  In this paper, we describe how a patient-specific, ultrasound-probe-induced
prostate motion model can be directly generated from a single preoperative MR
image. Our motion model allows for sampling from the conditional distribution
of dense displacement fields, is encoded by a generative neural network
conditioned on a medical image, and accepts random noise as additional input.
The generative network is trained by a minimax optimisation with a second
discriminative neural network, tasked to distinguish generated samples from
training motion data. In this work, we propose that 1) jointly optimising a
third conditioning neural network that pre-processes the input image, can
effectively extract patient-specific features for conditioning; and 2)
combining multiple generative models trained separately with heuristically
pre-disjointed training data sets can adequately mitigate the problem of mode
collapse. Trained with diagnostic T2-weighted MR images from 143 real patients
and 73,216 3D dense displacement fields from finite element simulations of
intraoperative prostate motion due to transrectal ultrasound probe pressure,
the proposed models produced physically-plausible patient-specific motion of
prostate glands. The ability to capture biomechanically simulated motion was
evaluated using two errors representing generalisability and specificity of the
model. The median values, calculated from a 10-fold cross-validation, were
2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced
approach demonstrates the feasibility of applying state-of-the-art machine
learning algorithms to generate organ motion models from patient images, and
shows significant promise for future research.
