title: [Improvement of the image quality of MSCT of the pelvis with a raw data-based, multidimensional filter].
abstract: OBJECTIVE
  To evaluate the potential of raw data-based multidimensional adaptive filtering (MAF) by determining its effects on image noise and image quality in multi-slice spiral CT (MSCT) of the pelvis.
  
  
  METHODS
  Fifty patients with rectal and bladder cancer were examined with MSCT using a high-resolution protocol. Reconstructions were performed with dedicated reconstruction software and a standard algorithm, both without and with MAF, with four different modification fractions selected from 5 % to 20 %. The noise was measured at six anatomic sites of the pelvis. Image quality and image noise were rated on a 5-point-scale.
  
  
  RESULTS
  Modification fractions of 15 % (15 % MAF) and 20 % (20 % MAF) significantly reduced the noise level at all measurement points in comparison with lower modification fractions (p < 0.05). Overall quality of the reconstructed images was rated better with 15 % MAF and 20 % MAF than with other modification fractions (p < 0.05). No further improvement of the image quality was observed by changing from 15 % MAF to 20 % MAF (p > 0.05). 15 % MAF achieved a mean noise reduction of 33 %. All examinations showed an improved visualization of the rectal wall and perirectal lymph nodes.
  
  
  CONCLUSIONS
  MAF improves the image quality by reducing the noise level and by removing noise structures. This technique offers new perspectives to radiation dose reduction in CT.
authors: Institut für Diagnostische Radiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg. ulrich.baum@idr.imed.uni-erlangen.de
authors: U
authors: U
authors: Baum
authors: A
authors: A
authors: Noemayr
authors: A
authors: A
authors: Reissig
authors: M
authors: M
authors: Lell
authors: A
authors: A
authors: Cavallaro
authors: M
authors: M
authors: Kachelriess
authors: T
authors: T
authors: Riedel
authors: W A
authors: WA
authors: Kalender
authors: W
authors: W
authors: Bautz
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted; D010388:Pelvis / Q000000981:diagnostic imaging*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D036542:Tomography, Spiral Computed / Q000379:methods* / Q000592:standards