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Applications of CS-MRI in Bioinformatics and Neuroinformatics

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 9))

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Abstract

MRI has a number of applications in bioinformatics and neuroinformatis, like, functional MRI (fMRI), diffusion weighted MRI (DW-MRI), and magnetic resonance spectroscopy (MRS). It gives valuable information about anatomical structure, the functioning of organs, neuronal activity, and abnormality inside the human body. Although MRI has a number of clinical advantages, it suffers from a fundamental limitation, i.e., slow data acquisition resulting in low SNR, poor resolution, and patient discomfort. Applications of CS-MRI for clinical practice is only a few till date but this new technology has a tremendous potential to overcome the fundamental limit of conventional MRI.

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References

  1. Akasaka, T., Fujimoto, K., Yamamoto, T., Okada, T., Fushumi, Y., Yamamoto, A., Tanaka, T., Togashi, K.: Optimization of regularization parameters in compressed sensing of magnetic resonance angiography: can statistical image metrics mimic radiologists perception? PLOS ONE 13(5), 1–14 (2018)

    Article  Google Scholar 

  2. Bilgic, B., Setsompop, K., Cohen-Adad, J., Wedeen, V., Wald, L.L., Adalsteinsson, E.: Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, pp. 1–9. Springer, Heidelberg (2012)

    Google Scholar 

  3. Blasiak, B., van Veggel, F.C.J.M., Tomanek, B.: Applications of nanoparticles for MRI cancer diagnosis and therapy. J. Nanomater. 2013, 1–13 (2013)

    Article  Google Scholar 

  4. Chavhan, G.B., Babyn, P.S.: Whole-body MR imaging in children: principles, technique, current applications, and future directions. RadioGraphics 31(6), 1757–1772 (2011)

    Article  Google Scholar 

  5. Cheng, J., Shen, D., Basser, P.J., Yap, P.: Joint 6D k-q space compressed sensing for accelerated high angular resolution diffusion MRI. IPMI, Lect Notes Comput Sci 9123, 782–793 (2015). Springer

    Google Scholar 

  6. Crasto, C.J. (ed.): Neuroinformatics. Humana Press, New Jersey (2007)

    Google Scholar 

  7. Deka, B., Datta, S., Handique, S.: Wavelet tree support detection for compressed sensing MRI reconstruction. IEEE Signal Process. Lett. 25(5), 730–734 (2018)

    Article  Google Scholar 

  8. Duarte-Carvajalino, J.M, Lenglet, C., Ugurbil, K., Moeller, S., Carin, L., Sapiro, G.: A framework for multi-task bayesian compressive sensing of DW-MRI. In: Proceedings of the CDMRI MICCAI Workshop, pp. 1–13 (2012)

    Google Scholar 

  9. Fang, Z., Le, N.V., Choy, M., Lee, J.H.: Fang z, van le n, choy m, lee jh. High spatial resolution compressed sensing (hsparse) functional magnetic resonance imaging. Magn. Reson. Med. 76, 440–455 (2016)

    Google Scholar 

  10. Faster MRI scans with compressed sensing from Siemens Healthineers. Siemens Healthineers. https://www.siemens.com/press/en/pressrelease/?press=/en/pressrelease/2016/healthcare/pr. Accessed 29 Jun 2018

  11. Friedman, P.D., Swaminathan, S.V., Herman, K., Kalisher, L.: Breast mri: the importance of bilateral imaging. Am. J. Roentgenol. 187(2), 345–349 (2006)

    Article  Google Scholar 

  12. Ganguly D. Chakraborty S., B.M.K.T.: Security-Enriched Urban Computing and Smart Grid. Communications in Computer and Information Science, vol. 78, chap. In: Medical Imaging: A Review, pp. 504–516. Springer, Heidelberg (2010)

    Google Scholar 

  13. Geerts-Ossevoort, L., de Weerdt, E., Duijndam, A., van IJperen, G., Peeters, H., Doneva, M., Nijenhuis, M., Huang, A.: Compressed SENSE speed done right. every time. Philips (2018). Accessed 29 Jun 2018

    Google Scholar 

  14. Geethanath, S., Baek, H.M., Ganji, S.K., Ding, Y., Maher, E.A., Sims, R.D., Choi, C., Lewis, M.A., Kodibagkar, V.D.: Compressive sensing could accelerate 1H MR metabolic imaging inthe clinic. Radiology 262(3), 985–994 (2012)

    Article  Google Scholar 

  15. Gujar, S.K., Maheshwari, S., Bjrkman-Burtscher, I., Sundgren, P.C.: Magnetic resonance spectroscopy. J. Neuro-Ophthalmol. 25(3), 217–226 (2005)

    Article  Google Scholar 

  16. Guo, Y., Zhu, Y., Lingala, S.G., Lebel, R.M., Shiroishi, M., Law, M., Nayak, K.: Highresolution whole-brain DCE-MRI using constrained reconstruction: prospective clinical evaluation in brain tumor patients. Med. Phys. 43(5), 2013–2023 (2016)

    Article  Google Scholar 

  17. Han, P.K.J., Park, S.H., Kim, S.G., Ye, J.C.: Compressed sensing for fMRI: Feasibility study on the acceleration of non-EPI fMRI at 9.4T. BioMed. Res. Int. 1–24 (2015)

    Google Scholar 

  18. Hartung, M.P., Grist, T.M., Francois, C.J.: Magnetic resonance angiography: current status and future directions. J. Cardiovasc. Magn. Reson. 13(1), 1–11 (2011)

    Article  Google Scholar 

  19. Huang, J., Wang, L., Chu, C., Zhang, Y., Liu, W., Zhu, Y.: Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation. Technol. Health Care: Off. J. Eur. Soc. Eng. Med. 24(2), S593–S599 (2016)

    Article  Google Scholar 

  20. Kasabov, N.K. (ed.): Springer Handbook of Bio-/Neuro-Informatics. Springer, Heidelberg (2014)

    MATH  Google Scholar 

  21. Kherlopian, A.R., Song, T., Duan, Q., Neimark, M.A., Po, M.J., Gohagan, J.K., Laine, A.F.: A review of imaging techniques for systems biology. BMC Syst. Biol. 2(1), 1–18 (2008)

    Article  Google Scholar 

  22. King, K.: HyperSense enables shorter scan times without compromising image quality. GE Healthcare (2016). Accessed 29 Jun 2018

    Google Scholar 

  23. Koh, D.M., Collins, D.J.: Diffusion-weighted MRI in the body: applications and challenges in oncology. Am. J. Roentgenol. 188, 1622–1635 (2007)

    Article  Google Scholar 

  24. Lee, B., Andrew, N.: Neuroimaging in traumatic brain imaging. NeuroRx 2(2), 372–383 (2005)

    Article  Google Scholar 

  25. Lustig, M., Keutzer, K., V.S., : The Berkeley Par Lab: progress in the parallel computing landscape, chap. In: Introduction to Parallelizing Compressed Sensing Magnetic Resonance Imaging, pp. 105–139. Microsoft Corporation (2013)

    Google Scholar 

  26. MAGNETOM Vida embrace human nature at 3T. Siemens Healthcare. https://www.healthcare.siemens.co.in/magnetic-resonance-imaging/3t-mri-scanner/magnetom. Accessed 29 Jun 2018

  27. Mori, S., Oishi, K., Faria, A.V., Miller, M.I.: Atlas-based neuroinformatics via MRI: harnessing information from past clinical cases and quantitative image analysis for patient care. Ann. Rev. Biomed. Eng. 15, 71–92 (2013)

    Article  Google Scholar 

  28. Moseley, M.E., Liu, C., Sandra Rodriguez, B., RT(R)(MR), Brosnan, T., : Advances in magnetic resonance neuroimaging. Neurol. Clin. 27(1), 1–24 (2009)

    Article  Google Scholar 

  29. Nakamura, M., Kido, T., Kido, T., Watanabe, K., Schmidt, M., Forman, C., Mochizuki, T.: Non-contrast compressed sensing whole-heart coronary magnetic resonance angiography at 3T: A comparison with conventional imaging. Radiology 104, 43–48 (2018)

    Google Scholar 

  30. Novotny, E., Ashwal, S., Shevell, M.: Proton magnetic resonance spectroscopy: An emerging technology in pediatric neurology research. Pediatr. Res. 44, 1–10 (1998)

    Article  Google Scholar 

  31. New compressed sensing technology could reduce MRI scan times. Rice University (2017)

    Google Scholar 

  32. Padhani, A.R., Koh, D.M., Collins, D.J.: Whole-body diffusion-weighted MR imaging in cancer: current status and research directions. Radiology 261(3), 700–718 (2011)

    Article  Google Scholar 

  33. Park, I., Hu, S., Bok, R., Ozawa, T., Ito, M., Mukherjee, J., Phillips, J., James, C., Pieper, R., Ronen, S., Vigneron, D., Nelson, S.: Evaluation of heterogeneous metabolic profile in an orthotopic human glioblastoma xenograft model using compressed sensing hyperpolarized 3D \(^13\)C magnetic resonance spectroscopic imaging. Magn. Reson. Med. 70(1), 33–39 (2013)

    Article  Google Scholar 

  34. Pernet, C.R., Gorgolewski, K.J., Job, D., Rodriguez, D., Whittle, I., Wardlaw, J.: A structural and functional magnetic resonance imaging dataset of brain tumour patients. Sci. Data 3, 1–6 (2016)

    Article  Google Scholar 

  35. Petrella, J.R., Provenzale, J.M.: MR perfusion imaging of the brain. Am. J. Roentgenol. 175(1), 207–219 (2000)

    Article  Google Scholar 

  36. Rapacchi, S., Han, F., Natsuaki, Y., Kroeker, R.M., Plotnik, A.N., Lehrman, E., Sayre, J., Laub, G., Finn, J.P., Hu, P.: High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography (CE-MRA) using compressed sensing with magnitude image subtraction. J. Cardiovasc. Magn. Reson. 15(1), 1–3 (2013)

    Article  Google Scholar 

  37. Rubin, D.L., Greenspan, H., Brinkley, J.F.: Biomedical Informatics, fourth edition edn., chap. In: Biomedical Imaging Informatics. Computer Applications in Health Care and Biomedicine, pp. 285–327. Springer, London, Heidelberg, New York (2014)

    Google Scholar 

  38. Smith, K.: Brain imaging: fMRI 2.0. Nature 484, 24–26 (2012)

    Article  Google Scholar 

  39. Symms, M., Jager, H.R., Schmierer, K., Yousry, T.A.: A review of structural magnetic resonance neuroimaging. J. Neurol. Neurosurg. Psychiatry 75(9), 1235–1244 (2004)

    Article  Google Scholar 

  40. Tesfamicael, S.A., Barzideh, F.: Clustered compressed sensing in fMRI data analysis using a bayesian framework. International Journal of Information and Electronics Engineering 4(2), 1–7 (2014)

    Article  Google Scholar 

  41. Tognarelli, M., J., Dawood, M., I.F. Shariff, M., P.B. Grover, V., M.E. Crossey, M., JaneCox, I., D. Taylor-Robinson, S., J.W. McPhail, M., : Magnetic resonance spectroscopy: Principles and techniques: Lessons for clinicians. Journal of Clinical and Experimental Hepatology 5(4), 320–328 (2015)

    Article  Google Scholar 

  42. Toledano-Massiah, S., Sayadi, A., de Boer, R.A., Gelderblom, J., Mahdjoub, R., Gerber, S., Zuber, M., Zins, M., Hodel, J.: Accuracy of the compressed sensing accelerated 3D-FLAIR sequence for the detection of MS plaques at 3T. AJNR. American journal of neuroradiology 1–5 (2018)

    Google Scholar 

  43. Yamamoto, T., Okada, T., Fushimi, Y., Yamamoto, A., Fujimoto, K., Okuchi, S., Fukutomi, H., Takahashi, J.C., Funaki, T., Miyamoto, S., Stalder, A.F., Natsuaki, Y., Speier, P., Togashi, K.: Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease. PLoS ONE 13(1), 1–11 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Tezpur University, Tezpur, Assam, India

    Bhabesh Deka & Sumit Datta

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  1. Bhabesh Deka
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  2. Sumit Datta
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Correspondence to Bhabesh Deka .

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Deka, B., Datta, S. (2019). Applications of CS-MRI in Bioinformatics and Neuroinformatics. In: Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms. Springer Series on Bio- and Neurosystems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-13-3597-6_6

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  • DOI: https://doi.org/10.1007/978-981-13-3597-6_6

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