title: Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging.
abstract: Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size  >  1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution, CNR = 11.9 compared to CNR = 5.6 and CNR = 9.9, respectively) and substantially reduced image noise especially in challenging regions such as skull base. The results support the hypothesis that with high-fidelity artifact correction and statistical reconstruction using an accurate post-artifact-correction noise model, FPD-CBCT can achieve image quality allowing reliable detection of intracranial hemorrhage.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: H
authors.initials: H
authors.lastname: Dang
authors.forename: J W
authors.initials: JW
authors.lastname: Stayman
authors.forename: A
authors.initials: A
authors.lastname: Sisniega
authors.forename: J
authors.initials: J
authors.lastname: Xu
authors.forename: W
authors.initials: W
authors.lastname: Zbijewski
authors.forename: X
authors.initials: X
authors.lastname: Wang
authors.forename: D H
authors.initials: DH
authors.lastname: Foos
authors.forename: N
authors.initials: N
authors.lastname: Aygun
authors.forename: V E
authors.initials: VE
authors.lastname: Koliatsos
authors.forename: J H
authors.initials: JH
authors.lastname: Siewerdsen
mesh_terms: D000465:Algorithms; D016477:Artifacts; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006257:Head / Q000000981:diagnostic imaging*; D006801:Humans; D015233:Models, Statistical; D059629:Signal-To-Noise Ratio