Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization

Gokhan Ertas

Abstract


Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D*(r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.

Keywords


Breast; Diffusion; Intravoxel Incoherent Motion; Fit; Optimization

Full Text:

PDF

References


Le Bihan, D., Lima, M. (2015). Diffusion magnetic resonance imaging: What water tells us about biological tissues. PLoS Biol, 13(7): e1002203. http://doi.org/10.1371/journal.pbio.1002203.

Pinker, K., Moy, L., Sutton, E.J., Mann, R.M., Weber, M., Thakur, S.B., Jochelson, M.S., Bago-Horvath, Z., Morris, E.A., Baltzer, P.A., Helbich, T.H. (2018). Diffusion-weighted imaging with apparent diffusion coefficient mapping for breast cancer detection as a stand-alone parameter: comparison with dynamic contrast-enhanced and multiparametric magnetic resonance imaging. Invest Radiol. http://doi:10.1097/RLI.0000000000000465.

Pasquier, D., Hadj Henni, A., Escande, A., Tresch, E., Reynaert, N., Colot, O., Lartigau, E., Betrouni, N. (2018). Diffusion weighted MRI as an early predictor of tumor response to hypofractionated stereotactic boost for prostate cancer. Scientific Reports, 8, 10407. http://doi.org/10.1038/s41598-018-28817-9.

Keil, V. C., Mädler, B. , Gielen, G. H., Pintea, B. , Hiththetiya, K. , Gaspranova, A. R., Gieseke, J. , Simon, M. , Schild, H. H. and Hadizadeh, D. R. (2017). Intravoxel incoherent motion MRI in the brain: Impact of the fitting model on perfusion fraction and lesion differentiability. Journal of Magnetic Resonance Imaging, 46(4), 1187-1199.

Barbieri, S., Donati, O.F., Froehlich, J.M., Thoeny, H.C. (2016). Impact of the calculation algorithm on bi-exponential fitting of diffusion-weighted MRI in upper abdominal organs. Magnetic Resonance in Medicine, 75(5), 2175-2184.

Marquardt, D.W. (1963). An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math, 11(1), 431-441.

Coleman, T.F., Li, Y. (1996). An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6(1), 418-445.

Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 1942-1945.

Devraj Mandal, D., Chatterjee, A., Maitra, M. (2014). Robust medical image segmentation using particle swarm optimization aided level set based global fitting energy active contour approach. Engineering Applications of Artificial Intelligence, 35(1), 199-214.

Kaur, S., Kaur, M. (2010) Medical image enhancement using particle swarm optimization. Artificial Intelligent Systems and Machine Learning, 2(5), 54-59.

Lin, C.-L., Mimori, A., Chen, Y.-W. (2012). Hybrid particle swarm optimization and its application to multimodal 3D medical image registration. Computational Intelligence and Neuroscience, 561406. http://doi.org/10.1155/2012/561406.

Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., Laval-Jeantet, M. (1986). MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology, 161(2), 401-417.

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): e782-9. http://doi:10.1016/j.ejrad.2013.08.006.

Cho, G. Y., Moy, L., Zhang, J. L., Baete, S., Lattanzi, R., Moccaldi, M., Sigmund, E.E. (2015). Comparison of Fitting Methods and b-Value Sampling Strategies for Intravoxel Incoherent Motion in Breast Cancer. Magnetic Resonance in Medicine, 74(4), 1077–1085.

Harel, O. (2009). The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics, 36(10), 1109-1118.

Zhou, T., Han, D. (2008). A weighted least squares method for scattered data fitting. Journal of Computational and Applied Mathematics, 217(1), 56-63.

While, P. T. (2017). A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion‐weighted MRI. Magnetic Resonance in Medicine, 78(6), 2373-2387.




DOI: http://dx.doi.org/10.11121/ijocta.01.2019.00642

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Gokhan Ertas

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

footer_771

   ithe_170     crossref_284         ind_131_43_x_117_117  Scopus  EBSCO_Host    ULAKBIM   PROQUEST   ZBMATH more...