A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy
Authors: Li Hua , Dolly Steven , Chen Hsin‐Chen , Anastasio Mark A. , Low Daniel A. , Li Harold H. , Michalski Jeff M. , Thorstad Wade L. , Gay Hiram , Mutic Sasa 
Published: February 2017

CT image reconstruction is typically evaluated based on the ability to reduce the radiation dose to as‐low‐as‐reasonably‐achievable (ALARA) while maintaining acceptable image quality. However, the determination of common image quality metrics, such as noise, contrast, and contrast‐to‐noise ratio, is often insufficient for describing clinical radiotherapy task performance. In this study we designed and implemented a new comparative analysis method associating image quality, radiation dose, and patient size with radiotherapy task performance, with the purpose of guiding the clinical radiotherapy usage of CT reconstruction algorithms. The iterative reconstruction algorithm was selected as the target for comparison, wherein filtered back‐projection (FBP) reconstruction was regarded as the baseline. Both phantom and patient images were analyzed. A layer‐adjustable anthropomorphic pelvis phantom capable of mimicking 38–58 cm lateral diameter‐sized patients was imaged and reconstructed by the FBP and algorithms with varying noise‐reduction‐levels, respectively. The resulting image sets were quantitatively assessed by two image quality indices, noise and contrast‐to‐noise ratio, and two clinical task‐based indices, target CT Hounsfield number (for electron density determination) and structure contouring accuracy (for dose‐volume calculations). Additionally, CT images of 34 patients reconstructed with with six noise reduction levels were qualitatively evaluated by two radiation oncologists using a five‐point scoring mechanism. For the phantom experiments, achieved noise reduction up to 66.1% and CNR improvement up to 53.2%, compared to FBP without considering the changes of spatial resolution among images and the clinical acceptance of reconstructed images. Such improvements consistently appeared across different noise reduction levels, exhibiting limited interlevel noise () and target CT number variations (). The radiation dose required to achieve similar contouring accuracy decreased when using in place of FBP, up to 32%. Contouring accuracy improvement for images, when compared to FBP, was greater in larger patients than smaller‐sized patients. Overall, the algorithm provided superior radiation dose control while maintaining or improving task performance, when compared to FBP. The reader study on image quality improvement of patient cases shows that physicians preferred ‐reconstructed images on all cases compared to those from FBP algorithm with overall quality score: 1.21 vs. 3.15, However, qualitative evaluation strongly indicated that the radiation oncologists chose noise reduction levels of 3–4 with additional consideration of task performance, instead of image quality metrics alone. Although higher noise reduction levels improved the CNR through the further reduction of noise, there was pixelization of anatomical/tumor structures. Very‐low‐dose scans yielded severe photon starvation artifacts, which decreased target visualization on both FBP and reconstructions, especially for the 58 cm phantom size. The algorithm with a moderate noise reduction level is hence suggested for CT simulation and treatment planning. Quantitative task‐based image quality metrics should be further investigated to accommodate additional clinical applications.

PACS number(s): 87.57.C‐, 87,57.Q‐