Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI.

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. DISCUSSION: 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.

Using surface markers for MRI guided breast conserving surgery: a feasibility survey.

Breast MRI is frequently performed prior to breast conserving surgery in order to assess the location and extent of the lesion. Ideally, the surgeon should also be able to use the image information during surgery to guide the excision and this requires that the MR image is co-registered to conform to the patient's position on the operating table. Recent progress in MR imaging techniques has made it possible to obtain high quality images of the patient in the supine position which significantly reduces the complexity of the registration task. Surface markers placed on the breast during imaging can be located during surgery using an external tracking device and this information can be used to co-register the images to the patient. There remains the problem that in most clinical MR scanners the arm of the patient has to be placed parallel to the body whereas the arm is placed perpendicular to the patient during surgery. The aim of this study is to determine the accuracy of co-registration based on a surface marker approach and, in particular, to determine what effect the difference in a patient's arm position makes on the accuracy of tumour localization. Obtaining a second MRI of the patient where the patient's arm is perpendicular to body axes (operating room position) is not possible. Instead we obtain a secondary MRI scan where the patient's arm is above the patient's head to validate the registration. Five patients with enhancing lesions ranging from 1.5 to 80 cm(3) in size were imaged using contrast enhanced MRI with their arms in two positions. A thin-plate spline registration scheme was used to match these two configurations. The registration algorithm uses the surface markers only and does not employ the image intensities. Tumour outlines were segmented and centre of mass (COM) displacement and Dice measures of lesion overlap were calculated. The relationship between the number of markers used and the COM-displacement was also studied. The lesion COM-displacements ranged from 0.9 to 9.3 mm and the Dice overlap score ranged from 20% to 80%. The registration procedure took less than 1 min to run on a standard PC. Alignment of pre-surgical supine MR images to the patient using surface markers on the breast for co-registration therefore appears to be feasible.

Monte Carlo bias field correction in endorectal diffusion imaging.

Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multiparametric magnetic resonance imaging (MRI) has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci, which is required to plan the treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multiparametric datasets. An endorectal receiver coil is often used to improve signal-to-noise ratio and aid in detection of smaller cancer foci. Moreover, computed high b-value diffusion-weighted imaging show improved delineation of tumors but is subject to strong bias fields near the coil. Here, a nonparametric approach to bias field correction for endorectal diffusion imaging via Monte Carlo sampling is introduced. It will be shown that the delineation between the prostate gland and the background and intensity inhomogeneity may be improved using the proposed approach. High b-value generated results also show improved visualization of tumor regions. The results suggest that Monte Carlo bias correction may have potential as a preprocessing tool for endorectal diffusion images for the prostate cancer detection and localization or segmentation.

Multiparametric MRI prostate cancer analysis via a hybrid morphological-textural model.

Multiparametric MRI has shown considerable promise as a diagnostic tool for prostate cancer grading. Diffusion-weighted MRI (DWI) has shown particularly strong potential for improving the delineation between cancerous and healthy tissue in the prostate gland. Current automated diagnostic methods using multiparametric MRI, however, tend to either use low-level features, which are difficult to interpret by radiologists and clinicians, or use highly subjective heuristic methods. We propose a novel strategy comprising a tumor candidate identification scheme and a hybrid textural-morphological feature model for delineating between cancerous and non-cancerous tumor candidates in the prostate gland via multiparametric MRI. Experimental results using clinical multiparametric MRI datasets show that the proposed strategy has strong potential as a diagnostic tool to aid radiologists and clinicians identify and detect prostate cancer more efficiently and effectively.

Low-dose computed tomography via spatially adaptive Monte-Carlo reconstruction.

Low-dose computed tomography (CT) reduces radiation exposure but decreases signal-to-noise ratio (SNR) and diagnostic capabilities. Noise compensation can improve SNR so low-dose CT can provide valuable information for diagnosis without risking patient radiation exposure. In this study, a novel noise-compensated CT reconstruction method that uses spatially adaptive Monte-Carlo sampling to produce noise-compensated reconstructions is investigated. By adapting to local noise statistics, a non-parametric estimation of the noise-free image is computed that successfully handles non-stationary noise found in low-dose CT images. Using phantom and real low-dose CT images, effective noise suppression is shown to be accomplished while maintaining structures and details.