Quantitative material decomposition using linear iterative near-field phase retrieval dual-energy x-ray imaging. (16th September 2020)

• Record Type: Journal Article
• Authors: Li, Heyang Thomas; Schaff, Florian; Croton, Linda C P; Morgan, Kaye S; Kitchen, Marcus J
• Is Part Of: Physics in medicine & biology. Volume 65:Number 18(2020:Sep.)
• Publication Date: 2020-09-16
• Subjects: dual-energy -- phase retrieval -- electron density -- atomic number -- phase contrast x-ray imaging -- iterative reconstruction; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab9558
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: This paper expands the linear iterative near-field phase retrieval (LIPR) formalism to achieve quantitative material thickness decomposition. Propagation-based phase contrast x-ray imaging with subsequent phase retrieval has been shown to improve the signal-to-noise ratio (SNR) by factors of up to hundreds compared to conventional x-ray imaging. This is a key step in biomedical imaging, where radiation exposure must be kept low without compromising the SNR. However, for a satisfactory phase retrieval from a single measurement, assumptions must be made about the object investigated. To avoid such assumptions, we use two measurements collected at the same propagation distance but at different x-ray energies. Phase retrieval is then performed by incorporating the Alvarez-Macovski (AM) model, which models the x-ray interactions as being comprised of distinct photoelectric and Compton scattering components. We present the first application of dual-energy phase retrieval with the AM model to monochromatic experimental x-ray projections at two different energies for obtaining split x-ray interactions. Our phase retrieval method allows us to separate the object investigated into the projected thicknesses of two known materials. Our phase retrieval output leads to no visible loss in spatial resolution while the SNR improves by factors of 2 to 10. This corresponds to a possible x-ray dose reduction by a factor of 4 to 100, under the Poisson noise assumption.