Harada, Masafumi

Hayashi, Hiroaki

Kanazawa, Yuki

Konishi, Yuto

Matsuda, M.

Matsumoto, Yuki

Usuda, Takatoshi

Tokushima University Institutional Repository

Page 1 of 14Noise estimation for diffusion weighted MR imagePoster No.: C-0591Congress: ECR 2016Type: Scientific ExhibitAuthors: Y. Kanazawa1, Y. Konishi1, T. Usuda1, Y. Matsumoto1, H.Hayashi1, M. Matsuda2, M. Harada1; 1Tokushima/JP, 2Tokyo/JPKeywords: MR physics, MR-Diffusion/Perfusion, MR, Physics, Technicalaspects, Education, Image verification, Quality assuranceAny information contained in this pdf file is automatically generated from digital materialsubmitted to EPOS by third parties in the form of scientific presentations. Referencesto any names, marks, products, or services of third parties or hypertext links to third-party sites or information are provided solely as a convenience to you and do not inany way constitute or imply ECR's endorsement, sponsorship or recommendation of thethird party, information, product or service. ECR is not responsible for the content ofthese pages and does not make any representations regarding the content or accuracyof material in this file.As per copyright regulations, any unauthorised use of the material or parts thereof aswell as commercial reproduction or multiple distribution by any traditional or electronicallybased reproduction/publication method ist strictly prohibited.You agree to defend, indemnify, and hold ECR harmless from and against any and allclaims, damages, costs, and expenses, including attorneys' fees, arising from or relatedto your use of these pages.Please note: Links to movies, ppt slideshows and any other multimedia files are notavailable in the pdf version of presentations.www.myESR.orgPage 2 of 14Aims and objectivesDiffusion weighted (Dw) magnetic resonance image (MRI) is used for analysis of tissuecharacterization, i.e., intravoxel incoherent motion [1]. Some reports have been publishedbeing demonstrated the analysis method, e.g., q-space-analyzed Dw MRI [2], diffusionalkurtosis imaging [3], and modified tri-exponential analysis [4]. These most analysismethods have been required high-b-values data of Dw MRI. On the other hand, the signalintensity (SI) tends to become progressively lower as the b-value increases since longStejskal-Tanner diffusion gradients prolong echo time (TE) [5].MR images are visualized as magnitude images after applying a pulse sequences.The pulse sequences have reported various methods to improve image quality, e.g.,signal-to-noise ratio (SNR), contrast, and imaging artifacts. Particularly, SNR assessmentis important for image analysis not to occur systematic error. The noisy MRI data isindicated by Rician statistical distribution [6]. The correction scheme based on the Riciandistribution were applied in low signal image analysis, R2* estimation [7], and diffusiontensor imaging [8].Thus, there might a possibility of evaluation of signal of high-b-value Dw MRI betweensome pulse sequences. To improve SNR on high-b-value Dw MR images, we developedto signal correction method of noise estimation using Rician probably distribution function(PDF).Methods and materialsCorrection methodFig. 1 on page 5 shows an outline of signal correction methodology in our study.The probability density function (PDF) for the Rician distribution for the measured pixelintensity M is given byPage 3 of 14Fig. 8: Equation (1), Rician probability distribution function.References: Health Biosciences, Tokushima University - Tokushima/JPwhere A is the true pixel intensity, I0 is the modified zeroth order Bessel function, µis mean, and # is the standard deviation (S.D.). When the SNR is high, the Riciandistribution is approximated by Eq. (1) [6]:Fig. 9: Equation (2), Assumption of of Rician probability distribution function asGaussian distribution.References: Health Biosciences, Tokushima University - Tokushima/JP Then, it waspossible to reduce noise by using the following relationship [6]:Fig. 10: Equation (3), correction signal.References: Health Biosciences, Tokushima University - Tokushima/JPwhere Ã is the corrected SI. We applied equation (3) as correction scheme in Dw MRimages.Fig. 2 on page 5 shows schematic procedure for estimation of noise signal. Truesignal values were calculated using curve fitting of Rician distribution function fromeach Dw MR data. Then, we evaluated the correction factor µ between measured andestimation values of different imaging sequences. The correction factor µ were describedas follows:Page 4 of 14Fig. 11: Equation (4), correction factor.References: Health Biosciences, Tokushima University - Tokushima/JPThen, we compared between phantom signal and background (BG) signal.MR imagingOn a 1.5 T MR scanner system (Signa HDxt, GE Healthcare, Waukesha, WI, USA), DwMR imaging data of sucrose (Suc) samples (10, 30, and 50 wt%) were acquired. Differentimaging sequences of single shot echo-planner imaging (SS-EPI) and dual spin-echo EPI(DSE-EPI) were performed with multi b-values (0 - 7000 s/mm2). Table 1 shows detailedimaging parameters. All imaging analysis were applied by using an in-house programfrom MATLAB (MathWorks, Natick, MA, USA).Table 1 Imaging parameters of Dw MRI inour studyImagingparametersSetting valuesSequences SS-EPI & DSE-EPIb-values [s/mm2] 0#20#200#500#1000#2000#3000#4000#4500#5000#5500#6000#6500#7000TR/TE [ms] 5000/129Slice thickness[mm]10FOV [cm2] 12.8 × 6.4Band width [Hz/pixel]166.7Page 5 of 14Images for this section:Fig. 1: Outline of signal correction methodology with Rician distibution in our study.© Health Biosciences, Tokushima University - Tokushima/JPPage 6 of 14Page 7 of 14Fig. 2: Schematic noise estimation procedure.© Health Biosciences, Tokushima University - Tokushima/JPPage 8 of 14ResultsFig. 3 on page 8 shows relation between b-value and correction factor in Dw MRIof SS-EPI. Mean correction factors of SS-EPI data were Suc 10 wt%, 0.99; Suc 30 wt%, 1.00; and Suc 50 wt%, 1.00.Fig. 4 on page 9 shows relation between b-value and correction factor in Dw MRIof DSE-EPI. Mean correction factor of DSE-EPI data were Suc 10 wt%, 0.99; Suc 30 wt%, 0.98; and Suc 50 wt%, 1.00. At these high values, the ratio of mean signal value tostandard deviation value, i.e., SNR were all less than 3.Fig. 5 on page 9 shows poor correction case of 10 wt % sucrose SS-EPI Dwi MRI.Fig. 6 on page 10 shows poor correction case of 10 wt % sucrose DSE-EPI Dwi MRI.Fig. 7 on page 11 shows poor correction case of 30 wt % sucrose DSE-EPI Dwi MRI.In all poor correction case, these indicated that SNRs of phantom were lower value thanBG.Images for this section:Page 9 of 14Fig. 3: Scatter Plots shows relation between each b-value and correction factor in DwMRI of SS-EPI.© Health Biosciences, Tokushima University - Tokushima/JPFig. 4: Scatter Plots shows relation between b-value and correction factor in Dw MRIof DSE-EPI.© Health Biosciences, Tokushima University - Tokushima/JPPage 10 of 14Fig. 5: Poor correction case of 10 wt% sucrose SS-EPI Dwi MRI. The correction factorindicated 0.93 at b-value = 5500 s/mm2.© Health Biosciences, Tokushima University - Tokushima/JPPage 11 of 14Fig. 6: Poor correction case of 10 wt% sucrose DSE-EPI Dwi MRI. The correction factorindicated 0.92 at b-value = 3000 s/mm2.© Health Biosciences, Tokushima University - Tokushima/JPPage 12 of 14Fig. 7: Poor correction case of 30 wt% sucrose DSE-EPI Dwi MRI. The correction factorindicated 0.80 at b-value = 4000 s/mm2.© Health Biosciences, Tokushima University - Tokushima/JPPage 13 of 14ConclusionSignal correction method of noise estimation with Rician PDF makes it possible to obtainmore detailed information on Dw MR image having high-b-values.Personal informationYuki KANAZAWA, PhD, Assistant ProfessorInstitute of Biomedical Sciences, Tokushima University Graduate School3-18-15, Kuramoto-Cho, Toksuhima City, Tokushima, 770-8509, JapanE-mail: yk@tokushima-u.ac.jpReferences1. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval Jeantet M.MR Imaging of Intravoxel Incoherent Motions: application to Diffusion andPerfusion in Neurologic Disorders. Radiology. 1986;161:401-407.2. Assaf Y, Ben-Bashat D, Chapman J, Peled S, Biton IE, et al. High b-value q-Space Analyzed Diffusion-Weighted MRI: Application to Multiple Sclerosis.Magn Reson Med. 2002;47:115-126.3. Fieremans Els, Jensen JH, Helpern JA. White matter characterization withdiffusional kurtosis imaging. NeuroImage. 2011;58:177-188.4. Ohno N, Miyati T, Kobayashi S, Gabata T. Modified Triexponential Analysisof Intravoxel Incoherent Motion for Brain Perfusion and Diffusion. J MagnReson Imaging. 2015; e-pub. doi: 10.1002/jmri.25048.5. Tanner J, Stejskal E. Restricted self-diffusion of protons in colloidalsystems by the pulsed-gradient, spin echo method. J Chem Phys.1968;49:1768-1777.6. Gudbjartsson H, Patz S. The Rician Distribution of Noisy MRI Data. MagnReson Med.1995;34(6):910-914.7. Yokoo T, Yuan Q, Senegas J, Wiethoff AJ, Pedrosa I. Quantitative MRI ofthe Liver With Rician Noise Models for Evaluation of Hepatic Iron Overload :Simulation, Phantom, and Early Clinical Expertence. J Magn ResonImaging. 2015; e-pub. doi: 10.1002/jmri.249488. Taylor PA, Biwal B. Geometric analysis of the b-dependent effects of Riciansignal noise on diffusion tensor imaging estimates and determining anoptimal b value. Magn Reson Imaging. 2011;29:777-788.Page 14 of 14

Noise estimation for diffusion weighted MR image

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Noise estimation for diffusion weighted MR image

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