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This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human\u2010scale dark\u2010field CT (DF\u2010CT) prototype has been built for lung imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aimed to develop a thorough optimization method for human\u2010scale dark\u2010field lung CT and guide the system design.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We introduced a task\u2010based metric formulated as the contrast\u2010to\u2010noise ratio (CNR) between normal and lesioned alveoli for system parameter optimization and designed a digital human\u2010thorax phantom to fit the task of lung disease detection. Furthermore, a computational framework was developed to model the signal propagation in DF\u2010CT and established the link between system parameters and the CNR metric.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We showed that for a DF\u2010CT system, its CNR first increases and then decreases with the system auto\u2010correlation length (ACL). The optimal ACL is mostly independent of system's visibility, and is only related to the phantom's properties, that is, its size and absorption. For our phantom, the optimal ACL is about 0.35 \u00b5m at the design energy of 60\u00a0keV. As for system geometry, increasing source\u2010detector and isocenter\u2010detector distance can extend the system's maximal ACL, making it easier for the system to meet the optimal ACL and relaxing the grating pitches. We proposed a set of parameters for a projective fringe system that can satisfy the simulated optimal ACL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study introduced a task\u2010based metric and a process for DF\u2010CT optimization. We demonstrated that for a given phantom, the detection performance of the system is optimized at a specific ACL. The optimization method and design principles are independent from the underlying dark\u2010field imaging method and can be applied to DF\u2010CT system design using different grating\u2010based implementations such as Talbot\u2010Lau interferometer (TLI) or projective fringe method.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17630","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T23:24:31Z","timestamp":1738020271000},"page":"2155-2166","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimization of x\u2010ray dark\u2010field CT for human\u2010scale lung imaging"],"prefix":"10.1002","volume":"52","author":[{"given":"Peiyuan","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Engineering Physics Tsinghua University  Beijing China"},{"name":"Key Laboratory of Particle &amp; Radiation Imaging (Tsinghua University) of Ministry of Education  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Spindler","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Engineering ETH Z\u00fcrich and University of Z\u00fcrich  Z\u00fcrich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michal","family":"Rawlik","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Engineering ETH Z\u00fcrich and University of Z\u00fcrich  Z\u00fcrich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Engineering Physics Tsinghua University  Beijing China"},{"name":"Key Laboratory of Particle &amp; 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