Development and Application of Patient-Informed Metrics of Image Quality in CT

The purpose of this dissertation was to develop methods of measuring patient-specific image quality in computed tomography. The methods developed in this dissertation enable noise power spectrum, low contrast resolution, and ultimately a detectability index to be measured in a patient-specific manner. The project is divided into three part: 1) demonstrating the utility of currently developed patient-specific measures of image quality, 2) developing a method to estimate noise power spectrum and low contrast task transfer function from patient images, 3) and applying the extended metrology to the calculation of a patient-specific and task-specific detectability index of the future.In part 1, (chapters 2 and 3) the value of patient-specific image quality is demonstrated in two ways. First, patient-specific measures of noise magnitude and high-contrast resolution were deployed on a broad clinical dataset of chest and abdomen-pelvis exams. Image quality and dose were measured for 87,629 cases across 97 medical facilities, and variability in each outcome are reported. Such measurements of variability would be impossible in a phantom-derived image quality paradigm. Secondly, patient-specific measures of noise magnitude and high-contrast resolution were combined with a phantom-derived noise power spectrum to yield a detectability index. The hybrid (patient, and phantom-derived) detectability index was measured and retrospectively compared to the results of a detection observer study. The results show that the measured hybrid detectability index is shown to be correlated with human observer detection performance, further demonstrating the value of measuring patient-specific image quality. In part 2, (chapters 4 and 5) two image quality aspects are extended from a phantom-derived to a patient-specific paradigm. In chapter 4, a method to measure noise power spectrum from patient images is developed and validated using virtual imaging trial and physical phantom data. The method is applied to unseen clinical cases to demonstrate its feasibility, and the method’s sensitivity to expected trends across image reconstructions. Since the method relies on a sufficient area within the patient’s liver to make a measurement, the sensitivity of measurement accuracy of the method region size is assessed. Results show that the measurements can be accurate with as few as 106 included pixels, and that measurements are sensitive to ground truth differences in reconstruction algorithm. In chapter 5, a method to measure low contrast resolution from patient images is developed and validated using low contrast insert phantom scans. The method uses a support vector machine to learn the connection between the patient-specific noise power spectrum measured in chapter 4 and the low contrast task transfer function. The estimation method is compared to clinical alternative and results show that it is more accurate on the basis of RMSE for iterative reconstructions (especially high strength reconstructions). In part 3, (chapter 6 and appendix section 8.1) the developed patient-specific image quality metrology are applied to calculated fully patient-specific detectability index. Here, patient-specific image quality measures are re-applied to the detectability index calculations from chapter 3, converting the calculations from a hybrid method to a fully patient-specific method. To do so, the patient-specific noise power spectrum estimates from chapter 4 were combined with the patient-specific low contrast task transfer functions from chapter 5 to inform the detectability index calculations. The purpose of this chapter was to show the positive impact of measuring a task-based measure of image quality in a fully patient-specific paradigm. The results show that the fully patient-specific detectability index show a statistically significant improvement in its relation with human detection accuracy over the hybrid measurements. This section also served as an indirect validation methodologies in chapters 4 and 5. Finally, all patient-specific measures are deployed over a variety of clinical cases to demonstrate feasibility of using the methods to monitor image quality. In conclusion, this dissertation developed methods to assess task based and task generic image quality directly from patient images, and demonstrated the utility and value of patient-specific image quality assessment.