates that the proposed body-mounted robotic system is able to perform MRI-guided low back injection in a phantom study with sufficient accuracy and with minimal visible image degradation that should not affect the procedure.

Multiresolution vessel detection in magnetic particle imaging using wavelets and a Gaussian mixture model Abstract Purpose Magnetic particle imaging is a tomographic imaging technique that allows one to measure the spatial distribution of superparamagnetic nanoparticles, which are used as tracer. The magnetic particle imaging scanner measures the voltage induced due to the nonlinear magnetization behavior of the nanoparticles. The tracer distribution can be reconstructed from the voltage signal by solving an inverse problem. A possible application is the imaging of vessel structures. In this and many other cases, the tracer is only located inside the structures and a large part of the image is related to background. A detection of the tracer support in early stages of the reconstruction process could improve reconstruction results. Methods In this work, a multiresolution wavelet-based reconstruction combined with a segmentation of the foreground structures is performed. For this, different wavelets are compared with respect to their reconstruction quality. For the detection of the foreground, a segmentation with a Gaussian mixture model is performed, which leads to a threshold-based binary segmentation. This segmentation is done on a coarse level of the reconstruction and then transferred to the next finer level, where it is used as prior knowledge for the reconstruction. This is repeated until the finest resolution is reached. Results The approach is evaluated on simulated vessel phantoms and on two real measurements. The results show that this method improves the structural similarity index of the reconstructed images significantly. Among the compared wavelets, the 9/7 wavelets led to the best reconstruction results. Conclusions The early detection of the vessel structures at low resolution helps to improve the image quality. For the wavelet decomposition, the use of 9/7 wavelets is recommended.

Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning Abstract Purpose For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment. Method The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments. Results The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy. Conclusion Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.

Soft tissue deformation tracking by means of an optimized fiducial marker layout with application to cancer tumors Abstract Objective Interventional radiology methods have been adopted for intraoperative control of the surgical region of interest (ROI) in a wide range of minimally invasive procedures. One major obstacle that hinders the success of procedures using interventional radiology methods is the preoperative and intraoperative deformation of the ROI. While fiducial markers (FM) tracing has been shown to be promising in tracking such deformations, determining the optimal placement of the FM in the ROI remains a significant challenge. The current study proposes a computational framework to address this problem by preoperatively optimizing the layout of FM, thereby enabling an accurate tracking of the ROI deformations. Methods The proposed approach includes three main components: (1) creation of virtual deformation benchmarks, (2) method of predicting intraoperative tissue deformation based on FM registration, and (3) FM layout optimization. To account for the large variety of potential ROI deformations, virtual benchmarks are created by applying a multitude of random force fields on the tumor surface in physically based simulations. The ROI deformation prediction is carried out by solving the inverse problem of finding the smoothest force field that leads to the observed FM displacements. Based on this formulation, a simulated annealing approach is employed to optimize the FM layout that produces the best prediction accuracy. Results The proposed approach is capable of finding an FM layout that outperforms the rationally chosen layouts by 40% in terms of ROI prediction accuracy. For a maximum induced displacement of 20 mm on the tumor surface, the average maximum error between the benchmarks and our FM-optimized predictions is about 1.72 mm, which falls within the typical resolution of ultrasound imaging. Conclusions The proposed framework can optimize FM layout to effectively reduce the errors in the intraoperative deformation prediction process, thus bridging the gap between preoperative imaging and intraoperative tissue deformation.

A microsurgical robot research platform for robot-assisted microsurgery research and training Abstract Purpose Ocular surgery, ear, nose and throat surgery and neurosurgery are typical types of microsurgery. A versatile training platform can assist microsurgical skills development and accelerate the uptake of robot-assisted microsurgery (RAMS). However, the currently available platforms are mainly designed for macro-scale minimally invasive surgery. There is a need to develop a dedicated microsurgical robot research platform for both research and clinical training. Methods A microsurgical robot research platform (MRRP) is introduced in this paper. The hardware system includes a slave robot with bimanual manipulators, two master controllers and a vision system. It is flexible to support multiple microsurgical tools. The software architecture is developed based on the robot operating system, which is extensible at high-level control. The selection of master–slave mapping strategy was explored, while comparisons were made between different interfaces. Results Experimental verification was conducted based on two microsurgical tasks for training evaluation, i.e. trajectory following and targeting. User study results indicated that the proposed hybrid interface is more effective than the traditional approach in terms of frequency of clutching, task completion time and ease of control. Conclusion Results indicated that the MRRP can be utilized for microsurgical skills training, since motion kinematic data and vision data can provide objective means of verification and scoring. The proposed system can further be used for verifying high-level control algorithms and task automation for RAMS research.

Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches Abstract Background The determination of surgeons’ psychomotor skills in minimally invasive surgery techniques is one of the major concerns of the programs of surgical training in several hospitals. Therefore, it is important to assess and classify objectively the level of experience of surgeons and residents during their training process. The aim of this study was to investigate three classification methods for establishing automatically the level of surgical competence of the surgeons based on their psychomotor laparoscopic skills. Methods A total of 43 participants, divided into an experienced surgeons group with ten experts (> 100 laparoscopic procedures performed) and non-experienced surgeons group with 24 residents and nine medical students (< 10 laparoscopic procedures performed), performed three tasks in the EndoViS training system. Motion data of the instruments were captured with a video-tracking system built into the EndoViS simulator and analyzed using 13 motion analysis parameters (MAPs). Radial basis function networks (RBFNets), K-star (K*), and random forest (RF) were used for classifying surgeons based on the MAPs’ scores of all participants. The performance of the three classifiers was examined using hold-out and leave-one-out validation techniques. Results For all three tasks, the K-star method was superior in terms of accuracy and AUC in both validation techniques. The mean accuracy of the classifiers was 93.33% for K-star, 87.58% for RBFNets, and 84.85% for RF in hold-out validation, and 91.47% for K-star, 89.92% for RBFNets, and 83.72% for RF in leave-one-out cross-validation. Conclusions The three proposed methods demonstrated high performance in the classification of laparoscopic surgeons, according to their level of psychomotor skills. Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscopic box trainers.

Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences Abstract Purpose For planning and guidance of minimally invasive mitral valve repair procedures, 3D+t transesophageal echocardiography (TEE) sequences are acquired before and after the intervention. The valve is then visually and quantitatively assessed in selected phases. To enable a quantitative assessment of valve geometry and pathological properties in all heart phases, as well as the changes achieved through surgery, we aim to provide a new 4D segmentation method. Methods We propose a tracking-based approach combining gradient vector flow (GVF) and position-based dynamics (PBD). An open-state surface model of the valve is propagated through time to the closed state, attracted by the GVF field of the leaflet area. The PBD method ensures topological consistency during deformation. For evaluation, one expert in cardiac surgery annotated the closed-state leaflets in 10 TEE sequences of patients with normal and abnormal mitral valves, and defined the corresponding open-state models. Results The average point-to-surface distance between the manual annotations and the final tracked model was \(1.00\,\hbox {mm} \pm 1.08\,\hbox {mm}\) . Qualitatively, four cases were satisfactory, five passable and one unsatisfactory. Each sequence could be segmented in 2–6 min. Conclusion Our approach enables to segment the mitral valve in 4D TEE image data with normal and pathological valve closing behavior. With this method, in addition to the quantification of the remaining orifice area, shape and dimensions of the coaptation zone can be analyzed and considered for planning and surgical result assessment.

Correction to: On the reproducibility of expert-operated and robotic ultrasound acquisitions The original version of this article unfortunately contained a mistake.

Prostate cancer detection using residual networks Abstract Purpose To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI). Methods A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study. Results The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations. Conclusion This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)—phase II: rupture risk assessment Abstract Purpose Assessing the rupture probability of intracranial aneurysms (IAs) remains challenging. Therefore, hemodynamic simulations are increasingly applied toward supporting physicians during treatment planning. However, due to several assumptions, the clinical acceptance of these methods remains limited. Methods To provide an overview of state-of-the-art blood flow simulation capabilities, the Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH) was conducted. Seventeen research groups from all over the world performed segmentations and hemodynamic simulations to identify the ruptured aneurysm in a patient harboring five IAs. Although simulation setups revealed good similarity, clear differences exist with respect to the analysis of aneurysm shape and blood flow results. Most groups (12/71%) included morphological and hemodynamic parameters in their analysis, with aspect ratio and wall shear stress as the most popular candidates, respectively. Results The majority of groups (7/41%) selected the largest aneurysm as being the ruptured one. Four (24%) of the participating groups were able to correctly select the ruptured aneurysm, while three groups (18%) ranked the ruptured aneurysm as the second most probable. Successful selections were based on the integration of clinically relevant information such as the aneurysm site, as well as advanced rupture probability models considering multiple parameters. Additionally, flow characteristics such as the quantification of inflow jets and the identification of multiple vortices led to correct predictions. Conclusions MATCH compares state-of-the-art image-based blood flow simulation approaches to assess the rupture risk of IAs. Furthermore, this challenge highlights the importance of multivariate analyses by combining clinically relevant metadata with advanced morphological and hemodynamic quantification.