Browsing by Subject "patient-specific"

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.

Recently, there has been renewed interest in decreasing radiation dose to patients from diagnostic imaging procedures. So far, efforts to decrease radiation dose have focused on the amount of radiation delivered from typical techniques and fail to capture the variation in radiation dose between patients. Despite the feasibility of estimating patient-specific radiation doses and the potential for this practice to aid in protocol optimization, it is not currently standard procedure for hospitals to monitor radiation dose for all patients. To address this shortcoming, we have developed an institution-wide patient-specific radiation dose monitoring program for computed tomography, digital radiography, and nuclear medicine.

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.