Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements.

ABSTRACT: A model based noise correction algorithm was developed to improve the accuracy of CT perfusion (CTp) blood flow (BF) measurements affected by image noise. The algorithm used tissue attenuation curves (TACs), generated by convolving an impulse response function (IRF) with an arterial input function (AIF) averaged from 59 patient datasets. Gaussian noise was introduced to simulate noise, and BF was measured using deconvolution. The algorithm iteratively compared BF without added noise against noise-impacted BF to estimate ground-truth BF (GTBF). Performance was evaluated with digital perfusion phantoms (DPPs) for GTBF values of 5-420 ml/100 ml/min and added noise (standard deviation 25 HU), measuring absolute difference from GTBF and contrast-to-noise ratio (CNR). For clinical evaluation, CT

SUBMITTER: Vats N 

PROVIDER: S-EPMC12550029 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature