A Review of Low-Cost Ultrasound Compatible Phantoms.

Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost phantoms were provided. Overall, this article aims to summarize a breadth of ultrasound-compatible phantom research to enable informed phantom methods selection.

Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction.

PURPOSE: In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US). METHODS: US scans of the neck vasculature were collected to produce a dataset of 2439 images and their respective manual segmentations. Fourfold cross-validation was employed to train and evaluate Mask RCNN and U-Net models. The U-Net algorithm includes a post-processing step that selects the largest connected segmentation for each class. A Mask R-CNN-based vascular reconstruction pipeline was validated by performing a surface-to-surface distance comparison between US and CT reconstructions from the same patient. RESULTS: The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were [Formula: see text] and [Formula: see text]. The average Dice scores produced by the post-processed U-Net were [Formula: see text] and [Formula: see text], for the CA and IJV, respectively. The reconstruction algorithm utilizing the Mask R-CNN was capable of producing accurate 3D reconstructions with majority of US reconstruction surface points being within 2 mm of the CT equivalent. CONCLUSIONS: On average, the Mask R-CNN produced more accurate vascular segmentations compared to U-Net. The Mask R-CNN models were used to produce 3D reconstructed vasculature with a similar accuracy to that of a manually segmented CT scan. This implementation of the Mask R-CNN network enables automatic analysis of the neck vasculature and facilitates 3D vascular reconstruction.

Accuracy assessment for the co-registration between optical and VIVE head-mounted display tracking.

PURPOSE: We report on the development and accuracy assessment of a hybrid tracking system that integrates optical spatial tracking into a video pass-through head-mounted display. METHODS: The hybrid system uses a dual-tracked co-calibration apparatus to provide a co-registration between the origins of an optical dynamic reference frame and the VIVE Pro controller through a point-based registration. This registration provides the location of optically tracked tools with respect to the VIVE controller's origin and thus the VIVE's tracking system. RESULTS: The positional accuracy was assessed using a CNC machine to collect a grid of points with 25 samples per location. The positional trueness and precision for the hybrid tracking system were [Formula: see text] and [Formula: see text], respectively. The rotational accuracy was assessed through inserting a stylus tracked by all three systems into a hemispherical phantom with cylindrical openings at known angles and collecting 25 samples per cylinder for each system. The rotational trueness and precision for the hybrid tracking system were [Formula: see text] and [Formula: see text], respectively. The difference in position and rotational trueness between the OTS and the hybrid tracking system was [Formula: see text] and [Formula: see text], respectively. CONCLUSIONS: We developed a hybrid tracking system that allows the pose of optically tracked surgical instruments to be known within a first-person HMD visualization system, achieving submillimeter accuracy. This research validated the positional and rotational accuracy of the hybrid tracking system and subsequently the optical tracking and VIVE tracking systems. This work provides a method to determine the position of an optically tracked surgical tool with a surgically acceptable accuracy within a low-cost commercial-grade video pass-through HMD. The hybrid tracking system provides the foundation for the continued development of virtual reality or augmented virtuality surgical navigation systems for training or practicing surgical techniques.

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.