Methods for online calibration of Q-matrix and item parameters for polytomous responses in cognitive diagnostic computerized adaptive testing

The ability to rapidly provide examinees with detailed and effective diagnostic information is a critical topic in psychology. Knowing what diagnostic criteria the examinees have met enables the practitioner to seek the solution to help them in a timely manner, and this can be achieved by cognitive diagnostic computerized adaptive testing (CD-CAT). However, the pervasive challenge of replenishing items in the CD-CAT item bank limits its practical application. Online calibration is a means to address item replenishment, but in CD-CAT, most existing online calibration methods that jointly calibrate the Q-matrix and item parameters of the new items are developed only for dichotomous responses and are time-consuming. Notably, previous studies pay no attention to polytomously scored items that are frequently observed in testing, even though they can offer additional evidence for the examinees' diagnosis. To fill this gap, we propose a SCAD-based method (SCAD-EM) to calibrate the Q-matrix and item parameters of the new items with polytomous response data in order to promote the application of CD-CAT in practice. The performance of the SCAD-EM was investigated in two comprehensive simulation studies and compared against the revised single-item estimation method (SIE-BIC). Results indicated that the SCAD-EM produces a higher calibration accuracy for the category-level Q-matrix and is computationally more efficient across all conditions, but it produces a lower calibration accuracy for the item-level Q-matrix. An empirical study further demonstrated the utility of the SCAD-EM and the SIE-BIC methods in calibrating new items with a real dataset. The advantages of the proposed method, its limitations, and possible future research directions are offered at the end.

An iterative two-step method for online item calibration in CD-CAT

The development and maintenance of the item bank is a critical element to a CD-CAT (cognitive diagnostic computerized adaptive testing; Cheng, 2009) system. For continuous testing, it is important to replenish the item bank with new items that have been calibrated. This requires pretesting to estimate the parameters of the new items. For CD-CAT, the structural parameters that need to be estimated include both item parameters and attribute vectors. In this paper, we propose three residual-statistic-based methods: RMA, ROEM, and RMEM, to estimate the attribute vectors and item parameters all together for new items. An iterative two-step online calibration procedure is developed to estimate the attribute vectors for the new items in the first step, and estimate the item parameters in the second step, then proceed iteratively until convergence is reached. An extensive simulation study was conducted to evaluate the performance of the three proposed methods and compare them with two existing methods, namely the Joint Estimation Algorithm (JEA; Chen & Xin, 2011) and Single Item Estimation (SIE; Chen et al., 2015) methods. In terms of the estimation of the attribute vector, the RMEM method performs the best in most of the cases. In terms of item parameter estimation, RMEM still has some advantages, and RMA outperforms JEA and SIE. Taken together, results suggest that the RMEM is superior to the other methods, especially when sample size is relatively small. A real-data example is provided to illustrate the application of RMEM in practice.

Pareto-like sequential sampling heuristic for global optimisation

In this paper, we propose a simple global optimisation algorithm inspired by Pareto's principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening of the prominent domains while the greediness of the algorithm increases over time (iterations). Unlike traditional metaheuristics, the proposed method has no direct mutation- or crossover-like operations. It depends solely on the sequential random sampling that can be used in diversification and intensification processes while keeping the information-flow between generations and the structural bias at a minimum. By using a simple topology, the algorithm avoids premature convergence by sampling new solutions every generation. A simple theoretical derivation revealed that the exploration of this approach is unbiased and the rate of the diversification is constant during the runtime. The trade-off balance between the diversification and the intensification is explained theoretically and experimentally. This proposed approach has been benchmarked against standard optimisation problems as well as a selected set of simple and complex engineering applications. We used 26 standard benchmarks with different properties that cover most of the optimisation problems' nature, three traditional engineering problems, and one real complex engineering problem from the state-of-the-art literature. The algorithm performs well in finding global minima for nonconvex and multimodal functions, especially with high dimensional problems and it was found very competitive in comparison with the recent algorithmic proposals. Moreover, the algorithm outperforms and scales better than recent algorithms when it is benchmarked under a limited number of iterations for the composite CEC2017 problems. The design of this algorithm is kept simple so it can be easily coupled or hybridised with other search paradigms. The code of the algorithm is provided in C++14, Python3.7, and Octave (Matlab).

Homography-based robust pose compensation and fusion imaging for augmented reality based endoscopic navigation system

BACKGROUND

Augmented reality (AR) based fusion imaging in endoscopic surgeries rely on the quality of image-to-patient registration and camera calibration, and these two offline steps are usually performed independently to get the target transformation separately. The optimal solution can be obtained under independent conditions but may not be globally optimal. All residual errors will be accumulated and eventually lead to inaccurate AR fusion.

METHODS

After a careful analysis of the principle of AR imaging, a robust online calibration framework was proposed for an endoscopic camera to enable accurate AR fusion. A 2D checkerboard-based homography estimation algorithm was proposed to estimate the local pose of the endoscopic camera, and the least square method was used to calculate the compensation matrix in combination with the optical tracking system.

RESULTS

In comparison with conventional methods, the proposed compensation method improved the performance of AR fusion, which reduced physical error by up to 82%, reduced pixel error by up to 83%, and improved target coverage by up to 6%. Experimental results of simulating mechanical noise revealed that the proposed compensation method effectively corrected the fusion errors caused by the rotation of the endoscopic tube without recalibrating the camera. Furthermore, the simulation results revealed the robustness of the proposed compensation method to noises.

CONCLUSIONS

Overall, the experiment results proved the effectiveness of the proposed compensation method and online calibration framework, and revealed a considerable potential in clinical practice.

Online mobile C-arm calibration using inertial sensors: a preliminary study in order to achieve CBCT

PURPOSE

Cone beam computed tomography (CBCT) became increasingly popular over the last years. It allows more accurate diagnosis and treatment planning with a lower effective radiation dose. However, volume reconstruction algorithms require a very precise knowledge of the imaging geometry. Due to mechanical instabilities, mobile C-arms are incompatible with existing tomography algorithms. Therefore, C-arm online calibration is essential in order to achieve an accurate volume reconstruction.

METHODS

We present an online calibration method for mobile C-arms. It is based on tracking the detector and the X-ray source of the C-arm using three-axis gyroscopes and accelerometers. It aims to be precise and noninvasive. The performance of the calibration algorithm is evaluated in regard to the precision of the sensors and to whether or not dynamic models are considered. In addition, we present an algorithm which propagate the errors from the positions and orientations estimates to the 2D projections on the detector plane. Thus, we can evaluate the impact of the estimation errors on the acquired images.

RESULTS

The experiments are conducted on an experimental C-arm. The reached accuracy is [Formula: see text] for orientation and [Formula: see text] for position. These errors propagate as an error of [Formula: see text] for the 2D projections on the detector plane.

CONCLUSIONS

The proposed calibration algorithm achieves an accuracy comparable to the precision of existing calibration methods. The required angle accuracy by CBCT algorithms is reached. However, improvements are needed to achieve the required position precision. The in-plane translations of the X-ray source and the detector are the most crucial parameters to estimate in order to conduct CBCT on mobile C-arms.

Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems

PURPOSE

Cone beam computed tomography (CBCT) systems offer physicians crucial 3D and 2D imaging capabilities during interventions. However, certain medical applications only require very specific information from the CBCTs (e.g., determination of the position of high-contrast objects). In diagnostics, tomosynthesis techniques can be used in these cases to minimize dose exposure. Therefore, integrating such techniques on CBCT systems could also be beneficial for interventions. In this paper, we investigate the performance of our implementation of circular tomosynthesis on a CBCT device.

METHODS

The tomosynthesis scan trajectory is realized with step-and-shoot on a clinical C-arm device. The online calibration algorithm uses conventionally acquired 3D CBCT of the scanned object as prior knowledge to correct the imaging geometries. The online calibration algorithm was compared to an offline calibration to test its performance. A ball bearing phantom was used to evaluate the reconstructions with respect to geometric distortions. The evaluation was done for three different scenarios to test the robustness of our tomosynthesis implementation against object deviations (e.g., pen) and different object positioning.

RESULTS

The circular tomosynthesis was tested on a ball bearing and an anthropomorphic phantom. The results show that the calibration is robust against isocenter shifts and object deviations in the CBCT. All reconstructions used 100 projections and displayed limited angle artifacts. The accuracy of the positions and shapes of high-contrast objects were, however, determined precisely. (The maximal center position deviation is 0.31 mm.) CONCLUSION: For medical procedures that primarily determine the precise position of high-contrast objects, circular tomosynthesis could offer an approach to reduce dose exposure.

Gaze gesture based human robot interaction for laparoscopic surgery

While minimally invasive surgery offers great benefits in terms of reduced patient trauma, bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic challenges. Laparoscopic surgery requires the surgeon to bimanually control surgical instruments during the operation. A dedicated assistant is thus required to manoeuvre the camera, which is often difficult to synchronise with the surgeon's movements. This article introduces a robotic system in which a rigid endoscope held by a robotic arm is controlled via the surgeon's eye movement, thus forgoing the need for a camera assistant. Gaze gestures detected via a series of eye movements are used to convey the surgeon's intention to initiate gaze contingent camera control. Hidden Markov Models (HMMs) are used for real-time gaze gesture recognition, allowing the robotic camera to pan, tilt, and zoom, whilst immune to aberrant or unintentional eye movements. A novel online calibration method for the gaze tracker is proposed, which overcomes calibration drift and simplifies its clinical application. This robotic system has been validated by comprehensive user trials and a detailed analysis performed on usability metrics to assess the performance of the system. The results demonstrate that the surgeons can perform their tasks quicker and more efficiently when compared to the use of a camera assistant or foot switches.