
A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy
Author(s) -
Li Hua,
Dolly Steven,
Chen HsinChen,
Anastasio Mark A.,
Low Daniel A.,
Li Harold H.,
Michalski Jeff M.,
Thorstad Wade L.,
Gay Hiram,
Mutic Sasa
Publication year - 2016
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1120/jacmp.v17i4.5763
Subject(s) - image quality , task (project management) , computer science , quality (philosophy) , algorithm , medical physics , iterative reconstruction , artificial intelligence , image (mathematics) , medicine , philosophy , management , epistemology , economics
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. TheiDose 4 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 andiDose 4algorithms 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 withiDose 4with six noise reduction levels were qualitatively evaluated by two radiation oncologists using a five‐point scoring mechanism. For the phantom experiments,iDose 4achieved 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 differentiDose 4noise reduction levels, exhibiting limited interlevel noise ( < 5 HU ) and target CT number variations ( < 1 HU ). The radiation dose required to achieve similar contouring accuracy decreased when usingiDose 4in place of FBP, up to 32%. Contouring accuracy improvement foriDose 4images, when compared to FBP, was greater in larger patients than smaller‐sized patients. Overall, theiDose 4algorithm 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 preferrediDose 4 ‐reconstructed images on all cases compared to those from FBP algorithm with overall quality score: 1.21 vs. 3.15, p = 0.0022. However, qualitative evaluation strongly indicated that the radiation oncologists choseiDose 4noise reduction levels of 3–4 with additional consideration of task performance, instead of image quality metrics alone. Although higheriDose 4noise 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 andiDose 4reconstructions, especially for the 58 cm phantom size. TheiDose 4algorithm 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‐