Prospective Regularization Analysis and Design for Prior-Image-Based Reconstruction of X-ray CT

Hao Zhang, Hao Dang, Grace J. Gang, J. Webster Stayman


Published in:Fully3D 2017 Proceedings


Prior-image-based reconstruction (PIBR) methods, which incorporate a high-quality patient-specific prior image into the reconstruction of subsequent low-dose CT acquisitions, have demonstrated great potential to dramatically reduce data fidelity requirements while maintaining or improving image quality. However, one challenge with the PIBR methods is in the selection of the prior image regularization parameter which controls the balance between information from current measurements and in-formation from the prior image. Too little prior information yields few improvements for PIBR, and too much prior information can lead to PIBR results too similar to the prior image obscuring or misrepresenting features in the reconstruction. While exhaustive parameter searches can be used to establish prior image regulari-zation strength, this process can be time consuming (involving a series of iterative reconstructions) and particular settings may not generalize for different acquisition protocols, anatomical sites, pa-tient sizes, etc. Moreover, optimal regularization strategies can be dependent on the location within the object further complicating selection… 

Hao Zhang
The Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA
Hao Dang
The Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA
Grace J. Gang
The Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA
J. Webster Stayman
The Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA
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