title: Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening.
abstract: OBJECTIVE
  While the acquisition of projection data in a computed tomography (CT) scanner is generally cqrried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening.
  
  
  METHODS
  The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects.
  
  
  RESULTS
  Filtered images for all combinations of three types of reconstruction kernels ("smooth," "standard," and "sharp" kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for the difference between filtered images and reconstructed images ranged from 1.9 to 23.5 Hounsfield units for all kernel combinations; these SDs were much smaller than the noise SDs in the reconstructed images.
  
  
  CONCLUSIONS
  The proposed method has good performance and is clinically feasible in lung cancer screening. This method can be applied to images reconstructed on any scanner by measuring the PSFs in each system.
authors.affiliation: Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, Niigata 951-8518, Japan.
authors.forename: Masaki
authors.initials: M
authors.lastname: Ohkubo
authors.forename: Shinichi
authors.initials: S
authors.lastname: Wada
authors.forename: Akihiro
authors.initials: A
authors.lastname: Kayugawa
authors.forename: Toru
authors.initials: T
authors.lastname: Matsumoto
authors.forename: Kohei
authors.initials: K
authors.lastname: Murao
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008403:Mass Screening / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*