A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy

Hua Li, Steven Dolly, Hsin Chen Chen, Mark A. Anastasio, Daniel A. Low, Harold H. Li, Jeff M. Michalski, Wade L. Thorstad, Hiram Gay, Sasa Mutic

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

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 iDose4 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 iDose4 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 iDose4 with six noise reduction levels were qualitatively evaluated by two radiation oncologists using a five-point scoring mechanism. For the phantom experiments, iDose4 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 iDose4 noise 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 using iDose4 in place of FBP, up to 32%. Contouring accuracy improvement for iDose4 images, when compared to FBP, was greater in larger patients than smaller-sized patients. Overall, the iDose4 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 iDose4-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 chose iDose4 noise reduction levels of 3-4 with additional consideration of task performance, instead of image quality metrics alone. Although higher iDose4 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 iDose4 reconstructions, especially for the 58 cm phantom size. The iDose4 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.

Original languageEnglish
Pages (from-to)377-390
Number of pages14
JournalJournal of applied clinical medical physics
Volume17
Issue number4
DOIs
StatePublished - Jan 1 2016

Keywords

  • ALARA principle
  • IDose reconstruction
  • Radiotherapy
  • Simulation CT
  • Task-based image quality

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