Intensity-based image registration for 3D spatial compounding using a freehand 3D ultrasound system

3D spatial compounding involves the combination of two or more 3D ultrasound (US) data sets, acquired under different insonation angles and windows, to form a higher quality 3D US data set. An important requirement for this method to succeed is the accurate registration between the US images used to form the final compounded image. We have developed a new automatic method for rigid and deformable registration of 3D US data sets, acquired using a freehand 3D US system. Deformation is provided by using a 3D thin-plate spline (TPS). Our method is fundamentally different from previous ones in that the acquired scattered US 2D slices are registered and compounded directly into the 3D US volume. Our approach has several benefits over the traditional registration and spatial compounding methods: (i) we only peform one 3D US reconstruction for the first acquired data set, therefore we save the computation time required to reconstruct subsequent acquired scans, (H) for our registration we use (except for the first scan) the acquired high-resolution 2D US images rather than the 3D US reconstruction data which are of lower quality due to the interpolation and potential subsampling associated with 3D reconstruction, and (iii) the scans performed after the first one are not required to follow the typical 3D US scanning protocol. where a large number of dense slices have to be acquired; slices can be acquired in any fashion in areas where compounding is desired. We show that by taking advantage of the similar information contained in adjacent acquired 2D US slices, we can reduce the computation time of linear and nonlinear registrations by a factor of more than 7:1, without compromising registration accuracy. Furthermore, we implemented an adaptive approximation to the 3D TPS with local bilinear transformations allowing additional reduction of the nonlinear registration computation time by a factor of approximately 3.5. Our results are based on a commercially available tissue-mimicking abdominal phantom and in-vivo muscle data.