Harada, Masafumi

Hayashi, Hiroaki

Kanazawa, Yuki

Konishi, Yuto

Matsuda, Tsuyoshi

Matsumoto, Yuki

Usuda, Takatoshi

Tokushima University Institutional Repository

Page 1 of 22Comparison with statistical analysis of signal correction indiffusion weighted MR imagePoster No.: C-0291Congress: ECR 2016Type: Scientific ExhibitAuthors: Y. Konishi1, Y. Kanazawa1, T. Usuda1, Y. Matsumoto1, H.Hayashi1, M. Matsuda2, M. Harada1; 1Tokushima/JP, 2Tokyo/JPKeywords: MR physics, MR, MR-Diffusion/Perfusion, Physics, Statistics,Technical aspects, 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 22Aims and objectivesDiffusion weighted (Dw) magnetic resonance (MR) imaging has recently become a usefultechnique for evaluating various diseases, e.g., acute cerebral infractions and tumors[1]. Dw MR imaging is a method for imaging of incoherent motions. In Dw MR imaging,the b-values represents the intensity of the gradient magnetic field. Dw images with highb-values are influenced less by perfusion but more by diffusion. It is becoming moreimportant to apply an analysis method with high b-values, e.g., q-space -analyzed DwMRI [2], diffusional kurtosis imaging [3], and modified tri-exponential analysis of intravoxelincoherent motion [4]. The signal intensity (SI) of the Dw image decreases by echo time(TE) prolongation, and this leads to a low signal-to-noise ratio (SNR) [5]. Additionally, ithas been reported that a low SNR influences the determination of the apparent diffusioncoefficients (ADC) [6], or leads to failure in measuring diffusion anisotropy [7].MR images are commonly presented as magnitude images. The magnitude imagesare made by reconstruction of real images and imaginary images [8]. These imagesare acquired by a quadrature detector system; they are influenced by the statisticalimage noise. Before reconstruction, the noise from these images has characteristicsof a Gaussian distribution. On the other hand, the magnitude image has not only thecharacteristics of a Gaussian distribution, but also of a Rician distribution [9]. Theyindicate that the magnitude image does not have a negative value. This distribution hascharacteristics that differ from those of the Gaussian distribution. Many reports appliedto the Rician distribution, e.g.,R2* estimation of the liver [10], and determination of theoptimal b-value in diffusion tensor imaging [11]. Thus, it is important for MR imaging toassess the relationship between the signal values and the Rician distribution.Therefore, the signal correction in high-b-value images is expected to yield improvedimages as a consequence of application of the Rician distribution. We became interestedin correction schemes from past reports. Previous articles did not describe high-b-valueDw signals corrected with use of these schemes. Our purpose in this our study was tocorrect the noise in order to improve the SNR of Dw images with high b-value by usingtwo correction schemes based on the probability distributions.Methods and materials1 Magnitude image of MR imagingMR images consist of reconstruction of real images and imaginary images. The SI fromthe jth pixel in a complex MR image was described by Henkelman [4]:Page 3 of 22Fig. 1References: Tokushima University - Tokushima/JPwhere Sj is the measured signal amplitude, Aj is the true signal amplitude of the jth pixel,and NRj and NIj are the noise components. The magnitude of the SI, Mj, is described asfollows:Fig. 2References: Tokushima University - Tokushima/JP2 Probability distributionThe probability density function (PDF) for the Gaussian distribution for the measured pixelintensity M is given byFig. 3References: Tokushima University - Tokushima/JPwhere µ is mean, and # is the standard deviation (S.D.). This probability densitydistribution show in Fig. 4.Page 4 of 22Fig. 4: The probability density distribution of the Gaussian distribution for M.References: Tokushima University - Tokushima/JPThe PDF for the Rician distribution for M is given in reference [4]:Fig. 5References: Tokushima University - Tokushima/JPwhere A is the true pixel intensity, and I0 is the modified zeroth order Bessel function.Besides, this probability density distribution show in Fig.6.Page 5 of 22Fig. 6: The probability density distribution of the Rician distribution for M. Standarddeviation # is 1.References: Tokushima University - Tokushima/JPThe characteristics of the Rician distribution differ from those of the Gaussian distributionwhen the SNR is low. However, when the SNR is high, it is approximately equal to theGaussian distribution [9]. When the SNR is high, the Rician distribution is approximatedby Eq. (5) [9]:Fig. 7References: Tokushima University - Tokushima/JPThe noise distribution of a magnitude image at a high SNR can be regarded as theGaussian distribution with mean #(A2+#2) and variance # as determined by this equation.Page 6 of 22The postprocessing correction scheme introduced by Gudbjartson and Patz suggestedthat it was possible to reduce noise by using the following relationship [9]:Fig. 8References: Tokushima University - Tokushima/JPwhere Ã1 is corrected SI. We used Eq. (6) as behaving the correction scheme based onthe Rician distribution.There is a relationship, M2=A2+2#2, between the mean of the measured SI and the true SIin the magnitude image [4]. Then, the following correction scheme has been established[9]:Fig. 9References: Tokushima University - Tokushima/JPwhere Ã2 is the corrected SI. We established Eq. (7) as behaving the correction schemebased on the Gaussian distribution.3 MR imagingOn a 1.5T MRI system (Signa HDxt, GE Healthcare, Waukesha, WI, USA), Dw imagingof a phantom was performed with single-shot spin-echo echo-planner imaging (SS-SE-EPI). To acquire Dw images of ADC, we made phantoms by using sucrose solutions ofdifferent concentrations (10, 30, and 50 wt%) [12]. SI decay model show in Fig. 10.Page 7 of 22Fig. 10: Signal intensity decay model in sucrose phantom. The signal intensity of thisphantom follow mono-exponential decay model.References: Tokushima University - Tokushima/JPEach phantom was placed in the center of a standard head coil. The other imagingparameters show Table 1.Page 8 of 22Table 1: Imaging parameters.References: Tokushima University - Tokushima/JP4 Data analysisA typical example of the region-of-interest (ROI) setting is shown in Fig. 1. We measuredSIs at the ROI (for the size for 15 × 15 pixels). Then we measured the mean SI M and S.D.# in the ROI. Next, we calculated each PDF by using an in-house program from MATLAB(MathWorks, Natick, MA, USA). We derived the median value A from each PDF, i.e., weregarded A as the true SI. Furthermore, we calculated each corrected SI (Ã1 and Ã2)from each Eq. (6) and Eq. (7).To assess the results of correcting the measured SI in Dw imaging, we evaluated errorvalues in each b-value Dw image. The error value is defined by Eq. (8):Fig. 11References: Tokushima University - Tokushima/JP When the error was small, weregarded the effect as accurate performance.The chart of this experiment shows in Fig. 12.Page 9 of 22Fig. 12: Chart of this experiments.References: Tokushima University - Tokushima/JPPage 10 of 22Images for this section:Fig. 4: The probability density distribution of the Gaussian distribution for M.© Tokushima University - Tokushima/JPPage 11 of 22Fig. 6: The probability density distribution of the Rician distribution for M. Standarddeviation # is 1.© Tokushima University - Tokushima/JPPage 12 of 22Fig. 10: Signal intensity decay model in sucrose phantom. The signal intensity of thisphantom follow mono-exponential decay model.© Tokushima University - Tokushima/JPPage 13 of 22Table 1: Imaging parameters.© Tokushima University - Tokushima/JPPage 14 of 22Fig. 12: Chart of this experiments.Page 15 of 22© Tokushima University - Tokushima/JPPage 16 of 22ResultsTable 2 shows the relationship between b-values and several signal-to-standarddeviation ratios for each phantom.Table 2: Relationship between b-values and several signal-to-standard deviationratios.References: Tokushima University - Tokushima/JPFigure 13 shows the Dw images (a, b, and c) and the PDFs from the Rician distribution(d, e, and f). Each SI for a Dw image was shown to decrease at the higher b-values. ThePDFs shifted to low intensity, and the variances were low at higher b-values.Page 17 of 22Fig. 13: Diffusion weighted images and probability distribution function from the Riciandistribution.References: Tokushima University - Tokushima/JPFigure 14 shows the error between the correction SI and the true SI. The highesterror values for each phantom concentration (10, 30, and 50 wt%), in correction bythe correction scheme based on the Rician distribution, were 7.3, 2.4, and 0.1 [%],respectively. The highest error values for each of the three phantom concentration incorrection by the correction scheme based on the Gaussian distribution, were 20.3, 11.6,and 3.4 [%], respectively. When A/# was lower than 1.4, the error value was determinedby application of the correction scheme based on the Rician distribution, which was higherthan 5.0 [%]. In addition, when A/# was lower than 3.0, the error values were determinedby application of the correction scheme based on the Gaussian distribution, which werehigher than 5.0 [%] (Table 2 and Fig.14).Page 18 of 22Fig. 14: Error value between the correction signal intensity and true signal intensity.References: Tokushima University - Tokushima/JPImages for this section:Table 2: Relationship between b-values and several signal-to-standard deviation ratios.© Tokushima University - Tokushima/JPPage 19 of 22Fig. 13: Diffusion weighted images and probability distribution function from the Riciandistribution.© Tokushima University - Tokushima/JPFig. 14: Error value between the correction signal intensity and true signal intensity.© Tokushima University - Tokushima/JPPage 20 of 22ConclusionIn Dw imaging, the correction of the SI makes it possible to apply a correction schemebased on the Rician distribution.Personal informationAuthor Names:Yuto Konishi1, Yuki Kanazawa2, Takatoshi Usuda1, Yuki Matsumoto1, Hiroaki Hayashi1,Tsuyoshi Matsuda3, and Masafumi Harada4Author Affiliations:1 School of Health Sciences, Tokushima University2 Institute of Biomedical Sciences, Tokushima University Graduate School3 MR Applications and Workflow Asia Pacific GE Healthcare Japan Corporation4 Department of Radiology and Radiation Oncology, Institute of Biomedical Sciences,Tokushima University Graduate SchoolCorresponding Author and Reprint Info:Yuki Kanazawa, PhDInstitute of Biomedical Sciences, Tokushima University Graduate School3-18-15, Kuramoto-Cho, Toksuhima City, Tokushima, 770-8509, JapanE-mail: yk@tokushima-u.ac.jpReferencesPage 21 of 221. 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Comparison with statistical analysis of signal correction in diffusion weighted MR image

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Comparison with statistical analysis of signal correction in diffusion weighted MR image

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