Large area robotically assisted optical coherence tomography (LARA-OCT).

We demonstrate large-area robotically assisted optical coherence tomography (LARA-OCT), utilizing a seven-degree-of-freedom robotic arm in conjunction with a 3.3 MHz swept-source OCT to raster scan samples of arbitrary shape. By combining multiple fields of view (FOV), LARA-OCT can probe a much larger area than conventional OCT. Also, nonplanar and curved surfaces like skin on arms and legs can be probed. The lenses in the LARA-OCT scanner with their normal FOV can have fewer aberrations and less complex optics compared to a single wide field design. This may be especially critical for high resolution scans. We directly use our fast MHz-OCT for tracking and stitching, making additional machine vision systems like cameras, positioning, tracking or navigation devices obsolete. This also eliminates the need for complex coordinate system registration between OCT and the machine vision system. We implemented a real time probe-to-surface control that maintains the probe alignment orthogonal to the sample by only using surface information from the OCT images. We present OCT data sets with volume sizes of 140 × 170 × 20 mm3, captured in 2.5 minutes.

Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series.

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient's neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning methods.Since decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch.

PURPOSE: Endovascular intervention is the state-of-the-art treatment for common cardiovascular diseases, such as heart attack and stroke. Automation of the procedure may improve the working conditions of physicians and provide high-quality care to patients in remote areas, posing a major impact on overall treatment quality. However, this requires the adaption to individual patient anatomies, which currently poses an unsolved challenge. METHODS: This work investigates an endovascular guidewire controller architecture based on recurrent neural networks. The controller is evaluated in-silico on its ability to adapt to new vessel geometries when navigating through the aortic arch. The controller's generalization capabilities are examined by reducing the number of variations seen during training. For this purpose, an endovascular simulation environment is introduced, which allows guidewire navigation in a parametrizable aortic arch. RESULTS: The recurrent controller achieves a higher navigation success rate of 75.0% after 29,200 interventions compared to 71.6% after 156,800 interventions for a feedforward controller. Furthermore, the recurrent controller generalizes to previously unseen aortic arches and is robust towards size changes of the aortic arch. Being trained on 2048 aortic arch geometries gives the same results as being trained with full variation when evaluated on 1000 different geometries. For interpolation a gap of 30% of the scaling range and for extrapolation additional 10% of the scaling range can be navigated successfully. CONCLUSION: Adaption to new vessel geometries is essential in the navigation of endovascular instruments. Therefore, the intrinsic generalization to new vessel geometries poses an essential step towards autonomous endovascular robotics.

Navigation with Polytopes: A Toolbox for Optimal Path Planning with Polytope Maps and B-spline Curves.

To deal with the problem of optimal path planning in 2D space, this paper introduces a new toolbox named "Navigation with Polytopes" and explains the algorithms behind it. The toolbox allows one to create a polytopic map from a standard grid map, search for an optimal corridor, and plan a safe B-spline reference path used for mobile robot navigation. Specifically, the B-spline path is converted into its equivalent Bézier representation via a novel calculation method in order to reduce the conservativeness of the constrained path planning problem. The conversion can handle the differences between the curve intervals and allows for efficient computation. Furthermore, two different constraint formulations used for enforcing a B-spline path to stay within the sequence of connected polytopes are proposed, one with a guaranteed solution. The toolbox was extensively validated through simulations and experiments.

Augmenting Image-Guided Procedures through In Situ Visualization of 3D Ultrasound via a Head-Mounted Display.

Medical ultrasound (US) is a commonly used modality for image-guided procedures. Recent research systems providing an in situ visualization of 2D US images via an augmented reality (AR) head-mounted display (HMD) were shown to be advantageous over conventional imaging through reduced task completion times and improved accuracy. In this work, we continue in the direction of recent developments by describing the first AR HMD application visualizing real-time volumetric (3D) US in situ for guiding vascular punctures. We evaluated the application on a technical level as well as in a mixed-methods user study with a qualitative prestudy and a quantitative main study, simulating a vascular puncture. Participants completed the puncture task significantly faster when using 3D US AR mode compared to 2D US AR, with a decrease of 28.4% in time. However, no significant differences were observed regarding the success rate of vascular puncture (2D US AR-50% vs. 3D US AR-72%). On the technical side, the system offers a low latency of 49.90 ± 12.92 ms and a satisfactory frame rate of 60 Hz. Our work shows the feasibility of a system that visualizes real-time 3D US data via an AR HMD, and our experiments show, furthermore, that this may offer additional benefits in US-guided tasks (i.e., reduced task completion time) over 2D US images viewed in AR by offering a vividly volumetric visualization.

Landmark tracking in 4D ultrasound using generalized representation learning.

PURPOSE: In this study, we present and validate a novel concept for target tracking in 4D ultrasound. The key idea is to replace image patch similarity metrics by distances in a latent representation. For this, 3D ultrasound patches are mapped into a representation space using sliced-Wasserstein autoencoders. METHODS: A novel target tracking method for 4D ultrasound is presented that performs tracking in a representation space instead of in images space. Sliced-Wasserstein autoencoders are trained in an unsupervised manner which are used to map 3D ultrasound patches into a representation space. The tracking procedure is based on a greedy algorithm approach and measuring distances between representation vectors to relocate the target . The proposed algorithm is validated on an in vivo data set of liver images. Furthermore, three different concepts for training the autoencoder are presented to provide cross-patient generalizability, aiming at minimal training time on data of the individual patient. RESULTS: Eight annotated 4D ultrasound sequences are used to test the tracking method. Tracking could be performed in all sequences using all autoencoder training approaches. A mean tracking error of 3.23 mm could be achieved using generalized fine-tuned autoencoders. It is shown that using generalized autoencoders and fine-tuning them achieves better tracking results than training subject individual autoencoders. CONCLUSION: It could be shown that distances between encoded image patches in a representation space can serve as a meaningful measure of the image patch similarity, even under realistic deformations of the anatomical structure. Based on that, we could validate the proposed tracking algorithm in an in vivo setting. Furthermore, our results indicate that using generalized autoencoders, fine-tuning on only a small number of patches from the individual patient provides promising results.

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation.

PURPOSE: During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators. METHODS: Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated. RESULTS: Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results. CONCLUSION: This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.

UltrARsound: in situ visualization of live ultrasound images using HoloLens 2.

PURPOSE: Augmented Reality (AR) has the potential to simplify ultrasound (US) examinations which usually require a skilled and experienced sonographer to mentally align narrow 2D cross-sectional US images in the 3D anatomy of the patient. This work describes and evaluates a novel approach to track retroreflective spheres attached to the US probe using an inside-out technique with the AR glasses HoloLens 2. Finally, live US images are displayed in situ on the imaged anatomy. METHODS: The Unity application UltrARsound performs spatial tracking of the US probe and attached retroreflective markers using the depth camera integrated into the AR glasses-thus eliminating the need for an external tracking system. Additionally, a Kalman filter is implemented to improve the noisy measurements of the camera. US images are streamed wirelessly via the PLUS toolkit to HoloLens 2. The technical evaluation comprises static and dynamic tracking accuracy, frequency and latency of displayed images. RESULTS: Tracking is performed with a median accuracy of 1.98 mm/1.81[Formula: see text] for the static setting when using the Kalman filter. In a dynamic scenario, the median error was 2.81 mm/1.70[Formula: see text]. The tracking frequency is currently limited to 20 Hz. 83% of the displayed US images had a latency lower than 16 ms. CONCLUSIONS: In this work, we showed that spatial tracking of retroreflective spheres with the depth camera of HoloLens 2 is feasible, achieving a promising accuracy for in situ visualization of live US images. For tracking, no additional hardware nor modifications to HoloLens 2 are required making it a cheap and easy-to-use approach. Moreover, a minimal latency of displayed images enables a real-time perception for the sonographer.

Using Deep Neural Networks to Improve Contact Wrench Estimation of Serial Robotic Manipulators in Static Tasks.

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches. We used a redundant serial light-weight manipulator (KUKA iiwa 7 R800) with integrated force estimation based on the joint torques measured in each of the robot's seven axes. Firstly, a simulated dataset is created to let a feed-forward net learn the relationship between end-effector contact wrenches and joint torques for a static case. Secondly, an extensive real training dataset was acquired with 330,000 randomized robot positions and end-effector contact wrenches and used for retraining the simulated trained feed-forward net. We can show that the wrench prediction error could be reduced by around 57% for the forces compared to the manufacturer's proprietary force estimation model. In addition, we show that the number of high outliers can be reduced substantially. Furthermore we prove that the approach could be also transferred to another robot (KUKA iiwa 14 R820) with reasonable prediction accuracy and without the need of acquiring new robot specific data.

Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research. Thus, there is a high demand to estimate the individual shape of inassessible structures given only information on the geometry of the surrounding tissue. This leads to a domain adaptation problem, where the domain gap could be very large while typically only small datasets are available. Hence, classical approaches for domain adaptation are not capable of providing sufficient predictions. In this work, we present a new framework for bridging this domain gap in the scope of estimating anatomical shapes based on the surrounding tissue's morphology. Thus, we propose deep representation learning to not map from one image to another but to predict a latent shape representation. We formalize this framework and present two different approaches to solve the given problem. Furthermore, we perform a proof-of-concept study for estimating the individual shape of the aortic valve leaflets based on a volumetric ultrasound image of the aortic root. Therefore, we collect an ex-vivo porcine data set consisting of both, ultrasound volume images as well as high-resolution leaflet images, evaluate both approaches on it and perform an analysis of the model's hyperparameters. Our results show that using deep representation learning and domain mapping between the identified latent spaces, a robust prediction of the unknown leaflet shape only based on surrounding tissue information is possible, even in limited data scenarios. The concept can be applied to a wide range of modeling tasks, not only in the scope of heart modeling but also for all kinds of inassessible structures within the human body.

Medical Device Development for Children and Young People-Reviewing the Challenges and Opportunities.

Development of specific medical devices (MDs) is required to meet the healthcare needs of children and young people (CYP). In this context, MD development should address changes in growth and psychosocial maturation, physiology, and pathophysiology, and avoid inappropriate repurposing of adult technologies. Underpinning the development of MD for CYP is the need to ensure MD safety and effectiveness through pediatric MD-specific regulations. Contrary to current perceptions of limited market potential, the global pediatric healthcare market is expected to generate around USD 15,984 million by 2025. There are 1.8 billion young people in the world today; 40% of the global population is under 24, creating significant future healthcare market opportunities. This review highlights a number of technology areas that have led to successful pediatric MD, including 3D printing, advanced materials, drug delivery, and diagnostic imaging. To ensure the targeted development of MD for CYP, collaboration across multiple professional disciplines is required, facilitated by a platform to foster collaboration and drive innovation. The European Pediatric Translational Research Infrastructure (EPTRI) will be established as the European platform to support collaboration, including the life sciences industrial sector, to identify unmet needs in child health and support the development, adoption, and commercialization of pediatric MDs.

Instrument localisation for endovascular aneurysm repair: Comparison of two methods based on tracking systems or using imaging.

BACKGROUND: In endovascular aneuysm repair (EVAR) procedures, medical instruments are currently navigated with a two-dimensional imaging based guidance requiring X-rays and contrast agent. METHODS: Novel approaches for obtaining the three-dimensional instrument positions are introduced. Firstly, a method based on fibre optical shape sensing, one electromagnetic sensor and a preoperative computed tomography (CT) scan is described. Secondly, an approach based on image processing using one 2D fluoroscopic image and a preoperative CT scan is introduced. RESULTS: For the tracking based method, average errors from 1.81 to 3.13 mm and maximum errors from 3.21 to 5.46 mm were measured. For the image-based approach, average errors from 3.07 to 6.02 mm and maximum errors from 8.05 to 15.75 mm were measured. CONCLUSION: The tracking based method is promising for usage in EVAR procedures. For the image-based approach are applications in smaller vessels more suitable, since its errors increase with the vessel diameter.

Towards automated ultrasound imaging-robotic image acquisition in liver and prostate for long-term motion monitoring.

Real-time volumetric (4D) ultrasound has shown high potential for diagnostic and therapy guidance tasks. One of the main drawbacks of ultrasound imaging to date is the reliance on manual probe positioning and the resulting user dependence. Robotic assistance could help overcome this issue and facilitate the acquisition of long-term image data to observe dynamic processesin vivoover time. The aim of this study is to assess the feasibility of robotic probe manipulation and organ motion quantification during extended imaging sessions. The system consists of a collaborative robot and a 4D ultrasound system providing real-time data access. Five healthy volunteers received liver and prostate scans during free breathing over 30 min. Initial probe placement was performed with real-time remote control with a predefined contact force of 10 N. During scan acquisition, the probe position was continuously adjusted to the body surface motion using impedance control. Ultrasound volumes, the pose of the end-effector and the estimated contact forces were recorded. For motion analysis, one anatomical landmark was manually annotated in a subset of ultrasound frames for each experiment. Probe contact was uninterrupted over the entire scan duration in all ten sessions. Organ drift and imaging artefacts were successfully compensated using remote control. The median contact force along the probe's longitudinal axis was 10.0 N with maximum values of 13.2 and 21.3 N for liver and prostate, respectively. Forces exceeding 11 N only occurred in 0.3% of the time. Probe and landmark motion were more pronounced in the liver, with median interquartile ranges of 1.5 and 9.6 mm, compared to 0.6 and 2.7 mm in the prostate. The results show that robotic ultrasound imaging with dynamic force control can be used for stable, long-term imaging of anatomical regions affected by motion. The system facilitates the acquisition of 4D image datain vivoover extended scanning periods for the first time and holds the potential to be used for motion monitoring for therapy guidance as well as diagnostic tasks.

Interdisciplinary Clinical Target Volume Generation for Cardiac Radioablation: Multicenter Benchmarking for the RAdiosurgery for VENtricular TAchycardia (RAVENTA) Trial.

PURPOSE: Cardiac radioablation is a novel treatment option for therapy-refractory ventricular tachycardia (VT) ineligible for catheter ablation. Three-dimensional clinical target volume (CTV) definition is a key step, and this complex interdisciplinary procedure includes VT-substrate identification based on electroanatomical mapping (EAM) and its transfer to the planning computed tomography (PCT). Benchmarking of this process is necessary for multicenter clinical studies such as the RAVENTA trial. METHODS AND MATERIALS: For benchmarking of the RAVENTA trial, patient data (epicrisis, electrocardiogram, high-resolution EAM, contrast-enhanced cardiac computed tomography, PCT) of 3 cases were sent to 5 university centers for independent CTV generation, subsequent structure analysis, and consensus finding. VT substrates were first defined on multiple EAM screenshots/videos and manually transferred to the PCT. The generated structure characteristics were then independently analyzed (volume, localization, surface distance and conformity). After subsequent discussion, consensus structures were defined. RESULTS: VT substrate on the EAM showed visible variability in extent and localization for cases 1 and 2 and only minor variability for case 3. CTVs ranged from 6.7 to 22.9 cm3, 5.9 to 79.9 cm3, and 9.4 to 34.3 cm3; surface area varied from 1087 to 3285 mm2, 1077 to 9500 mm2, and 1620 to 4179 mm2, with a Hausdorff-distance of 15.7 to 39.5 mm, 23.1 to 43.5 mm, and 15.9 to 43.9 mm for cases 1 to 3, respectively. The absolute 3-dimensional center-of-mass difference was 5.8 to 28.0 mm, 8.4 to 26 mm, and 3.8 to 35.1 mm for cases 1 to 3, respectively. The entire process resulted in CTV structures with a conformity index of 0.2 to 0.83, 0.02 to 0.85, and 0.02 to 0.88 (ideal 1) with the consensus CTV as reference. CONCLUSIONS: Multicenter efficacy endpoint assessment of cardiac radioablation for therapy-refractory VT requires consistent CTV transfer methods from the EAM to the PCT. VT substrate definition and CTVs were comparable with current clinical practice. Remarkable differences regarding the degree of agreement of the CTV definition on the EAM and the PCT were noted, indicating a loss of agreement during the transfer process between EAM and PCT. Cardiac radioablation should be performed under well-defined protocols and in clinical trials with benchmarking and consensus forming.

Tumor-dose-rate variations during robotic radiosurgery of oligo and multiple brain metastases.

PURPOSE: For step-and-shoot robotic stereotactic radiosurgery (SRS) the dose delivered over time, called local tumor-dose-rate (TDR), may strongly vary during treatment of multiple lesions. The authors sought to evaluate technical parameters influencing TDR and correlate TDR to clinical outcome. MATERIAL AND METHODS: A total of 23 patients with 162 oligo (1-3) and multiple (>3) brain metastases (OBM/MBM) treated in 33 SRS sessions were retrospectively analyzed. Median PTV were 0.11 cc (0.01-6.36 cc) and 0.50 cc (0.12-3.68 cc) for OBM and MBM, respectively. Prescription dose ranged from 16 to 20 Gy prescribed to the median 70% isodose line. The maximum dose-rate for planning target volume (PTV) percentage p in time span s during treatment (TDRs,p) was calculated for various p and s based on treatment log files and in-house software. RESULTS: TDR60min,98% was 0.30 Gy/min (0.23-0.87 Gy/min) for OBM and 0.22 Gy/min (0.12-0.63 Gy/min) for MBM, respectively, and increased by 0.03 Gy/min per prescribed Gy. TDR60min,98% strongly correlated with treatment time (ρ = -0.717, p < 0.001), monitor units (MU) (ρ = -0.767, p < 0.001), number of beams (ρ = -0.755, p < 0.001) and beam directions (ρ = -0.685, p < 0.001) as well as lesions treated per collimator (ρ = -0.708, P < 0.001). Median overall survival (OS) was 20 months and 1­ and 2­year local control (LC) was 98.8% and 90.3%, respectively. LC did not correlate with any TDR, but tumor response (partial response [PR] or complete response [CR]) correlated with all TDR in univariate analysis (e.g., TDR60min,98%: hazard ration [HR] = 0.974, confidence interval [CI] = 0.952-0.996, p = 0.019). In multivariate analysis only concomitant targeted therapy or immunotherapy and breast cancer tumor histology remained a significant factor for tumor response. Local grade ≥2 radiation-induced tissue reactions were noted in 26.3% (OBM) and 5.2% (MBM), respectively, mainly influenced by tumor volume (p < 0.001). CONCLUSIONS: Large TDR variations are noted during MBM-SRS which mainly arise from prolonged treatment times. Clinically, low TDR corresponded with decreased local tumor responses, although the main influencing factor was concomitant medication.

Three-dimensional guidance including shape sensing of a stentgraft system for endovascular aneurysm repair.

PURPOSE: During endovascular aneurysm repair (EVAR) procedures, medical instruments are guided with two-dimensional (2D) fluoroscopy and conventional digital subtraction angiography. However, this requires X-ray exposure and contrast agent is used, and the depth information is missing. To overcome these drawbacks, a three-dimensional (3D) guidance approach based on tracking systems is introduced and evaluated. METHODS: A multicore fiber with fiber Bragg gratings for shape sensing and three electromagnetic (EM) sensors for locating the shape were integrated into a stentgraft system. A model for obtaining the located shape of the first 38 cm of the stentgraft system with two EM sensors is introduced and compared with a method based on three EM sensors. Both methods were evaluated with a vessel phantom containing a 3D-printed vessel made of silicone and agar-agar simulating the surrounding tissue. RESULTS: The evaluation of the guidance methods resulted in average errors from 1.35 to 2.43 mm and maximum errors from 3.04 to 6.30 mm using three EM sensors, and average errors from 1.57 to 2.64 mm and maximum errors from 2.79 to 6.27 mm using two EM sensors. Moreover, the videos made from the continuous measurements showed that a real-time guidance is possible with both approaches. CONCLUSION: The results showed that an accurate real-time guidance with two and three EM sensors is possible and that two EM sensors are already sufficient. Thus, the introduced 3D guidance method is promising to use it as navigation tool in EVAR procedures. Future work will focus on developing a method with less EM sensors and a detailed latency evaluation of the guidance method.

Feasibility of an endovascular training and research environment with exchangeable patient specific 3D printed vascular anatomy: Simulator with exchangeable patient-specific 3D-printed vascular anatomy for endovascular training and research.

PURPOSE: Endovascular interventions have become standard procedures for the therapy of abdominal aortic aneurysms. Therefore, endovascular surgeons need special skills which have to be learned and trained. Additionally, authentic simulators are needed for further development of new endovascular devices and procedures. The aim of this project was to develop an authentic and modular endovascular simulation environment with patient-specific vascular anatomy for training and research purposes. MATERIAL AND METHODS: We first designed a prototype with exchangeable 3D-printed patient-specific vascular anatomy. Then, the feasibility of the prototype was validated by a simulation of an EVAR procedure in a clinical setting. RESULTS: We developed an authentic endovascular simulator with an exchangeable patient-specific vascular anatomy and performed an EVAR procedure under realistic conditions. The evaluation of the accuracy of the vascular models showed little deviation when compared with the original CT data. CONCLUSION: Endovascular simulators based on patient-specific 3D-printed vascular models can realistically mimic endovascular procedures and have the potential to be used for further development of new devices and grafts as well as for training purposes. Furthermore, in our opinion they can reduce the use of animals during developmental processes.

In-vivo treatment accuracy analysis of active motion-compensated liver SBRT through registration of plan dose to post-therapeutic MRI-morphologic alterations.

BACKGROUND/PURPOSE: In-vivo-accuracy analysis (IVA) of dose-delivery with active motion-management (gating/tracking) was performed based on registration of post-radiotherapeutic MRI-morphologic-alterations (MMA) to the corresponding dose-distributions of gantry-based/robotic SBRT-plans. METHODS: Forty targets in two patient cohorts were evaluated: (1) gantry-based SBRT (deep-inspiratory breath-hold-gating; GS) and (2) robotic SBRT (online fiducial-tracking; RS). The planning-CT was deformably registered to the first post-treatment contrast-enhanced T1-weighted MRI. An isodose-structure cropped to the liver (ISL) and corresponding to the contoured MMA was created. Structure and statistical analysis regarding volumes, surface-distance, conformity metrics and center-of-mass-differences (CoMD) was performed. RESULTS: Liver volume-reduction was -43.1 ±â€¯148.2 cc post-RS and -55.8 ±â€¯174.3 cc post-GS. The mean surface-distance between MMA and ISL was 2.3 ±â€¯0.8 mm (RS) and 2.8 ±â€¯1.1 mm (GS). ISL and MMA volumes diverged by 5.1 ±â€¯23.3 cc (RS) and 16.5 ±â€¯34.1 cc (GS); the median conformity index of both structures was 0.83 (RS) and 0.80 (GS). The average relative directional errors were ≤0.7 mm (RS) and ≤0.3 mm (GS); the median absolute 3D-CoMD was 3.8 mm (RS) and 4.2 mm (GS) without statistically significant differences between the two techniques. Factors influencing the IVA included GTV and PTV (p = 0.041 and p = 0.020). Four local relapses occurred without correlation to IVA. CONCLUSIONS: For the first time a method for IVA was presented, which can serve as a benchmarking-tool for other treatment techniques. Both techniques have shown median deviations <5 mm of planned dose and MMA. However, IVA also revealed treatments with errors ≥5 mm, suggesting a necessity for patient-specific safety-margins. Nevertheless, the treatment accuracy of well-performed active motion-compensated liver SBRT seems not to be a driving factor for local treatment failure.

Efficient Registration of High-Resolution Feature Enhanced Point Clouds.

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

A visual probe positioning tool for 4D ultrasound-guided radiotherapy.

Ultrasound (US) guidance is a rapidly growing area in image-guided radiotherapy. For motion compensation, the therapy target needs to be visualized with the US probe to continuously determine its position and adapt for shifts. While US has obvious benefits such as real-time capability and proven safety, one of the main drawbacks to date is its user dependency - high quality results require long years of clinical experience. To provide positioning assistance for the setup of US equipment by non-experts, we developed a visual guidance tool combining real-time US volume and CT visualization in a geometrically calibrated setup. By using a 4D US station with real-time data access and an optical tracking system, we achieved a calibration accuracy of 1.2 mm and a mean 2D contour distance of 1.7 mm between organ boundaries identified in US and CT. With this low calibration error as well as the good visual alignment of the structures, the developed probe positioning tool could be a valuable aid for ultrasound-guided radiotherapy and other interventions by guiding the user to a suitable acoustic window while potentially improving setup reproducibility.