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Background: Prostate cancer is one of the most common forms of cancer foundin males making early diagnosis important. Magnetic resonance imaging (MRI) hasbeen useful in visualizing and localizing tumor candidates and with the use ofendorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. Thecoils introduce intensity inhomogeneities and the surface coil intensitycorrection built into MRI scanners is used to reduce these inhomogeneities.However, the correction typically performed at the MRI scanner level leads tonoise amplification and noise level variations. Methods: In this study, weintroduce a new Monte Carlo-based noise compensation approach for coilintensity corrected endorectal MRI which allows for effective noisecompensation and preservation of details within the prostate. The approachaccounts for the ERC SNR profile via a spatially-adaptive noise model forcorrecting non-stationary noise variations. Such a method is usefulparticularly for improving the image quality of coil intensity correctedendorectal MRI data performed at the MRI scanner level and when the originalraw data is not available. Results: SNR and contrast-to-noise ratio (CNR)analysis in patient experiments demonstrate an average improvement of 11.7 dBand 11.2 dB respectively over uncorrected endorectal MRI, and provides strongperformance when compared to existing approaches. Conclusions: A new noisecompensation method was developed for the purpose of improving the quality ofcoil intensity corrected endorectal MRI data performed at the MRI scannerlevel. We illustrate that promising noise compensation performance can beachieved for the proposed approach, which is particularly important forprocessing coil intensity corrected endorectal MRI data performed at the MRIscanner level and when the original raw data is not available.

Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI