Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Compressive Sensing Technique on MRI Reconstruction—Methodical Survey

  • Conference paper
  • First Online:
Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1415))

  • 618 Accesses

Abstract

Magnetic resonance imaging (MRI) in medical imaging plays a vital role in the clinical diagnostic. The motivation behind reconstruction of MRI is to reduce the radiation exposure time on patients which is the main cause of motion artifacts. The concept of compressive sensing (CS) has an advantage of compression during acquisition which reduces the acquisition time. The goal of this paper is to have a systematic survey on CS techniques on MRI focusing sparse transformation, measurement matrix, reconstruction methods and performance evaluation parameters. Analyze and tabulate the various compressive sensing techniques on MRI with their performance parameters, advantages and limitations. This survey provides knowledge of CS strategies, sparse transformation process, recovery design constraints and performance indices which are considered to be important in enhancing MRI reconstruction with good image quality.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Compressed Sensing Cardiac Cine (2017) FDA clears compressed sensing MRI acceleration technology from Siemens Healthineers, pp 13–14

    Google Scholar 

  2. Hansen MS, Kellman P (2015) Image reconstruction: an overview for clinicians. J Magn Reson Imaging 41(3):573–585. https://doi.org/10.1002/jmri.24687

    Article  Google Scholar 

  3. White Paper (2019) Understanding how compressed SENSE makes MRI faster, pp 1–4

    Google Scholar 

  4. Nan Y, Yi Z, Bingxia C (2016) Review of compressed sensing for biomedical imaging. In: Proceedings—2015 7th International conference on information technology in medicine and education, ITME 2015, pp 225–228. https://doi.org/10.1109/ITME.2015.119

  5. Nan Y, Yi Z, Bingxia C (2016) Review of compressed sensing for biomedical imaging. In: Proceedings—2015 7th International conference on information technology in medicine and education, ITME 2015, pp 225–228. https://doi.org/10.1109/ITME.2015.119

  6. Yousufi M et al (2019) Application of compressive sensing to ultrasound images: a review. Biomed Res Int. https://doi.org/10.1155/2019/7861651

  7. Salahdine F, Kaabouch N, El Ghazi H (2018) One-bit compressive sensing vs. multi-bit compressive sensing for cognitive radio networks. Proc IEEE Int Conf Ind Technol 2018(1):1610–1615. https://doi.org/10.1109/ICIT.2018.8352422

    Article  Google Scholar 

  8. Sreeharitha S, Sabarinath G, Jose BR (2018) Compressive sensing recovery algorithms and applications—a survey. IOP Conf Ser Mater Sci Eng 396(1). https://doi.org/10.1088/1757-899X/396/1/012037

  9. Candès E, Romberg J (2007) Sparsity and incoherence in compressive sampling. Inverse Probl. 23(3):969–985. https://doi.org/10.1088/0266-5611/23/3/008

    Article  MathSciNet  MATH  Google Scholar 

  10. Rani M, Dhok SB, Deshmukh RB (2018) A systematic review of compressive sensing: concepts, implementations and applications. IEEE Access 6:4875–4894. https://doi.org/10.1109/ACCESS.2018.2793851

    Article  Google Scholar 

  11. Bhatt U, Bamniya K (2015) Medical image compression and reconstruction using compressive sensing 2(5):1610–1616

    Google Scholar 

  12. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  Google Scholar 

  13. Pfander GE, Rauhut H, Tropp JA (2013) The restricted isometry property for time-frequency structured random matrices. Probab Theory Relat Fields 156(3–4):707–737. https://doi.org/10.1007/s00440-012-0441-4

    Article  MathSciNet  MATH  Google Scholar 

  14. Rauhut H, Romberg J, Tropp JA (2012) Restricted isometries for partial random circulant matrices. Appl Comput Harmon Anal 32(2):242–254. https://doi.org/10.1016/j.acha.2011.05.001

    Article  MathSciNet  MATH  Google Scholar 

  15. Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D (2018) A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 37(2):491–503. https://doi.org/10.1109/TMI.2017.2760978

    Article  Google Scholar 

  16. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195. https://doi.org/10.1002/mrm.21391

    Article  Google Scholar 

  17. Hu Z, Wang Q, Ming C, Wang L, Hu Y, Zou J (2016) Compressed sensing MRI reconstruction algorithm based on contourlet transform and Split Bregman method. In: Proceedings—2015 8th International symposium on computational Intelligence and Design, ISCID 2015, vol 2, pp 164–167. https://doi.org/10.1109/ISCID.2015.97

  18. Zhang Y, Dong Z, Phillips P, Wang S, Ji G, Yang J (2015) Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inf Sci (NY) 322:115–132. https://doi.org/10.1016/j.ins.2015.06.017

    Article  MathSciNet  Google Scholar 

  19. Sungheetha A, Sharma R (2020) GTIKF—Gabor-transform incorporated K-means and fuzzy C means clustering for edge detection in CT and MRI. J Soft Comput Paradig 2(2):111–119. https://doi.org/10.36548/jscp.2020.2.004

  20. Forman C (2016) Compressed sensing: a paradigm shift in MRI, vol 1. MAGNETOM Flash, pp 8–13

    Google Scholar 

  21. Dhengre N, Sinha S (2021) K sparse autoencoder-based accelerated reconstruction of magnetic resonance imaging. Vis Comput. https://doi.org/10.1007/s00371-020-02054-6

    Article  Google Scholar 

  22. Ravishankar S, Ye JC, Fessler JA (2020) Image reconstruction: from sparsity to data-adaptive methods and machine learning. Proc IEEE 108(1):86–109. https://doi.org/10.1109/JPROC.2019.2936204

    Article  Google Scholar 

  23. Kim SJ, Koh K, Lustig M, Boyd S, Gorinevsky D (2007) An interior-point method for large-scale 1-regularized least squares. IEEE J Sel Top Signal Process 1(4):606–617. https://doi.org/10.1109/JSTSP.2007.910971

    Article  Google Scholar 

  24. DeVore RA, Temlyakov VN (1996) Some remarks on greedy algorithms. Adv Comput Math 5(1):173–187. https://doi.org/10.1007/bf02124742

    Article  MathSciNet  MATH  Google Scholar 

  25. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666. https://doi.org/10.1109/TIT.2007.909108

    Article  MathSciNet  MATH  Google Scholar 

  26. Dhasmana M, Budhiraja S (2015) A survey of compressive sensing based greedy pursuit reconstruction algorithms, Sept 2015, 2016. https://doi.org/10.5815/ijigsp.2015.10.01

  27. Caballero J, Price AN, Rueckert D, Hajnal JV (2014) Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans Med Imaging 33(4):979–994. https://doi.org/10.1109/TMI.2014.2301271

    Article  Google Scholar 

  28. Sandilya M, Nirmala SR (2017) Compressed sensing trends in magnetic resonance imaging. Eng Sci Technol Int J 20(4):1342–1352. https://doi.org/10.1016/j.jestch.2017.07.001

    Article  Google Scholar 

  29. Palani U, Vasanthi D, Rabiya Begam S (2020) Enhancement of medical image fusion using image processing. J Innov Image Process (JIIP) 02(04):165–174. https://doi.org/10.36548/jiip.2020.4.001

  30. Shashi Kiran S, Suresh KV (2019) Reconstruction of MRI images based on compressive sensing. In: Proceedings of 2019 IEEE international conference on communication and signal processing, ICCSP 2019, no I, pp 787–791. https://doi.org/10.1109/ICCSP.2019.8698052

  31. Islam S (2021) Multiscale wavelet-based regularized reconstruction algorithm for three-dimensional compressed sensing magnetic resonance imaging. Signal Image Video Process. https://doi.org/10.1007/s11760-021-01881-x

    Article  Google Scholar 

  32. Kiragu H, Mwangi E, Kamucha G (2020) An efficacious MRI sparse recovery method based on differential under-sampling and k-space interpolation. In: 20th IEEE Mediterranean electrotechnical conference, MELECON 2020—Proceedings, pp 382–387. https://doi.org/10.1109/MELECON48756.2020.9140563

  33. Yuan L, Li Y, Dai F, Long Y, Cheng X, Gui G (2019) Analysis L1/2 regularization: iterative half thresholding algorithm for CS-MRI. IEEE Access 7(2):79366–79373. https://doi.org/10.1109/ACCESS.2019.2923171

    Article  Google Scholar 

  34. Babapour S, Lakestani M, Fatholahzadeh A (2021) AFISTA: accelerated FISTA for sparse signal recovery and compressive sensing

    Google Scholar 

  35. Gamper U, Boesiger P, Kozerke S (2008) Compressed sensing in dynamic MRI 373:365–373. https://doi.org/10.1002/mrm.21477

  36. Fiandrotti A, Fosson SM, Ravazzi C, Magli E (2013) PISTA: parallel iterative soft thresholding algorithm for sparse image recovery, July 2015, 2013. https://doi.org/10.1109/PCS.2013.6737749

  37. Lakshminarayana M, Sarvagya M (2018) MICCS: a novel framework for medical image compression using compressive sensing 8(5):2818–2828. https://doi.org/10.11591/ijece.v8i5.pp2818-2828

  38. Huang Z (2021) Model-based reconstruction with learning: from unsupervised to supervised and beyond, pp 1–20

    Google Scholar 

  39. Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002

    Article  Google Scholar 

  40. Christilin DMAB, Mary MS (2018) Image reconstruction using compressive sensing techniques—a survey Feb 2018

    Google Scholar 

  41. Lakshminarayana M, Sarvagya M (2018) OFCS: optimized framework of compressive sensing for medical images in bottleneck network condition. Int J Electr Comput Eng 8(5):2829. https://doi.org/10.11591/ijece.v8i5.pp2829-2838

  42. Irawati ID, Hadiyoso S, Hariyani YS (2020) Multi-wavelet level comparison on compressive sensing for MRI image reconstruction. Bull Electr Eng Inf 9(4):1461–1467. https://doi.org/10.11591/eei.v9i4.2347

  43. Zhang Y, Peterson BS, Ji G, Dong Z (2014) Energy preserved sampling for compressed sensing MRI. Comput Math Methods Med 2014. https://doi.org/10.1155/2014/546814

  44. Sridhar N, Ramrao N, Singh MK (2014) PID controller auto tuning using ASBO technique. J Control Eng Technol 4(3):192–204. https://doi.org/10.14511/jcet.2014.040305

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Sambhram Institute of Technology, Visvesvaraya Technological University, Belagavi, India

    A. N. Shilpa

  2. Government Polytechnic, Channasandra, Bengaluru, India

    A. N. Shilpa

  3. Department of ECE, Sambhram Institute of Technology, Bengaluru, India

    C. S. Veena

Authors
  1. A. N. Shilpa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. S. Veena
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Vaigai College of Engineering, Madurai, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Department of Business Administration, The Gerald Schwartz School of Business, Nova Scotia, NS, Canada

    Ram Palanisamy

  3. Department of Computer Science Engineering, Malla Reddy College of Engineering, Secunderabad, Telangana, India

    M. Narayanan

  4. University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shilpa, A.N., Veena, C.S. (2022). Compressive Sensing Technique on MRI Reconstruction—Methodical Survey. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_20

Download citation

Publish with us

Policies and ethics

Search

Navigation