Algorithm

Image quality

Materials science

Image noise

Iterative reconstruction

Reconstruction algorithm

Computer science

Resolution (logic)

Scanner

Image resolution

Imaging phantom

Physics

Mathematics

Optics

Image (mathematics)

Artificial intelligence

Medicine

Nuclear medicine

Noise reduction

Noise (video)

Computer vision

Medical Physics

Purpose To characterize the noise and spatial resolution properties of a commercially available deep learning‐based computed tomography (CT) reconstruction algorithm. Methods Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI vol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR‐V), and deep learning‐based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high‐contrast (air–polyethylene interface), and intermediate‐contrast (water–polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (f av ) and frequency at which the TTF was reduced to 50% (f 50% ), respectively. The second experiment used a custom phantom with low‐contrast rods and lung texture sections for the assessment of low‐contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDI vol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, , following AAPM TG233 methods. All measurements were compared among the algorithms. Results Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR‐V (at “100%” setting) and True Fidelity (at “High” setting), respectively. The noise texture from ASiR‐V had substantially lower noise frequency content with 55 ± 4% lower NPS f av compared to FBP while True Fidelity had only marginally different noise frequency content with 9 ± 5% lower NPS f av compared to FBP. Both ASiR‐V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high‐contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level values were 314% and 271% higher in ASiR‐V and True Fidelity compared to FBP, respectively. High‐contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (&lt;3% difference in TTF f 50% ). Compared to FBP, low‐contrast spatial resolution was lower for ASiR‐V and True Fidelity with a reduction of TTF f 50% of up to 42% and 36%, respectively. Conclusions The deep learning‐based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high‐contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low‐contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm