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

An efficient low-dose CT reconstruction technique using partial derivatives based guided image filter

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Low-dose Computed Tomography (CT) reconstruction techniques have been implemented to minimize the X-ray radiation in a human body. Many researchers have designed different low-dose CT reconstruction techniques to reduce the effect of radiation in a human body. However, the majority of these techniques suffer from over-smoothing, edge distortion, halo artifacts, gradient reversal artifacts etc. problems. Therefore, in this paper, novel partial differential equations and dictionary learning based reconstruction technique have been designed to reconstruct the low-dose CT images. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique that existing image reconstruction techniques. It has been observed that the proposed technique significantly preserves the radiometric information of low-dose CT images with a lesser number of edge distortion, halo and gradient reversal artifacts. Also, the proposed technique is computationally faster than existing techniques, therefore most suitable for real-time low-dose CT reconstruction systems.

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

Access this article

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

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

References

  1. Akashita S, Tachibana Y, Sakamaki K, Sogawa K, Inoue T (2015) Detection of pure ground-glass nodules in the lung by low-dose multi-detector computed tomography, with use of an iterative reconstruction method: a comparison with conventional image reconstruction by the filtered back-projection method. Jpn J Radiol 33(3):113–121

    Article  Google Scholar 

  2. Albertina B, Watson M, Holback C, Jarosz R, Kirk S, Lee Y, Lemmerman J (2016) Radiology data from the cancer genome atlas lung adenocarcinoma [tcga-luad] collection. The Cancer Imaging Archive

  3. Anuj Kumar MKS, Srivastava Vishal, Hancke G (2014) Current status of the IEEE 1451 standard based sensor applications. IEEE Sens 15(5):2505–2513

    Article  Google Scholar 

  4. Azeem Ahmad VD, Srivastava Vishal, Mehta D (2015) Ultra-shot longitudinal spatial coherence length of laser light with combined effect of spatial, angular and temporal diversity. Appl Phys Lett 106:093701

    Article  Google Scholar 

  5. Bouman C, Sauer K (1993) A generalized gaussian image model for edge-preserving map estimation. IEEE Trans Image Process 2(3):296–310

    Article  Google Scholar 

  6. Brown AA, Scarfe WC, Scheetz JP, Silveira AM, Farman AG (2009) Linear accuracy of cone beam ct derived 3d images. Angle Orthod 79(1):150–157

    Article  Google Scholar 

  7. Chen Y, Shi L, Feng Q, Yang J, Shu H, Luo L, Coatrieux J-L, Chen W (2014) Artifact suppressed dictionary learning for low-dose ct image processing. IEEE Trans Med Imaging 33(12):2271–2292

    Article  Google Scholar 

  8. Elbakri IA, Fessler JA (2002) Statistical image reconstruction for polyenergetic x-ray computed tomography. IEEE Trans Med Imaging 21(2):89–99

    Article  Google Scholar 

  9. Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp 4558–4567

  10. Fang J, Zhang D, Wilcox C, Heidinger B, Raptopoulos V, Brook A, Brook OR (2017) Metal implants on ct: comparison of iterative reconstruction algorithms for reduction of metal artifacts with single energy and spectral ct scanning in a phantom model. Abdominal Radiology 42(3):742–748

    Article  Google Scholar 

  11. Gilboa G, Sochen N, Zeevi YY (2004) Image enhancement and denoising by complex diffusion processes. IEEE Trans Pattern Anal Mach Intell 26(8):1020–1036

    Article  Google Scholar 

  12. Gnahm C, Nagel AM (2015) Anatomically weighted second-order total variation reconstruction of 23 na mri using prior information from 1 h mri. NeuroImage 105:452–461

    Article  Google Scholar 

  13. Golub GH, Heath M, Wahba G (1979) Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2):215–223

    Article  MathSciNet  MATH  Google Scholar 

  14. Greffier J, Macri F, Larbi A, Fernandez A, Khasanova E, Pereira F, Mekkaoui C, Beregi J (2015) Dose reduction with iterative reconstruction: optimization of ct protocols in clinical practice. Diagnostic and interventional imaging 96(5):477–486

    Article  Google Scholar 

  15. Han-Ming Z, Lin-Yuan W, Bin Y, Lei L, Xiao-Qi X, Li-Zhong L (2013) Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography. Chinese Physics B 22(7):078701

    Article  Google Scholar 

  16. Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W (2009) Iterative reconstruction technique for reducing body radiation dose at ct: feasibility study. Am J Roentgenol 193(3):764–771

    Article  Google Scholar 

  17. Katsura M, Sato J, Akahane M, Matsuda I, Ishida M, Yasaka K, Kunimatsu A, Ohtomo K (2013) Comparison of pure and hybrid iterative reconstruction techniques with conventional filtered back projection: image quality assessment in the cervicothoracic region. Eur J Radiol 82(2):356–360

    Article  Google Scholar 

  18. Kim SH, Yoon J-H, Lee JH, Lim Y-J, Kim OH, Ryu JH, Son J-H (2015) Low-dose ct for patients with clinically suspected acute appendicitis: optimal strength of sinogram affirmed iterative reconstruction for image quality and diagnostic performance. Acta Radiol 56(8):899–907

    Article  Google Scholar 

  19. Kim Y, Kim YK, Lee BE, Lee SJ, Ryu YJ, Lee JH, Chang JH (2015) Ultra-low-dose ct of the thorax using iterative reconstruction: evaluation of image quality and radiation dose reduction. Am J Roentgenol 204(6):1197–1202

    Article  Google Scholar 

  20. Kojima S, Shinohara H, Hashimoto T, Hirata M, Ueno E (2015) Iterative image reconstruction that includes a total variation regularization for radial mri. Radiol Phys Technol 8(2):295–304

    Article  Google Scholar 

  21. Lee H-J, Kim J, Kim KW, Lee S-K, Yoon JS (2018) Feasibility of a low-dose orbital ct protocol with a knowledge-based iterative model reconstruction algorithm for evaluating graves’ orbitopathy. Clin Imaging 51:327–331

    Article  Google Scholar 

  22. Li L, Wang B, Wang G (2016) Edge-oriented dual-dictionary guided enrichment (edge) for mri-ct image reconstruction. J Xray Sci Technol 24(1):161–175

    MathSciNet  Google Scholar 

  23. Liu Y, Liang Z, Ma J, Lu H, Wang K, Zhang H, Moore W (2014) Total variation-stokes strategy for sparse-view x-ray ct image reconstruction. IEEE Trans Med Imaging 33(3):749–763

    Article  Google Scholar 

  24. Mahmood U, Erdi Y, Wang W (2014) Su-e-i-89: assessment of ct radiation dose and image quality for an automated tube potential selection algorithm using pediatric anthropomorphic and acr phantoms. Med Phys 41(6):151–151

    Article  Google Scholar 

  25. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11(Jan):19–60

    MathSciNet  MATH  Google Scholar 

  26. Miller K (1970) Least squares methods for ill-posed problems with a prescribed bound. SIAM J Math Anal 1(1):52–74

    Article  MathSciNet  MATH  Google Scholar 

  27. Moloney F, Twomey M, James K, Kavanagh R, Fama D, O’Neill S, Grey T, Moore N, Murphy M, O’Connor O, Maher M (2018) A phantom study of the performance of model-based iterative reconstruction in low-dose chest and abdominal ct: When are benefits maximized?. Radiography 24(4):345–351

    Article  Google Scholar 

  28. Nagata K, Fujiwara M, Kanazawa H, Mogi T, Iida N, Mitsushima T, Lefor AT, Sugimoto H (2015) Evaluation of dose reduction and image quality in ct colonography: comparison of low-dose ct with iterative reconstruction and routine-dose ct with filtered back projection. Eur Radiol 25(1):221–229

    Article  Google Scholar 

  29. Nelson TR (2014) Practical strategies to reduce pediatric ct radiation dose. J Am Coll Radiol 11(3):292–299

    Article  Google Scholar 

  30. Nien H, Fessler JA (2015) Fast x-ray ct image reconstruction using a linearized augmented lagrangian method with ordered subsets. IEEE Trans Med Imaging 34 (2):388–399

    Article  Google Scholar 

  31. Olcott EW, Shin LK, Sommer G, Chan I, Rosenberg J, Molvin FL, Boas FE, Fleischmann D (2014) Model-based iterative reconstruction compared to adaptive statistical iterative reconstruction and filtered back-projection in ct of the kidneys and the adjacent retroperitoneum. Acad Radiol 21(6):774–784

    Article  Google Scholar 

  32. Padole A, Singh S, Ackman JB, Wu C, Do S, Pourjabbar S, Khawaja RA, Otrakji A, Digumarthy S, Shepard J-A, et al. (2014) Submillisievert chest ct with filtered back projection and iterative reconstruction techniques. Am J Roentgenol 203(4):772–781

    Article  Google Scholar 

  33. Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 conference record of the twenty-seventh asilomar conference on signals, systems and computers, 1993. IEEE, pp 40–44

  34. Qian W-L, Zhou D-J, Jiang Y, Feng C, Chen Q, Wang H, Zhang J-B, XU J-M (2018) Ultra-low radiation dose ct angiography of the lower extremity using the iterative model reconstruction (imr) algorithm. Clin Radiol 73(11):985.e13—985.e19

    Article  Google Scholar 

  35. Rampinelli C, Origgi D, Vecchi V, Funicelli L, Raimondi S, Deak P, Bellomi M (2015) Ultra-low-dose ct with model-based iterative reconstruction (mbir): detection of ground-glass nodules in an anthropomorphic phantom study. La Radiologia Medica 120(7):611–617

    Article  Google Scholar 

  36. Samei E, Richard S (2015) Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. Med Phys 42(1):314–323

    Article  Google Scholar 

  37. Takahashi M, Kimura F, Umezawa T, Watanabe Y, Ogawa H (2016) Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium. J Cardiovasc Comput Tomogr 10(1):61–68

    Article  Google Scholar 

  38. Thibault J-B, Sauer KD, Bouman CA, Hsieh J (2007) A three-dimensional statistical approach to improved image quality for multislice helical ct. Med Phys 34 (11):4526–4544

    Article  Google Scholar 

  39. Vishal Srivastava SN, Mehta DS (2013) High-resolution corneal topography and tomography of fish eye using wide field white light interference microscopy. Appl Phys Lett 102:153701

    Article  Google Scholar 

  40. Vishal Srivastava SN, Mehta DS (2013) High-resolution full-field optical coherence tomography using a spatially incoherent monochromatic light source. Appl Phys Lett 50(34):6343–6351

  41. Wu W, Zhang Y, Wang Q, Liu F, Chen P, Yu H (2018) Low-dose spectral ct reconstruction using image gradient l 0–norm and tensor dictionary. Appl Math Model 63:538–557

    Article  MathSciNet  Google Scholar 

  42. Xu Q, Yu H, Mou X, Zhang L, Hsieh J, Wang G (2012) Low-dose x-ray ct reconstruction via dictionary learning. IEEE Trans Med Imaging 31(9):1682–1697

    Article  Google Scholar 

  43. Xu Q, Han H, Xing L (2016) Su-f-i-12: region-specific dictionary learning for low-dose x-ray ct reconstruction. Med Phys 43(6):3389–3389

    Article  Google Scholar 

  44. Xu Q, Liu H, Yu H, Wang G, Xing L (2016) Mo-de-207a-05: dictionary learning based reconstruction with low-rank constraint for low-dose spectral ct. Med Phys 43(6):3701–3701

    Article  Google Scholar 

  45. Yoon J, Lee JM, Yu MH, Baek JH, Jeon JH, Hur BY, Dhanantwari A, Chung SY, Han JK, Choi BI (2014) Comparison of iterative model–based reconstruction versus conventional filtered back projection and hybrid iterative reconstruction techniques: Lesion conspicuity and influence of body size in anthropomorphic liver phantoms. J Comput Assist Tomogr 38(6):859–868

    Article  Google Scholar 

  46. You Y-L, Kaveh M (2000) Fourth-order partial differential equations for noise removal. IEEE Trans Image Process 9(10):1723–1730

    Article  MathSciNet  MATH  Google Scholar 

  47. Yuki H, Oda S, Utsunomiya D, Funama Y, Kidoh M, Namimoto T, Katahira K, Honda K, Tokuyasu S, Yamashita Y (2016) Clinical impact of model-based type iterative reconstruction with fast reconstruction time on image quality of low-dose screening chest ct. Acta Radiol 57(3):295–302

    Article  Google Scholar 

  48. Zhang H, Han H, Wang J, Ma J, Liu Y, Moore W, Liang Z (2014) Deriving adaptive mrf coefficients from previous normal-dose ct scan for low-dose image reconstruction via penalized weighted least-squares minimization. Med Phys 41(4)

  49. Zhang H, Ma J, Wang J, Liu Y, Lu H, Liang Z (2014) Statistical image reconstruction for low-dose ct using nonlocal means-based regularization. Comput Med Imaging Graph 38(6):423–435

    Article  Google Scholar 

  50. Zhang C, Zhang T, Zheng J, Li M, Lu Y, You J, Guan Y (2015) A model of regularization parameter determination in low-dose x-ray ct reconstruction based on dictionary learning. Comput Math Methods Med 2015

  51. Zhang C, Zhang T, Li M, Peng C, Liu Z, Zheng J (2016) Low-dose ct reconstruction via l1 dictionary learning regularization using iteratively reweighted least-squares. Biomed Eng Online 15(1):66

    Article  Google Scholar 

  52. Zhang H, Zhang L, Sun Y, Zhang J, Chen L (2017) Low dose ct image statistical iterative reconstruction algorithms based on off-line dictionary sparse representation. Optik 131:785–797

    Article  Google Scholar 

  53. Zhang Y, Mou X, Wang G, Yu H (2017) Tensor-based dictionary learning for spectral ct reconstruction. IEEE Trans Med Imaging 36(1):142–154

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia and Information Security Lab, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, 474015, India

    Yadunath Pathak & K. V. Arya

  2. Thapar University, Patiala, Punjab, India

    Shailendra Tiwari

Authors
  1. Yadunath Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. V. Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shailendra Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yadunath Pathak.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this article

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

Pathak, Y., Arya, K.V. & Tiwari, S. An efficient low-dose CT reconstruction technique using partial derivatives based guided image filter. Multimed Tools Appl 78, 14733–14752 (2019). https://doi.org/10.1007/s11042-018-6840-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6840-5

Keywords

Search

Navigation