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A method for characterizing and matching CT image quality across CT scanners from different manufacturers
Author(s) -
Winslow James,
Zhang Yakun,
Samei Ehsan
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12554
Subject(s) - iterative reconstruction , noise (video) , image quality , kernel (algebra) , weighting , optical transfer function , imaging phantom , artificial intelligence , computer science , image resolution , computer vision , metric (unit) , pattern recognition (psychology) , mathematics , optics , physics , image (mathematics) , operations management , combinatorics , acoustics , economics
Purpose The purpose of this study was to quantitatively characterize the fundamental aspects of image quality ( IQ ) associated with different computed tomography ( CT ) reconstruction algorithms, the resolution, noise texture, noise magnitude per dose, and use those data to devise a methodology to match IQ between different CT systems. Methods and materials This study entailed a 3‐step methodology involving (a) characterizing the noise magnitude, texture, and resolution for a CT system‐reconstruction using the relationship between noise magnitude and Computed Tomography Dose Index ( CTDI ), noise power spectrum ( NPS ), and modulation transfer function ( MTF ), (b) developing clinically relevant strategies of weighting the differences among system‐reconstructions as a means to determine the best match (c) identifying for each target system‐reconstruction, system‐reconstructions with matched in terms of that minimum IQ differences. Images of the ACR CT phantom were acquired at two dose levels on each of two CT scanners. Images were reconstructed using all available reconstruction kernels and multiple iterative reconstruction ( IR ) settings. Each reconstruction was characterized as described above. Percent changes for each IQ metric were calculated for every possible pair of system‐reconstructions. Weighting functions, reflecting the human visual system's limit to discriminate between spatial frequencies with differences below 5%, were applied to the differences and the product of the weighted values was used to indicate the best match for each system‐reconstruction. Results Noise texture and resolution are governed by choice of reconstruction kernel and IR strength, while noise magnitude is additionally dependent on dose. Harder kernels have better resolution, finer noise texture, and increase the required dose for a given noise magnitude, and vice versa. Increasing IR strength generally improves resolution, coarsens noise texture, and lowers the required dose. Seventy‐one percent of Siemens matches for GE target reconstructions had percent changes in noise texture/resolution under 5%. Seventy‐three percent of GE matches for Siemens target reconstructions had percent changes in noise texture/resolution under 5%. ACR phantom images for each matched reconstruction pair appeared similar in both noise magnitude and noise texture. Conclusion Matching image appearance in terms of resolution, noise magnitude, and noise texture provides a quantitative and reproducible strategy to improve consistency in image quality among different CT scanners and reconstructions.