title: Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI.
abstract: BACKGROUND
  Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations.
  
  
  METHODS
  In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.
  
  
  RESULTS
  SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches.
  
  
  CONCLUSIONS
  Experimental results using both phantom and patient data showed that ACER provided strong performance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing approaches.
  
  
  CONCLUSIONS
  A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada. dorothy.lui88@gmail.com.
authors.forename: Dorothy
authors.initials: D
authors.lastname: Lui
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada. aamhjx@gmail.com.
authors.forename: Amen
authors.initials: A
authors.lastname: Modhafar
authors.affiliation: Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada. masoom.haider@sunnybrook.ca.
authors.forename: Masoom A
authors.initials: MA
authors.lastname: Haider
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada. Alex.S.Wong@gmail.com.
authors.forename: Alexander
authors.initials: A
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D055088:Early Detection of Cancer; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio