Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine.

Abstract

X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.