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Methods. Two novel methodologies predicate the work: (1) Known-Component Registration (KC-Reg) for 3D localization of the patient and interventional devices from 2D radiographs; and (2) Penalized-Likelihood reconstruction (PLH) for improved 3D image quality and dose reduction. A thorough assessment of geometric stability, dosimetry, and image quality was performed to define algorithm parameters for imaging and guidance protocols. Laboratory studies included: evaluation of KC-Reg in localization of spine screws delivered in cadaver; and PLH performance in contrast, noise, and resolution in phantoms/cadaver compared to filtered backprojection (FBP).
Results. KC-Reg was shown to successfully register screw implants within ~1 mm based on as few as 3 radiographs. PLH was shown to improve soft-tissue visibility (61% improvement in CNR) compared to FBP at matched resolution. Cadaver studies verified the selection of algorithm parameters and the methods were successfully translated to clinical studies under an IRB protocol.
Conclusions. Model-based registration and reconstruction approaches were shown to reduce dose and provide improved visualization of anatomy and surgical instrumentation. Immediate future work will focus on further integration of KC-Reg and PLH for Known-Component Reconstruction (KC-Recon) to provide high-quality intraoperative imaging in the presence of dense instrumentation.
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.
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