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Prospective image quality analysis and control for prior-image-based reconstruction of low-dose CT
Author(s): Hao Zhang; Grace J. Gang; Hao Dang; Marc C. Sussman; Cheng Ting Lin; Jeffrey H. Siewerdsen; J. Webster Stayman
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Paper Abstract

Purpose: Prior-image-based reconstruction (PIBR) is a powerful tool for low-dose CT, however, the nonlinear behavior of such approaches are generally difficult to predict and control. Similarly, traditional image quality metrics do not capture potential biases exhibited in PIBR images. In this work, we identify a new bias metric and construct an analytical framework for prospectively predicting and controlling the relationship between prior image regularization strength and this bias in a reliable and quantitative fashion. Methods: Bias associated with prior image regularization in PIBR can be described as the fraction of actual contrast change (between the prior image and current anatomy) that appears in the reconstruction. Using local approximation of the nonlinear PIBR objective, we develop an analytical relationship between local regularization, fractional contrast reconstructed, and true contrast change. This analytic tool allows prediction bias properties in a reconstructed PIBR image and includes the dependencies on the data acquisition, patient anatomy and change, and reconstruction parameters. Predictions are leveraged to provide reliable and repeatable image properties for varying data fidelity in simulation and physical cadaver experiments. Results: The proposed analytical approach permits accurate prediction of reconstructed contrast relative to a gold standard based on exhaustive search based on numerous iterative reconstructions. The framework is used to control regularization parameters to enforce consistent change reconstructions over varying fluence levels and varying numbers of projection angles – enabling bias properties that are less location- and acquisition-dependent. Conclusions: While PIBR methods have demonstrated a substantial ability for dose reduction, image properties associated with those images have been difficult to express and quantify using traditional metrics. The novel framework presented in this work not only quantifies this bias in an intuitive fashion, but it gives a way to predict and control the bias. Reliable and predictable reconstruction methods are a requirement for clinical imaging systems and the proposed framework is an important step translating PIBR methods to clinical application.

Paper Details

Date Published: 9 March 2018
PDF: 7 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057329 (9 March 2018); doi: 10.1117/12.2293135
Show Author Affiliations
Hao Zhang, Johns Hopkins Univ. (United States)
Grace J. Gang, Johns Hopkins Univ. (United States)
Hao Dang, Johns Hopkins Univ. (United States)
Marc C. Sussman, Johns Hopkins Univ. (United States)
Cheng Ting Lin, Johns Hopkins Univ. (United States)
Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
J. Webster Stayman, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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