Quantification and homogenization of image noise between two CT scanner models

• Published in: Journal of applied clinical medical physics Vol. 21; no. 1; pp. 174 - 178
• Main Authors: Einstein, Samuel A., Rong, Xiujiang John, Jensen, Corey T., Liu, Xinming
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2020; John Wiley and Sons Inc
• Subjects: Abdomen; Algorithms; Anthropomorphism; computed tomography; Energy; image noise; image quality; Liver; Medical Imaging; Noise; noise power spectrum; Quality control; Scanners; Spectrum analysis; Standard deviation; United States > US; computed tomography; image quality; image noise; noise power spectrum
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.12798

Feedback from radiologists indicated that differences in image appearance and noise impeded reading of post‐contrast computed tomography (CT) scans from an updated CT scanner that was recently added to a fleet of existing scanners from the same vendor, despite using identically named reconstruction algorithms. The goals of this work were to quantify and possibly standardize image quality on the new and an existing scanner using phantom images. Three months of daily quality control images were analyzed to determine the mean CT number and noise magnitude in a water phantom. Next, subtraction images from the uniformity section of an American College of Radiology CT phantom were used to generate noise power spectra for both scanners. Then, a semi‐anthropomorphic liver phantom was imaged with both scanners in triplicate using identical body protocols to quantify differences CT number and noise magnitude. Finally, the scanner dependence of CT number and noise magnitude on material attenuation was quantified using a multi‐energy CT phantom with 15 material inserts. Significant differences between scanners were determined using a paired or Welch's t test as appropriate. In daily quality control images, the new scanner exhibited slightly higher CT number (0.697 vs. 0.412, P < 0.001, n = 85) and slightly lower noise magnitude (4.85 vs. 4.94, P < 0.001, n = 85). Measured NPS was not significantly different between the existing and new scanners. Interestingly, it was observed that the noise magnitude from the new scanner increased with increasing material attenuation in both the liver (P = 0.008) and multi‐energy (P < 0.001) phantoms. Using an alternate reconstruction algorithm with the new scanner eliminated this deviation at high material attenuations. While standard noise evaluation in a water phantom was unable to discern differences between the scanners, more comprehensive testing with higher attenuation materials allowed for the characterization and homogenization of image quality.