Volumetric T1 and T2 magnetic resonance brain toolkit for relaxometry mapping simulation
Senra Filho, Antonio Carlos da Silva
http://dx.doi.org/10.1590/2446-4740.00916
Res. Biomed. Eng., vol.32, n3, p.301-305, 2016
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Views: 886
Abstract
Introduction: Relaxometry images are an important magnetic resonance imaging (MRI) technique in the clinical routine. Many diagnoses are based on the relaxometry maps to infer abnormal state in the tissue characteristic relaxation constant. In order to study the performance of these image processing approaches, a controlled simulated environment is necessary. However, a simulated relaxometry image tool is still lacking. This study proposes a computational anatomical brain phantom for MRI relaxometry images, which aims to offer an easy and flexible toolkit to test different image processing techniques, applied to MRI relaxometry maps in a controlled simulated environment. Methods: A pipeline of image processing techniques such as brain extraction, image segmentation, normalization to a common space and signal relaxation decay simulation, were applied to a brain structural ICBM brain template, on both T1 and T2 weighted images, in order to simulate a volumetric brain relaxometry phantom. The FMRIB Software Library (FSL) toolkits were used here as the base image processing needed to all the relaxometry reconstruction. Results: All the image processing procedures are performed using automatic algorithms. In addition, different artefact levels can be set from different sources such as Rician noise and radio-frequency inhomogeneity noises. Conclusion: The main goal of this project is to help researchers in their future image processing analysis involving MRI relaxometry images, offering reliable and robust brain relaxometry simulation modelling. Furthermore, the entire pipeline is open-source, which provides a wide collaboration between researchers who may want to improve the software and its functionality.
Keywords
Relaxometry, Magnetic resonance imaging, Brain phantom, Simulation.
References
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Senra ACS, Fo, Barbosa JHO, Salmon CEG, Murta, LOM Jr. Anisotropic anomalous diffusion filtering applied to relaxation time estimation in Magnetic Resonance Imaging. In: EMBC 2014: Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2014 Aug 26-30; Chicago, USA. New York: IEEE; 2014. p. 3893-6. http://dx.doi.org/10.1109/EMBC.2014.6944474.
Van De Walle R, Barrett HH, Myers KJ, Altbach MI, Desplanques B, Gmitro AF, Cornelis J, Lemahieu I. Reconstruction of MR images from data acquired on a general nonregular grid by pseudoinverse calculation. IEEE Transactions on Medical Imaging. 2000; 19(12):1160-7. PMid:11212364. http://dx.doi.org/10.1109/42.897806.
Wansapura JP, Holland SK, Dunn RS, Ball WS Jr. NMR relaxation times in the human brain at 3.0 tesla. Journal of Magnetic Resonance Imaging. 1999; 9(4):531-8. PMid:10232510. http://dx.doi.org/10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L.
Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging data in FSL. NeuroImage. 2009; 45(1 Suppl):S173-86. PMid:19059349. http://dx.doi.org/10.1016/j.neuroimage.2008.10.055.
Burgetova A, Seidl Z, Krasensky J, Horakova D, Vaneckova M. Multiple sclerosis and the accumulation of iron in the Basal Ganglia: quantitative assessment of brain iron using MRI t(2) relaxometry. European Neurology. 2010; 63(3):136-43. PMid:20130410. http://dx.doi.org/10.1159/000279305.
Cárdenas-Blanco A, Tejos C, Irarrazaval P, Cameron I. Noise in magnitude magnetic resonance images. Concepts in Magnetic Resonance – Part A. 2008; 32(6):409-16. http://dx.doi.org/10.1002/cmr.a.20124.
Carneiro AAO, Vilela GR, Araujo DB, Baffa O. MRI relaxometry: methods and applications. Brazilian Journal of Physics. 2006; 36(1a):9-15. http://dx.doi.org/10.1590/S0103-97332006000100005.
Chau W, McIntosh AR. The Talairach coordinate of a point in the MNI space: how to interpret it. NeuroImage. 2005; 25(2):408-16. PMid:15784419. http://dx.doi.org/10.1016/j.neuroimage.2004.12.007.
Cheng HL, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. Journal of Magnetic Resonance Imaging. 2012; 36(4):805-24. PMid:22987758. http://dx.doi.org/10.1002/jmri.23718.
Cocosco CA, Kollokian V, Kwan RK, Pike GB, Evans AC. BrainWeb: online interface to a 3D MRI simulated brain database. In: Proceedings of 3rd International Conference on Functional Mapping of the Human Brain; 1997 May; Copenhagen, Denmark. Montréal: Springer; 1997. p. 1996.
Collins DL, Zijdenbos P, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans C. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging. 1998; 17(3):463-8. PMid:9735909. http://dx.doi.org/10.1109/42.712135.
Deoni SCL. Quantitative relaxometry of the brain. Topics in Magnetic Resonance Imaging. 2010; 21(2):101-13. PMid:21613875. http://dx.doi.org/10.1097/RMR.0b013e31821e56d8.
Drobnjak I, Gavaghan D, Pitt-Francis J, Jenkinson M. Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magnetic Resonance in Medicine. 2006; 56(2):364-80. PMid:16841304. http://dx.doi.org/10.1002/mrm.20939.
Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Lalezari S, Zaw T, Motevalibashinaeini K, Mischel PS, Pope WB. Quantification of edema reduction using differential quantitative T2 (DQT2) relaxometry mapping in recurrent glioblastoma treated with bevacizumab. Journal of Neuro-Oncology. 2012; 106(1):111-9. PMid:21706273. http://dx.doi.org/10.1007/s11060-011-0638-x.
Feng Y, He T, Feng M, Carpenter JP, Greiser A, Xin X, Chen W, Pennell DJ, Yang GZ, Firmin DN. Improved pixel-by-pixel MRI R2* relaxometry by nonlocal means. Magnetic Resonance in Medicine. 2014; 72(1):260-8. PMid:23963595. http://dx.doi.org/10.1002/mrm.24914.
Grabner G, Janke AL, Budge MM, Smith D, Pruessner J, Collins DL. Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. In: MICCAI 2006: 9th International Conference on Medical Image Computing and Computer Assisted Intervention; 2006 Oct 1-6; Copenhagen, Denmark. Montréal: Springer; 2006. p. 58-66. PMID:17354756.
Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance imaging: physical principles and sequence design. 4th ed. Hoboken: Wiley; 1999.
Hasan KM, Walimuni IS, Kramer LA, Narayana PA. Human brain iron mapping using atlas-based T2 relaxometry. Magnetic Resonance in Medicine. 2012; 67(3):731-9. PMid:21702065. http://dx.doi.org/10.1002/mrm.23054.
Hellerbach A, Schuster V, Jansen A, Sommer J. MRI phantoms: are there alternatives to Agar? PLoS One. 2013; 8(8):e70343. PMid:23940563. http://dx.doi.org/10.1371/journal.pone.0070343.
House MJ, St. Pierre, TG, Foster JK, Martins RN, Clarnette R. Quantitative MR imaging R2 relaxometry in elderly participants reporting memory loss. American Journal of Neuroradiology. 2006; 27(2):430-9. PMD 16484425.
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage. 2012; 62(2):782-90. PMid:21979382. http://dx.doi.org/10.1016/j.neuroimage.2011.09.015.
Koay CG, Sarlls JE, Özarslan E. Three-dimensional analytical magnetic resonance imaging phantom in the Fourier domain. Magnetic Resonance in Medicine. 2007; 58(2):430-6. PMid:17616967. http://dx.doi.org/10.1002/mrm.21292.
Kosior RK, Lauzon ML, Federico P, Frayne R. Algebraic T2 estimation improves detection of right temporal lobe epilepsy by MR T2 relaxometry. NeuroImage. 2011; 58(1):189-97. PMid:21689766. http://dx.doi.org/10.1016/j.neuroimage.2011.06.002.
Kumar D, Nguyen TD, Gauthier SA, Raj A. Bayesian algorithm using spatial priors for multiexponential T 2 relaxometry from multiecho spin echo MRI. Magnetic Resonance in Medicine. 2012; 68(5):1536-43. PMid:22266707. http://dx.doi.org/10.1002/mrm.24170.
Lebel RM, Wilman AH. Transverse relaxometry with stimulated echo compensation. Magnetic Resonance in Medicine. 2010; 64(4):1005-14. PMid:20564587. http://dx.doi.org/10.1002/mrm.22487.
Popescu V, Battaglini M, Hoogstrate WS, Verfaillie SCJ, Sluimer IC, van Schijndel RA, van Dijk BW, Cover KS, Knol DL, Jenkinson M, Barkhof F, de Stefano N, Vrenken H. Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. NeuroImage. 2012; 61(4):1484-94. PMid:22484407. http://dx.doi.org/10.1016/j.neuroimage.2012.03.074.
Rykhlevskaia E, Gratton G, Fabiani M. Combining structural and functional neuroimaging data for studying brain connectivity: A review. Psychophysiology. 2008; 45(2):173-87. PMid:17995910. http://dx.doi.org/10.1111/j.1469-8986.2007.00621.x.
Senra ACS, Fo, Barbosa JHO, Salmon CEG, Murta, LOM Jr. Anisotropic anomalous diffusion filtering applied to relaxation time estimation in Magnetic Resonance Imaging. In: EMBC 2014: Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2014 Aug 26-30; Chicago, USA. New York: IEEE; 2014. p. 3893-6. http://dx.doi.org/10.1109/EMBC.2014.6944474.
Van De Walle R, Barrett HH, Myers KJ, Altbach MI, Desplanques B, Gmitro AF, Cornelis J, Lemahieu I. Reconstruction of MR images from data acquired on a general nonregular grid by pseudoinverse calculation. IEEE Transactions on Medical Imaging. 2000; 19(12):1160-7. PMid:11212364. http://dx.doi.org/10.1109/42.897806.
Wansapura JP, Holland SK, Dunn RS, Ball WS Jr. NMR relaxation times in the human brain at 3.0 tesla. Journal of Magnetic Resonance Imaging. 1999; 9(4):531-8. PMid:10232510. http://dx.doi.org/10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L.
Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging data in FSL. NeuroImage. 2009; 45(1 Suppl):S173-86. PMid:19059349. http://dx.doi.org/10.1016/j.neuroimage.2008.10.055.
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