Image Quality Variation with Gantry Rotation Time and Reconstruction Algorithm in Ultra-high-resolution CT.
Authors
Affiliations (4)
Affiliations (4)
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto 862-0976, Japan (M.H.). Electronic address: [email protected].
- Department of Radiological Technology, Tohoku University Hospital, Sendai, Japan (S.K.).
- Department of Medical Support, Division of Radiology, Okayama University Hospital, Okayama, Japan (N.A., T.I.).
- Department of Medical Image Analysis, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan (Y.F.).
Abstract
In ultra-high-resolution CT (U-HRCT), longer gantry rotation times are sometimes used to maintain image quality when using a small focal spot. This study aimed to evaluate the impact of gantry rotation time on image quality for deep learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and filtered back projection (FBP). A phantom was scanned on a U-HRCT scanner at four dose levels and four gantry rotation times, with images reconstructed using DLR, MBIR, and FBP algorithms. Image quality was evaluated for noise characteristics and high-contrast resolution. Noise was characterized using the noise power spectrum (NPS) to compute the noise magnitude ratio and central frequency ratio for MBIR and DLR relative to FBP, while high-contrast resolution was determined from the profile curve. MBIR and FBP demonstrated consistent image quality across all rotation times, with no statistically significant differences observed. In contrast, DLR showed significantly lower high-contrast resolution at a 1.0 s rotation time compared to 0.5-0.75 s (p<0.05). At 1.0 s, DLR also exhibited an unfavorable shift of the NPS toward lower frequencies, indicating degraded noise texture. While DLR delivers superior image quality at gantry rotation times of 0.5-0.75s, it exhibits a loss of resolution and altered noise texture at 1.0 s. This degradation is likely attributable to the algorithm's limitations when processing data distributions that were underrepresented in its training set. Therefore, to optimize diagnostic performance, scan parameters must be carefully tailored to the specific reconstruction algorithm.