Automatic spatial calibration of freehand ultrasound probe with a multilayer N-wire phantom

Automatic robotic doppler sonography of leg arteries

PURPOSE

Robot-assisted systems offer an opportunity to support the diagnostic and therapeutic treatment of vascular diseases to reduce radiation exposure and support the limited medical staff in vascular medicine. In the diagnosis and follow-up care of vascular pathologies, Doppler ultrasound has become the preferred diagnostic tool. The study presents a robotic system for automatic Doppler ultrasound examinations of patients' leg vessels.

METHODS

The robotic system consists of a redundant 7 DoF serial manipulator, to which a 3D ultrasound probe is attached. A compliant control was employed, whereby the transducer was guided along the vessel with a defined contact force. Visual servoing was used to correct the position of the probe during the scan so that the vessel can always be properly visualized. To track the vessel's position, methods based on template matching and Doppler sonography were used.

RESULTS

Our system was able to successfully scan the femoral artery of seven volunteers automatically for a distance of 20 cm. In particular, our approach using Doppler ultrasound data showed high robustness and an accuracy of 10.7 (±3.1) px in determining the vessel's position and thus outperformed our template matching approach, whereby an accuracy of 13.9 (±6.4) px was achieved.

CONCLUSIONS

The developed system enables automated robotic ultrasound examinations of vessels and thus represents an opportunity to reduce radiation exposure and staff workload. The integration of Doppler ultrasound improves the accuracy and robustness of vessel tracking, and could thus contribute to the realization of routine robotic vascular examinations and potential endovascular interventions.

Coplanarity Constrained Ultrasound Probe Calibration Based on N-Wire Phantom

OBJECTIVE

N-wire phantom-based ultrasound probe calibration has been used widely in many freehand tracked ultrasound imaging systems. The calibration matrix is obtained by registering the coplanar point cloud in ultrasound space and non-coplanar point cloud in tracking sensor space based on the least squares method. This method is sensitive to outliers and loses the coplanar information of the fiducial points. In this article, we describe a coplanarity-constrained calibration algorithm focusing on these issues.

METHODS

We verified that the out-of-plane error along the oblique wire in the N-wire phantom followed a normal distribution and used it to remove the experimental outliers and fit the plane with the Levenberg-Marquardt algorithm. Then, we projected the points to the plane along the oblique wire. Coplanarity-constrained point cloud registration was used to calculate the transformation matrix.

RESULTS

Compared with the other two commonly used methods, our method had the best calibration precision and achieved 25% and 36% improvement of the mean calibration accuracy than the closed-form solution and in-plane error method respectively at depth 16. Experiments at different depths revealed that our algorithm had better performance in our setup.

CONCLUSION

Our proposed coplanarity-constrained calibration algorithm achieved significant improvement in both precision and accuracy compared with existing algorithms with the same N-wire phantom. It is expected that calibration accuracy will improve when the algorithm is applied to all other N-wire phantom-based calibration procedures.

Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound

Objective.Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method.Approach.An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data.Main results.Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22-0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32-1.83 dB and 0.01-0.05, respectively.Significance.With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research.