Method: Registration of 2D US (slice) images is performed via the initialization obtained from a fast dictionary search that determines probe pose within a predefined set of pose configurations. 2D slices are extracted from a static 3D US (volume) image to construct a feature dictionary representing different probe poses. Haar features are computed in a fourlevel pyramid that transforms 2D image intensities to a 1D feature vector, which are in turn matched to the 2D target image. 3D-2D registration was performed with the Haar-based initialization with normalized cross-correlation as the metric and Powell’s method as the optimizer. Reduction to 1D feature vectors presents the potential for major gains in speed compared to registration of the 3D and 2D images directly. The method was validated in experiments conducted in a lumbar spine phantom and a cadaver specimen with known translations imparted by a computerized motion stage.
Results: The Haar feature matching method demonstrated initialization accuracy (mean ± std) = (1.9 ± 1.4) mm and (2.1 ± 1.2) mm in phantom and cadaver studies, respectively. The overall registration accuracy was (2.0 ± 1.3) mm and (1.7 ± 0.9) mm, and the initialization was a necessary and important step in the registration process.
Conclusions: The proposed image-based registration method demonstrated promising results for compensating motion of the US probe. This image-based solution could be an important step toward an entirely image-based, real-time registration method of 2D US to 3D US and pre-procedure MRI, eliminating hardware-based tracking systems in a manner more suitable to clinical workflow.