The Accuracy of Noise Measurements in Patient CT Images

RATIONALE AND OBJECTIVE:Noise magnitude is one of the image quality indicators in computed tomography (CT) for assessing imaging performance, and the CMS (Centers for Medical and Medicaid Services) recently included a measure of noise magnitude in terms of "global noise". Despite its importance, no standard method currently exists to measure noise magnitude in patient images. Theoretically, the most accurate approach is to assess voxel value variation across repeated images, the so-called ensemble noise. Obviously, such a method is not ethically feasible in actual patients. To surmount this impasse, we deployed virtual imaging techniques to benchmark three noise magnitude calculation methods against two gold standard ensemble noise measures across 36 imaging conditions. MATERIALS AND METHODS:Over 1800 virtual image datasets were generated from imaging an American College of Radiology (ACR) phantom and Extended Cardiac-Torso (XCAT) human models using a validated, scanner-specific CT simulator (DukeSim). The ACR phantom was imaged under 36 different imaging conditions defined by combinations of chest and abdominopelvic protocols, three dose levels, three reconstruction kernels, and both Filtered Back Projection and Iterative Reconstruction algorithms. Under the same conditions, XCAT models were repeatedly imaged 50 times. Noise magnitudes in the ACR phantom were calculated in 5 circular regions of interest. In patients, noise was measured in the air surrounding the body and in soft tissues by applying HU < -900 and -300 ≤ HU ≤ 100 thresholds, respectively. For each imaging condition, measured noise magnitudes were compared against the ensemble noise in soft tissue, liver, and lungs. RESULTS:Across all imaging conditions, noise measurements in the ACR phantom and in the air surrounding the patient underestimated ensemble noise by approximately 60% and 50%, respectively. In contrast, soft tissue-based noise measurements were closer to the gold standard with median differences between -8% and +4%. CONCLUSION:This study introduced a virtual imaging-based framework to benchmark clinical CT noise metrics against ensemble noise measures. Virtual imaging enabled objective comparison of different noise magnitude calculation methods in large, realistic populations simulating clinical conditions. Noise measured in the air cannot represent soft tissue noise. The results validated soft tissue-based noise measurements as a reliable surrogate to inform protocol design, technology assessment, and equitable healthcare reimbursement.