A Novel Approach of Surface Texture Mapping for Cone-beam Computed Tomography in Image-guided Surgical Navigation.

The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

A decision tree network with semi-supervised entropy learning strategy for spectroscopy aided detection of blood hemoglobin.

Non-invasive techniques for rapid blood testing are gaining traction in global healthcare as they optimize medical screening, diagnosis and clinical decisions. Fourier transform infrared (FT-IR) spectroscopy is one of the most common technologies that can be used for non-destructive aided medical detection. Typically, after acquiring the Fourier transform infrared spectrum, spectral data preprocessing and feature extraction and quantitative analysis of several indicators of blood samples can be accomplished, in combination with chemometric method studies. At present, blood hemoglobin (HGB) concentration is one of the most valuable information for the clinical diagnosis of patient's health status. FT-IR spectroscopy is employed as a green technique aided medical test of blood HGB. Then the acquired HGB concentration data is switched to the spectral feature data by the studies of advanced chemometric method, in help for hiding the sensitive medical information to protect the privacy of patients. The decision tree network architecture is proposed for feature extraction of FT-IR data in order to find the small set of wavenumbers that are able to quantify HGB. A semi-supervised learning strategy is designed for tuning the number of network neuron nodes, in the way of searching for the maximum entropy increment. Each neuron is optimized by the growing of a semi-supervised decision tree, to accurately identify the informative FT-IR wavenumbers. The features extracted by the semi-supervised learning decision tree network guarantees the FT-IR aided detection model has high efficiency and high prediction accuracy. A model of quantifying the HGB concentration shows that the proposed decision tree network with semi-supervised entropy learning strategy outperforms the usual methods of full spectrum partial least square model and the fully connected neural network model in prediction accuracy. The framework is expected to support the FT-IR spectral technology for aided detection of medical and clinical data.

Optimization Model for the Distribution of Fiducial Markers in Liver Intervention.

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

Rapid Detection of Pomelo Fruit Quality Using Near-Infrared Hyperspectral Imaging Combined With Chemometric Methods.

Pomelo is an important agricultural product in southern China. Near-infrared hyperspectral imaging (NIRHI) technology is applied to the rapid detection of pomelo fruit quality. Advanced chemometric methods have been investigated for the optimization of the NIRHI spectral calibration model. The partial least squares (PLS) method is improved for non-linear regression by combining it with the kernel Gaussian radial basis function (RBF). In this study, the core parameters of the PLS latent variables and the RBF kernel width were designed for grid search selection to observe the minimum prediction error and a relatively high correlation coefficient. A deep learning architecture was proposed for the parametric scaling optimization of the RBF-PLS modeling process for NIRHI data in the spectral dimension. The RBF-PLS models were established for the quantitative prediction of the sugar (SU), vitamin C (VC), and organic acid (OA) contents in pomelo samples. Experimental results showed that the proposed RBF-PLS method performed well in the parameter deep search progress for the prediction of the target contents. The predictive errors for model training were 1.076% for SU, 41.381 mg/kg for VC, and 1.136 g/kg for OA, which were under 15% of their reference chemical measurements. The corresponding model testing results were acceptably good. Therefore, the NIRHI technology combined with the study of chemometric methods is applicable for the rapid quantitative detection of pomelo fruit quality, and the proposed algorithmic framework may be promoted for the detection of other agricultural products.

An Accurate Recognition of Infrared Retro-Reflective Markers in Surgical Navigation.

Marker-based optical tracking systems (OTS) are widely used in clinical image-guided therapy. However, the emergence of ghost markers, which is caused by the mistaken recognition of markers and the incorrect correspondences between marker projections, may lead to tracking failures for these systems. Therefore, this paper proposes a strategy to prevent the emergence of ghost markers by identifying markers based on the features of their projections, finding the correspondences between marker projections based on the geometric information provided by markers, and fast-tracking markers in a 2D image between frames based on the sizes of their projections. Apart from validating its high robustness, the experimental results show that the proposed strategy can accurately recognize markers, correctly identify their correspondences, and meet the requirements of real-time tracking.

Geometric calibration of markerless optical surgical navigation system.

BACKGROUND: Patient-to-image registration is required for image-guided surgical navigation, but marker-based registration is time consuming and is subject to manual error. Markerless registration is an alternative solution to avoid these issues. METHODS: This study designs a calibration board and proposes a geometric calibration method to calibrate the near-infrared tracking and structured light components of the proposed optical surgical navigation system simultaneously. RESULTS: A planar board and a cylinder are used to evaluate the accuracy of calibration. The mean error for the board experiment is 0.035 mm, and the diameter error for the cylinder experiment is 0.119 mm. A calibration board is reconstructed to evaluate the accuracy of the calibration, and the measured mean error is 0.012 mm. A head phantom is reconstructed and tracked by the proposed optical surgical navigation system. The tracking error is less than 0.3 mm. CONCLUSIONS: Experimental results show that the proposed method obtains high accessibility and accuracy and satisfies application requirements.

[Automatic Robotic Puncture System for Accurate Liver Cancer Ablation Based on Optical Surgical Navigation].

This paper designed an automatic robotic puncture system for accurate liver cancer ablation based on optical surgical navigation. The near-infrared optical surgical navigation system we constructed for liver ablation was applied to carry out surgical planning and simulation, the near-infrared cameras dynamically tracked the current position of puncture needle relative to the location of the patient's anatomy, then guided the surgery robot to position precisely in three-dimensional space and performed the surgery.

Simulation of multi-probe radiofrequency ablation guided by optical surgery navigation system under different active modes.

Radiofrequency ablation (RFA) is a crucial alternative treatment for liver cancer with the advantages of minimal invasion and a fast prognosis. However, two problems limit its further application: the orientation of the puncture point and the ablation of large tumors. The optical surgery navigation system in the RFA presents a promising approach for solving the localization problem in the puncturing process, which greatly increases puncture accuracy and has overcome the disadvantages of traditional RFA surgery. In addition, the use of multiple electrodes in the RFA (multi-probe RFA) is proposed and is applied clinically to deal with large tumors. In this study, we present a multi-probe RFA model using the finite element method (FEM) combined with a self-developed optical surgical navigation system. A real 3D liver model was adopted as an effective reference. Based on this model, two-probe RFA simulations were performed under different active modes. An analysis was conducted from the perspective of the temperature and electric potential fields and cell necrosis. The simulation results showed that different active modes had separate advantages and were suitable for different situations. Understanding their advantages can not only help doctors make surgical plans that fit the patients' conditions, but also the understanding can offer a virtual surgery platform for further development in the preoperative planning of RFA incorporated with the surgery navigation system.

Tracking multiple surgical instruments in a near-infrared optical system.

Surgical navigation systems can assist doctors in performing more precise and more efficient surgical procedures to avoid various accidents. The near-infrared optical system (NOS) is an important component of surgical navigation systems. However, several surgical instruments are used during surgery, and effectively tracking all of them is challenging. A stereo matching algorithm using two intersecting lines and surgical instrument codes is proposed in this paper. In our NOS, the markers on the surgical instruments can be captured by two near-infrared cameras. After automatically searching and extracting their subpixel coordinates in the left and right images, the coordinates of the real and pseudo markers are determined by the two intersecting lines. Finally, the pseudo markers are removed to achieve accurate stereo matching by summing the codes for the distances between a specific marker with the other two markers on the surgical instrument. Experimental results show that the markers on the different surgical instruments can be automatically and accurately recognized. The NOS can accurately track multiple surgical instruments.

Development and Validation of a Near-Infrared Optical System for Tracking Surgical Instruments.

Surgical navigation systems can help doctors maximize the accuracy of surgeries, minimize operation durations, avoid mistakes, and improve the survival chances of patients. The tracking of device is an important component in surgical navigation systems. However, commercial surgical tracking devices are expensive, thus hindering the development of surgical navigation systems, particularly in developing countries. Therefore, an accurate and low-cost near-infrared optical tracking system is presented in this study for the real-time tracking of surgical tools and for measuring and displaying the positions of these tools relative to lesions and other targets inside a patient's body. A relative algorithm for the registration of surgical tools is also proposed in this paper to yield easy, safe, and precise tracking. Experiments are conducted to test the performance of the system. Results show that the mean square errors of the distances between the light-emitting points on the surgical tools are less than 0.3 mm, with the mean square error of distance between the tip and light-emitting points is less than 0.025 mm and that between two adjacent corner points is 0.2714 mm.

Near-Infrared Camera Calibration for Optical Surgical Navigation.

Near-infrared optical tracking devices, which are important components of surgical navigation systems, need to be calibrated for effective tracking. The calibration results has a direct influence on the tracking accuracy of an entire system. Therefore, the study of calibration techniques is of theoretical significance and practical value. In the present work, a systematic calibration method based on movable plates is established, which analyzes existing calibration theories and implements methods using calibration reference objects. First, the distortion model of near-infrared cameras (NICs) is analyzed in the implementation of this method. Second, the calibration images from different positions and orientations are used to establish the required linear equations. The initial values of the NIC parameters are calculated with the direct linear transformation method. Finally, the accurate internal and external parameters of the NICs are obtained by conducting nonlinear optimization. Analysis results show that the relative errors of the left and right NICs in the tracking system are 0.244 and 0.282 % for the focal lengths and 0.735 and 1.111 % for the principal points, respectively. The image residuals of the left and right image sets are both less than 0.01 pixel. The standard error of the calibration result is lower than 1, and the measurement error of the tracking system is less than 0.3 mm. The experimental data show that the proposed method of calibrating NICs is effective and can generate favorable calibration results.

Strategy for accurate liver intervention by an optical tracking system.

Image-guided navigation for radiofrequency ablation of liver tumors requires the accurate guidance of needle insertion into a tumor target. The main challenge of image-guided navigation for radiofrequency ablation of liver tumors is the occurrence of liver deformations caused by respiratory motion. This study reports a strategy of real-time automatic registration to track custom fiducial markers glued onto the surface of a patient's abdomen to find the respiratory phase, in which the static preoperative CT is performed. Custom fiducial markers are designed. Real-time automatic registration method consists of the automatic localization of custom fiducial markers in the patient and image spaces. The fiducial registration error is calculated in real time and indicates if the current respiratory phase corresponds to the phase of the static preoperative CT. To demonstrate the feasibility of the proposed strategy, a liver simulator is constructed and two volunteers are involved in the preliminary experiments. An ex-vivo porcine liver model is employed to further verify the strategy for liver intervention. Experimental results demonstrate that real-time automatic registration method is rapid, accurate, and feasible for capturing the respiratory phase from which the static preoperative CT anatomical model is generated by tracking the movement of the skin-adhered custom fiducial markers.