Hybrid Cone-Beam Tomographic Reconstruction: Incorporation of Prior Anatomical Models to Compensate for Missing Data

Abstract

We propose a method for improving the quality of cone-beam tomographic reconstruction done with a C-arm. C-arm scans frequently suffer from incomplete information due to image truncation, limited scan length, or other limitations. Our proposed "hybrid reconstruction" method injects information from a prior anatomical model, derived from a subject-specific CT or from a statistical database (atlas), where the C-arm x-ray data is missing. This significantly reduces reconstruction artifacts with little loss of true information from the x-ray projections. The methods consist of constructing anatomical models, fast rendering of digitally reconstructed radiograph (DRR) projections of the models, rigid or deformable registration of the model and the x-ray images, and fusion of the DRR and x-ray projections, all prior to a conventional filtered backprojection algorithm. Our experiments, conducted with a mobile image intensifier C-arm, demonstrate visually and quantitatively the contribution of data fusion to image quality, which we assess through comparison to a "ground truth" CT. Importantly, we show that a significantly improved reconstruction can be obtained from a C-arm scan as short as 90° by complementing the observed projections with DRRs of two prior models, namely an atlas and a pre-operative samepatient CT. The hybrid reconstruction principles are applicable to other types of C-arms as well.

3D Prior Image Constrained Projection Completion for X-ray CT Metal Artifact Reduction

“…This idea has recently motivated some recent attempts to iteratively improve the derivation of the prior image [61], [62]. There are also other approaches enabling to avoid segmentation errors and possibly to improve the accuracy of recovered projections by defining a prior image from statistical anatomical atlases [63].…”

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

“…The series of researches targeted the pelvis and proximal femur [30]- [34]. A tetrahedral mesh density model was used for representing both shape and density distributions [31], and PCA was simultaneously applied to both of them to construct statistical shape and density model (SSDM) [32].…”

“…A tetrahedral mesh density model was used for representing both shape and density distributions [31], and PCA was simultaneously applied to both of them to construct statistical shape and density model (SSDM) [32]. In the latest work, the femur in which bone cement was injected was tomographically reconstructed from incomplete projection datasets by using SSDM [33], [34].…”

A Multi-view Active Contour Method for Bone Cement Reconstruction from C-Arm X-Ray Images

“…If prior information such as a statistical shape model is available, then this information may be used to assist in reconstruction [1]. However, such information is not always available, especially if the object is highly deformable or its shape is created and/or substantially modified during the procedure.…”

“…In these methods, the segmentation process is formulated as the minimization of an objective function that incorporates information from acquired XRay images in log space, linear attenuation coefficients in patient coordinates, and geometric properties of the deformable model: (1) In this paper, we formulate the segmentation process as an optimization problem that permits the segmentation algorithm to (1) reconstruct deformable objects for which the background partially occludes the object in X-Ray images, (2) use X-Ray images acquired on a non-circular trajectory, and (3) incorporate prior CT information. Subsequently, we describe a method for optimizing the objective function and evaluate the feasibility and performance of the Sparse X-Ray Multi-view Active Contour algorithm (SxMAC -pronounced "smack") to reconstruct injected bone cement.…”