Transrectal ultrasound (TRUS) is considered the standard of care for imaging the prostate
during biopsy and brachytherapy procedures. However, interpretation of TRUS images
is challenging due to high specularity, making it difficult to recognize prostate
boundaries. Image-guided brachytherapy and fusion-guided prostate biopsies require
accurate non-rigid registration of magnetic resonance pre-operative image to intra-operative
TRUS. State of the art techniques suggest semi-automated segmentation of the prostate
on the TRUS images. However, due to the high variability, segmentation of the prostate
is challenging. Segmentation errors could lead into poor localization of the biopsy
target and can impact the registration of pre-operative images. In general, this kind
of registration is challenging since the prostate anatomy undergoes motion due to
TRUS probe pressure. In this paper, we propose a non-rigid surface registration approach
for MR-TRUS fusion based on a statistical deformation model. Our method builds a statistical
deformation model (SDM) of pre-operative to intra-operative deformations on a prostate
dataset In order to compute the fusion for an unseen MR-TRUS pair, the trained SDM
is incorporated into the registration process to increase the fusion accuracy. The
proposed approach is evaluated on a dataset of 23 patients with prostate cancer, for
which the MRI-TRUS scans were available. We compared the proposed non-rigid SDM registration
to non-rigid Iterative closest point (NICP) and rigid ICP approaches. Experiments
demonstrate that the proposed SDM based method outperforms both NICP and ICP approaches,
yielding a mean squared distance of 0.52 +/- 0.26mm at the base, 0.45 +/- 0.17mm mid-gland
and 0.59 +/- 0.13mm at the apex. These results show the advantage of integrating prior
knowledge of deformation fields due to probe pressure for MR-TRUS fusion prostate
interventions.
