Assessing signal intensity change on well-registered images: comparing subtraction, color-encoded subtraction, and parallel display formats.
case_control · Level III
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- Record sourced from PubMed, PMID 21502385.
- Also identified by DOI 10.1148/radiol.11101092.
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Abstract
To assess the sensitivity, specificity, accuracy, and interobserver reliability of subtraction, color-encoded subtraction, and parallel display formats in assessing signal intensity (SI) differences between well-registered images. Institutional ethics approval for the study and a waiver of individual patient consent were obtained. Five radiologists graded the severity of fatty liver by using a seven-point scale for four imaging sets created from 179 pairs of dual-echo in- and opposed-phase magnetic resonance images from 179 patients. The four sets contained images displayed in parallel, subtraction images, color-encoded subtraction images, and images from the three previous formats presented together. The order of the images and sets was randomized. Sensitivity and specificity were assessed with the McNemar test. Accuracy was assessed by using three-way analysis of variance, with Tukey post hoc methods used to assess differences between the four formats. Interobserver reliability was assessed by using the Fleiss κ value. Subtraction (P = .016 at a 5% SI difference threshold) and color-encoded subtraction (P = .031 at a 4% SI difference threshold) formats had higher sensitivity than did the parallel format. The accuracy of the subtraction format was superior to that of the parallel format (P < .0001). Interobserver reliability of the subtraction (κ = 0.53) and color-encoded subtraction (κ = 0.39) formats was superior to that of the parallel format (κ = 0.33) (P < .0001 and P = .0085, respectively). When images are well registered, subtraction and color-encoded subtraction techniques offer advantages over the traditional parallel presentation format for the assessment of SI differences.
Medical subject headings
- Fatty Liver
- Magnetic Resonance Imaging