Skip to main content

Advertisement

SpringerLink
Log in

MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Several noise sources, such as the Johnson–Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity–based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of Data and Material

Images were centralized at the PRIMAGE platform repository.

References

  1. Lakshmi Devasena, C., Hemalatha, M.: Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique: International Journal of Computer Applications. 2011.

  2. Páez Aguilar, S.E., Mújica-Vargas, D., Vianney Kinani, J.M.: Supresión de ruido Riciano en imágenes de resonancia magnética del cerebro utilizando un algoritmo de promedio local y global: Research in Computing Science. 2018.

  3. V.R., S., Edla, D.R., Joseph, J., Kuppili, V.: Analysis of controversies in the formulation and evaluation of restoration algorithms for MR Images: Expert Systems with Applications. 2019.

  4. Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI: Presented at the 2008.

  5. Anand, C.S., Sahambi, J.S.: MRI denoising using bilateral filter in redundant wavelet domain: In: IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2008.

  6. Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R.G.P.M., Granton, P., Zegers, C.M.L., Gillies, R., Boellard, R., Dekker, A., Aerts, H.J.W.L.: Radiomics: Extracting more information from medical images using advanced feature analysis: European Journal of Cancer. 2012.

  7. Exhibit, S., Company, F., Palomo, R.: Analysis of weekly MR image quality assurance controls in spectroscopy quantification. 1–7 , 2013.

  8. Martí-Bonmatí, L., Alberich-Bayarri, Á., Ladenstein, R., Blanquer, I., Segrelles, J.D., Cerdá-Alberich, L., Gkontra, P., Hero, B., García-Aznar, J.M., Keim, D., Jentner, W., Seymour, K., Jiménez-Pastor, A., González-Valverde, I., Martínez de las Heras, B., Essiaf, S., Walker, D., Rochette, M., Bubak, M., Mestres, J., Viceconti, M., Martí-Besa, G., Cañete, A., Richmond, P., Wertheim, K.Y., Gubala, T., Kasztelnik, M., Meizner, J., Nowakowski, P., Gilpérez, S., Suárez, A., Aznar, M., Restante, G., Neri, E.: PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers: European Radiology Experimental. 2020.

  9. Isa, I.S., Sulaiman, S.N., Mustapha, M., Darus, S.: Evaluating denoising performances of fundamental filters for T2-weighted MRI images: In: Procedia Computer Science 2015.

  10. Alvarez, L., Lions, P.L., Morel, J.M.: Image selective smoothing and edge detection by nonlinear diffusion. II: SIAM Journal on Numerical Analysis. 1992.

  11. Sethian, J. a.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 1999.

  12. Cappabianco, F.A.M., Dos Santos, S.R.B., Ide, J.S., Da Silva, P.P.C.E.: Non-Local Operational Anisotropic Diffusion Filter: In: Proceedings - International Conference on Image Processing, ICIP. 2019.

  13. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. II: 60–65 , 2005.

  14. Manjón, J. V., Carbonell-Caballero, J., Lull, J.J., García-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using Non-Local Means: Medical Image Analysis. 2008.

  15. Udomhunsakul, S., Wongsita, P.: Feature extraction in medical MRI images: In: 2004 IEEE Conference on Cybernetics and Intelligent Systems. pp. 340–344 2004.

  16. Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods, https://doi.org/10.1007/s10462-010-9155-0. 2010.

  17. Xiao, K., Ho, S.H., Salih, Q.: A study: Segmentation of lateral ventricles in brain MRI using fuzzy C-means clustering with gaussian smoothing: In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 161–170. Springer Verlag 2007.

  18. Das, P., Pal, C., Chakrabarti, A., Acharyya, A., Basu, S.: Adaptive denoising of 3D volumetric MR images using local variance based estimator: Biomedical Signal Processing and Control. 2020.

  19. Nair, R.R., David, E., Rajagopal, S.: A robust anisotropic diffusion filter with low arithmetic complexity for images: Eurasip Journal on Image and Video Processing. 2019.

  20. Kaimal, A.B., Priestly Shan, B.: Removing the traces of median filtering via unsharp masking as an anti-forensic approach in medical imaging: Biomedical and Pharmacology Journal. 2019.

  21. Biswas, S., Aggarwal, H.K., Jacob, M.: Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM: Magnetic Resonance in Medicine. 82: 485–494 , 2019.

    PubMed  PubMed Central  Google Scholar 

  22. Kidoh, M., Shinoda, K., Kitajima, M., Isogawa, K., Nambu, M., Uetani, H., Morita, K., Nakaura, T., Tateishi, M., Yamashita, Y., Yamashita, Y.: Deep learning based noise reduction for brain MR imaging: Tests on phantoms and healthy volunteers: Magnetic Resonance in Medical Sciences. 19: 195–206 , 2020.

    PubMed  Google Scholar 

  23. Zhang, X., Feng, X., Wang, W., Xue, W.: Edge strength similarity for image quality assessment: IEEE Signal Processing Letters. 2013.

  24. Isaksson, L.J., Raimondi, S., Botta, F., Pepa, M., Gugliandolo, S.G., De Angelis, S.P., Marvaso, G., Petralia, G., De Cobelli, O., Gandini, S., Cremonesi, M., Cattani, F., Summers, P., Jereczek-Fossa, B.A.: Effects of MRI image normalization techniques in prostate cancer radiomics: Physica Medica. 71: 7–13 , 2020.

    PubMed  Google Scholar 

  25. Aetesam, H., Maji, S.K.: ℓ2-ℓ1 Fidelity based Elastic Net Regularisation for Magnetic Resonance Image Denoising: 2020 International Conference on Contemporary Computing and Applications, IC3A. 2020: 137–142 , 2020.

  26. Roy, S., Whitehead, T.D., Quirk, J.D., Salter, A., Ademuyiwa, F.O., Li, S., An, H., Shoghi, K.I.: Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging: EBioMedicine. 59: 102963 , 2020.

  27. Bologna, M., Corino, V., Mainardi, L.: Technical Note : Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. 1–8 , 2019.

  28. Moradmand, H., Aghamiri, S.M.R., Ghaderi, R.: Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma: Journal of Applied Clinical Medical Physics. 21: 179–190 , 2020.

    PubMed  Google Scholar 

Download references

Acknowledgements

PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalised diaGnosis and prognosis, empowered by imaging biomarkers) Business Place is a Horizon 2020 | RIA (Topic SC1-DTH-07-2018) project with grant agreement no: 826494.

Funding

This work was supported by Horizon 2020 project (RIA, topic SC1-DTH-07–2018).

Author information

Authors and Affiliations

  1. Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026, Valencia, Spain

    Matías Fernández Patón, Leonor Cerdá Alberich & Luis Martí-Bonmatí

  2. Área Clínica de Imagen Médica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain

    Cinta Sangüesa Nebot, Diana Veiga Canuto & Luis Martí-Bonmatí

  3. Unidad de Oncohematología Pediátrica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain

    Blanca Martínez de las Heras & Adela Cañete Nieto

Authors
  1. Matías Fernández Patón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonor Cerdá Alberich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cinta Sangüesa Nebot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Blanca Martínez de las Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diana Veiga Canuto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adela Cañete Nieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luis Martí-Bonmatí
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matías Fernández Patón.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernández Patón, M., Cerdá Alberich, L., Sangüesa Nebot, C. et al. MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging. J Digit Imaging 34, 1134–1145 (2021). https://doi.org/10.1007/s10278-021-00512-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-021-00512-8

Keywords

Search

Navigation