Although non-linear CT systems offer improved image quality over conventional linear systems, they disrupt certain assumptions of the dependency of noise and resolution on radiation dose that are true of linear systems. As such, simplistic phantoms do not fully represent the actual performance of current systems in the clinic. Assessing image quality from clinical images address this limitation, but full realization of image quality attributes, particularly noise, requires the knowledge of the exact heterogeneous anatomy of the patient (not knowable) and/or repeated imaging (ethically unattainable). This limitation can be overcome through realistic simulations enabled by virtual clinical trials (VCTs). This study aimed to characterize the noise properties of CT images reconstructed with filtered back-projection (FBP) and non-linear iterative reconstruction (IR) algorithms through a VCT. The study deployed a new generation version of the Extended Cardio-Torso (XCAT) phantom enhanced with anatomically-based intra-organ heterogeneities. The phantom was virtually “imaged” using a scanner-specific simulator, with fifty repeats, and reconstructed using clinical FBP and IR algorithms. The FBP and IR noise magnitude maps and the relative noise reduction maps were calculated to quantify the amount of noise reduction achieved by IR. Moreover, the 2D noise power spectra were measured for both FBP and IR images. The noise reduction maps showed that IR images have lower noise magnitude in uniform regions but higher noise magnitude at edge voxels, thus the noise reduction attributed to IR is less than what could be expected from uniform phantoms (29% versus 60%). This work demonstrates the utility of our CT simulator and “textured” XCAT phantoms in performing VCT that would be otherwise infeasible.
Ehsan Abadi, W. Paul Segars, Brian Harrawood, Anuj Kapadia, and Ehsan Samei, "Virtual clinical trial in action: textured XCAT phantoms and scanner-specific CT simulator to characterize noise across CT reconstruction algorithms," Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057317 (Presented at SPIE Medical Imaging: February 14, 2018; Published: 9 March 2018); https://doi.org/10.1117/12.2294599.
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