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A comparison of fitting algorithms for diffusion-weighted MRI data analysis using an intravoxel incoherent motion model

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Abstract

Object

The objective of this study is to propose a modified VARiable PROjection (VARPRO) algorithm specifically tailored for fitting the intravoxel incoherent motion (IVIM) model to diffusion-weighted magnetic resonance imaging (DW-MRI) data from locally advanced rectal cancer (LARC).

Materials and methods

The proposed algorithm is compared with classical non-linear least squares (NLLS) analysis using the Levenberg-Marquardt (LM) algorithm and with two recently proposed algorithms for ‘segmented’ analysis. These latter two comprise two consecutive steps: first, a subset of parameters is estimated using a portion of data; second, the remaining parameters are estimated using the whole data and the previous estimates. The comparison between the algorithms was based on the \(R^2\) goodness-of-fit measure: performance analysis was carried out on real data obtained by DW-MRI on 40 LARC patients.

Results

The performance of the proposed algorithm was higher than that of LM in 64 % of cases; ‘segmented’ methods were poorer than our algorithm in 100 % of cases.

Conclusion

The proposed modified VARPRO algorithm can lead to better fit of the IVIM model to LARC DW-MRI data compared to other techniques.

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References

  1. Bisdas S, Klose U (2015) IVIM analysis of brain tumors: an investigation of the relaxation effects of CSF, blood, and tumor tissue on the estimated perfusion fraction. Magn Reson Mater Phy 28(4):377–383

    Article  Google Scholar 

  2. Le Bihan D (2008) Intravoxel incoherent motion perfusion MR imaging: a wake-up call. Radiology 249(3):748–752

    Article  PubMed  Google Scholar 

  3. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168(2):497–505

    Article  PubMed  Google Scholar 

  4. Bokacheva L, Kaplan JB, Giri DD, Patil S, Gnanasigamani M, Nyman CG, Deasy JO, Morris EA, Thakur SB (2014) Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging 40(4):813–823

    Article  PubMed  Google Scholar 

  5. Liu C, Liang C, Liu Z, Zhang S, Huang B (2013) Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol 82(12):e782–e789

    Article  PubMed  Google Scholar 

  6. Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, Hua J, Xu J (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve-fitting methods. J Magn Reson Imaging 42(2):362–370

    Article  PubMed  Google Scholar 

  7. Fusco R, Sansone M, Petrillo A (2015) The use of the Levenberg-Marquardt and variable projection curve-fitting algorithm in intravoxel incoherent motion method for DW-MRI data analysis. Appl Magn Reson 46(5):551–558

    Article  Google Scholar 

  8. Seber GAF, Wild CJ (2003) Non-linear regression. Wiley, New York

  9. Young Cho G, Moy L, Zhang JL, Baete S, Lattanzi R, Moccaldi M, Babb JS, Kim S, Sodickson DK, Sigmund EE (2014) Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer. Magn Reson Med 74(4):1077–1085

    Google Scholar 

  10. Chan JHM, Tsui EYK, Luk SH, Fung ASL, Yuen MK, Szeto ML, Cheung YK, Wong KPC (2001) Diffusion-weighted MR imaging of the liver: distinguishing hepatic abscess from cystic or necrotic tumor. Abdom Imaging 26(2):161–165

    Article  CAS  PubMed  Google Scholar 

  11. Gyu Na Dong, Thijs Vincent N, Albers Gregory W, Moseley Michael E, Marks Michael P (2004) Diffusion-weighted MR imaging in acute ischemia: value of apparent diffusion coefficient and signal intensity thresholds in predicting tissue at risk and final infarct size. Am J Neuroradiol 25(8):1331–1336

    Google Scholar 

  12. Zhang JL, Sigmund EE, Rusinek H, Chandarana H, Storey P, Chen Q, Lee VS (2012) Optimization of b-value sampling for diffusion-weighted imaging of the kidney. Magn Reson Med 67(1):89–97

    Article  PubMed  Google Scholar 

  13. Concia M, Sprinkart AM, Penner AH, Brossart P, Gieseke J, Schild HH, Willinek WA, Mürtz P (2014) Diffusion-weighted magnetic resonance imaging of the pancreas: diagnostic benefit from an intravoxel incoherent motion model-based 3 b-value analysis. Investig Radiol 49(2):93–100

    Article  Google Scholar 

  14. Orton MR, Collins DJ, Koh DM, Leach MO (2014) Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling. Magn Reson Med 71(1):411–420

    Article  PubMed  Google Scholar 

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Correspondence to Mario Sansone.

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All authors declare that they have no conflict of interest to disclose.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants in the study.

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Fusco, R., Sansone, M. & Petrillo, A. A comparison of fitting algorithms for diffusion-weighted MRI data analysis using an intravoxel incoherent motion model. Magn Reson Mater Phy 30, 113–120 (2017). https://doi.org/10.1007/s10334-016-0591-y

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  • DOI: https://doi.org/10.1007/s10334-016-0591-y

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