title: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT.
abstract: OBJECTIVE
  To investigate a novel locally adaptive projection space denoising algorithm for low-dose CT data.
  
  
  METHODS
  The denoising algorithm is based on bilateral filtering, which smooths values using a weighted average in a local neighborhood, with weights determined according to both spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus maintaining high spatial resolution. A CT noise model that takes into account the bowtie filter and patient-specific automatic exposure control effects is also incorporated into the denoising process. The authors evaluated the noise-resolution properties of bilateral filtering incorporating such a CT noise model in phantom studies and preliminary patient studies with contrast-enhanced abdominal CT exams.
  
  
  RESULTS
  On a thin wire phantom, the noise-resolution properties were significantly improved with the denoising algorithm compared to commercial reconstruction kernels. The noise-resolution properties on low-dose (40 mA s) data after denoising approximated those of conventional reconstructions at twice the dose level. A separate contrast plate phantom showed improved depiction of low-contrast plates with the denoising algorithm over conventional reconstructions when noise levels were matched. Similar improvement in noise-resolution properties was found on CT colonography data and on five abdominal low-energy (80 kV) CT exams. In each abdominal case, a board-certified subspecialized radiologist rated the denoised 80 kV images markedly superior in image quality compared to the commercially available reconstructions, and denoising improved the image quality to the point where the 80 kV images alone were considered to be of diagnostic quality.
  
  
  CONCLUSIONS
  The results demonstrate that bilateral filtering incorporating a CT noise model can achieve a significantly better noise-resolution trade-off than a series of commercial reconstruction kernels. This improvement in noise-resolution properties can be used for improving image quality in CT and can be translated into substantial dose reduction.
authors.affiliation: Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. manduca@mayo.edu
authors.forename: Armando
authors.initials: A
authors.lastname: Manduca
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yu
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Trzasko
authors.forename: Natalia
authors.initials: N
authors.lastname: Khaylova
authors.forename: James M
authors.initials: JM
authors.lastname: Kofler
authors.forename: Cynthia M
authors.initials: CM
authors.lastname: McCollough
authors.forename: Joel G
authors.initials: JG
authors.lastname: Fletcher
mesh_terms: D000465:Algorithms; D016477:Artifacts; D023881:Colonography, Computed Tomographic / Q000295:instrumentation / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011860:Radiography, Abdominal / Q000295:instrumentation / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*