Accuracy of Noise Magnitude Measurements from Patient CT Images

Abstract

Purpose Noise magnitude is a main CT image quality indicator. In vivo measurements emerged as a patient-specific methodology to assess and qualify CT noise, yet methods to do so vary. Current noise measurement methods in soft tissues and air surrounding the patient use distinct image segmentations, HU thresholds, and region-of-interests, resulting in noise estimation variations. In this study, we compared two noise magnitude calculation methods against the gold standard ensemble noise in two cohorts of virtually-generated patient images across 36 imaging conditions. Methods 1800 image datasets were generated using a virtual trial platform based on anthropomorphic phantoms (XCAT) and a validated, scanner-specific CT simulator (DukeSim). XCAT phantoms were repeatedly imaged 50 times using Chest and Abdominopelvic protocols, three dose levels, and three reconstruction kernels, using both FBP and IR algorithms. Noise magnitudes were calculated in the air surrounding the patient (An) and soft tissues (GNI) by applying HU<-900 and -300<HU<100 thresholds, respectively. Per each imaging condition, An and GNI were compared to the ensemble noise calculated in soft tissue (En) and to the ensemble noise calculated in the liver and in the lungs (On) for abdominopelvic and chest studies, respectively. Results Across the three kernels and dose levels, An largely underestimated En and On for both FBP (median differences: -46% and -49%) and IR (median differences: -49% and -51%); whereas the GNI showed closer values to the En and On for FBP (median differences: 4% and 3%) and IR (median differences: -3% and -8%). Conclusion Applying virtual imaging techniques enabled an unbiased comparison of different noise magnitude calculation methods in large and realistic populations simulating clinical conditions. The noise measured in the air surrounding the patient cannot represent noise magnitude in soft tissues. The results affirmed the validation of soft tissue-based noise measurements as a close surrogate to inform protocol design and technology assessment.