Keywords:
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
- H. Yu, S. Zhao, E. A. Hoffman, and G. Wang, “Ultra-low Dose Lung CT Perfusion Regularized by a Previous Scan,” Acad. Radiol., vol. 16, no. 3, pp. 363–373, 2009.
- B. Nett, J. Tang, B. Aagaard-Kienitz, H. Rowley, and G.-H. Chen, “Low radiation dose C-arm cone-beam CT based on prior image constrained compressed sensing (PICCS): including compensation for image volume mismatch between multiple data acquisitions,” Proc. SPIE Medical Imaging, vol. 7258, p. 725803, 2009.
- J. W. Stayman, W. Zbijewski, Y. Otake, A. Uneri, S. Schafer, J. Lee, J. L. Prince, and J. H. Siewerdsen, “Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties,” Proc. SPIE Medical Imaging, vol. 7961, p. 79611L, 2011.
- J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan.,” Med. Phys., vol. 38, no. 10, pp. 5713–31, Oct. 2011.
- H. Lee, L. Xing, R. Davidi, R. Li, J. Qian, and R. Lee, “Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints,” Phys. Med. Biol., vol. 57, no. 8, pp. 2287–2307, Apr. 2012.
- W. Xu and K. Mueller, “Efficient low-dose CT artifact mitigation using an artifact-matched prior scan.,” Med. Phys., vol. 39, no. 8, pp. 4748–60, Aug. 2012.
- J. Xu and B. Tsui, “C-arm CT image reconstruction from sparse projections,” International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, vol. 5, pp. 34–7, 2013.
- J. W. Stayman, H. Dang, Y. Ding, and J. H. Siewerdsen, “PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction.,” Phys. Med. Biol., vol. 58, no. 21, pp. 7563–82, Nov. 2013.
- H. Zhang, J. Huang, J. Ma, Z. Bian, Q. Feng, H. Lu, Z. Liang, and W. Chen, “Iterative reconstruction for x-ray computed tomography using prior-image induced nonlocal regularization.,” IEEE Trans. Biomed. Eng., vol. 61, no. 9, pp. 2367–78, Sep. 2014.
- H. Dang, A. S. Wang, M. S. Sussman, J. H. Siewerdsen, and J. W. Stayman, “dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images.,” Phys. Med. Biol., vol. 59, no. 17, pp. 4799–826, Sep. 2014.
- H. Zhang, H. Han, J. Wang, J. Ma, Y. Liu, W. Moore, and Z. Liang, “Deriving adaptive MRF coefficients from previous normal-dose CT scan for low-dose image reconstruction via penalized weighted least-squares minimization.,” Med. Phys., vol. 41, no. 4, p. 41916, Apr. 2014.
- H. Zhang, H. Han, Z. Liang, Y. Hu, Y. Liu, W. Moore, J. Ma, and H. Lu, “Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images.,” IEEE Trans. Med. Imaging, vol. 35, no. 3, pp. 860–70, Mar. 2016.
- A. Pourmorteza, H. Dang, J. H. Siewerdsen, and J. W. Stayman, “Reconstruction of difference in sequential CT studies using penalized likelihood estimation.,” Phys. Med. Biol., vol. 61, no. 5, pp. 1986–2002, Mar. 2016.
- M. Wu, Q. Yang, A. Maier, and R. Fahrig, “Approximate path seeking for statistical iterative reconstruction,” Proc. SPIE Medical Imaging, vol. 9412, p. 94121D, 2015.
- J. W. Stayman, J. L. Prince, and J. H. Siewerdsen, “Information Propagation in Prior-Image-Based Reconstruction.,” Conf. proceedings. Int. Conf. Image Form. X-Ray Comput. Tomogr., vol. 2012, pp. 334–338, 2012.
- H. Dang, J. H. Siewerdsen, and J. W. Stayman, “Prospective regularization design in prior-image-based reconstruction.,” Phys. Med. Biol., vol. 60, no. 24, pp. 9515–36, Dec. 2015.
- H. Erdoğan and J. A. Fessler, “Ordered subsets algorithms for transmission tomography.,” Phys. Med. Biol., vol. 44, no. 11, pp. 2835–51, Nov. 1999.
- J. A. Fessler and W. L. Rogers, “Resolution properties of regularized image reconstruction methods,” Technical Report No. 297, 1995.
- J. A. Fessler and W. L. Rogers, “Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs.,” IEEE Trans. Image Process., vol. 5, no. 9, pp. 1346–58, Jan. 1996.
- B. B. Tan, K. R. Flaherty, E. A. Kazerooni, M. D. Iannettoni, and American College of Chest Physicians, “The solitary pulmonary nodule.,” Chest, vol. 123, no. 1 Suppl, p. 89S–96S, Jan. 2003.