Automatic localization of the center of fiducial markers in 3D CT/MRI images for image-guided neurosurgery

Tele-manipulative Neuro-registration in Robot-assisted Neurosurgery

BACKGROUND

Accurate neuro-registration is important as the success of the surgical procedure highly depends on it. This article deals with neuro-registration using tele-manipulation (Master-Slave Manipulation) to facilitate tele-surgery and enhance the overall accuracy and reach of the robot-assisted neurosurgery.

METHODS

A 6 degrees-of-freedom parallel kinematic mechanism (6D-PKM) master-slave robot in tele-manipulation mode is utilized for both neuro-registration and neurosurgery. Real-time kinematic control of 6D-PKM is made possible by solving its forward kinematics using the trajectory modifier algorithm with an accuracy of 1 μm and 0.001° in translation and orientation, respectively, in real time. The master operator using the 6D-PKM master mechanism moves the 6D-PKM slave robot equipped with a touch probe stylus (4 mm diameter) in tele-manipulation mode. In neuro-registration, the slave is remotely guided to touch the fiducial marker in a predetermined order. A correlation between the medical image space and the real patient space is made to establish the neuro-registration. The accuracy of neuro-registration is validated through experiments on skull phantoms. These phantoms are designed to simulate the neurosurgical process.

RESULTS

The neuro-registration process successfully registers the phantoms, and maximum registration error is found to be 0.6 mm. The accuracy of neurosurgery is validated using several target points in phantom. The accuracy of registration is also verified by robot piercing a 2-mm-diameter surgical needle through a predesignated 3-mm-diameter cylindrical target hole with radial clearance of 500 μm.

CONCLUSION

Accurate neuro-registration using tele-manipulation has been demonstrated. The overall accuracy of the robot-based neurosurgery is tabulated. This approach eliminates line-of-sight issue and the requirement of an additional unit for neuro-registration. This minimizes the registration time and makes intraoperative registration feasible.

A fully automatic surgical registration method for percutaneous abdominal puncture surgical navigation

Surgical registration that maps surgical space onto image space plays an important role in surgical navigation. Accurate surgical registration can help surgeons efficiently locate surgical instruments. The complicated marker-based surgical registration method is highly accurate, but it is time-consuming. Therefore, a marker-less surgical registration method with high-precision and high-efficiency is proposed without human intervention. Firstly, the surgical navigation system based on the multi-vision system is calibrated by using a specially-designed calibration board. When extracting the abdominal point cloud acquired by the structured light vision system, the constraint is constructed by using Computed Tomography (CT) image to filter out the points in irrelevant areas to improve the computational efficiency. The Coherent Point Drift (CPD) algorithm based on Gaussian Mixture Model (GMM) is applied in the registration of abdominal point cloud with lack of surface features. To enhance the efficiency of the CPD algorithm, firstly, the system calibration result is used in rough registration of the point cloud, and then the proper point cloud pretreatment method and its parameters are studied through experiments. Finally, the puncturing simulation experiments were carried out by using the abdominal phantom. The experimental results show that the proposed surgical registration method has high accuracy and efficiency, and has potential clinical application value.

A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

ROBUST SURFACE-MATCHING REGISTRATION BASED ON THE STRUCTURE INFORMATION FOR IMAGE-GUIDED NEUROSURGERY SYSTEM

Image-to-patient space registration is to make the accurate alignment between the actual operating space and the image space. Although the image-to-patient space registration using paired-point is used in some image-guided neurosurgery systems, the current paired-point registration method has some drawbacks and usually cannot achieve the best registration result. Therefore, surface-matching registration is proposed to solve this problem. This paper proposes a surface-matching method that accomplishes image-to-patient space registration automatically. We represent the surface point clouds by the Gaussian Mixture Model (GMM), which can smoothly approximate the probability density distribution of an arbitrary point set. We also use mutual information as the similarity measure between the point clouds and take into account the structure information of the points. To analyze the registration error, we introduce a method for the estimation of Target Registration Error (TRE) by generating simulated data. In the experiments, we used the point sets of the cranium surface and the model of the human head determined by a CT and laser scanner. The TRE was less than 2[Formula: see text]mm, and the TRE had better accuracy in the front and the posterior region. Compared to the Iterative Closest Point algorithm, the surface registration based on GMM and the structure information of the points proved superior in registration robustness and accurate implementation of image-to-patient registration.

Automated fiducial marker detection and fiducial point localization in CT images for lung biopsy image-guided surgery systems

In the lung biopsy image-guided surgery systems, the fiducial markers are used for point-based registration of the patient space to the CT image space. Fiducial marker detection and fiducial point localization in CT images have great influence on the accuracy of registration and guidance. This study proposes a fiducial marker detection approach based on the features of marker image slice sequences and a fiducial point localization approach according to marker projection images, without depending on the priori-knowledge of the marker default parameters provided by the manufacturers. The accuracy of our method was validated based on a CT image dataset of 24 patients. The experimental results showed that all 144 markers of 24 patients were correctly detected, and the fiducial points were localized with the average error of 0.35 mm. In addition, the localization accuracy of the proposed method was improved by an average of 12.5% compared with the accuracy of the previous method using the marker default parameters provided by the manufacturers. Thus, the study demonstrated that the proposed detection and localization methods are accurate and robust, which is quite encouraging to meet the requirement of future clinical applications in the image guided lung biopsy and surgery systems.

Fiducial-based registration with a touchable region model

PURPOSE

Image-guided surgery requires registration between an image coordinate system and an intraoperative coordinate system that is typically referenced to a tracking device. In fiducial-based registration methods, this is achieved by localizing points (fiducials) in each coordinate system. Often, both localizations are performed manually, first by picking a fiducial point in the image and then by using a hand-held tracked pointer to physically touch the corresponding fiducial on the patient. These manual procedures introduce localization error that is user-dependent and can significantly decrease registration accuracy. Thus, there is a need for a registration method that is tolerant of imprecise fiducial localization in the preoperative and intraoperative phases.

METHODS

We propose the iterative closest touchable point (ICTP) registration framework, which uses model-based localization and a touchable region model. This method consists of three stages: (1) fiducial marker localization in image space, using a fiducial marker model, (2) initial registration with paired-point registration, and (3) fine registration based on the iterative closest point method.

RESULTS

We perform phantom experiments with a fiducial marker design that is commonly used in neurosurgery. The results demonstrate that ICTP can provide accuracy improvements compared to the standard paired-point registration method that is widely used for surgical navigation and surgical robot systems, especially in cases where the surgeon introduces large localization errors.

CONCLUSIONS

The results demonstrate that the proposed method can reduce the effect of the surgeon's localization performance on the accuracy of registration, thereby producing more consistent and less user-dependent registration outcomes.

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.

Co-registration of glucose metabolism with positron emission tomography and vascularity with fluorescent diffuse optical tomography in mouse tumors

BACKGROUND

Bimodal molecular imaging with fluorescence diffuse optical tomography (fDOT) and positron emission tomography (PET) has the capacity to provide multiple molecular information of mouse tumors. The objective of the present study is to co-register fDOT and PET molecular images of tumors in mice automatically.

METHODS

The coordinates of bimodal fiducial markers (FM) in regions of detection were automatically detected in planar optical images (x, y positions) in laser pattern optical surface images (z position) and in 3-D PET images. A transformation matrix was calculated from the coordinates of the FM in fDOT and in PET and applied in order to co-register images of mice bearing neuroendocrine tumors.

RESULTS

The method yielded accurate non-supervised co-registration of fDOT and PET images. The mean fiducial registration error was smaller than the respective voxel sizes for both modalities, allowing comparison of the distribution of contrast agents from both modalities in mice. Combined imaging depicting tumor metabolism with PET-[18 F]2-deoxy-2-fluoro-d-glucose and blood pool with fDOT demonstrated partial overlap of the two signals.

CONCLUSIONS

This automatic method for co-registration of fDOT with PET and other modalities is efficient, simple and rapid, opening up multiplexing capacities for experimental in vivo molecular imaging.

Automated detection of fiducial screws from CT/DVT volume data for image-guided ENT surgery

This paper presents an automated solution for precise detection of fiducial screws from three-dimensional (3D) Computerized Tomography (CT)/Digital Volume Tomography (DVT) data for image-guided ENT surgery. Unlike previously published solutions, we regard the detection of the fiducial screws from the CT/DVT volume data as a pose estimation problem. We thus developed a model-based solution. Starting from a user-supplied initialization, our solution detects the fiducial screws by iteratively matching a computer aided design (CAD) model of the fiducial screw to features extracted from the CT/DVT data. We validated our solution on one conventional CT dataset and on five DVT volume datasets, resulting in a total detection of 24 fiducial screws. Our experimental results indicate that the proposed solution achieves much higher reproducibility and precision than the manual detection. Further comparison shows that the proposed solution produces better results on the DVT dataset than on the conventional CT dataset.