PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Joint Maximum Likelihood Estimation of Motion and T 1 Parameters from Magnetic Resonance Images in a Super-resolution Framework : a Simulation Study

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson's disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.
Wydawca
Rocznik
Strony
105--128
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
  • Department of Physics, University of Antwerp, Antwerp, Belgium
  • Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
  • Department of Physics, University of Antwerp, Antwerp, Belgium
  • Departments of Medical informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
  • David Hartley Chair of Radiology, Royal Perth Hospital, Perth, WA, Australia
  • Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
autor
  • Department of Physics, University of Antwerp, Antwerp, Belgium
  • Department of Physics, University of Antwerp, Antwerp, Belgium
Bibliografia
  • [1] Deoni SCL, Rutt BK, Peters TM. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn. Reson. Med., 2003. 49(3):515-526. doi:10.1002/mrm.10407.
  • [2] Larsson HBW, Frederiksen J, Petersen J, Nordenbo A, Zeeberg I, Henriksen O, Olesen J. Assessment of demyelination, edema, and gliosis by in vivo determination of T1 and T2 in the brain of patients with acute attack of multiple sclerosis. Magn. Reson. Med., 1989. 11(3):337-348. doi:10.1002/mrm.1910110308.
  • [3] Vrenken H, Geurts JJG, Knol DL, van Dijk LN, Dattola V, Jasperse B, van Schijndel RA, Polman CH, Castelijns JA, Barkhof F, Pouwels PJW. Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology, 2006. 240(3):811-820. doi:10.1148/radiol.2403050569. PMID: 16868279.
  • [4] Papadopoulos K, Tozer DJ, Fisniku L, Altmann DR, Davies G, Rashid W, Thompson AJ, Miller DH, Chard DT. T1-relaxation time changes over five years in relapsing-remitting multiple sclerosis. Mult. Scler. J., 2010. 16(4):427-433. doi:10.1177/1352458509359924.
  • [5] Conlon P, Trimble MR, Rogers D, Callicott C. Magnetic resonance imaging in epilepsy: a controlled study. Epilepsy Res., 1988. 2(1):37-43. doi:10.1016/0920-1211(88)90008-3.
  • [6] Erkinjuntti T, Ketonen L, Sulkava R, Vuorialho M, Livanainen M. Do white matter changes on MRI and CT differentiate vascular dementia from Alzheimer’s disease? J. Neurol. Neurosurg. Psychiatry, 1987. 50(1):37-42. doi:10.1136/jnnp.50.1.37.
  • [7] Taylor AJ, Salerno M, Dharmakumar R, Jerosch-Herold M. T1 Mapping Basic Techniques and Clinical Applications. JACC: Cardiovasc. Imaging, 2016. 9(1, SI):67-81. doi:10.1016/j.jcmg.2015.11.005.
  • [8] Van Steenkiste G, Poot DHJ, Jeurissen B, den Dekker AJ, Vanhevel F, Parizel PM, Sijbers J. Superresolution T1 estimation: Quantitative high resolution T1 mapping from a set of low resolution T1-weighted images with different slice orientations. Magn. Reson. Med., 2017. 77(5):1818-1830. doi:10.1002/mrm.26262.
  • [9] Poot DHJ, Van Meir V, Sijbers J. General and efficient super-resolution method for multi-slice MRI. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2010, volume 6361. 2010 pp. 615-622. doi:10.1007/978-3-642-15705-9_75.
  • [10] Ramos-Llordén G, den Dekker AJ, Van Steenkiste G, Jeurissen B, Vanhevel F, Van Audekerke J, Verhoye M, Sijbers J. A unified maximum likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping. IEEE Trans. Med. Imag., 2017. 36(2):433-446. doi:10.1109/TMI.2016.2611653.
  • [11] Hahn EL. An accurate nuclear magnetic resonance method for measuring spin-lattice relaxation times. Phys. Rev., 1949. 76(1):145-146. doi:10.1103/physrev.76.145.
  • [12] Drain LE. A direct method of measuring nuclear spin-lattice relaxation times. Proc. Phys. Soc. London, Sect. A, 1949. 62:301-306. doi:10.1088/0370-1298/62/5/306.
  • [13] Crawley AP, Henkelman RM. A comparison of one-shot and recovery methods in T1 imaging. Magn. Reson. Med., 1988. 7(1):23-34. doi:10.1002/mrm.1910070104.
  • [14] Bernstein MA, King KF, Zou XJ. Handbook of MRI pulse sequences. Elsevier Academic press, London, 2004.
  • [15] Li Y, Matej S, Metzler SD. Image reconstructions from super-sampled data sets with resolution modeling in PET imaging. Med. Phys., 2014. 41(12):121912. doi:10.1118/1.4901552.
  • [16] Tofts P. Quantitative MRI of the brain: measuring changes caused by disease. Chichester, England: John Wiley & Sons, 2004.
  • [17] Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn. Reson. Med., 1995. 34(6):910-914. doi:10.1002/mrm.1910340618.
  • [18] Andersen AH, Kirsch JE. Analysis of noise in phase contrast MR imaging. Med. Phys., 1996. 23(6):857-869. doi:10.1118/1.597729.
  • [19] Constantinides CD, Atalar E, McVeigh ER. Signal-to-noise measurements in magnitude images from NMR phased arrays. Magn. Reson. Med., 1997. 38(5):852-857. doi:10.1002/mrm.1910380524.
  • [20] den Dekker AJ, Sijbers J. Data distributions in magnetic resonance images: A review. Physica Med., 2014. 30(7):725-741. doi:10.1016/j.ejmp.2014.05.002.
  • [21] van den Bos A. Parameter Estimation for Scientists and Engineers. Hoboken, New Yersey, USA: John Wiley & Sons, 2007. doi:10.1002/9780470173862.
  • [22] Fessler JA, Kim D. Axial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction. In: Proc. Intl. Mtg. on Fully 3D image Recon. in Rad. and Nuc. Med. 2011 pp. 262-5.
  • [23] Beck A, Tetruashvili L. On the Convergence of Block Coordinate Descent Type Methods. SIAM J. Optim., 2013. 23(4):2037-2060. doi:10.1137/120887679.
  • [24] Fan J, Farmen M, Gijbels I. Local maximum likelihood estimation and inference. J. R. Stat. Soc. Series B. Stat. Methodol., 1998. 60(3):591-608. doi:10.1111/1467-9868.00142.
  • [25] Nocedal J, Wright S. Numerical optimization. New York, NJ, USA: Springer-Verlag, 2006.
  • [26] Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W. PET-CT image registration in the chest using free-form deformations. IEEE Trans. Med. Imag., 2003. 22(1):120-128. doi:10.1109/tmi.2003.809072.
  • [27] Pluim JPW, Maintz JB, Viergever MA. Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag., 2003. 22(8):986-1004. doi:10.1109/tmi.2003.815867.
  • [28] The Mathworks, Inc., Natick, Massachusetts, United States. MATLAB and Image Processing Toolbox Release 2017b.
  • [29] Wright PJ, Mougin OE, Totman JJ, Peters AM, Brookes MJ, Coxon R, Morris PE, Clemence M, Francis ST, Bowtell RW, Gowland PA. Water proton T1 measurements in brain tissue at 7, 3, and 1.5 T using IR-EPI, IR-TSE, and MPRAGE: results and optimization. Magn. Reson. Mater. Phys., Biol. Med., 2008. 21(1-2):121-130. doi:10.1007/s10334-008-0104-8.
  • [30] Larkin KG, Oldfield MA, Klemm H. Fast Fourier method for the accurate rotation of sampled images. Opt. Commun., 1997. 139(1-3):99-106. doi:10.1016/s0030-4018(97)00097-7.
  • [31] Plenge E, Poot DHJ, Bernsen M, Kotek G, Houston G, Wielopolski P, Van Der Weerd L, Niessen WJ, Meijering E. Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn. Reson. Med., 2012. 68(6):1983-1993. doi:10.1002/mrm.24187.
  • [32] Van Steenkiste G, Jeurissen B, Veraart J, den Dekker AJ, Parizel PM, Poot DHJ, Sijbers J. Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations. Magn. Reson. Med., 2016. 75(1):181-195. doi:10.1002/mrm.25597.
  • [33] Xue H, Shah S, Greiser A, Guetter C, Littmann A, Jolly MP, Arai AE, Zuehlsdorff S, Guehring J, Kellman P. Motion correction for myocardial T1 mapping using image registration with synthetic image estimation. Magn. Reson. Med., 2012. 67(6):1644-1655. doi:10.1002/mrm.23153.
  • [34] Roujol S, Foppa M, Weingartner S, Manning W J, Nezafat R. Adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC): Application to T1 mapping. Magn. Reson. Med., 2015. 73(4):1469-1482. doi:10.1002/mrm.25270.
  • [35] Barral JK, Gudmundson E, Stikov N, Etezadi-Amoli M, Stoica P, Nishimura DG. A robust methodology for in vivo T1 mapping. Magn. Reson. Med., 2010. 64(4):1057-1067. doi:10.1002/mrm.22497.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7545bc94-a96c-4fc2-bc94-442b82bc56e1
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.