FDG PET/MR Imaging in Major Neurocognitive Disorders

Author(s): Ismini C. Mainta, Daniela Perani, Benedicte M.A. Delattre, Frederic Assal, Sven Haller, Maria I. Vargas, Dina S. Zekry, Giovanni B. Frisoni, Habib Zaidi, Osman Ratib, Valentina Garibotto.

Journal Name: Current Alzheimer Research

Volume 14 , Issue 2 , 2017

  Journal Home
Translate in Chinese
Become EABM
Become Reviewer

Abstract:

PET/MRI tomographs represent the latest development in hybrid molecular imaging, opening new perspectives for clinical and research applications and attracting a large interest among the medical community. This new hybrid modality is expected to play a pivotal role in a number of clinical applications and among these the assessment of neurodegenerative disorders. PET and MRI, acquired separately, are already the imaging biomarkers of choice for a comprehensive assessment of the changes occurring in dementias (major cognitive disorders) as well as in their prodromal phase.

In this paper we review the current evidence on the use of integrated PET/MRI scanners to investigate patients with neurodegenerative conditions, and in particular major neurocognitive disorders. The number of studies performed is still limited and shows that the use of PET/MRI gives results overall comparable to PET/CT and MRI acquired independently. We also address the challenges for quantitative aspects in PET/MRI, namely attenuation, partial volume and motion correction and the use of semi-quantitative approaches for FDG PET image analysis in this framework.

The recent development of PET tracers for the in vivo differential diagnosis of dementias, able to visualize amyloid and tau deposits, suggests that in the future PET/MRI might represent the investigation of choice for a single session evaluation of morphological, functional and molecular markers.

Keywords: PET, fluorodeoxyglucose, MRI, hybrid imaging, statistical parametric mapping, major cognitive disorders.

[1]
Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 9(1): 63-75. e2 (2013)
[2]
Dementia, Fact sheet No 362, USA: WHO. (2012)
[3]
Mosconi L, Murray J, Tsui WH, Li Y, Spector N, Goldowsky A, et al. Brain imaging of cognitively normal individuals with 2 parents affected by late-onset AD. Neurology 82(9): 752-60. (2014)
[4]
Wallin AK, Andreasen N, Eriksson S, Batsman S, Nasman B, Ekdahl A, et al. Donepezil in Alzheimer’s disease: what to expect after 3 years of treatment in a routine clinical setting. Dement Geriatr Cogn Disord 23(3): 150-60. (2007)
[5]
Winblad B, Wimo A, Engedal K, Soininen H, Verhey F, Waldemar G, et al. 3-year study of donepezil therapy in Alzheimer’s disease: effects of early and continuous therapy. Dement Geriatr Cogn Disord 21(5-6): 353-63. (2006)
[6]
Zamrini E, De Santi S, Tolar M. Imaging is superior to cognitive testing for early diagnosis of Alzheimer’s disease. Neurobiol Aging 25(5): 685-91. (2004)
[7]
Atkinson AJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69(3): 89-95. (2001)
[8]
Nordberg A. Molecular imaging in Alzheimer’s disease: new perspectives on biomarkers for early diagnosis and drug development. Alzheimers Res Ther 3(6): 34. (2011)
[9]
Mosconi L. Glucose metabolism in normal aging and Alzheimer’s disease: methodological and physiological considerations for PET studies Clin Trans Imag 1(4): 217-33. (2013)
[10]
Perani D. FDG PET and cognitive symptoms of dementia. Clin Transl Imaging 1(4): 247-60. (2013)
[11]
Hitz S, Habekost C, Furst S, Delso G, Forste S, Ziegler S, et al. Systematic comparison of the performance of integrated whole-body PET/MR imaging to conventional PET CT for 18F-FDG brain imaging in patients examined for suspected dementia. J Nucl Med 55(6): 923-31. (2014)
[12]
Haller S, Garibotto V, Kovari E, Bouras C, Xekardaki A, Rodriguez C, et al. Neuroimaging of dementia in 2013: what radiologists need to know. Eur Radiol 23(12): 3393-404. (2013)
[13]
Sorbi S, Hort J, Erkinjuntti T, Fladby T, Gainotti G, Gurvit H, et al. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol 19(9): 1159-79. (2012)
[14]
Frisoni GB, Bocchetta M, Chetelat G, Rabinovici GD, de Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: which vs. how. Neurology 81(5): 487-500. (2013)
[15]
Jones DT, Machulda MM, Vemuri P, McDade EM, Zeng G, Senjem ML, et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology 77(16): 1524-31. (2011)
[16]
Sheline YI, Raichle ME, Snyder AZ, Morris JC, Head D, Wang S, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry 67(6): 584-7. (2010)
[17]
Werner P, Barthel H, Drzezga A, Sabri O. Current status and future role of brain PET/MRI in clinical and research settings. Eur J Nucl Med Mol Imaging 42(3): 512-26. (2015)
[18]
Griffa A, Baumann PS, Thiran JP, Hagmann P. Structural connectomics in brain diseases. Neuroimage 80: 515-26. (2013)
[19]
Haller S, Nguyen D, Rodriguez C, Emch J, Gold G, Bartsch A, et al. Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J Alzheimers Dis 22(1): 315-27. (2010)
[20]
Alsop DC, Dai W, Grossman M, Detre JA. Arterial spin labeling blood flow MRI: its role in the early characterization of Alzheimer’s disease. J Alzheimers Dis 20(3): 871-80. (2010)
[21]
Binnewijzend MA, Kuijer JP, Benedictus MR, an der Flier WM, Wink AM, Wattjes MP, et al. Cerebral blood flow measured with 3D pseudocontinuous arterial spin-labeling MR imaging in Alzheimer disease and mild cognitive impairment: a marker for disease severity. Radiology 267(1): 221-30. (2013)
[22]
Xekardaki A, Rodriguez C, Montandon ML, Toma S, Tombeur E, Herrmann FR, et al. Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology 274(2): 490-9. (2015)
[23]
Toledo JB, Arnold SE, Raible K, Brettschneider J, Xie SX, Grossman M, et al. Contribution of cerebrovascular disease in autopsy confirmed neurodegenerative disease cases in the National Alzheimer’s Coordinating Centre. Brain 136(Pt 9): 2697-706. (2013)
[24]
Viswanathan A, Rocca WA, Tzourio C. Vascular risk factors and dementia: how to move forward? Neurology 72(4): 368-74. (2009)
[25]
Baskin A, Giannakopoulos P, Ratib O, Seimbille Y, Assal F, Perani D, et al. PET radiotracers for molecular imaging in dementia. Curr Radiopharm 6(4): 215-30. (2013)
[26]
Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3): 270-9. (2011)
[27]
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3): 263-9. (2011)
[28]
Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3): 280-92. (2011)
[29]
Catana C, Drzezga A, Heiss WD, Rosen BR. PET/MRI for neurologic applications. J Nucl Med 53(12): 1916-25. (2012)
[30]
Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia. Neuroimage Clin 7187-94. (2015)
[31]
Drzezga A. Diagnosis of Alzheimer’s disease with [18F]PET in mild and asymptomatic stages. Behav Neurol 21(1): 101-15. (2009)
[32]
Silverman DH, Small GW, Chang CY, Lu CS, Kung De Aburto MA, Chen W, et al. Positron emission tomography in evaluation of dementia: Regional brain metabolism and long-term outcome. JAMA 286(17): 2120-7. (2001)
[33]
Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin •••: 6445-54. (2014)
[34]
Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 49(3): 390-8. (2008)
[35]
Anchisi D, Borroni B, Franceschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, et al. Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol 62(11): 1728-33. (2005)
[36]
Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N, Siebner H, Alexopoulus P, et al. Prediction of individual clinical outcome in MCI by means of genetic assessment and (18)F-FDG PET. J Nucl Med 46(10): 1625-32. (2005)
[37]
Laforce R Jr, Buteau JP, Paquet N, Verret L, Houde M, Bouchard RW. The value of PET in mild cognitive impairment, typical and atypical/unclear dementias: A retrospective memory clinic study. Am J Alzheimers Dis Other Demen 25(4): 324-32. (2010)
[38]
Nasrallah IM, Wolk DA. Multimodality imaging of Alzheimer disease and other neurodegenerative dementias. J Nucl Med 55(12): 2003-11. (2014)
[39]
Perani D. FDG-PET and amyloid-PET imaging: the diverging paths. Curr Opin Neurol 27(4): 405-13. (2014)
[40]
Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA 305(3): 275-83. (2011)
[41]
Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol 10(5): 424-35. (2011)
[42]
Nordberg A, Carter SF, Rinne J, Drzezga A, Brooks DJ, Vandenberghe R, et al. A European multicentre PET study of fibrillar amyloid in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 40(1): 104-14. (2013)
[43]
Rowe CC, Villemagne VL. Brain amyloid imaging. J Nucl Med 52(11): 1733-40. (2011)
[44]
Fu L, Liu L, Zhang J, Xu B, Fan Y, Tian J. Comparison of dual-biomarker PIB-PET and dual-tracer PET in AD diagnosis. Eur Radiol 24(11): 2800-9. (2014)
[45]
Hsiao IT, Huang CC, Hsieh CJ, Hsu WC, Wey SP, Yen TC, et al. Correlation of early-phase 18F-florbetapir (AV-45/Amyvid) PET images to FDG images: preliminary studies. Eur J Nucl Med Mol Imaging 39(4): 613-20. (2012)
[46]
Wolk DA, Klunk W. Update on amyloid imaging: from healthy aging to Alzheimer’s disease. Curr Neurol Neurosci Rep 9(5): 345-52. (2009)
[47]
Johnson KA, Minoshima S, Bohnen NI, Donohoe KJ, Foster NL, Herscovitch P, et al. Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. J Nucl Med 54(3): 476-90. (2013)
[48]
Rowe CC, Ng S, Ackermann U, Gong SJ, Pike K, Savage G, et al. Imaging beta-amyloid burden in aging and dementia. Neurology 68(20): 1718-25. (2007)
[49]
Schlemmer HP, Pichler BJ, Schmand M, Burbar Z, Michel C, Ladebeck R, et al. Simultaneous MR/PET imaging of the human brain: feasibility study. Radiology 248(3): 1028-35. (2008)
[50]
Beyer T, Pichler B. A decade of combined imaging: from a PET attached to a CT to a PET inside an MR. Eur J Nucl Med Mol Imaging 36: 1S1-2. (2009)
[51]
Catana C, Wu Y, Judenhofer MS, Qi J, Pichler BJ, Cherry SR. Simultaneous acquisition of multislice PET and MR images: initial results with a MR-compatible PET scanner. J Nucl Med 47(12): 1968-76. (2006)
[52]
Judenhofer MS, Wehrl HF, Newport DF, Catana C, Siegel SB, Becker M, et al. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat Med 14(4): 459-65. (2008)
[53]
Zaidi H, Del Guerra A. An outlook on future design of hybrid PET/MRI systems. Med Phys 38(10): 5667-89. (2011)
[54]
Torigian DA, Zaidi H, Kwee TC, Saboury B, Udupa JK, Cho ZH, et al. PET/MR imaging: technical aspects and potential clinical applications. Radiology 267(1): 26-44. (2013)
[55]
Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z, Maniawski P, et al. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys Med Biol 56(10): 3091-106. (2011)
[56]
Veit-Haibach P, Kuhn FP, Wiesinger F, Delso G, von Schulthess G. PET-MR imaging using a tri-modality PET/CT-MR system with a dedicated shuttle in clinical routine. MAGMA 26(1): 25-35. (2013)
[57]
Disselhorst JA, Bezrukov I, Kolb A, Parl C, Pichler BJ. Principles of PET/MR Imaging. J Nucl Med 55(2): 2S-10S. (2014)
[58]
von Schulthess GK, Schlemmer HP. A look ahead: PET/MR versus PET/CT. Eur J Nucl Med Mol Imaging 36: 1S3-9. (2009)
[59]
Kolb A, Sauter AW, Eriksson L, Vandenbrouke A, Liu CC, Levin C, et al. Shine-Through in PET/MR Imaging: effects of the magnetic field on positron range and subsequent image artifacts. J Nucl Med 56(6): 951-4. (2015)
[60]
Cho ZH, Son YD, Kim HK, Kim ST, Lee SY, Chi JG, et al. Substructural hippocampal glucose metabolism observed on PET/MRI. J Nucl Med 51(10): 1545-8. (2010)
[61]
Jung JH, Choi Y, Jung J, Kim S, Lim HK, Im KC, et al. Development of PET/MRI with insertable PET for simultaneous PET and MR imaging of human brain. Med Phys 42(5): 2354. (2015)
[62]
Zaidi H, Montandon M-L, Alavi A. Advances in Attenuation Correction Techniques in PET. PET Clin 2(2): 191-217. (2007)
[63]
Wagenknecht G, Kaiser HJ, Mottaghy FM, Herzog H. MRI for attenuation correction in PET: methods and challenges. MAGMA 26(1): 99-113. (2013)
[64]
Hofmann M, Pichler B, Scholkopf B, Beyer T. Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques. Eur J Nucl Med Mol Imaging 36: 1S93-04. (2009)
[65]
Hofmann M, Steinke F, Schecl V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med 49(11): 1875-83. (2008)
[66]
Kops ER, Herzog H. Template based Attenuation Correction for PET in MR-PET Scanners Nucl Sci Symp 3786-. (2008)
[67]
Montandon ML, Zaidi H. Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging. Neuroimage 25(1): 278-86. (2005)
[68]
Rota Kops E, H Herzog. Alternative Methods for Attenuation Correction for PET. Images in MR-PET Scanners 4327-30. (2007)
[69]
Malone IB, Ansorge RE, Williams GB, Nestor PJ, Carpenter TA, Fryer TD. Attenuation correction methods suitable for brain imaging with a PET/MRI scanner: a comparison of tissue atlas and template attenuation map approaches. J Nucl Med 52(7): 1142-9. (2011)
[70]
Burgo N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging 33(12): 2332-41. (2014)
[71]
Schulz V, Torres-Espallardo I, Renisch S, Hu Z, Ojha N, Bornert P, et al. Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data. Eur J Nucl Med Mol Imaging 38(1): 138-52. (2011)
[72]
Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd’hotel C, Ziegler SI, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med 50(4): 520-6. (2009)
[73]
Andersen FL, Ladefoged CN, Beyer T, Keller SH, Hansen AE, Hojgaard L, et al. Combined PET/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone. Neuroimage 84: 206-16. (2014)
[74]
Catana C, van der Kouwe A, Benner T, Michel CJ, Hamm M, Fenchel M, et al. Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med 51(9): 1431-8. (2010)
[75]
Zaidi H, Montandon ML, Slosman DO. Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography. Med Phys 30(5): 937-48. (2003)
[76]
Wagenknecht G, Rota Kops E, Kaffanke J, Tellmann L, Mottaghy F, Piroth M, et al. CT-based evaluation of segmented head regions for attenuation correction in MR-PET systems. Knoxville, TN IEEE (2010); 2793-7.
[77]
Wagenknecht G, Rota Kops E, Tellmann L, Herzog H. Knowledge-based segmentation of attenuation-relevant regions of the head in T1-weighted MR images for attenuation correction in MR/PET systems. Orlando, FL IEEE (2009); 3338-43.
[78]
Berker Y, Franke J, Salomon A, Palmowski M, Donker HC, Temur Y, et al. MRI-based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence. J Nucl Med 53(5): 796-804. (2012)
[79]
Keereman V, Fierens Y, Broux T, De Deene Y, Lonneux M, Vandenberghe S. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med 51(5): 812-8. (2010)
[80]
Boellaard R, Hofman MB, Hoekstra OS, Lammertsma AA. Accurate PET/MR quantification using time of flight MLAA image reconstruction. Mol Imaging Biol 16(4): 469-77. (2014)
[81]
Mehranian A, Zaidi H. Clinical assessment of emission- and segmentation-based MRI-guided attenuation correction in whole-body TOF PET/MRI. J Nucl Med 56(6): 877-83. (2015)
[82]
Rezaei A, Defrise M, Bal G, Michel C, Conti M, Watson C, et al. Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans Med Imaging 31(12): 2224-33. (2012)
[83]
Tsoumpas C, Mackewn JE, Halsted P, King AP, Buerger C, Totman JJ, et al. Simultaneous PET-MR acquisition and MR-derived motion fields for correction of non-rigid motion in PET. Ann Nucl Med 24(10): 745-50. (2010)
[84]
Ouyang J, Li Q, El Fakhri G. Magnetic resonance-based motion correction for positron emission tomography imaging. Semin Nucl Med 43(1): 60-7. (2013)
[85]
Green MV, Seidel J, Stein SD, Tedder TE, Kempner KM, Kertzman C, et al. Head movement in normal subjects during simulated PET brain imaging with and without head restraint. J Nucl Med 35(9): 1538-46. (1994)
[86]
Pilipuf MN, Goble JC, Kassell NF. A noninvasive thermoplastic head immobilization system. Technical note. J Neurosurg 82(6): 1082-5. (1995)
[87]
Picard Y, Thompson CJ. Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imaging 16(2): 137-44. (1997)
[88]
Catana C, Benner T, van der Kouwe A, Byars L, Hamm M, Chonde DB, et al. MRI-assisted PET motion correction for neurologic studies in an integrated MR-PET scanner. J Nucl Med 52(1): 154-61. (2011)
[89]
Chun SY, Reese TG, Ouyang J, Guerin B, Catana C, Zhu X, et al. MRI-based nonrigid motion correction in simultaneous PET/MRI. J Nucl Med 53(8): 1284-91. (2012)
[90]
Ullisch MG, Scheins JJ, Weirich C, Rota Kops E, Celik A, Tellmann L, et al. MR-based PET motion correction procedure for simultaneous MR-PET neuroimaging of human brain. PLoS One 7(11)e48149 (2012)
[91]
Haller S, Monsch AU, Richiardi J, Barkhof F, Kressig RW, Radue EW. Head motion parameters in fMRI differ between patients with mild cognitive impairment and Alzheimer disease versus elderly control subjects. Brain Topogr 27(6): 801-7. (2014)
[92]
Zaidi H, Montandon M-L, Assal F. Structure-function–based quantitative brain image analysis. PET Clin 5(2): 155-68. (2010)
[93]
Garibotto V, Forster S, Haller S, Vargas MI, Drzezga A. Molecular Neuroimaging with PET/MRI. Clin Transl Imaging 1(1): 53-63. (2013)
[94]
Ishii K, Willoch F, Minoshima S, Drzezga A, Ficaro EP, Cross DJ, et al. Statistical brain mapping of 18F-FDG PET in Alzheimer’s disease: validation of anatomic standardization for atrophied brains. J Nucl Med 42(4): 548-57. (2001)
[95]
Nishimiya M, Matsuda H, Imabayashi E, Kuji I, Sato N. Comparison of SPM and NEUROSTAT in voxelwise statistical analysis of brain SPECT and MRI at the early stage of Alzheimer’s disease. Ann Nucl Med 22(10): 921-7. (2008)
[96]
Roland PE, Graufelds CJ. Human brain atlas: For high-resolution functional and anatomical mapping. Hum Brain Mapp 1(3): 173-84. (1994)
[97]
Friston KJ, Holmes AP, Worsley KJ, Poline J-P, Frith CD, Frackowiak RSJ. Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp 2(4): 189-210. (1995)
[98]
Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 12(4): 575-93. (2014)
[99]
Pagani M, Salmaso D, Borbely K. Optimisation of statistical methodologies for a better diagnosis of neurological and psychiatric disorders by means of SPECT. Nucl Med Rev Cent East Eur 8(2): 140-9. (2005)
[100]
Minoshima S, Berger KL, Lee KS, Mintun MA. An automated method for rotational correction and centering of three-dimensional functional brain images. J Nucl Med 33(8): 1579-85. (1992)
[101]
Van Laere KJ, Warwick J, Versijpt J, Goethals I, Audenaert K, Van Heerden B, et al. Analysis of clinical brain SPECT data based on anatomic standardization and reference to normal data: an ROC-based comparison of visual, semiquantitative, and voxel-based methods. J Nucl Med 43(4): 458-69. (2002)
[102]
Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17(1): 302-16. (2002)
[103]
Haense C, Herholz K, Jagust WJ, Heiss WD. Performance of FDG PET for detection of Alzheimer’s disease in two independent multicentre samples (NEST-DD and ADNI). Dement Geriatr Cogn Disord 28(3): 259-66. (2009)
[104]
Hosaka K, Ishii K, Sakamoto S, Sadato N, Fukuda H, Kato T, et al. Validation of anatomical standardization of FDG PET images of normal brain: comparison of SPM and NEUROSTAT. Eur J Nucl Med Mol Imaging 32(1): 92-7. (2005)
[105]
von Borczyskowski D, Wilke F, Martin B, Brenner W, Clausen M, Mester J, et al. Evaluation of a new expert system for fully automated detection of the Alzheimer’s dementia pattern in FDG PET. Nucl Med Commun 27(9): 739-43. (2006)
[106]
Garibotto V, Heinzer S, Vulliemoz S, Guignard R, Wissmeyer M, Seeck M, et al. Clinical applications of hybrid PET/MRI in neuroimaging. Clin Nucl Med 38(1): e13-8. (2013)
[107]
Schwenzer NF, Stegger L, Bisdas S, Schraml C, Kolb A, Boss A, et al. Simultaneous PET/MR imaging in a human brain PET/MR system in 50 patients--current state of image quality. Eur J Radiol 81(11): 3472-8. (2012)
[108]
Jena A, Taneja S, Goel R, Renjen P, Negi P. Reliability of semiquantitative (1)(8)F-FDG PET parameters derived from simultaneous brain PET/MRI: a feasibility study. Eur J Radiol 83(7): 1269-74. (2014)
[109]
Moodley KK, Perani D, Minati L, Della Rosa PA, Pennycook F, Dickson JC, et al. Simultaneous PET-MRI studies of the concordance of atrophy and hypometabolism in syndromic variants of Alzheimer’s disease and frontotemporal dementia: an extended case series. J Alzheimers Dis 46(3) (2015)
[110]
Drzezga A, Barthel H, Minoshima S, Sabri O. Potential clinical applications of PET/MR imaging in neurodegenerative diseases. J Nucl Med 55(2): 47S-55S. (2014)
[111]
Barthel H, Schroeter ML, Hoffmann KT, O Sabri. PET/MR in dementia and other neurodegenerative diseases. Semin Nucl Med 45(3): 224-33. (2015)
[112]
Zhang K, Herzog H, Mauler J, Filss C, Okell TW, Kops ER, et al. Comparison of cerebral blood flow acquired by simultaneous [15O]water positron emission tomography and arterial spin labeling magnetic resonance imaging. J Cereb Blood Flow Metab 34(8): 1373-80. (2014)
[113]
Aiello M, Salvatore E, Cachia A, Pappata S, Cavaliere C, Prinster A, et al. Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study. Neuroimage 113111-21. (2015)
[114]
Andersen JB, Henning WS, Lindberg U, Ladefoged CN, Hojgaard L, Greisen G, et al. Positron emission tomography/magnetic resonance hybrid scanner imaging of cerebral blood flow using O-water positron emission tomography and arterial spin labeling magnetic resonance imaging in newborn piglets. J Cereb Blood Flow Metab 35(11): 1703-10. (2015)
[115]
Vitali P, Maccagnano E, Caverzasi E, Henry RG, Haman A, Torres-Chae C, et al. Diffusion-weighted MRI hyperintensity patterns differentiate CJD from other rapid dementias. Neurology 76(20): 1711-9. (2011)
[116]
Kantarci K, Petersen RC, Boeve BF, Knopman DS, Weigand SD, O’Brien PC, et al. DWI predicts future progression to Alzheimer disease in amnestic mild cognitive impairment. Neurology 64(5): 902-4. (2005)
[117]
Ray KM, Wang H, Chu Y, Chen YF, Bert A, Hasso AN, et al. Mild cognitive impairment: apparent diffusion coefficient in regional gray matter and white matter structures. Radiology 241(1): 197-205. (2006)
[118]
Graff-Radford J, Kantarci K. Magnetic resonance spectroscopy in Alzheimer’s disease. Neuropsychiatr Dis Treat 9687-96. (2013)
[119]
Villemagne VL, Fodero-Tavoletti MT, Masters CL, Rowe CC. Tau imaging: early progress and future directions. Lancet Neurol 14(1): 114-24. (2015)
[120]
Wehner J, Weissler B, Dueppenbecker P, Gebhardt P, Schug D, Ruetten W, et al. PET/MRI insert using digital SiPMs: Investigation of MR-compatibility. Nucl Instrum Methods Phys Res A 734116-21. (2014)
[121]
Olcott P, Kim E, Hong K, Lee BJ, Grant AM, Chang CM, et al. Prototype positron emission tomography insert with electro-optical signal transmission for simultaneous operation with MRI. Phys Med Biol 60(9): 3459-78. (2015)


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 14
ISSUE: 2
Year: 2017
Page: [186 - 197]
Pages: 12
DOI: 10.2174/1567205013666160620115130
Price: $58

Article Metrics

PDF: 34
HTML: 8
EPUB: 1
PRC: 1

Special-new-year-discount