ArticleMedical physics2019
Statistical properties of cerebral CT perfusion imaging systems. Part I. Cerebral blood volume maps generated from nondeconvolution-based systems.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CT perfusion-focused preoperative assessment of the blood-supplying state among imaging modalities in skull base chordoma: a retrospective single-center cohort.Journal of neuro-oncology · 2026Article
- [Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- [Low-dose cerebral perfusion CT image restoration using prior image constrained diffusion tensor].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Leveraging non-contrast head CT to improve the image quality of cerebral CT perfusion maps.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution-based systems.Medical physics · 2019Article
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Abstract
purposeThe development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method.
methodsThe CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models.
resultsThe noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV.
conclusionsThe statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.
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