  Imaging modalities provide clinicians with real-time visualization of
anatomical regions of interest (ROI) for the purpose of minimally invasive
surgery. During the procedure, low-resolution image data are acquired and
registered with high-resolution preoperative 3D reconstruction to guide the
execution of surgical preplan. Unfortunately, due to the potential large strain
and nonlinearities in the deformation of soft biological tissues, significant
mismatch may be observed between ROI shapes during pre- and intra-operative
imaging stages, making the surgical preplan prone to failure. In an effort to
bridge the gap between the two imaging stages, this paper presents a
data-driven approach based on artificial neural network for predicting the ROI
deformation in real-time with sparsely registered fiducial markers. For a
head-and-neck tumor model with an average maximum displacement of 30 mm, the
maximum surface offsets between benchmarks and predictions using the proposed
approach for 98% of the test cases are under 1.0 mm, which is the typical
resolution of high-quality interventional ultrasound. Each of the prediction
processes takes less than 0.5 s. With the resulting prediction accuracy and
computational efficiency, the proposed approach demonstrates its potential to
be clinically relevant.
