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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>X-ray computed tomography (CT) is a nondestructive imaging technique to reconstruct cross-sectional images of an object using x-ray measurements taken from different view angles for medical diagnosis, therapeutic planning, security screening, and other applications. In clinical practice, the x-ray tube emits polychromatic x-rays, and the x-ray detector array operates in the energy-integrating mode to acquire energy intensity. This physical process of x-ray imaging is accurately described by an energy-dependent non-linear integral equation on the basis of the Beer\u2013Lambert law. However, the non-linear model is not invertible using a computationally efficient solution and is often approximated as a linear integral model in the form of the Radon transform, which basically loses energy-dependent information. This approximate model produces an inaccurate quantification of attenuation images, suffering from beam-hardening effects. In this paper, a machine learning-based approach is proposed to correct the model mismatch to achieve quantitative CT imaging. Specifically, a one-dimensional network model is proposed to learn a non-linear transform from a training dataset to map a polychromatic CT image to its monochromatic sinogram at a pre-specified energy level, realizing virtual monochromatic (VM) imaging effectively and efficiently. Our results show that the proposed method recovers high-quality monochromatic projections with an average relative error of less than 2%. The resultant x-ray VM imaging can be applied for beam-hardening correction, material differentiation and tissue characterization, and proton therapy treatment planning.<\/jats:p>","DOI":"10.1088\/2632-2153\/abdbff","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T03:30:58Z","timestamp":1610681458000},"page":"025032","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Monochromatic image reconstruction via machine learning"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2536-8488","authenticated-orcid":false,"given":"Wenxiang","family":"Cong","sequence":"first","affiliation":[]},{"given":"Yan","family":"Xi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7250-3406","authenticated-orcid":false,"given":"Bruno","family":"De Man","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2656-7705","authenticated-orcid":false,"given":"Ge","family":"Wang","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2021,4,14]]},"reference":[{"key":"mlstabdbffbib1","author":"Buzug","year":"2008"},{"key":"mlstabdbffbib2","author":"Hsieh","year":"2009"},{"key":"mlstabdbffbib3","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1109\/42.959297","article-title":"An iterative maximum-likelihood polychromatic algorithm for CT","volume":"20","author":"de Man","year":"2001","journal-title":"IEEE Trans. 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