title: Assessment of low contrast detection in CT using model observers: Developing a clinically-relevant tool for characterising adaptive statistical and model-based iterative reconstruction.
abstract: OBJECTIVE
  This study aims to assess CT image quality in a way that would meet specific requirements of clinical practice. Physics metrics like Fourier transform derived metrics were traditionally employed for that. However, assessment methods through a detection task have also developed quite extensively lately, and we chose here to rely on this modality for image quality assessment. Our goal was to develop a tool adapted for a fast and reliable CT image quality assessment in order to pave the way for new CT benchmarking techniques in a clinical context. Additionally, we also used this method to estimate the benefits brought by some IR algorithms.
  
  
  METHODS
  A modified QRM chest phantom containing spheres of 5 and 8mm at contrast levels of 10 and 20HU at 120kVp was used. Images of the phantom were acquired at CTDIvolof 0.8, 3.6, 8.2 and 14.5mGy, before being reconstructed using FBP, ASIR 40 and MBIR on a GE HD 750 CT scanner. They were then assessed by eight human observers undergoing a 4-AFC test. After that, these data were compared with the results obtained from two different model observers (NPWE and CHO with DDoG channels). The study investigated the effects of the acquisition conditions as well as reconstruction methods.
  
  
  RESULTS
  NPWE and CHO models both gave coherent results and approximated human observer results well. Moreover, the reconstruction technique used to retrieve the images had a clear impact on the PC values. Both models suggest that switching from FBP to ASIR 40 and particularly to MBIR produces an increase of the low contrast detection, provided a minimum level of exposure is reached.
  
  
  CONCLUSIONS
  Our work shows that both CHO with DDoG channels and NPWE models both approximate the trend of humans performing a detection task. Both models also suggest that the use of MBIR goes along with an increase of the PCs, indicating that further dose reduction is still possible when using those techniques. Eventually, the CHO model associated to the protocol we described in this study happened to be an efficient way to assess CT images in a clinical environment. In the future, this simple method could represent a sound basis to benchmark clinical practice and CT devices.
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland. Electronic address: ottjulien1@gmail.com.
authors.forename: Julien G
authors.initials: JG
authors.lastname: Ott
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Ba
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland.
authors.forename: Damien
authors.initials: D
authors.lastname: Racine
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland.
authors.forename: Anais
authors.initials: A
authors.lastname: Viry
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland.
authors.forename: François O
authors.initials: FO
authors.lastname: Bochud
authors.affiliation: Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand Pré, 1, 1007 Lausanne, Switzerland.
authors.forename: Francis R
authors.initials: FR
authors.lastname: Verdun
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D006801:Humans; D015588:Observer Variation; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000592:standards*
keywords: Bildqualität; Computed Tomography (CT); Computed tomography (CT); Dose reduction; Dosisreduktion; Image quality; Iterative reconstruction (IR); Iterative-Verfahren; Model observer; Model-Beobachter