Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis\nimportant. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with\nthe use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity\ninhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these\ninhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification\nand noise level variations.\nMethods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity\ncorrected endorectal MRI which allows for effective noise compensation and preservation of details within the\nprostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting\nnon-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity\ncorrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.\nResults: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement\nof 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared\nto existing approaches.\nDiscussion: Experimental results using both phantom and patient data showed that ACER provided strong\nperformance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing\napproaches.\nConclusions: A new noise compensation method was developed for the purpose of improving the quality of coil\nintensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise\ncompensation performance can be achieved for the proposed approach, which is particularly important for\nprocessing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw\ndata is not available.
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