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Patient‐specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images
Author(s) -
Sanders Jeremiah,
Hurwitz Lynne,
Samei Ehsan
Publication year - 2016
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 - 10.1118/1.4961984
Subject(s) - imaging phantom , iterative reconstruction , image quality , image resolution , artificial intelligence , optical transfer function , computer vision , reconstruction algorithm , computer science , mathematics , nuclear medicine , algorithm , pattern recognition (psychology) , image (mathematics) , medicine , mathematical analysis
Purpose To develop and validate an automated technique for evaluating the spatial resolution characteristics of clinical computed tomography (CT) images. Methods Twenty one chest and abdominopelvic clinical CT datasets were examined in this study. An algorithm was developed to extract a CT resolution index (RI) analogous to the modulation transfer function from clinical CT images by measuring the edge‐spread function (ESF) across the patient's skin. A polygon mesh of the air‐skin boundary was created. The faces of the mesh were then used to measure the ESF across the air‐skin interface. The ESF was differentiated to obtain the line‐spread function (LSF), and the LSF was Fourier transformed to obtain the RI. The algorithm's ability to detect the radial dependence of the RI was investigated. RIs measured with the proposed method were compared with a conventional phantom‐based method across two reconstruction algorithms (FBP and iterative) using the spatial frequency at 50% RI, f 50 , as the metric for comparison. Three reconstruction kernels were investigated for each reconstruction algorithm. Finally, an observer study was conducted to determine if observers could visually perceive the differences in the measured blurriness of images reconstructed with a given reconstruction method. Results RI measurements performed with the proposed technique exhibited the expected dependencies on the image reconstruction. The measured f 50 values increased with harder kernels for both FBP and iterative reconstruction. Furthermore, the proposed algorithm was able to detect the radial dependence of the RI. Patient‐specific measurements of the RI were comparable to the phantom‐based technique, but the patient data exhibited a large spread in the measured f 50 , indicating that some datasets were blurrier than others even when the projection data were reconstructed with the same reconstruction algorithm and kernel. Results from the observer study substantiated this finding. Conclusions Clinically informed, patient‐specific spatial resolution can be measured from clinical datasets. The method is sufficiently sensitive to reflect changes in spatial resolution due to different reconstruction parameters. The method can be applied to automatically assess the spatial resolution of patient images and quantify dependencies that may not be captured in phantom data.