Surface coil MR imaging: utility of image intensity correction filter.

Gelber, Nicholas; Ragland, Ronald L.; Knorr, John R.

doi:10.2214/ajr.162.3.8109524

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…This approach has been realized in commercial systems such as Phased array UnifoRmity Enhancement (PURE -General Electric (GE)), Prescan Normalize (Siemens), CLEAR (Philips) and NATURAL (Hitachi). One of the consequences of using such intensity correction approaches is it creates a spatial dependence on background noise (which is uniformly distributed [9,10] prior to correction). This results in increasing noise levels as we move away from the coil in the corrected images, which is particularly visible in regions distant from the coil [10].…”

Section: Introductionmentioning

confidence: 99%

“…One of the consequences of using such intensity correction approaches is it creates a spatial dependence on background noise (which is uniformly distributed [9,10] prior to correction). This results in increasing noise levels as we move away from the coil in the corrected images, which is particularly visible in regions distant from the coil [10]. An example is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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BackgroundProstate 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.MethodsIn 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.ResultsSNR 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.DiscussionExperimental 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.ConclusionsA 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…A common challenge faced in bias correction, particularly when dealing with the significant intensity inhomogeneities exhibited in DW-MR acquisitions using endorectal coil, is the undesired noise amplification which can have significant negative effects on not only interpretation but also tasks such as segmentation and registration [46], [20]. Most existing bias correction methods either do not take into account the issue of noise amplification [38], [40], [47], [48] or add a simple spatial filter to the designed method to control the effect of data noise as an independent pre-or post-processing step [49], [50].…”

Section: Related Workmentioning

confidence: 99%

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Boroomand

Shafiee

Khalvati

et al. 2016

IEEE Trans. Med. Imaging

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Abstract-Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter-and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, biascorrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.

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“…In many MRI scanners, a common feature is a built-in surface coil intensity correction (SCIC) algorithm that postprocesses the images [33]- [35] or calibrates the acquisition to correct for the intensity variations [19]. A precalibration correction approach such as the one proposed by Liney et al, uses a series of proton density (PD) weighted images to determine the bias profile so that a correction matrix can be generated and applied to correct upon acquisition [19].…”

Section: Monte Carlo Bias Field Correction In Endorectalmentioning

confidence: 99%

Monte Carlo Bias Field Correction in Endorectal Diffusion Imaging

Lui

Modhafar

Glaister

et al. 2014

IEEE Trans. Biomed. Eng.

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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.

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Surface coil MR imaging: utility of image intensity correction filter.

Cited by 6 publications

References 6 publications

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

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

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Monte Carlo Bias Field Correction in Endorectal Diffusion Imaging

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