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16 March 2020 In vivo noise texture estimation: development and validation of an automated methodology
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Abstract
The purpose of this study was to develop, validate, and evaluate a method for measuring noise texture directly from patient CT images (i.e., “in vivo”). The method identifies target regions within patient scans which are least likely to have major contribution of patient anatomy, detrends these regions locally, and measures noise power spectrum (NPS) there using previously phantomvalidated techniques. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom at three phases of contrast enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120kV at 3 dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, I50) algorithms using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of 1) a measurement in parenchymal regions of anatomy-subtracted (i.e. “noise only”) scans, and 2) a measurement in the same region of “noise-free” (pre-noise-insertion) images. To assess the performance of the in vivo NPS, the accuracy and bias of NPS average frequency (favg), and integrated noise magnitude were reported across the simulated scan population representing 2 reconstruction algorithms, 3 kernels, 3 dose levels, and 3 liver vasculature-to-parenchyma contrast levels. Pearson and Spearman correlation coefficient pairs were 0.97 and 0.93, and 1.0 and 0.99 for favg and noise magnitude, respectively. Finally, the NPS estimation method was further deployed on clinical cases to assess the feasibility of clinical analysis.
Conference Presentation
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Taylor Brunton Smith, Ehsan Abadi, T. J. Sauer, Wanyi Fu, Justin Solomon, and Ehsan Samei "In vivo noise texture estimation: development and validation of an automated methodology", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113121U (16 March 2020); https://doi.org/10.1117/12.2549268
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