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.

A novel ultrasound probe calibration method for multimodal image guidance of needle placement in cervical cancer brachytherapy.

PURPOSE: Interstitial needles placement is a critical component of combined intracavitary/interstitial (IC/IS) brachytherapy (BT). To ensure precise placement of interstitial needles, we proposed a novel ultrasonic (US) probe calibration method to accurately register the US image in the magnetic resonance imaging (MRI) image and provide multimodal image guidance for needle placement. METHODS: A wire-based calibration phantom combined with the stylus was developed for the calibration of US probe. The calibration phantom helps to quickly align the imaging plane of the US probe with the fiducial points to obtain US images of these points. The coordinates of fiducial points in US images were located automatically by feature extraction algorithms and were further corrected by the proposed correction method. Ingenious structures were designed on both sides of the calibration phantom to accurately obtain the coordinates of the fiducial points relative to the tracking device. Marker validation and pelvic phantom study were performed to evaluate the accuracy of the proposed calibration method. RESULTS: In the marker validation, the US probe calibration method with corrected transformation achieves a registration accuracy of 0.694 ± 0.014 mm, and the uncorrected one is 0.746 ± 0.018 mm. In the pelvic phantom study, the needle tip difference was 1.096 ± 0.225 mm and trajectory difference was 1.416 ± 0.284 degrees. CONCLUSION: The proposed US probe calibration method is helpful to achieve more accurate multimodality image guidance for needle placement.

Deep learning approach for automatic out-of-plane needle localisation for semi-automatic ultrasound probe calibration.

The authors present a deep learning algorithm for the automatic centroid localisation of out-of-plane US needle reflections to produce a semi-automatic ultrasound (US) probe calibration algorithm. A convolutional neural network was trained on a dataset of 3825 images at a 6 cm imaging depth to predict the position of the centroid of a needle reflection. Applying the automatic centroid localisation algorithm to a test set of 614 annotated images produced a root mean squared error of 0.62 and 0.74 mm (6.08 and 7.62 pixels) in the axial and lateral directions, respectively. The mean absolute errors associated with the test set were 0.50 ± 0.40 mm and 0.51 ± 0.54 mm (4.9 ± 3.96 pixels and 5.24 ± 5.52 pixels) for the axial and lateral directions, respectively. The trained model was able to produce visually validated US probe calibrations at imaging depths on the range of 4-8 cm, despite being solely trained at 6 cm. This work has automated the pixel localisation required for the guided-US calibration algorithm producing a semi-automatic implementation available open-source through 3D Slicer. The automatic needle centroid localisation improves the usability of the algorithm and has the potential to decrease the fiducial localisation and target registration errors associated with the guided-US calibration method.

Efficient image based method using water-filled balloons for improving probe spatial calibration in 3D freehand ultrasonography.

The ultrasound (US) probe spatial calibration is a key prerequisite for enabling the use of the 3D freehand US technique. Several methods have been proposed for achieving an accurate and precise calibration, although these methods still require specialised equipment. This equipment is often not available in research or clinical facilities. Therefore, the present investigation aimed to propose an efficient US probe calibration method that is accessible in terms of cost, easy to apply and capable of achieving results suitable for clinical applications. The data acquisition was carried out by performing two perpendicular US sweeps over water filled balloon phantoms. The data analysis was carried out by computing the similarity measures between 2D images from the first sweep and the corresponding images of the 3D reconstruction of the second sweep. These measures were maximized by using the Nelder-Mead algorithm, to find the optimal solution for the calibration parameters. The calibration results were evaluated in terms of accuracy and precision by comparing known phantom geometries with those extracted from the US images. The accuracy and the precision after applying the calibration method were improved. By using the parameters obtained from the plane phantom method as initialization of the calibration parameters, the accuracy and the precision in the best scenario was 0.4 mm and 1.5 mm, respectively. These results were in line with the methods requiring specialised equipment. However, the applied method was unable to consistently produce this level of accuracy and precision. The calibration parameters were also tested in a musculoskeletal application, revealing sufficient matching of the relevant anatomical features when multiple US sweeps are combined in a 3D reconstruction. To improve the current results and increase the reproducibility of this research, the developed software is made available.