Methods for molecular imaging of brain tumours in a hybrid MR-PET context: Water content, T2∗, diffusion indices and FET-PET

Methods - Tập 130 - Trang 135-151 - 2017
A.M. Oros-Peusquens1, R. Loução1, M. Zimmermann1, K.-J. Langen1,2, N.J. Shah1,3
1Institute of Neuroscience and Medicine-4, Forschungszentrum Juelich, 52425 Juelich, Germany
2Clinic of Nuclear Medicine, University Hospital, RWTH Aachen University, 52074 Aachen, Germany
3Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, 52074 Aachen, Germany

Tài liệu tham khảo

Dunet, 2016, Performance of 18F-FET versus 18F-FDG-PET for the diagnosis and grading of brain tumors: systematic review and meta-analysis, Neuro-Oncology, 18, 426, 10.1093/neuonc/nov148 Langen, 2017, Advances in neuro-oncology imaging, Nat. Rev. Neurol., 13, 279, 10.1038/nrneurol.2017.44 Pauleit, 2005, O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas, Brain, 128, 678, 10.1093/brain/awh399 Pauleit, 2009, Comparison of (18)F-FET and (18)F-FDG PET in brain tumors, Nucl. Med. Biol., 36, 779, 10.1016/j.nucmedbio.2009.05.005 Lescher, 2015, Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: earlier detection of tumor progression compared to conventional MRI, Neuroradiology, 57, 11, 10.1007/s00234-014-1445-9 Müller, 2017, Quantitative T1-mapping detects cloudy-enhancing tumor compartments predicting outcome of patients with glioblastoma, Cancer Med., 6, 89, 10.1002/cam4.966 Louis, 2016, The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary, Acta Neuropathol., 131, 803, 10.1007/s00401-016-1545-1 Bertossi, 1997, Ultrastructural and morphometric investigation of human brain capillaries in normal and peritumoral tissues, Ultrastruct. Pathol., 21, 41, 10.3109/01913129709023246 Klatzo, 1987, Pathophysiological aspects of brain edema, Acta Neuropathol., 72, 236, 10.1007/BF00691095 Kuroiwa, 1985, Role of extracellular proteins in the dynamics of vasogenic brain edema, Acta Neuropathol., 66, 3, 10.1007/BF00698288 Wick, 2004, Brain edema in neurooncology: radiological assessment and management, Onkologie, 27, 261 Strugar, 1994, Vascular permeability factor in brain metastases: correlation with vasogenic brain edema and tumor angiogenesis, J. Neurosurg., 81, 560, 10.3171/jns.1994.81.4.0560 Thapar, 1995, Brain edema, increased intracranial pressure, vascular effects, and other epiphenomena of human brain tumors, 163 Reulen, 1977, Role of pressure gradients and bulk flow in dynamics of vasogenic brain edema, J. Neurosurg., 46, 24, 10.3171/jns.1977.46.1.0024 Eidel, 2016, Automatic analysis of cellularity in glioblastoma and correlation with ADC using trajectory analysis and automatic nuclei counting, PLoS One, 11, e0160250, 10.1371/journal.pone.0160250 Shah, 2011, Measuring the absolute water content of the brain using quantitative MRI, Methods Mol. Biol., 711, 29, 10.1007/978-1-61737-992-5_3 Neeb, 2006, Fully-automated detection of cerebral water content changes: study of age- and gender-related H2O patterns with quantitative MRI, Neuroimage, 29, 910, 10.1016/j.neuroimage.2005.08.062 Neeb, 2006, A new method for fast quantitative mapping of absolute water content in vivo, Neuroimage, 31, 1156, 10.1016/j.neuroimage.2005.12.063 Neeb, 2008, Fast quantitative mapping of absolute water content with full brain coverage, Neuroimage, 42, 1094, 10.1016/j.neuroimage.2008.03.060 Volz, 2012, Correction of systematic errors in quantitative proton density mapping, Magn. Reson. Med., 68, 74, 10.1002/mrm.23206 Volz, 2012, Quantitative proton density mapping: correcting the receiver sensitivity bias via pseudo proton densities, Neuroimage, 63, 540, 10.1016/j.neuroimage.2012.06.076 Warntjes, 2007, Novel method for rapid, simultaneous T1, T2*, and proton density quantification, Magn. Reson. Med., 57, 528, 10.1002/mrm.21165 Beaulieu, 2002, The basis of anisotropic water diffusion in the nervous system – a technical review, NMR Biomed., 15, 435, 10.1002/nbm.782 Chen, 2013, The Correlation between Apparent Diffusion Coefficient and Tumor Cellularity in Patients: A Meta-Analysis. Hess C.P., ed., PLoS ONE, 8, e79008, 10.1371/journal.pone.0079008 Lu, 2003, Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors, AJNR Am. J. Neuroradiol., 24, 93 Lu, 2004, Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index, Radiology, 232, 22, 10.1148/radiol.2321030653 Pavlisa, 2009, The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema, Clin. Imaging, 33, 96, 10.1016/j.clinimag.2008.06.035 Provenzale, 2004, Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging, Radiology, 232, 451, 10.1148/radiol.2322030959 Wang, 2009, Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy, AJNR Am. J. Neuroradiol., 30, 203, 10.3174/ajnr.A1303 De Belder, 2012, Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema, J. Comput. Assist. Tomogr., 36, 577, 10.1097/RCT.0b013e318261e913 Jiang, 2014, The value of diffusion tensor imaging in differentiating high-grade gliomas from brain metastases: a systematic review and meta-analysis, PLoS One, 9, e112550, 10.1371/journal.pone.0112550 Bai, 2016, Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging, Radiology, 278, 496, 10.1148/radiol.2015142173 Van Cauter, 2012, Gliomas: diffusion kurtosis MR imaging in grading, Radiology, 263, 492, 10.1148/radiol.12110927 Raab, 2010, Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences, Radiology, 254, 876, 10.1148/radiol.09090819 Chung, 2012, Molecular imaging of water binding state and diffusion in breast cancer using diffuse optical spectroscopy and diffusion weighted MRI, J. Biomed. Opt., 17, 071304, 10.1117/1.JBO.17.7.071304 Chung, 2008, In vivo water state measurements in breast cancer using broadband diffuse optical spectroscopy, Phys. Med. Biol., 53, 6713, 10.1088/0031-9155/53/23/005 Galldiks, 2015, The use of dynamic O-(2-18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma, Neuro Oncol., 17, 1293 Oros-Peusquens, 2012, (2012) A 7min protocol for quantitative, whole-brain, accurate water mapping at 3T for neurological applications, Proc. Intl. Soc. Mag. Reson. Med., 20, 4270 A.M. Oros-Peusquens, et al., Fast and accurate water content and T2* mapping in brain tumours localised with FET-PET. Nuclear Inst. and Methods in Physics Research, A, 2014, 734, 185–190. doi: 10.1016/j.nima.2013.09.045. van Gelderen, 2012, Nonexponential T2* decay in white matter, Magn. Reson. Med., 67, 110, 10.1002/mrm.22990 H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24(6), 1933, 417. 37. A. Hyvarinen, J. Karhunen, E. Oja, Independent component analysis, New York: John Wiley & Sons; 2001, pp. 125–44. I.T. Jolliffe, Principal Component Analysis, 2nd ed. Springer Series in Statistics. Springer-Verlag New York, Inc., 2002, ISBN-13: 978-0387954424. Bydder, 2006, Noise reduction in multiple-echo data sets using singular value decomposition, Magn. Reson. Imaging, 24, 849, 10.1016/j.mri.2006.03.006 Oros-Peusquens, 2008, Magnetic field dependence of the distribution of NMR relaxation times in the living human brain, Magma, 21, 131, 10.1007/s10334-008-0107-5 Ernst, 1966, Application of Fourier transform to magnetic resonance spectroscopy, Rev. Sci. Instrum., 37, 93, 10.1063/1.1719961 J.R. Reichenbach, R. Venkatesan, D.A. Yablonskiy, M.R. Thompson, S. Lai, E.M. Haacke, Theory and application of static field inhomogeneity effects in gradient-echo imaging, J. Magn. Reson. Imaging, 1997, 7(2), 266-279. Review. Dahnke, 2005, Limits of detection of SPIO at 3.0T using T2 relaxometry, Magn. Reson. Med., 53, 1202, 10.1002/mrm.20435 An, 2002, Cerebral oxygen extraction fraction and cerebral venous blood volume measurements using MRI: effects of magnetic field variation, Magn. Reson. Med., 47, 958, 10.1002/mrm.10148 Bakker, 2008, Phase gradient mapping as an aid in the analysis of object-induced and system-related phase perturbations in MRI, Phys. Med. Biol., 53, N349, 10.1088/0031-9155/53/18/N02 Ashburner, 2005, Unified segmentation, NeuroImage, 26, 839, 10.1016/j.neuroimage.2005.02.018 Hopkins, 1986, Multiple field strength in vivo T1 and T2 for cerebrospinal fluid protons, Magn. Reson. Med., 3, 303, 10.1002/mrm.1910030214 Rooney, 2007, Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo, Magn. Reson. Med., 57, 308, 10.1002/mrm.21122 Tofts, 2008, Imaging cadavers: Cold FLAIR and noninvasive brain thermometry using CSF diffusion, Magn. Reson. Med., 59, 190, 10.1002/mrm.21456 Hamacher, 2002, Efficient routine production of the 18F-labelled amino acid O-2-18F fluoroethyl-L-tyrosine, Appl. Radiat. Isot., 57, 853, 10.1016/S0969-8043(02)00225-7 Langen, 2011, German guidelines for brain tumour imaging by PET and SPECT using labelled amino acids, Nuklearmedizin, 50, 167 H. Herzog et al., Nuklearmedizin 50(2) (2011) 74, doi: 10.3413/Nukmed-0347-10-09. Gideon, 1999, MR-visible brain water content in human acute stroke, Magn. Reson. Imaging, 17, 301, 10.1016/S0730-725X(98)00161-1 Pierpaoli, 1996, Toward a quantitative assessment of diffusion anisotropy, Magn. Reson. Med., 36, 893, 10.1002/mrm.1910360612 Manjón, 2013, Diffusion weighted image denoising using overcomplete local PCA, PLoS ONE, 8, e73021, 10.1371/journal.pone.0073021 Andersson, 2016, An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging, Neuroimage, 125, 1063, 10.1016/j.neuroimage.2015.10.019 Tabesh, 2011, Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging, Magn. Reson. Med., 65, 823, 10.1002/mrm.22655 Fieremans, 2011, White matter characterization with diffusional kurtosis imaging, NeuroImage., 58, 177, 10.1016/j.neuroimage.2011.06.006 A.S. Ribeiro, L.M. Lacerda, H.A. Ferreira, Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox. Tavano A, ed. PeerJ. 2015;3:e1078. doi: 10.7717/peerj.1078. Neto Henriques, 2015, United Diffusion Kurtosis Imaging (UDKI) Toolbox, Magma, 28, 511 Mezer, 2013, Quantifying the local tissue volume and composition in individual brains with MRI, Nat. Med.., 19, 1667, 10.1038/nm.3390 Besson, 1990, Are NMR brain changes in chronic alcoholism related to water content or structuring?, Alcohol Clin. Exp. Res., 14, 952, 10.1111/j.1530-0277.1990.tb01845.x Laule, 2004, Water content and myelin water fraction in multiple sclerosis. A T2 relaxation study, J. Neurol., 251, 284, 10.1007/s00415-004-0306-6 Shah, 2008, Quantitative cerebral water content mapping in hepatic encephalopathy, Neuroimage, 41, 706, 10.1016/j.neuroimage.2008.02.057 Smith, 1985, Brain water in chronic alcoholic patients measured by magnetic resonance imaging, Lancet, 325, 1273, 10.1016/S0140-6736(85)92339-6 Winney, 1986, Changes in brain water with haemodialysis, Lancet, 328, 1107, 10.1016/S0140-6736(86)90516-7 Abbas, 2014, Analysis of proton-density bias corrections based on T1 measurement for robust quantification of water content in the brain at 3 Tesla, Magn. Reson. Med., 72, 1735, 10.1002/mrm.25086 Salimi-Khorshidi, 2014, Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers, Neuroimage, 90, 449, 10.1016/j.neuroimage.2013.11.046 Veraart, 2016, Denoising of diffusion MRI using random matrix theory, NeuroImage, 142, 394, 10.1016/j.neuroimage.2016.08.016 Koay, 2006, Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, J. Magn. Reson., 179, 317, 10.1016/j.jmr.2006.01.016 Raya, 2010, T2 measurement in articular cartilage: impact of the fitting method on accuracy and precision at low SNR, Magn. Reson. Med., 63, 181 Lätt, 2013, Regional values of diffusional kurtosis estimates in the healthy brain, J. Magn. Reson. Imaging, 37, 610, 10.1002/jmri.23857 Schoenegger, 2009, Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma?, Eur. J. Neurol., 16, 874, 10.1111/j.1468-1331.2009.02613.x Wu, 2015, Peritumoral edema shown by MRI predicts poor clinical outcome in glioblastoma, World J. Surg. Oncol., 13, 97, 10.1186/s12957-015-0496-7 Quail, 2013, Microenvironmental regulation of tumor progression and metastasis, Nat. Med., 19, 1423, 10.1038/nm.3394 Maedler, 2008, Is diffusion anisotropy an accurate monitor of myelination? Correlation of multicomponent T-2 relaxation and diffusion tensor anisotropy in human brain, Magn. Reson. Imaging, 26, 874, 10.1016/j.mri.2008.01.047 Billiet, 2014, (2014) Characterizing the microstructural basis of “unidentified bright objects” in neurofibromatosis type 1: a combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis, NeuroImage: Clin., 4, 649, 10.1016/j.nicl.2014.04.005 Maier, 2010, Diffusion imaging of brain tumors, NMR Biomed., 23, 849, 10.1002/nbm.1544 Paran, 2004, Water diffusion in the different microenvironments of breast cancer, NMR Biomed., 17, 170, 10.1002/nbm.882 Van As, 2009, MRI of intact plants, Photosynth. Res., 102, 213, 10.1007/s11120-009-9486-3 Eida, Sato, et al., Length of intact plasma membrane determines the diffusion properties of cellular water. Sci. Rep., 2016, 6.