Skip to main content
SpringerLink
Log in

Vessel Wall Imaging in the Era of Artificial Intelligence

  • Chapter
  • First Online:
Vessel Based Imaging Techniques

  • 822 Accesses

Abstract

Vessel wall imaging (VWI), particularly magnetic resonance VWI, can detect plaque burden and characterize plaque components, and it has gradually raised clinical attention in screening and diagnosis for vascular disease. The major remaining challenges for wider clinical usage of VWI focus on the trade-off between image quality and scan time, as well as the time and labor commitment required for comprehensive image interpretation. In this chapter, we will discuss artificial intelligence (AI) methods that leverage the power of prior knowledge and data to solve these challenges. In particular, deep learning techniques with recent advancements on both computational algorithms and hardware will be discussed with some recent example applications. In terms of image acquisition, AI methods can open new possibilities to optimize imaging paradigms, improve image quality, and reduce overall scan time. In addition, considering the superior capabilities of AI in image feature extraction and classification, more extensive applications of AI for VWI are provided from the image analysis perspective, including artery detection and tracking, vessel wall segmentation, plaque characterization, and prediction of clinical outcome.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mihai G, Chung YC, Merchant A, Simonetti OP, Rajagopalan S. T1-weighted-SPACE dark blood whole body magnetic resonance angiography (DB-WBMRA): initial experience. J Magn Reson Imaging. 2010;31(2):502–9.

    Article  PubMed  Google Scholar 

  2. Qiao Y, Steinman DA, Qin Q, Etesami M, Schär M, Astor BC, Wasserman BA. Intracranial arterial wall imaging using three-dimensional high isotropic resolution black bloodMRI at 3.0 Tesla. J Magn Reson Imaging. 2011;34(1):22–30.

    Article  PubMed  Google Scholar 

  3. J W, Yarnykh VL, Yuan C. Enhanced image quality in black-blood MRI using the improved motion-sensitized driven-equilibrium (iMSDE) sequence. J Magn Reson Imaging. 2010;31(5):1256–63.

    Article  Google Scholar 

  4. Li L, Miller KL, Jezzard P. DANTE-prepared pulse trains: a novel approach to motion-sensitized and motion-suppressed quantitative magnetic resonance imaging. Magn Reson Med. 2012;68(5):1423–38.

    Article  PubMed  Google Scholar 

  5. Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans Image Process. 2017;26(7):3142–55.

    Article  PubMed  Google Scholar 

  6. Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016, pp. 1646–54.

    Google Scholar 

  7. Lim B, Son S, Kim H, Nah S, Lee KM. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017, pp. 136–44.

    Google Scholar 

  8. Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017, pp. 4681–90.

    Google Scholar 

  9. Hennig J, Weigel M, Scheffler K. Calculation of flip angles for echo trains with predefined amplitudes with the extended phasegraph (EPG)-algorithm: principles and applications to hyperecho and TRAPS sequences. Magn Reson Med. 2004;51(1):68–80.

    Article  PubMed  Google Scholar 

  10. Busse RF, Hariharan H, Vu A, Brittain JH. Fast spin echo sequences with very long echo trains: design of variable refocusing flip angleschedules and generation of clinical T2 contrast. Magn Reson Med. 2006;55(5):1030–7.

    Article  PubMed  Google Scholar 

  11. Zhou Z, Chen S, Wu J, Bornert P, Yuan C. Deep convolutional neural network enhanced 3D high resolution turbo spin echo intracranial vessel wall imaging. In Proceedings of the 26th ISMRM Annual Meeting, Paris, France; 2018. p. 1049.

    Google Scholar 

  12. Zhu B, Liu J, Koonjoo N, Rosen B, Rosen M. AUTOmated pulse SEQuence generation (AUTOSEQ) using Bayesian reinforcement learning in an MRI physics simulation environment. In Proceedings of the 26th ISMRM Annual Meeting, Paris, France; 2018. p. 0438.

    Google Scholar 

  13. Chen Y, Fan Z, Shi F, Tian Z, Christodoulou A, Xie Y, Li D. Efficient super-resolution in intracranial vessel wall magnetic resonance imaging using 3D deep densely connected neural networks. In Proceedings of the 26th ISMRM Annual Meeting, Paris, France; 2018. p. 0657.

    Google Scholar 

  14. Yang Y, Sun J, Li H, Xu Z. Deep ADMM-Net for Compressive Sensing MRI. Advances in Neural Information Processing Systems (NIPS); 2016.

    Google Scholar 

  15. Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, Knoll F. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055–71.

    Article  PubMed  Google Scholar 

  16. Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM. Deep generative adversarial neural networks for compressive sensing (GANCS) MRI. IEEE Trans Med Imaging. 2019;38(1):167–79.

    Article  PubMed  Google Scholar 

  17. Chen F, Taviani V, Malkiel I, Cheng JY, Tamir JI, Shaikh J, Chang ST, Hardy CJ, Pauly JM, Vasanawala SS. Variable-density single-shot fast spin-echo MRI with deep learning reconstruction by using variational networks. Radiology. 2018;289(2):366–73.

    Article  PubMed  Google Scholar 

  18. Altaf N, MacSweeney ST, Gladman J, Auer DP. Carotid intraplaque hemorrhage predicts recurrent symptoms in patients with high-grade carotid stenosis. Stroke. 2007;38(5):1633–5.

    Article  PubMed  Google Scholar 

  19. Underhill HR, Hatsukami TS, Cai J, Yu W, DeMarco JK, Polissar NL, Ota H, Zhao X, Dong L, Oikawa M, Yuan C. A noninvasive imaging approach to assess plaque severity: the carotid atherosclerosis score. AJNR Am J Neuroradiol. 2010;31(6):1068–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van Dijk AC, Truijman MT, Hussain B, Zadi T, Saiedie G, de Rotte AA, Liem MI, van der Steen AF, Daemen MJ, Koudstaal PJ, Nederkoorn PJ, Hendrikse J, Kooi ME, van der Lugt A. Intraplaque hemorrhage and the plaque surface in carotid atherosclerosis: the plaque at RISK study (PARISK). AJNR Am J Neuroradiol. 2015;36(11):2127–33.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sun J, Underhill HR, Hippe DS, Xue Y, Yuan C, Hatsukami TS. Sustained acceleration in carotid atherosclerotic plaque progression with intraplaque hemorrhage: a long-term time course study. JACC Cardiovasc Imaging. 2012;5(8):798–804.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Liu F, Xu D, Ferguson MS, Chu B, Saam T, Takaya N, Hatsukami TS, Yuan C, Kerwin WS. Automated in vivo segmentation of carotid plaque MRI with Morphology-Enhanced probability maps. Magn Reson Med. 2006;55(3):659–68.

    Article  PubMed  Google Scholar 

  23. Gao S, van ’t Klooster R, van Wijk DF, Nederveen AJ, Lelieveldt BP, van der Geest RJ. Repeatability of in vivo quantification of atherosclerotic carotid artery plaque components by supervised multispectral classification. MAGMA. 2015;28(6):535–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sun B, Giddens DP, Long R Jr, Taylor WR, Weiss D, Joseph G, Vega D, Oshinski JN. Automatic plaque characterization employing quantitative and multicontrast MRI. Magn Reson Med. 2008;59(1):174–80.

    Article  PubMed  Google Scholar 

  25. Liu W, Balu N, Sun J, Zhao X, Chen H, Yuan C, Zhao H, Xu J, Wang G, Kerwin WS. Segmentation of carotid plaque using multicontrast 3D gradient echo MRI. J Magn Reson Imaging. 2012;35(4):812–9.

    Article  PubMed  Google Scholar 

  26. van ’t Klooster R, Naggara O, Marsico R, Reiber JH, Meder JF, van der Geest RJ, Touzé E, Oppenheim C. Automated versus manual in vivo segmentation of carotid plaque MRI. AJNR Am J Neuroradiol. 2012;33(8):1621–7.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Smits LP, van Wijk DF, Duivenvoorden R, Xu D, Yuan C, Stroes ES, Nederveen AJ. Manual versus automated carotid artery plaque component segmentation in high and lower quality 3.0 tesla MRI scans. PLoS One. 2016;11(12):e0164267.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Liu J, Balu N, Hippe DS, Ferguson MS, Martinez-Malo V, DeMarco JK, Zhu DC, Ota H, Sun J, Xu D, Kerwin WS, Hatsukami TS, Yuan C. Semi-automatic carotid intraplaque hemorrhage detection and quantification on Magnetization-Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) with optimized threshold selection. J Cardiovasc Magn Reson. 2016;18(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dong Y, Pan Y, Zhao X, Li R, Yuan C, Xu W. Identifying carotid plaque composition in MRI with convolutional neural networks. In Proceedings of the IEEE International Conference on Smart Computing (SMARTCOMP); 2017. https://doi.org/10.1109/SMARTCOMP.2017.7947015.

  30. Lekadir K, Galimzianova A, Betriu A, Del Mar Vila M, Igual L, Rubin DL, Fernandez E, Radeva P, Napel S. A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform. 2017;21(1):48–55.

    Article  PubMed  Google Scholar 

  31. Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2018; https://doi.org/10.1109/TMI.2018.2883807.

    Article  PubMed  Google Scholar 

  32. Chen L, Zhao H, Balu N, Zhao X, Li R, Xu J, Hatsukami TS, Hwang JN, Yuan C. Automatic segmentation of carotid vessel wall using convolutional neural network. In Proceedings of the 26th ISMRM Annual Meeting, Paris, France; 2018. p. 3494.

    Google Scholar 

  33. Chen L, Xie Y, Sun J, Balu N, Mossa-Basha M, Pimentel K, Hatsukami TS, Hwang JN, Yuan C. 3D intracranial artery segmentation using a convolutional autoencoder. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2017. pp. 714–7.

    Google Scholar 

  34. Takaya N, Cai J, Ferguson MS, Yarnykh VL, Chu B, Saam T, Polissar NL, Sherwood J, Cury RC, Anders RJ, Broschat KO, Hinton D, Furie KL, Hatsukami TS, Yuan C. Intra- and interreader reproducibility of magnetic resonance imaging for quantifying the lipid-rich necrotic core is improved with gadolinium contrast enhancement. J Magn Reson Imaging. 2006;24(1):203–10.

    Article  PubMed  Google Scholar 

  35. Kerwin WS, O’Brien KD, Ferguson MS, Polissar N, Hatsukami TS, Yuan C. Inflammation in carotid atherosclerotic plaque: a dynamic contrast-enhanced MR imaging study. Radiology. 2006;241(2):459–68.

    Article  PubMed  Google Scholar 

  36. Cai J, Balu N, Chen L, Zhou Z, Mossa-Basha M, Hatsukami TS, Yuan C. Contrast enhancement pattern study based on U-net. In Proceedings of the ISMRM Workshop on Machine Learning- Part II; 2018.

    Google Scholar 

  37. Molinari F, Raghavendra U, Gudigar A, Meiburger KM, Rajendra Acharya U. An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique. Med Biol Eng Comput. 2018;56(9):1579–93.

    Article  PubMed  Google Scholar 

  38. Sudha S, Jayanthi KB, Rajasekaran C, Madian N, Sunder T. Convolutional neural network for segmentation and measurement of intima media thickness. J Med Syst. 2018;42(8):154.

    Article  Google Scholar 

  39. Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7(22):e009476.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Washington, Department of Radiology, Seattle, WA, USA

    Niranjan Balu

  2. Philips Research North America, Cambridge, MA, USA

    Zechen Zhou

  3. University of Washington, Seattle, WA, USA

    Chun Yuan

Authors
  1. Niranjan Balu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zechen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chun Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chun Yuan .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, WA, USA

    Chun Yuan

  2. University of Washington, Seattle, WA, USA

    Thomas S. Hatsukami

  3. University of Washington, Seattle, WA, USA

    Mahmud Mossa-Basha

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Balu, N., Zhou, Z., Yuan, C. (2020). Vessel Wall Imaging in the Era of Artificial Intelligence. In: Yuan, C., Hatsukami, T., Mossa-Basha, M. (eds) Vessel Based Imaging Techniques . Springer, Cham. https://doi.org/10.1007/978-3-030-25249-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-25249-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25248-9

  • Online ISBN: 978-3-030-25249-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation