DETECTION OF DEGRADATION OF MAGNETIC-RESONANCE (MR) IMAGES - COMPARISON OF AN AUTOMATED MR IMAGE-QUALITY ANALYSIS SYSTEM WITH TRAINED HUMANOBSERVERS

Rationale and Objectives. Ther perceived need for magnetic resonance ( MR) imaging quality control (QC) is occasionally minimized on the assu mption that significant errors will be detected by the users. To evalu ate the validity of this assumption, we compared the sensitivity of a test object and automated image analysis system for MR imaging QC with the sensitivity of trained human observers by evaluating images that were intentionally degraded. Methods. Parameters for imaging the test object and normal human volunteers were set to values that decreased t he signal-to-noise ratio (SNR), caused distortion, and increased the s lice thickness and separation. Results. The human observers were able to detect a 6-13% reduction in the SNR and distortions of more than 15 % in human images. They were unable to identify 40% increases in the s lice thickness. Automated analysis of test object images was able to d etect all image degradations at the minimum levels applied. Conclusion . The poor sensitivity of the human observers indicated that degradati on, especially spatial measurements, could be significantly in error b efore being detected through visual analysis of clinical images. These errors would be detected by automated analysis of the test object use d. Further investigation is needed to better define the accuracy with which quantitative image-quality analysis predicts the effects of degr aded image quality on the ability of human observes to detect subtle a bnormalities in clinical images.