Lung nodule pre-diagnosis and insertion path planning for chest CT images.

Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.

Experimental study of the optimum puncture pattern of robot-assisted needle insertion into hyperelastic materials.

The robot-assisted insertion surgery plays a crucial role in biopsy and therapy. This study focuses on determining the optimum puncture pattern for robot-assisted insertion, aiming at the matching problem of needle insertion parameters, thereby to reduce the pain for patients and to improve the reachability to the lesion point. First, a 6-degrees of freedom (DOFs) Computed Tomography (CT)-guided surgical robotic system for minimally invasive percutaneous lung is developed and used to perform puncture experiments. The effects of four main insertion factors on the robotic puncture are verified by designing the orthogonal test, where the inserting object is the artificial skin-like specimen with high transparent property and a digital image processing method is used to analyze the needle tip deflection. Next, the various phases of puncture process are divided and analyzed in detail in view of the tissue deformation and puncture force. Then, short discussion on the comparison of puncture force with different effect factors for the same beveled needle is presented. The same pattern can be observed for all of the cases. Finally, based on the experimental data, the formulations of the puncture force and needle deflection which depends on Gauge size, insertion velocity, insertion angle, and insertion depth are developed using the multiple regression method, which can be used to get an optimum puncture pattern under the constrains of minimum peak force and minimum needle tip deflection. The developed models have the effectiveness and applicability on determining the optimum puncture pattern for one puncture event, and which can also provide insights useful for the setting of insertion parameters in clinical practice.

Lesion positioning method of a CT-guided surgical robotic system for minimally invasive percutaneous lung.

BACKGROUND: Robot-assisted puncture has gradually attracted more attention and practical clinical application. The lesion positioning and the needle positioning are the basis to ensure the accuracy of puncture and the key techniques in insertion operation. METHODS: A lesion positioning method is established which is realized only by the robot-CT system without using external positioning system, and an omnidirectional needle positioning method is also developed and realized by using VRCM, in order to make the puncture needle always keep pointing to the lesion point. A CT-guided surgical robotic system used for minimally invasive percutaneous lung is designed and the physical prototype is manufactured, to perform in-vitro experiments, thereby to validate the effectiveness of the lesion positioning method and the feasibility of omnidirectional needle positioning method. RESULTS: The accuracy of established lesion positioning method based on three non-collinear markers is within 3 mm, which is similar to that of the least squares method based on the five non-coplanar markers, but the positioning efficiency can be improved by about 40%, and the non-collinearity of markers is easier to be satisfied than non-coplanarity in practical applications. The average calculation error of the established positioning method is 0.997 mm. Moreover, the omnidirectional positioning of the puncture needle under the designed surgical robot is feasible. CONCLUSIONS: The designed surgical robot has good control accuracy and it can satisfy the requirements for use. The established lesion positioning method can provide a good precision basis for robot-assisted puncture surgery. The suitable insertion point and insertion posture can be determined by the developed omnidirectional needle positioning method. This study can provide theoretical reference for further study of path planning or autonomous positioning.