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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2015

Open Access 01-12-2015 | Research Article

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

Authors: Dorothy Lui, Amen Modhafar, Masoom A. Haider, Alexander Wong

Published in: BMC Medical Imaging | Issue 1/2015

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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.

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.
Literature
1.
2.
3.
Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level ≤ 4.0 ng per milliliter. N Engl J Med. 2004; 350(22):2239–246.CrossRefPubMed
4.
Carroll PR. Early detection of prostate cancer. Community Oncol. 2010; 7:25–7.
5.
Turkbey B, Merino MJ, Gallardo EC, Shah V, Aras O, Bernardo M, et al. Comparison of endorectal coil and nonendorectal coil T2W and diffusion-weighted MRI at 3 Tesla for localizing prostate cancer: Correlation with whole-mount histopathology. J Magn Reson Imaging. 2014; 39(6):1443–1448.CrossRefPubMed
6.
Noworolski SM, Crane JC, Vigneron DB, Kurhanewicz J. A clinical comparison of rigid and inflatable endorectal-coil probes for MRI and 3d MR spectroscopic imaging (MRSI) of the prostate. J Magn Reson Imaging. 2008; 27(5):1077–1082.CrossRefPubMedPubMedCentral
7.
Beyersdorff D, Taymoorian K, Knösel T, Schnorr D, Felix R, Hamm B, et al. MRI of prostate cancer at 1.5 and 3.0 T: comparison of image quality in tumor detection and staging. Am J Roentgenol. 2005; 185(5):1214–1220.CrossRef
8.
9.
Liney GP, Turnbull LW, Knowles AJ. A simple method for the correction of endorectal surface coil inhomogeneity in prostate imaging. J Magn Reson Imaging. 1998; 8(4):994–7.CrossRefPubMed
10.
Gelber ND, Ragland RL, Knorr JR. Surface coil MR imaging: utility of image intensity correction filter. Am J Roentgenol. 1994; 162(3):695–7.CrossRef
11.
Wong A, Mishra AK. Quasi-Monte Carlo estimation approach for denoising MRI data based on regional statistics. IEEE Trans Biomed Eng. 2011; 58(4):1076–1083.CrossRefPubMed
12.
Shafiee M, Haider S, Wong A, Lui D, Cameron A, Modhafar A, et al. Apparent ultra-high b-value diffusion-weighted image reconstruction via hidden conditional random fields. IEEE Trans Med Imaging. 2014; 34:1–15.
13.
Manjón JV, Thacker NA, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, Robles M. Multicomponent MR image denoising. J Biomed Imaging. 2009; 2009:18.
14.
Golshan HM, Hasanzadeh RP, Yousefzadeh SC. An MRI denoising method using image data redundancy and local SNR estimation. Magn Reson Imaging. 2013; 31(7):1206–1217.CrossRefPubMed
15.
Wong A, Orchard J. Robust multimodal registration using local phase-coherence representations. J Signal Process Syst. 2009; 54:89–100.CrossRef
16.
Wong A, Clausi D, Fieguth P. Cpol: Complex phase order likelihood as a similarity measure for mr-ct registration. Med Image Anal. 2010; 14:50–7.CrossRefPubMed
17.
Wong A, Bishop W. Efficient least squares fusion of mri and ct images using a phase congruency model. Pattern Recognit Lett. 2008; 29:173–80.CrossRef
18.
Wong A, Bishop W, Orchard J. Efficient multi-modal least-squares alignment of medical images using quasi-orientation maps. Proc. IPCV. 2006;:74–80.
19.
Foi A. Noise estimation and removal in MR imaging: the variance-stabilization approach. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Chicago, Illinois: IEEE: 2011. p. 1809–1814.
20.
21.
Sijbers J, Den Dekker A. Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn Reson Med. 2004; 51(3):586–94.CrossRefPubMed
22.
Wiest-Daesslé N, Prima S, Coupé P, Morrissey SP, Barillot C. Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: Applications to DT-MRI. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008. New York: Springer: 2008. p. 171–9.
23.
Keeling SL, Hintermüller M, Knoll F, Kraft D, Laurain A. A total variation based approach to correcting surface coil magnetic resonance images. Appl Math Comput. 2011; 218(2):219–32.CrossRef
24.
Varghees V, Manikandan M, Gini R. Adaptive MRI image denoising using total-variation and local noise estimation. In: 2012 International Conference on Advances in Engineering, Science and Management (ICAESM). Tamil Nadu, India: IEEE: 2012. p. 506–11.
25.
Martin A, Garamendi JF, Schiavi E. MRI TV-Rician denoising. In: Biomedical Engineering Systems and Technologies. Berlin Heidelberg: Springer: 2013. p. 255–68.
26.
Pizurica A, Philips W, Lemahieu I, Acheroy M. A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imaging. 2003; 22(3):323–31.CrossRefPubMed
27.
Anand CS, Sahambi JS. Wavelet domain non-linear filtering for MRI denoising. Magn Reson Imaging. 2010; 28(6):842–61.CrossRefPubMed
28.
Wang Y, Che X, Ma S. Nonlinear filtering based on 3D wavelet transform for MRI denoising. EURASIP J Adv Signal Process. 2012; 2012(1):1–14.CrossRef
29.
Coupé P, Manjón JV, Robles M, Collins LD. Adaptive multiresolution non-local means filter for 3D MR image denoising. IET Image Process. 2011; 6:558–8.CrossRef
30.
Manjón JV, Coupé P, Martí-Bonmatí L, Collins DL, Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging. 2010; 31(1):192–203.CrossRefPubMed
31.
Wong A, Fieguth P, Clausi D. A perceptually adaptive approach to image denoising using anisotropic non-local means. Proc IEEE Int Conf Image Proc. 2008;:537–540.
32.
Aja-Fernández S, Alberola-López C, Westin CF. Noise and signal estimation in magnitude mri and rician distributed images: a lmmse approach. IEEE Trans Image Process. 2008; 17(8):1383–1398.CrossRefPubMed
33.
Maximov II, Farrher E, Grinberg F, Jon Shah N. Spatially variable Rician noise in magnetic resonance imaging. Med Image Anal. 2012; 16(2):536–48.CrossRefPubMed
34.
Awate SP, Whitaker RT. Feature-preserving MRI denoising: a nonparametric empirical Bayes approach. IEEE Trans Med Imaging. 2007; 26(9):1242–1255.CrossRefPubMed
35.
Manjón JV, Coupé P, Buades A, Louis Collins D, Robles M. New methods for mri denoising based on sparseness and self-similarity. Med Image Anal. 2012; 16(1):18–27.CrossRefPubMed
36.
Fieguth P. Statistical Image Processing and Multidimensional Modeling. New York: Springer; 2010.
37.
Wong A, Mishra A, Zhang W, Fieguth P, Clausi DA. Stochastic image denoising based on markov-chain monte carlo sampling. Signal Process. 2011; 91(8):2112–120.CrossRef
38.
Milanfar P. A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Signal Proc Mag. 2013; 30(1):106–28.CrossRef
39.
Chen MH. Importance-weighted marginal Bayesian posterior density estimation. J Am Stat Assoc. 1994; 89:818–24.CrossRef
40.
Venugopal N, Krieger A, Jeufack H, Bradshaw K, McCurdy B, Ryner L. Signal characterization of a novel two-channel rigid endorectal coil for MR imaging of the prostate. In: International Society for Magnetic Resonance in Medicine Proceedings. Stockholm Sweden: ISMRM: 2010.
41.
Noworolski SM, Crane JC, Vigneron DB, Kurhanewicz J. A clinical comparison of rigid and inflatable endorectal-coil probes for MRI and 3D MR spectroscopic imaging (MRSI) of the prostate. J Magn Reson Imaging. 2008; 27(5):1077–1082.CrossRefPubMedPubMedCentral
42.
Arteaga de Castro C, van den Bergen B, Luijten P, van der Heide U, van Vulpen M, Klomp D. Improving SNR and B1 transmit field for an endorecta coil in 7 T MRI and MRS of prostate cancer. Magn Reson Med. 2012; 68(1):311–8.CrossRefPubMed
43.
National Electrical Manufacturers Association. Determination of signal-to-noise ratio (SNR) in diagnostic magnetic resonance imaging. NEMA Standards Publication MS 1-2008 (R2014). 2015.
44.
American College of Radiology. Magnetic resonance imaging quality control manual. Reston, Va; 2015.
45.
Sattar F, Floreby L, Salomonsson G, Lovstrom B. Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process. 1997; 6:888–95.CrossRefPubMed
46.
Walsh C, Dowling A, Meade A, Malone J. Subjective and objective measures of image quality in digital fluoroscopy. Radiat Prot Dosim. 2005; 117(1–3):34–7.
47.
Wong A, Glaister J, Cameron A, Haider MA. Correlated diffusion imaging. BMC Med Imaging. 2013; 13:1–7.CrossRef
48.
Wong A, Mishra AK. Sparse reconstruction of breast mri using homotopic minimization in a regional sparsified domain. IEEE Transactions on Biomedical Engineering. 2013; 60(3):743–52.CrossRefPubMed
Metadata
Title
Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI
Authors
Dorothy Lui
Amen Modhafar
Masoom A. Haider
Alexander Wong
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2015
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-015-0081-0

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