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Results for “CBCT image reconstruction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography

An optimization-based image reconstruction frame work is developed specifically for bone imaging. This framework exploits voxel-sparsity by use of ℓ 1 -norm image regularization and it enables image reconstruction from sparse-view cone-beam computed tomography (CBCT) acquisition. The effectiveness of the voxel-sparsity regularization is enhanced by using a blurred image representation. Ramp-filtering is included in the data discrepancy term and it has the effect of acting as a preconditioner, reducing the necessary number of iterations. The bone image reconstruction framework is demonstrated on CBCT data taken from an equine metacarpal condyle specimen.

Bone imaging↗
DOE OSTI2021

Scalable FBP decomposition for cone-beam CT reconstruction

Filtered Back-Projection (FBP) is a fundamental compute intense algorithm used in tomographic image reconstruction. Cone-Beam Computed Tomography (CBCT) devices use a cone-shaped X-ray beam, in comparison to the parallel beam used in older CT generations. Distributed image reconstruction of cone-beam datasets typically relies on dividing batches of images into different nodes. This simple input decomposition, however, introduces limits on input/output sizes and scalability.We propose a novel decomposition scheme and reconstruction algorithm for distributed FPB. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline and improves the scalability by replacing two communication collectives with only one segmented reduction. Finally, we implement the proposed decomposition scheme in a framework that is useful for all current-generation CT devices (7th gen). In our experiments using up to 1024 GPUs, our framework can construct 40963 volumes, for real-world datasets, in under 16 seconds (including I/O).

Chen, Peng↗