Skip to main content

Advertisement

SpringerLink
Log in

Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors

  • Head and Neck
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To investigate the impact of acceleration factors on reproducibility of radiomic features in sensitivity encoding (SENSE) and compressed SENSE (CS), compare between SENSE and CS, and identify reproducible radiomic features.

Methods

Three-dimensional turbo spin echo T1-weighted imaging was performed in 14 healthy volunteers (mean age, 57 years; range, 33–67 years; 7 men) under SENSE and CS with accelerator factors of 5.5, 6.8, and 9.7. Eight anatomical locations (brain parenchyma, salivary glands, masseter muscle, tongue, pharyngeal mucosal space, eyeballs) were evaluated. Reproducibility of radiomic features was evaluated by calculating concordance correlation coefficient (CCC) in reference to the original image (SENSE with acceleration factor of 3.5). Reproducibility of radiomic features among acceleration factors and between SENSE and CS was compared.

Results

Proportion of radiomic features with CCC > 0.85 in reference to the original image was lower with higher acceleration factors in both SENSE and CS across all anatomical locations (p < .001). Proportion of radiomic features with CCC > 0.85 in reference to the original image was higher in SENSE compared with CS (SENSE, 6.7–7.3% vs CS, 4.4–5.0%; p < .001). Run percentage of gray-level run-length matrix (GLRLM) with wavelet D showed CCC > 0.85 in reference to the original image in both SENSE and CS at acceleration factor of 9.7 in the highest number of anatomical locations.

Conclusions

Higher acceleration factors resulted in lower reproducibility of radiomic features in both SENSE and CS, and SENSE showed higher reproducibility of radiomic features than CS in reference to the original image. Run percentage of GLRLM with wavelet D was identified as the most reproducible feature.

Key Points

• Reproducibility of radiomic features in reference to the original image was lower with higher acceleration factors in both sensitivity encoding (SENSE) and compressed SENSE (CS) across all anatomical locations (p < .001).

• SENSE showed higher proportions of radiomic features with CCC > 0.85 in reference to the original image (SENSE, 6.7–7.3% vs CS, 4.4–5.0%; p < .001) compared with CS.

• Run percentage of gray-level run-length matrix (GLRLM) with wavelet D showed CCC > 0.85 in reference to the original image in both SENSE and CS with the highest acceleration factor.

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

Abbreviations

AFt :

Acceleration factor

CCC:

Concordance correlation coefficient

CS:

Compressed sensing combined with SENSE

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

IQR:

Interquartile range

ROI:

Region of interest

SENSE:

Sensitivity encoding

T1WI:

T1-weighted imaging

References

  1. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58:1182–1195

    Article  Google Scholar 

  2. Yang AC, Kretzler M, Sudarski S, Gulani V, Seiberlich N (2016) Sparse reconstruction techniques in magnetic resonance imaging: methods, applications, and challenges to clinical adoption. Invest Radiol 51:349–364

    Article  CAS  Google Scholar 

  3. Jaspan ON, Fleysher R, Lipton ML (2015) Compressed sensing MRI: a review of the clinical literature. Br J Radiol 88:20150487

    Article  Google Scholar 

  4. Toledano-Massiah S, Sayadi A, de Boer R et al (2018) Accuracy of the compressed sensing accelerated 3D-FLAIR sequence for the detection of MS plaques at 3T. AJNR Am J Neuroradiol https://doi.org/10.3174/ajnr.A5517

  5. Suh CH, Jung SC, Lee HB, Cho SJ (2019) High-resolution magnetic resonance imaging using compressed sensing for intracranial and extracranial arteries: comparison with conventional parallel imaging. Korean J Radiol 20:487–497

    Article  Google Scholar 

  6. Cho SJ, Choi YJ, Chung SR, Lee JH, Baek JH (2019) High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique. Clin Radiol 74:817.e819–817.e814

    Article  Google Scholar 

  7. Chandarana H, Feng L, Block TK et al (2013) Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling. Invest Radiol 48:10–16

    Article  Google Scholar 

  8. Otazo R, Kim D, Axel L, Sodickson DK (2010) Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 64:767–776

    Article  Google Scholar 

  9. Zhu C, Tian B, Chen L et al (2018) Accelerated whole brain intracranial vessel wall imaging using black blood fast spin echo with compressed sensing (CS-SPACE). MAGMA 31:457–467

    Article  CAS  Google Scholar 

  10. Vranic JE, Cross NM, Wang Y, Hippe DS, de Weerdt E, Mossa-Basha M (2019) Compressed Sensing-Sensitivity Encoding (CS-SENSE) accelerated brain imaging: reduced scan time without reduced image quality. AJNR Am J Neuroradiol 40:92–98

    Article  CAS  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  12. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  13. Balagurunathan Y, Kumar V, Gu Y et al (2014) Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27:805–823

    Article  Google Scholar 

  14. Berenguer R, Pastor-Juan MDR, Canales-Vázquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415

    Article  Google Scholar 

  15. Meyer M, Ronald J, Vernuccio F et al (2019) Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings. Radiology 293:583–591

    Article  Google Scholar 

  16. Park BW, Kim JK, Heo C, Park KJ (2020) Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep 10:3852

    Article  CAS  Google Scholar 

  17. O’Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186

    Article  Google Scholar 

  18. Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20:1124–1137

    Article  Google Scholar 

  19. Nolden M, Zelzer S, Seitel A et al (2013) The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620

    Article  Google Scholar 

  20. Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889

    Article  Google Scholar 

  21. Kang D, Park JE, Kim YH et al (2018) Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol. https://doi.org/10.1093/neuonc/noy021

  22. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv preprint arXiv:161207003

  23. Lin LI (1989) A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45:255–268

    Article  CAS  Google Scholar 

  24. Liang D, Liu B, Wang J, Ying L (2009) Accelerating SENSE using compressed sensing. Magn Reson Med 62:1574–1584

    Article  Google Scholar 

  25. Smith DS, Li X, Abramson RG, Quarles CC, Yankeelov TE, Welch EB (2013) Potential of compressed sensing in quantitative MR imaging of cancer. Cancer Imaging 13:633–644

    Article  Google Scholar 

  26. Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179

    Article  Google Scholar 

  27. Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55:414–422

    Article  CAS  Google Scholar 

  28. Parmar C, Rios Velazquez E, Leijenaar R et al (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107

    Article  Google Scholar 

  29. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  Google Scholar 

  30. Ford J, Dogan N, Young L, Yang F (2018) Quantitative radiomics: impact of pulse sequence parameter selection on MRI-based textural features of the brain. Contrast Media Mol Imaging 2018:1729071

  31. Cho SJ, Jung SC, Suh CH, Lee JB, Kim D (2019) High-resolution magnetic resonance imaging of intracranial vessel walls: comparison of 3D T1-weighted turbo spin echo with or without DANTE or iMSDE. PLoS One 14:e0220603

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The same cohort (all 14 healthy volunteers enrolled in the Acceleration Technique registry) was previously reported in a study evaluating image quality in SENSE and CS qualitatively by visual scoring systems and quantitatively by measurement of signal-to-noise ratio and contrast-to-noise ratio [5] while the current study investigated reproducibility of radiomic features in SENSE and CS. The same cohort was also reported in a study comparing 3D T1-weighted turbo spin echo with or without delay alternating with nutation for tailored excitation and improved motion-sensitized driven equilibrium [31].

Funding

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2019R1A2C1089939).

Author information

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul, 05505, Republic of Korea

    Minjae Kim, Seung Chai Jung, Ji Eun Park & Keum Mi Choi

  2. Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul, 05505, Republic of Korea

    Seo Young Park

  3. Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Olympic-ro 33, Seoul, 05505, Republic of Korea

    Hyunna Lee

Authors
  1. Minjae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seung Chai Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ji Eun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seo Young Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hyunna Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keum Mi Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seung Chai Jung.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Seung Chai Jung.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Seo Young Park kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The same cohort (all 14 healthy volunteers enrolled in the Acceleration Technique registry) was previously reported in a study evaluating image quality in SENSE and CS qualitatively by visual scoring systems and quantitatively by measurement of signal-to-noise ratio and contrast-to-noise ratio [1] while the current study investigated reproducibility of radiomic features in SENSE and CS. The same cohort was also reported in a study comparing 3D T1-weighted turbo spin echo with or without delay alternating with nutation for tailored excitation and improved motion-sensitized driven equilibrium [2].

1 Suh CH, Jung SC, Lee HB, Cho SJ (2019) High-Resolution Magnetic Resonance Imaging Using Compressed Sensing for Intracranial and Extracranial Arteries: Comparison with Conventional Parallel Imaging. Korean journal of radiology 20:487-497

2 Cho SJ, Jung SC, Suh CH, Lee JB, Kim D (2019) High-resolution magnetic resonance imaging of intracranial vessel walls: Comparison of 3D T1-weighted turbo spin echo with or without DANTE or iMSDE. PLoS One 14:e0220603

Methodology

• retrospective

• observational

• performed at one institution

Additional information

Publisher’s note

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

Supplementary Information

ESM 1

(DOCX 2515 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, M., Jung, S.C., Park, J.E. et al. Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors. Eur Radiol 31, 6457–6470 (2021). https://doi.org/10.1007/s00330-021-07760-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-07760-w

Keywords

Search

Navigation