Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions

Purpose

To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen.

Materials and methods

The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time.

Results

The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor.

Conclusion

The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.

     

Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model

Purpose

Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model.

Methods

Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles \((\hbox {act}_\mathrm{all})\), selecting the three muscles showing highest muscle activity bilaterally \((\hbox {act}_3)\)—this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance—and activating the muscles considered most relevant per instruction \((\hbox {act}_\mathrm{rel})\), bilaterally. The model’s lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients \((\rho )\).

Results

The correlation coefficient between simulations and measurements with \(\hbox {act}_\mathrm{rel}\) resulted in a median \(\rho \) of 0.77. \(\hbox {act}_3\) had a median \(\rho \) of 0.78, whereas with \(\hbox {act}_\mathrm{all}\) the median \(\rho \) decreased to 0.45.

Conclusion

We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median \(\rho \) of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient’s own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy.

     

Development of a vibration haptic simulator for shoulder arthroplasty

Purpose

Glenoid reaming is a technically challenging step during shoulder arthroplasty that could possibly be learned during simulation training. Creation of a realistic simulation using vibration feedback in this context is innovative. Our study focused on the development and internal validation of a novel glenoid reaming simulator for potential use as a training tool.

Methods

Vibration and force profiles associated with glenoid reaming were quantified during a cadaveric experiment. Subsequently, a simulator was fabricated utilizing a haptic vibration transducer with high- and low-fidelity amplifiers; system calibration was performed matching vibration peak–peak values for both amplifiers. Eight experts performed simulated reaming trials. The experts were asked to identify isolated layer profiles produced by the simulator. Additionally, experts’ efficiency to successfully perform a simulated glenoid ream based solely on vibration feedback was recorded.

Results

Cadaveric experimental cartilage reaming produced lower vibrations compared to subchondral and cancellous bones (\(p\le 0.03\)). Gain calibration of a lower-fidelity (3.5 \({g}_{\mathrm{pk-pk}}, 0.36\,{g}_{\mathrm{rms}})\) and higher-fidelity (3.4 \({g}_{\mathrm{pk-pk}}, 0.33\,{g}_{\mathrm{rms}})\) amplifier resulted in values similar to the cadaveric experimental benchmark (3.5 \({g}_{\mathrm{pk-pk}}, 0.30\,{g}_{\mathrm{rms}})\). When identifying random tissue layer samples, experts were correct \(52\pm 9\%\) of the time and success rate varied with tissue type (\(p=0.003\)). During simulated reaming, the experts stopped at the targeted subchondral bone with a success rate of \(78\pm 24\%\). The fidelity of the simulation did not have an effect on accuracy, applied force, or reaming time (\(p>0.05\)). However, the applied force tended to increase with trial number (\(p=0.047\)).

Conclusions

Development of the glenoid reaming simulator, coupled with expert evaluation furthered our understanding of the role of haptic vibration feedback during glenoid reaming. This study was the first to (1) propose, develop and examine simulated glenoid reaming, and (2) explore the use of haptic vibration feedback in the realm of shoulder arthroplasty.

     

Meaningful Assessment of Robotic Surgical Style using the Wisdom of Crowds

Objective

Quantitative assessment of surgical skills is an important aspect of surgical training; however, the proposed metrics are sometimes difficult to interpret and may not capture the stylistic characteristics that define expertise. This study proposes a methodology for evaluating the surgical skill, based on metrics associated with stylistic adjectives, and evaluates the ability of this method to differentiate expertise levels.

Methods

We recruited subjects from different expertise levels to perform training tasks on a surgical simulator. A lexicon of contrasting adjective pairs, based on important skills for robotic surgery, inspired by the global evaluative assessment of robotic skills tool, was developed. To validate the use of stylistic adjectives for surgical skill assessment, posture videos of the subjects performing the task, as well as videos of the task were rated by crowd-workers. Metrics associated with each adjective were found using kinematic and physiological measurements through correlation with the crowd-sourced adjective assignment ratings. To evaluate the chosen metrics’ ability in distinguishing expertise levels, two classifiers were trained and tested using these metrics.

Results

Crowd-assignment ratings for all adjectives were significantly correlated with expertise levels. The results indicate that naive Bayes classifier performs the best, with an accuracy of \(89\pm 12\), \(94\pm 8\), \(95\pm 7\), and \(100\pm 0\%\) when classifying into four, three, and two levels of expertise, respectively.

Conclusion

The proposed method is effective at mapping understandable adjectives of expertise to the stylistic movements and physiological response of trainees.

     

Ultrasound thermal monitoring with an external ultrasound source for customized bipolar RF ablation shapes

Purpose

Thermotherapy is a clinical procedure which delivers thermal energy to a target, and it has been applied for various medical treatments. Temperature monitoring during thermotherapy is important to achieve precise and reproducible results. Medical ultrasound can be used for thermal monitoring and is an attractive medical imaging modality due to its advantages including non-ionizing radiation, cost-effectiveness and portability. We propose an ultrasound thermal monitoring method using a speed-of-sound tomographic approach coupled with a biophysical heat diffusion model.

Methods

We implement an ultrasound thermometry approach using an external ultrasound source. We reconstruct the speed-of-sound images using time-of-flight information from the external ultrasound source and convert the speed-of-sound information into temperature by using the a priori knowledge brought by a biophysical heat diffusion model.

Results

Customized treatment shapes can be created using switching channels of radio frequency bipolar needle electrodes. Simulations of various ablation lesion shapes in the temperature range of 21–59 \(^\circ \)C are performed to study the feasibility of the proposed method. We also evaluated our method with ex vivo porcine liver experiments, in which we generated temperature images between 22 and 45 \(^\circ \)C.

Conclusion

In this paper, we present a proof of concept showing the feasibility of our ultrasound thermal monitoring method. The proposed method could be applied to various thermotherapy procedures by only adding an ultrasound source.

     

Reliability of computer-assisted periacetabular osteotomy using a minimally invasive approach

Background

Periacetabular osteotomy (PAO) is the treatment of choice for younger patients with developmental hip dysplasia. The procedure aims to normalize the joint configuration, reduce the peak-pressure, and delay the development of osteoarthritis. The procedure is technically demanding and no previous study has validated the use of computer navigation with a minimally invasive transsartorial approach.

Methods

Computer-assisted PAO was performed on ten patients. Patients underwent pre- and postoperative computed tomography (CT) scanning with a standardized protocol. Preoperative preparation consisted of outlining the lunate surface and segmenting the pelvis and femur from CT data. The Biomechanical Guidance System was used intra-operatively to automatically calculate diagnostic angles and peak-pressure measurements. Manual diagnostic angle measurements were performed based on pre- and postoperative CT. Differences in angle measurements were investigated with summary statistics, intraclass correlation coefficient, and Bland–Altman plots. The percentage postoperative change in peak-pressure was calculated.

Results

Intra-operative reported angle measurements show a good agreement with manual angle measurements with intraclass correlation coefficient between 0.94 and 0.98. Computer navigation reported angle measurements were significantly higher for the posterior sector angle (\(1.65^{\circ }\), \(p=0.001\)) and the acetabular anteversion angle (\(1.24^{\circ }\), \(p=0.004\)). No significant difference was found for the center-edge (\(p=0.056\)), acetabular index (\(p=0.212\)), and anterior sector angle (\(p=0.452\)). Peak-pressure after PAO decreased by a mean of 13% and was significantly different (\(p=0.008\)).

Conclusions

We found that computer navigation can reliably be used with a minimally invasive transsartorial approach PAO. Angle measurements generally agree with manual measurements and peak-pressure was shown to decrease postoperatively. With further development, the system will become a valuable tool in the operating room for both experienced and less experienced surgeons performing PAO. Further studies with a larger cohort and follow-up will allow us to investigate the association with peak-pressure and postoperative outcome and pave the way to clinical introduction.

     

Peripheral bronchial identification on chest CT using unsupervised machine learning

Purpose

To automatically identify small- to medium-diameter bronchial segments distributed throughout the lungs.

Methods

We segment the peripheral pulmonary vascular tree and construct cross-sectional images perpendicular to the lung vasculature. The bronchi running with pulmonary arteries appear as concentric rings, and potential center points that lie within the bronchi are identified by looking for circles (using the circular Hough transform) and rings (using a novel variable ring filter). The number of candidate bronchial center points are further reduced by using agglomerative hierarchical clustering applied to the points represented with 18 features pertaining to their 3D position, orientation and appearance of the surrounding cross-sectional image. Resulting clusters corresponded to bronchial segments. Parameters of the algorithm are varied and applied to two experimental data sets to find the best values for bronchial identification. The optimized algorithm was then applied to a further 21 CT studies obtained using two different CT vendors.

Results

The parameters that result in the most number of true positive bronchial center points with > 95% precision are a tolerance of 0.15 for the hierarchical clustering algorithm and a threshold of 75 HU with 10 spokes for the ring filter. Overall, the performance on all 21 test data sets from CT scans from both vendors demonstrates a mean number of 563 bronchial points detected per CT study, with a mean precision of 96%. The detected points across this group of test data sets are relatively uniformly distributed spatially with respect to spherical coordinates with the origin at the center of the test imaging data sets.

Conclusion

We have constructed a robust algorithm for automatic detection of small- to medium-diameter bronchial segments throughout the lungs using a combination of knowledge-based approaches and unsupervised machine learning. It appears robust over two different CT vendors with similar acquisition parameters.

     

AR in VR: assessing surgical augmented reality visualizations in a steerable virtual reality environment

Purpose 

Augmented reality (AR) has emerged as a promising approach to support surgeries; however, its application in real world scenarios is still very limited. Besides sophisticated registration tasks that need to be solved, surgical AR visualizations have not been studied in a standardized and comparative manner. To foster the development of future AR applications, a steerable framework is urgently needed to rapidly evaluate new visualization techniques, explore their individual parameter spaces and define relevant application scenarios.

Methods 

Inspired by its beneficial usage in the automotive industry, the underlying concept of virtual reality (VR) is capable of transforming complex real environments into controllable virtual ones. We present an interactive VR framework, called Augmented Visualization Box (AVB), in which visualizations for AR can be systematically investigated without explicitly performing an error-prone registration. As use case, a virtual laparoscopic scenario with anatomical surface models was created in a computer game engine. In a study with eleven surgeons, we analyzed this VR setting under different environmental factors and its applicability for a quantitative assessment of different AR overlay concepts.

Results 

According to the surgeons, the visual impression of the VR scene is mostly influenced by 2D surface details and lighting conditions. The AR evaluation shows that, depending on the visualization used and its capability to encode depth, 37% to 91% of the experts made wrong decisions, but were convinced of their correctness. These results show that surgeons have more confidence in their decisions, although they are wrong, when supported by AR visualizations.

Conclusion 

With AVB, intraoperative situations are realistically simulated to quantitatively benchmark current AR overlay methods. Successful surgical task execution in an AR system can only be facilitated if visualizations are customized toward the surgical task.

     

Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation

Purpose

Liver tumor extraction is essential for liver ablation surgery planning and treatment. For accurate and robust tumor segmentation, we propose a semiautomatic method using adaptive likelihood classification with modified likelihood model.

Methods

First, a minimal ellipse (or quasi-ellipsoid) that encloses a liver tumor is generated for initialization. Then, a hybrid intensity likelihood modification based on nonparametric density estimation is proposed to enhance local likelihood contrast and reduce its inhomogeneity. A prior elliptical (or quasi-ellipsoid) shape constraint is directly integrated into the likelihood to further prevent leakage of the algorithm into adjacent tissues with similar intensity. Finally, an adaptive likelihood classification is proposed for accurate segmentation of tumors with low contrast, high noise or heterogeneous densities.

Results

Experiments were performed on 3Dircadb and LiTS datasets. The average volumetric overlap errors of the 3Dircadb and LiTS datasets were 27.05 and 35.72%, respectively. The algorithm’s robustness was validated by comparing results of 5 operators with multiple selections on different tumors.

Conclusions

The proposed method achieved good results in different tumors, even in low-contrast tumors with blurred boundaries. Reliable results can still be achieved over different initializations by different operators using the proposed method.