  Various multi-modal imaging sensors are currently involved at different steps
of an interventional therapeutic work-flow. Cone beam computed tomography
(CBCT), computed tomography (CT) or Magnetic Resonance (MR) images thereby
provides complementary functional and/or structural information of the targeted
region and organs at risk. Merging this information relies on a correct spatial
alignment of the observed anatomy between the acquired images. This can be
achieved by the means of multi-modal deformable image registration (DIR),
demonstrated to be capable of estimating dense and elastic deformations between
images acquired by multiple imaging devices. However, due to the typically
different field-of-view (FOV) sampled across the various imaging modalities,
such algorithms may severely fail in finding a satisfactory solution.
  In the current study we propose a new fast method to align the FOV in
multi-modal 3D medical images. To this end, a patch-based approach is
introduced and combined with a state-of-the-art multi-modal image similarity
metric in order to cope with multi-modal medical images. The occurrence of
estimated patch shifts is computed for each spatial direction and the shift
value with maximum occurrence is selected and used to adjust the image
field-of-view.
  We show that a regional registration approach using voxel patches provides a
good structural compromise between the voxel-wise and "global shifts"
approaches. The method was thereby beneficial for CT to CBCT and MRI to CBCT
registration tasks, especially when highly different image FOVs are involved.
Besides, the benefit of the method for CT to CBCT and MRI to CBCT image
registration is analyzed, including the impact of artifacts generated by
percutaneous needle insertions. Additionally, the computational needs are
demonstrated to be compatible with clinical constraints in the practical case
of on-line procedures.
