  In Total Knee Replacement Arthroplasty (TKA), surgical robotics can provide
image-guided navigation to fit implants with high precision. Its tracking
approach highly relies on inserting bone pins into the bones tracked by the
optical tracking system. This is normally done by invasive, radiative manners
(implantable markers and CT scans), which introduce unnecessary trauma and
prolong the preparation time for patients. To tackle this issue,
ultrasound-based bone tracking could offer an alternative. In this study, we
proposed a novel deep learning structure to improve the accuracy of bone
tracking by an A-mode ultrasound (US). We first obtained a set of ultrasound
dataset from the cadaver experiment, where the ground truth locations of bones
were calculated using bone pins. These data were used to train the proposed
CasAtt-UNet to predict bone location automatically and robustly. The ground
truth bone locations and those locations of US were recorded simultaneously.
Therefore, we could label bone peaks in the raw US signals. As a result, our
method achieved sub millimeter precision across all eight bone areas with the
only exception of one channel in the ankle. This method enables the robust
measurement of lower extremity bone positions from 1D raw ultrasound signals.
It shows great potential to apply A-mode ultrasound in orthopedic surgery from
safe, convenient, and efficient perspectives.
