Skip to main content
SpringerLink
Log in

Simultaneous PET and MR Imaging of the Human Brain

  • Chapter
  • First Online:
Functional Neuroradiology

  • 3675 Accesses

Abstract

Complementary anatomical and functional information obtained from separately performed magnetic resonance imaging (MRI) and positron emission tomography (PET) examinations have long been combined either by performing a parallel analysis or by using software co-registration techniques to merge the two datasets. However, a major assumption that has been made in these cases is that no changes in underlying conditions have occurred between the two studies; increasingly investigators are recognizing that this is not the case, particularly when assessing a subject’s mental state, which may change on the order of seconds, but also in some diseases such as acute ischemic stroke where physiological and metabolic changes can occur on the order of minutes. As we probe illnesses of the mind more thoroughly, the state of the mind and the state of the brain at any given instant become more relevant.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO. The global burden of disease: 2004 update.http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_part4.pdfLast accessed September 9, 2010.

  2. Townsend DW, Carney JP, Yap JT, Hall NC. PET/CT today and tomorrow. J Nucl Med. 2004;45 Suppl 1:4S–14S.

    PubMed  Google Scholar 

  3. Christensen NL, Hammer BE, Heil BG, Fetterly K. Positron emission tomography within a magnetic field using photomultiplier tubes and lightguides. Phys Med Biol. 1995;40(4):691–7.

    Article  PubMed  CAS  Google Scholar 

  4. Shao Y, Cherry SR, Farahani K, et al. Development of a PET detector system compatible with MRI/NMR systems. Nucl Sci IEEE Trans. 1997;44(3):1167–71.

    Article  CAS  Google Scholar 

  5. Mackewn JE, Strul D, Hallett WA, et al. Design and development of an MR-compatible PET scanner for imaging small animals. IEEE Trans Nucl Sci. 2005;52(5):1376–80.

    Article  Google Scholar 

  6. Raylman RR, Majewski S, Lemieux SK, et al. Simultaneous MRI and PET imaging of a rat brain. Phys Med Biol. 2006;51(24):6371–9.

    Article  PubMed  Google Scholar 

  7. Shao Y, Cherry SR, Farahani K, et al. Simultaneous PET and MR imaging. Phys Med Biol. 1997;42(10):1965–70.

    Article  PubMed  CAS  Google Scholar 

  8. Garlick PB, Marsden PK, Cave AC, et al. PET and NMR dual acquisition (PANDA): applications to isolated, perfused rat hearts. NMR Biomed. 1997;10(3):138–42.

    Article  PubMed  CAS  Google Scholar 

  9. Raylman RR, Majewski S, Velan SS, et al. Simultaneous ­acquisition of magnetic resonance spectroscopy (MRS) data and positron emission tomography (PET) images with a prototype MR-compatible, small animal PET imager. J Magn Reson. 2007;186(2):305–10.

    Article  PubMed  CAS  Google Scholar 

  10. Lucas AJ, Hawkes RC, Ansorge RE, et al. Development of a combined microPET((R))-MR system. Technol Cancer Res Treat. 2006;5(4):337–41.

    PubMed  CAS  Google Scholar 

  11. Pichler B, Lorenz E, Mirzoyan R, et al. Performance test of a LSO-APD PET module in a 9.4 Tesla magnet. Paper presented at: Nuclear Science Symposium, 1997, IEEE, 1997.

    Google Scholar 

  12. Pichler BJ, Pimpl W, Buttler W, et al. Integrated low-noise low-power fast charge-sensitive preamplifier for avalanche photodiodes in JFET-CMOS technology. Nucl Sci IEEE Trans. 2001;48(6):2370–4.

    Article  Google Scholar 

  13. 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. 2006;47(12):1968–76.

    PubMed  Google Scholar 

  14. Judenhofer MS, Catana C, Swann BK, et al. PET/MR images acquired with a compact MR-compatible PET detector in a 7-T magnet. Radiology. 2007;244(3):807–14.

    Article  PubMed  Google Scholar 

  15. Woody C, Schlyer D, Vaska P, et al. Preliminary studies of a simultaneous PET/MRI scanner based on the RatCAP small animal tomograph. Nucl Instrum Meth Phys Res A Accel Spectrom Dect Assoc Equip. 2007;571(1–2):102–5.

    Article  CAS  Google Scholar 

  16. Catana C, Procissi D, Wu Y, et al. Simultaneous in vivo positron emission tomography and magnetic resonance imaging. Proc Natl Acad Sci USA. 2008;105(10):3705–10.

    Article  PubMed  CAS  Google Scholar 

  17. Judenhofer MS, Wehrl HF, Newport DF, et al. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat Med. 2008;14(4):459–65.

    Article  PubMed  CAS  Google Scholar 

  18. Schlemmer H-PW, Pichler BJ, Schmand M, et al. Simultaneous MR/PET Imaging of the Human Brain: Feasibility Study. Radiology. 2008;248(3):1028–35.

    Article  PubMed  Google Scholar 

  19. Catana C, van der Kouwe AJW, Benner T, et al. Towards Implementing an MR-based PET Attenuation Correction Method for Neurological Studies on the MR-PET Brain Prototype. The Journal of Nuclear Medicine. 2010:in press.

    Google Scholar 

  20. Hong IK, Chung ST, Kim HK, Kim YB, Son YD, Cho ZH. Ultra fast symmetry and SIMD-based projection-backprojection (SSP) algorithm for 3-D PET image reconstruction. IEEE Trans Med Imaging. 2007;26(6):789–803.

    Article  PubMed  CAS  Google Scholar 

  21. Byars LG, Sibomana M, Burbar Z. et al. Proc IEEE Medical Imaging Conf: Variance reduction on randoms from delayed coincidence histograms for the HRRT; 2005.

    Google Scholar 

  22. Watson CC. New, faster, image-based scatter correction for 3D PET. IEEE Trans Nucl Sci. 2000;47(4):1587–94.

    Article  Google Scholar 

  23. Bergstrom M, Litton J, Eriksson L, Bohm C, Blomqvist G. Determination of object contour from projections for attenuation correction in cranial positron emission tomography. J Comput Assist Tomogr. 1982;6(2):365–72.

    Article  PubMed  CAS  Google Scholar 

  24. Michel C, Bol A, Devolder AG, Goffinet AM. Online brain attenuation correction in PET – towards a fully automated data handling in a clinical environment. Eur J Nucl Med. 1989;15(11):712–8.

    Article  PubMed  CAS  Google Scholar 

  25. Siegel S, Dahlbom M. Implementation and evaluation of a calculated attenuation correction for PET. IEEE Trans Nucl Sci. 1992;39(4):1117–21.

    Article  CAS  Google Scholar 

  26. Weinzapfel BT, Hutchins GD. Automated PET attenuation correction model for functional brain imaging. J Nucl Med. 2001;42(3):483–91.

    PubMed  CAS  Google Scholar 

  27. Bailey DL. Transmission scanning in emission tomography. Eur J Nucl Med. 1998;25(7):774–87.

    Article  PubMed  CAS  Google Scholar 

  28. Carroll LR, Kretz P, Orcutt G. The orbiting rod source – improving performance in PET transmission correction scans. J Nucl Med. 1983;24(5):P11–1.

    Google Scholar 

  29. Dekemp RA, Nahmias C. Attenuation correction in PET using single-photon transmission measurement. Med Phys. 1994;21(6):771–8.

    Article  PubMed  CAS  Google Scholar 

  30. Karp JS, Muehllehner G, Qu H, Yan XH. Singles transmission in volume-imaging PET with a Cs-137 source. Phys Med Biol. 1995;40(5):929–44.

    Article  PubMed  CAS  Google Scholar 

  31. Carson RE, Daubewitherspoon ME, Green MV. A method for postinjection PET transmission measurements with a rotating source. J Nucl Med. 1988;29(9):1558–67.

    PubMed  CAS  Google Scholar 

  32. Thompson CJ, Ranger N, Evans AC, Gjedde A. Validation of simultaneous PET emission and transmission scans. J Nucl Med. 1991;32(1):154–60.

    PubMed  CAS  Google Scholar 

  33. Kinahan PE, Hasegawa BH, Beyer T. X-ray-based attenuation correction for positron emission tomography/computed tomography scanners. Semin Nucl Med. 2003;33(3):166–79.

    Article  PubMed  Google Scholar 

  34. Ay MR, Zaidi H. Assessment of errors caused by X-ray scatter and use of contrast medium when using CT-based attenuation correction in PET. Eur J Nucl Med Mol Imaging. 2006;33(11):1301–13.

    Article  PubMed  Google Scholar 

  35. Zaidi H, Montandon ML, Slosman DO. Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography. Med Phys. 2003;30(5):937–48.

    Article  PubMed  Google Scholar 

  36. Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–55.

    Article  PubMed  CAS  Google Scholar 

  37. Fischl B, Salat DH, van der Kouwe AJW, et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage. 2004;23:S69–84.

    Article  PubMed  Google Scholar 

  38. Zaidi H. Medical image segmentation: Quo Vadis. Comput Meth Programs Biomed. 2006;84(2–3):63–5.

    Article  Google Scholar 

  39. Montandon ML, Zaidi H. Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging. Neuroimage. 2005;25(1):278–86.

    Article  PubMed  Google Scholar 

  40. Hofmann M, Steinke F, Scheel V, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and Atlas registration. J Nucl Med. 2008;49(11):1875–83.

    Article  PubMed  Google Scholar 

  41. Rota Kops E, Herzog H. Template-based attenuation correction of PET in hybrid MR-PET scanners. Society of Nuclear Medicine Annual Meeting Abstracts. 2008;49(MeetingAbstracts_1):162P-c.

    Google Scholar 

  42. Reichert ILH, Robson MD, Gatehouse PD, et al. Magnetic resonance imaging of cortical bone with ultrashort TE pulse sequences. Magn Reson Imaging. 2005;23(5):611–8.

    Article  PubMed  Google Scholar 

  43. Robson MD, Bydder GM. Clinical ultrashort echo time imaging of bone and other connective tissues. NMR Biomed. 2006;19(7):765–80.

    Article  PubMed  Google Scholar 

  44. Catana C, van der Kouwe AJW, Benner T, et al. Rigid-body MR-assisted PET motion correction. Society of Nuclear Medicine, Annual Meeting. Toronto, Canada; 2009.

    Google Scholar 

  45. Bergstrom M, Boethius J, Eriksson L, Greitz T, Ribbe T, Widen L. Head fixation device for reproducible position alignment in transmission CT and positron emission tomography. J Comput Assist Tomogr. 1981;5(1):136–41.

    Article  PubMed  CAS  Google Scholar 

  46. Pilipuf MN, Goble JC, Kassell NF. A noninvasive thermoplastic head immobilization system – technical note. J Neurosurg. 1995;82(6):1082–5.

    Article  PubMed  CAS  Google Scholar 

  47. Picard Y, Thompson CJ. Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imaging. 1997;16(2):137–44.

    Article  PubMed  CAS  Google Scholar 

  48. Fulton RR, Meikle SR, Eberl S, Pfeiffer J, Constable CJ, Fulham MJ. Correction for head movements in positron emission tomography using an optical motion-tracking system. IEEE Trans Nucl Sci. 2002;49(1):116–23.

    Article  Google Scholar 

  49. Bloomfield PM, Spinks TJ, Reed J, et al. The design and implementation of a motion correction scheme for neurological PET. Phys Med Biol. 2003;48(8):959–78.

    Article  PubMed  Google Scholar 

  50. Carson RE, Barker WC, Liow J-S, Johnson CA. Design of a motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction for the HRRT. Nucl Sci Symp Conf Rec IEEE. 2003;5:3281–5.

    Google Scholar 

  51. Buhler P, Just U, Will E, Kotzerke J, van den Hoff J. An accurate method for correction of head movement in PET. IEEE Trans Med Imaging. 2004;23(9):1176–85.

    Article  PubMed  Google Scholar 

  52. Rahmim A, Bloomfield P, Houle S, et al. Motion compensation in histogram-mode and list-mode EM reconstructions: Beyond the event-driven approach. IEEE Trans Nucl Sci. 2004;51(5):2588–96.

    Article  Google Scholar 

  53. Herzog H, Tellmann L, Fulton R, et al. Motion artifact reduction on parametric PET images of neuroreceptor binding. J Nucl Med. 2005;46(6):1059–65.

    PubMed  Google Scholar 

  54. Raghunath N, Faber TL, Suryanarayanan S, Votaw JR. Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization. Phys Med Biol. 2009;54(3):813–29.

    Article  PubMed  CAS  Google Scholar 

  55. Olesen OV, Jørgensen MR, Paulsen RR, Højgaard L, Roed B, Larsen R. Structured light 3D tracking system for measuring motions in PET brain imaging. SPIE. 2010;Proceedings Vol. 7625.

    Google Scholar 

  56. Thesen S, Heid O, Mueller E, Schad LR. Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn Reson Med. 2000;44(3):457–63.

    Article  PubMed  CAS  Google Scholar 

  57. van der Kouwe AJ, Benner T, Dale AM. Real-time rigid body motion correction and shimming using cloverleaf navigators. Magn Reson Med. 2006;56(5):1019–32.

    Article  PubMed  Google Scholar 

  58. Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed-tomography. 1. Effect of object size. J Comput Assist Tomogr. 1979;3(3):299–308.

    Article  PubMed  CAS  Google Scholar 

  59. Mazziotta JC, Phelps ME, Plummer D, Kuhl DE. Quantitation in positron emission computed-tomography. 5. Physical-anatomical effects. J Comput Assist Tomogr. 1981;5(5):734–43.

    Article  PubMed  CAS  Google Scholar 

  60. Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48(6):932–45.

    Article  PubMed  Google Scholar 

  61. Meltzer CC, Leal JP, Mayberg HS, Wagner HN, Frost JJ. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14(4):561–70.

    Article  PubMed  CAS  Google Scholar 

  62. Mullergartner HW, Links JM, Prince JL, et al. Measurement of radiotracer concentration in brain gray-matter using positron emission tomography – MRI-based correction for partial volume effects. J Cereb Blood Flow Metab. 1992;12(4):571–83.

    Article  CAS  Google Scholar 

  63. Meltzer CC, Zubieta JK, Links JM, Brakeman P, Stumpf MJ, Frost JJ. MR-based correction of brain PET measurements for heterogeneous gray matter radioactivity distribution. J Cereb Blood Flow Metab. 1996;16(4):650–8.

    Article  PubMed  CAS  Google Scholar 

  64. Labbé C, Koepp M, Ashburner J, et al. Absolute PET quantification with correction for partial volume effects within cerebral structures. In: Carson RE D-WM, Herscovitch P, ed. Quantitative Functional Brain Imaging with Positron Emission Tomography. San Diego, CA: Academic Press; 1998:59-66.

    Google Scholar 

  65. Rousset OG, Ma YL, Evans AC. Correction for partial volume effects in PET: Principle and validation. J Nucl Med. 1998;39(5):904–11.

    PubMed  CAS  Google Scholar 

  66. Kirov AS, Piao JZ, Schmidtlein CR. Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology. Phys Med Biol. 2008;53(10):2577–91.

    Article  PubMed  CAS  Google Scholar 

  67. Tohka J, Reilhac A. Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method. Neuroimage. 2008;39(4):1570–84.

    Article  PubMed  Google Scholar 

  68. Boussion N, Le Rest CC, Hatt M, Visvikis D. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging. Eur J Nucl Med Mol Imaging. 2009;36(7):1064–75.

    Article  PubMed  CAS  Google Scholar 

  69. Boussion N, Hatt M, Lamare F, et al. A multiresolution image based approach for correction of partial volume effects in emission tomography. Phys Med Biol. 2006;51(7):1857–76.

    Article  PubMed  CAS  Google Scholar 

  70. Leahy R, Yan X. Incorporation of anatomical MR data for improved functional imaging with PET. In: Colchester DHaA, editor. Information Processing in Medical Imaging. New-York: Wiley-Liss; 1991. p. 105–20.

    Chapter  Google Scholar 

  71. Lipinski B, Herzog H, Kops ER, Oberschelp W, MullerGartner HW. Expectation maximization reconstruction of positron emission tomography images using anatomical magnetic resonance information. IEEE Trans Med Imaging. 1997;16(2):129–36.

    Article  PubMed  CAS  Google Scholar 

  72. Wang CH, Chen JC, Kao CM, Liu RS. Incorporation of correlated MR images in MAP reconstruction of PET images. J Nucl Med. 2003;44(5):278.

    Google Scholar 

  73. Wang CH, Chen JC, Liu RS. Development and evaluation of MRI based Bayesian image reconstruction methods for PET. Comput Med Imaging Graph. 2004;28(4):177–84.

    Article  PubMed  Google Scholar 

  74. Baete K, Nuyts J, Van Laere K, et al. Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET. Neuroimage. 2004;23(1):305–17.

    Article  PubMed  Google Scholar 

  75. Meltzer CC, Zubieta JK, Brandt J, Tune LE, Mayberg HS, Frost JJ. Regional hypometabolism in Alzheimer’s disease as measured by positron emission tomography after correction for effects of partial volume averaging. Neurology. 1996;47(2):454–61.

    PubMed  CAS  Google Scholar 

  76. Meltzer CC, Smith G, Price JC, et al. Reduced binding of [F-18]altanserin to serotonin type 2A receptors in aging: persistence of effect after partial volume correction. Brain Res. 1998;813(1):167–71.

    Article  PubMed  CAS  Google Scholar 

  77. Iida H, Law I, Pakkenberg B, et al. Quantitation of regional cerebral blood flow corrected for partial volume effect using O-15 water and PET: I. Theory, error analysis, and stereologic comparison. J Cereb Blood Flow Metab. 2000;20(8):1237–51.

    Article  PubMed  CAS  Google Scholar 

  78. Meltzer CC, Cantwell MN, Greer PJ, et al. Does cerebral blood flow decline in healthy aging? A PET study with partial-volume correction. J Nucl Med. 2000;41(11):1842–8.

    PubMed  CAS  Google Scholar 

  79. Rousset OG, Deep P, Kuwabara H, Evans AC, Gjedde AH, Cumming P. Effect of partial volume correction on estimates of the influx and cerebral metabolism of 6-[F-18]fluoro-L-dopa studied with PET in normal control and Parkinson’s disease subjects. Synapse. 2000;37(2):81–9.

    Article  PubMed  CAS  Google Scholar 

  80. Giovacchini G, Lerner A, Toczek MT, et al. Brain incorporation of C-11-arachidonic acid, blood volume, and blood flow in healthy aging: A study with partial-volume correction. J Nucl Med. 2004;45(9):1471–9.

    PubMed  CAS  Google Scholar 

  81. Bencherif B, Stumpf MJ, Links JM, Frost JJ. Application of MRI-based partial-volume correction to the analysis of PET images of mu-opioid receptors using statistical parametric mapping. J Nucl Med. 2004;45(3):402–8.

    PubMed  CAS  Google Scholar 

  82. Samuraki M, Matsunari I, Chen WP, et al. Partial volume effect-corrected FDG PET and grey matter volume loss in patients with mild Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2007;34(10):1658–69.

    Article  PubMed  Google Scholar 

  83. Rousset OG, Collins DL, Rahmim A, Wong DF. Design and implementation of an automated partial volume correction in PET: Application to dopamine receptor quantification in the normal human striatum. J Nucl Med. 2008;49(7):1097–106.

    Article  PubMed  Google Scholar 

  84. Thomas B, Thurfjell L, Rinne J, Ourselin S, Hutton B. The effects of iterative deconvolution partial-volume correction on beta-amyloid PET. J Cereb Blood Flow Metab. 2009;29:S337–8.

    Google Scholar 

  85. Goffin K, Van Paesschen W, Dupont P, et al. Anatomy-based reconstruction of FDG-PET images with implicit partial volume correction improves detection of hypometabolic regions in patients with epilepsy due to focal cortical dysplasia diagnosed on MRI. Eur J Nucl Med Mol Imaging. 2010;37(6):1148–55.

    Article  PubMed  Google Scholar 

  86. Meltzer CC, Kinahan PE, Greer PJ, et al. Comparative evaluation of MR-based partial-volume correction schemes for PET. J Nucl Med. 1999;40(12):2053–65.

    PubMed  CAS  Google Scholar 

  87. Rousset OG, Ma YL, Wong DF, Evans AC. Pixel- versus region-based partial volume correction in PET. In: Carson RED-WM, Herscovitch P, editors. Quantitative functional brain imaging with positron emission tomography. San Diego: Academic Press; 1998. p. 67–75.

    Chapter  Google Scholar 

  88. Strul D, Bendriem B. Robustness of anatomically guided pixel-by-pixel algorithms for partial volume effect correction in positron emission tomography. J Cereb Blood Flow Metab. 1999;19(5):547–59.

    Article  PubMed  CAS  Google Scholar 

  89. Zaidi H, Ruest T, Schoenahl F, Montandon ML. Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET. Neuroimage. 2006;32(4):1591–607.

    Article  PubMed  Google Scholar 

  90. Dhawan V, Takikawa S, Robeson W, et al. Quantitative brain FDG/PET studies using dynamic aortic imaging %Uhttp://stacks.iop.org/0031-9155/39/1475 Physics in Medicine and Biology; 1994:1475.

  91. Litton JE. Input function in PET brain studies using MR-defined arteries. J Comput Assist Tomogr. 1997;21(6):907–9.

    Article  PubMed  CAS  Google Scholar 

  92. Fang YH, Kao T, Liu RS, Wu LC. Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data. Eur J Nucl Med Mol Imaging. 2004;31(5):692–702.

    Article  PubMed  Google Scholar 

  93. Naganawa M, Kimura Y, Ishii K, Oda K, Ishiwata K, Matani A. Extraction of a plasma time-activity curve from dynamic brain PET images based on independent component analysis. IEEE Trans Biomed Eng. 2005;52(2):201–10.

    Article  PubMed  Google Scholar 

  94. de Geus-Oei L-F, Visser EP, Krabbe PFM, et al. Comparison of image-derived and arterial input functions for estimating the rate of glucose metabolism in therapy-monitoring 18F-FDG PET studies. J Nucl Med. 2006;47(6):945–9.

    PubMed  Google Scholar 

  95. Chen AK, Chen AX, Renaut AR, et al. Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images. Phys Med Biol. 2007;52:7055–71.

    Article  PubMed  CAS  Google Scholar 

  96. Yi-Gen WU. Noninvasive quantification of local cerebral metabolic rate of glucose for clinical application using positron emission tomography and F-fluoro-2-deoxy-D-glucose. J Cereb Blood Flow Metab. 2007;28(2):242–50.

    Article  CAS  Google Scholar 

  97. Guo H, Renaut RA, Chen K. An input function estimation method for FDG-PET human brain studies. Nucl Med Biol. 2007;34(5):483–92.

    Article  PubMed  CAS  Google Scholar 

  98. Jarrett BR, Gustafsson B, Kukis DL, Louie AY. Synthesis of Cu-64-labeled magnetic nanoparticles for multimodal imaging. Bioconjug Chem. 2008;19(7):1496–504.

    Article  PubMed  CAS  Google Scholar 

  99. Lee HY, Li Z, Chen K, et al. PET/MRI dual-modality tumor imaging using arginine-glycine-aspartic (RGD) – Conjugated radiolabeled iron oxide nanoparticles. J Nucl Med. 2008;49(8):1371–9.

    Article  PubMed  CAS  Google Scholar 

  100. Uppal R, Catana C, Ay I, et al. Simultaneous MR-PET imaging of thrombus with a fibrin-targeted dual MR-PET probe: a feasibility study. Radiology. in press.

    Google Scholar 

  101. Uppal R, Ay I, Dai GP, Kim YR, Sorensen AG, Caravan P. Molecular MRI of intracranial thrombus in a rat ischemic stroke model. Stroke. 2010;41(6):1271–7.

    Article  PubMed  Google Scholar 

  102. Louie AY, Huber MM, Ahrens ET, et al. In vivo visualization of gene expression using magnetic resonance imaging. Nat Biotechnol. 2000;18(3):321–5.

    Article  PubMed  CAS  Google Scholar 

  103. Frullano L, Catana C, Benner T, Sherry AD, Caravan P. Bimodal MR-PET Agent for Quantitative pH Imaging. Angew Chem-Int Edit. 49(13):2382–4.

    Google Scholar 

  104. Donahue M, Lu H, Jones C, Pekar J, van Zijl P. An account of the discrepancy between MRI and PET cerebral blood flow measures. A high-field MRI investigation. NMR Biomed. 2006;19:1043–54.

    Google Scholar 

  105. Kwong KK, Belliveau JW, Chesler DA, et al. Dynamic MRI of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA. 1992;89(12):5675–9.

    Article  PubMed  CAS  Google Scholar 

  106. CBTRUS. Primary Brain Tumors in the United States Statistical Report.http://wwwcbtrusorg/reports//2005-2006/2006reportpdf2005.

  107. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–96.

    Article  PubMed  CAS  Google Scholar 

  108. Valk PE, Mathis CA, Prados MD, Gilbert JC, Budinger TF. Hypoxia in human gliomas: demonstration by PET with fluorine-18-fluoromisonidazole. J Nucl Med. 1992;33(12):2133–7.

    PubMed  CAS  Google Scholar 

  109. Rampling R, Cruickshank G, Lewis A, Fitzsimmon S, Workman P. Direct measurement of PO2 distribution and bioreductive enzymes in human malignant brain tumors. Int J Radiat Oncol Biol Phys. 1994;29(3):427–31.

    Article  PubMed  CAS  Google Scholar 

  110. Plate KH, Mennel HD. Vascular morphology and angiogenesis in glial tumors. Exp Toxicol Pathol. 1995;47(2–3):89–94.

    PubMed  CAS  Google Scholar 

  111. Jain RK. Molecular regulation of vessel maturation. Nat Med. 2003;9(6):685–93.

    Article  PubMed  CAS  Google Scholar 

  112. Zagzag D, Hooper A, Friedlander DR, et al. In situ expression of angiopoietins in astrocytomas identifies angiopoietin-2 as an early marker of tumor angiogenesis. Exp Neurol. 1999;159(2):391–400.

    Article  PubMed  CAS  Google Scholar 

  113. Guo P, Hu B, Gu WS, et al. Platelet-derived growth factor-b enhances glioma angiogenesis by stimulating vascular endothelial growth factor expression in tumor endothelia and by promoting pericyte recruitment. Am J Pathol. 2003;162(4):1083–93.

    Article  PubMed  CAS  Google Scholar 

  114. Winkler F, Kozin SV, Tong RT, et al. Kinetics of vascular normalization by VEGFR2 blockade governs brain tumor response to radiation: Role of oxygenation, angiopoietin-1, and matrix metalloproteinases. Cancer Cell. 2004;6(6):553–63.

    PubMed  CAS  Google Scholar 

  115. Vredenburgh JJ, Desjardins A, Herndon II JE, et al. Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. J Clin Oncol. 2007;25(30):4722–9.

    Article  PubMed  CAS  Google Scholar 

  116. Sathornsumetee S, Cao Y, Marcello JE, et al. Tumor angiogenic and hypoxic profiles predict radiographic response and survival in malignant astrocytoma patients treated with bevacizumab and irinotecan. J Clin Oncol. 2008;26(2):271–8.

    Article  PubMed  CAS  Google Scholar 

  117. Hurwitz H, Fehrenbacher L, Novotny W, et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med. 2004;350(23):2335–42.

    Article  PubMed  CAS  Google Scholar 

  118. Sandler A, Gray R, Perry MC, et al. Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N Engl J Med. 2006;355(24):2542–50.

    Article  PubMed  CAS  Google Scholar 

  119. Jain RK, di Tomaso E, Duda DG, Loeffler JS, Sorensen AG, Batchelor TT. Angiogenesis in brain tumours. Nat Rev Neurosci. 2007;8(8):610–22.

    Article  PubMed  CAS  Google Scholar 

  120. Sorensen AG. Magnetic resonance as a cancer imaging biomarker. J Clin Oncol. 2006;24(20):3274–81.

    Article  PubMed  Google Scholar 

  121. Hylton N. Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. J Clin Oncol. 2006;24(20):3293–8.

    Article  PubMed  CAS  Google Scholar 

  122. Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic ­resonance imaging: A biomarker for treatment response in oncology. J Clin Oncol. 2007;25(26):4104–9.

    Article  PubMed  Google Scholar 

  123. Young RJ, Knopp EA. Brain MRI: tumor evaluation. J Magn Reson Imaging. 2006;24(4):709–24.

    Article  PubMed  Google Scholar 

  124. Medved M, Karczmar G, Yang C, et al. Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time. J Magn Reson Imaging. 2004;20(1):122–8.

    Article  PubMed  Google Scholar 

  125. Jackson A, Jayson GC, Li KL, et al. Reproducibility of quantitative dynamic contrast-enhanced MRI in newly presenting glioma. Br J Radiol. 2003;76(903):153–62.

    Article  PubMed  CAS  Google Scholar 

  126. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10(3):223–32.

    Article  PubMed  CAS  Google Scholar 

  127. Jain RK, Duda DG, Clark JW, Loeffler JS. Lessons from phase III clinical trials on anti-VEGF therapy for cancer. Nat Clin Pract Oncol. 2006;3(1):24–40.

    Article  PubMed  CAS  Google Scholar 

  128. Moffat BA, Chen M, Kariaapper MS, et al. Inhibition of vascular endothelial growth factor (VEGF)-A causes a paradoxical increase in tumor blood flow and up-regulation of VEGF-D. Clin Cancer Res. 2006;12(5):1525–32.

    Article  PubMed  CAS  Google Scholar 

  129. Dennie J, Mandeville JB, Boxerman JL, Packard SD, Rosen BR, Weisskoff RM. NMR imaging of changes in vascular morphology due to tumor angiogenesis. Magn Reson Med. 1998;40(6):793–9.

    Article  PubMed  CAS  Google Scholar 

  130. Packard SD, Mandeville JB, Ichikawa T, et al. Functional response of tumor vasculature to PaCO2: Determination of total and microvascular blood volume by MRI. Neoplasia. 2003;5(4):330–8.

    PubMed  Google Scholar 

  131. Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. Am J Neuroradiol. 2004;25(9):1524–32.

    PubMed  Google Scholar 

  132. Pathak AP, Schmainda KM, Ward BD, Linderman JR, Rebro KJ, Greene AS. MR-derived cerebral blood volume maps: issues regarding histological validation and assessment of tumor angiogenesis. Magn Reson Med. 2001;46(4):735–47.

    Article  PubMed  CAS  Google Scholar 

  133. Badruddoja MA, Krouwer HG, Rand SD, Rebro KJ, Pathak AP, Schmainda KM. Antiangiogenic effects of dexamethasone in 9L gliosarcoma assessed by MRI cerebral blood volume maps. Neuro Oncol. 2003;5(4):235–43.

    Article  PubMed  CAS  Google Scholar 

  134. Quarles CC, Krouwer HG, Rand SD, Schmainda KM. Dexamethasone normalizes brain tumor hemodynamics as indicated by dynamic susceptibility contrast MRI perfusion parameters. Technol Cancer Res Treat. 2005;4(3):245–9.

    PubMed  CAS  Google Scholar 

  135. Ogawa T, Shishido F, Kanno I, et al. Cerebral glioma – evaluation with methionine PET. Radiology. 1993;186(1):45–53.

    PubMed  CAS  Google Scholar 

  136. Chung JK, Kim YK, Kim SK, et al. Usefulness of C-11-methionine PET in the evaluation of brain lesions that are hypo- or isometabolic on F-18-FDG PET. Eur J Nucl Med Mol Imaging. 2002;29(2):176–82.

    Article  PubMed  CAS  Google Scholar 

  137. Singhal T, Narayanan TK, Jain V, Mukherjee J, Mantil J. C-11-L-methionine positron emission tomography in the clinical management of cerebral gliomas. Mol Imaging Biol. 2008;10(1):1–18.

    Article  PubMed  Google Scholar 

  138. Shields AF, Grierson JR, Kozawa SM, Zheng M. Development of labeled thymidine analogs for imaging tumor proliferation. Nucl Med Biol. 1996;23(1):17–22.

    Article  PubMed  CAS  Google Scholar 

  139. Mankoff DA, Shields AF, Graham MM, Link JM, Eary JF, Krohn KA. Kinetic analysis of 2-[Carbon-11]thymidine PET imaging studies: Compartmental model and mathematical analysis. J Nucl Med. 1998;39(6):1043–55.

    PubMed  CAS  Google Scholar 

  140. Grierson JR, Shields AF. Radiosynthesis of 3 ‘-deoxy-3 ‘-[F-18]fluorothymidine: [F-18]FLT for imaging of cellular proliferation in vivo. Nucl Med Biol. 2000;27(2):143–56.

    Article  PubMed  CAS  Google Scholar 

  141. Ricci PE, Karis JP, Heiserman JE, Fram EK, Bice AN, Drayer BP. Differentiating recurrent tumor from radiation necrosis: Time for re-evaluation of positron emission tomography? Am J Neuroradiol. 1998;19(3):407–13.

    PubMed  CAS  Google Scholar 

  142. Chen W, Cloughesy T, Kamdar N, et al. Imaging proliferation in brain tumors with F-18-FLT PET: Comparison with F-18-FDG. J Nucl Med. 2005;46(6):945–52.

    PubMed  CAS  Google Scholar 

  143. Chen W, Delaloye S, Silverman DHS, et al. Predicting treatment response of malignant gliomas to bevacizumab and irinotecan by imaging proliferation with [F-18] fluorothymidine positron emission tomography: A pilot study. J Clin Oncol. 2007;25(30):4714–21.

    Article  PubMed  CAS  Google Scholar 

  144. Muzi M, Spence AM, O’Sullivan F, et al. Kinetic analysis of 3’-­deoxy-3’-F-18-fluorothymidine in patients with gliomas. J Nucl Med. 2006;47(10):1612–21.

    PubMed  CAS  Google Scholar 

  145. Schiepers C, Dahlbom M, Chen W, et al. Kinetics of 3 ‘-Deoxy-3 ‘-F-18-Fluorothymidine During Treatment Monitoring of Recurrent High-Grade Glioma. J Nucl Med. 2010;51(5):720–7.

    Article  PubMed  Google Scholar 

  146. Bernsen H, Rijken P, Peters H, et al. Hypoxia in a human intracerebral glioma model. J Neurosurg. 2000;93(3):449–54.

    Article  PubMed  CAS  Google Scholar 

  147. Rijken PFJW, Bernsen HJJA, Peters JPW, Hodgkiss RJ, Raleigh JA, van der Kogel AJ. Spatial relationship between hypoxia and the (perfused) vascular network in a human glioma xenograft: a quantitative multi-parameter analysis. Int J Radiat Oncol Biol Phys. 2000;48(2):571–82.

    Article  PubMed  CAS  Google Scholar 

  148. Koh WJ, Rasey JS, Evans ML, et al. Imaging of hypoxia in human tumors with [F-18] fluoromisonidazole. Int J Radiat Oncol Biol Phys. 1992;22(1):199–212.

    Article  PubMed  CAS  Google Scholar 

  149. Bruehlmeier M, Roelcke U, Schubiger PA, Ametamey SM. Assessment of hypoxia and perfusion in human brain tumors using PET with F-18-fluoromisonidazole and O-15-H2O. J Nucl Med. 2004;45(11):1851–9.

    PubMed  Google Scholar 

  150. Rajendran JG, Mankoff DA, O’Sullivan F, et al. Hypoxia and glucose metabolism in malignant tumors: Evaluation by [F-18]fluoromisonidazole and [F-18]fluorodeoxyglucose positron emission tomography imaging. Clin Cancer Res. 2004;10(7):2245–52.

    Article  PubMed  CAS  Google Scholar 

  151. Cher LM, Murone C, Lawrentschuk N, et al. Correlation of hypoxic cell fraction and angiogenesis with glucose metabolic rate in gliomas using F-18-fluoromisonidazole, F-18-FDG PET, and immunohistochemical studies. J Nucl Med. 2006;47(3):410–8.

    PubMed  CAS  Google Scholar 

  152. Thorwarth D, Eschmann SM, Paulsen F, Alber M. A kinetic model for dynamic [F-18]-Fmiso PET data to analyse tumour hypoxia. Phys Med Biol. 2005;50(10):2209–24.

    Article  PubMed  Google Scholar 

  153. Mazziotta JC, Frackowiak RSJ, Phelps ME. The use of positron emission tomography in the clinical-assessment of dementia. Semin Nucl Med. 1992;22(4):233–46.

    Article  PubMed  CAS  Google Scholar 

  154. Salmon E, Sadzot B, Maquet P, et al. Differential-diagnosis of Alzheimer’s-disease with PET. J Nucl Med. 1994;35(3):391–8.

    PubMed  CAS  Google Scholar 

  155. Herholz K. FDG PET and differential-diagnosis of dementia. Alzheimer Dis Assoc Disord Spr. 1995;9(1):6–16.

    Article  CAS  Google Scholar 

  156. Convit A, DeLeon MJ, Tarshish C, et al. Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging. 1997;18(2):131–8.

    Article  PubMed  CAS  Google Scholar 

  157. Convit A, de Asis J, de Leon MJ, Tarshish CY, De Santi S, Rusinek H. Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiol Aging. 2000;21(1):19–26.

    Article  PubMed  CAS  Google Scholar 

  158. Silverman DHS, Small GW, Chang CY, et al. Positron emission tomography in evaluation of dementia – regional brain metabolism and long-term outcome. JAMA J Am Med Assoc. 2001;286(17):2120–7.

    Article  CAS  Google Scholar 

  159. Herholz K, Salmon E, Perani D, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002;17(1):302–16.

    Article  PubMed  CAS  Google Scholar 

  160. Kantarci K, Jack CR. Neuroimaging in Alzheimer disease: An evidence-based review. Neuroimaging Clin N Am. May 2003;13(2):197-+.

    Google Scholar 

  161. Dickerson BC, Salat DH, Bates JF, et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol. 2004;56(1):27–35.

    Article  PubMed  Google Scholar 

  162. Dickerson BC, Salat DH, Greve DN, et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology. 2005;65(3):404–11.

    Article  PubMed  CAS  Google Scholar 

  163. Lehericy S, Marjanska M, Mesrob L, Sarazin M, Kinkingnehun S. Magnetic resonance imaging of Alzheimer’s disease. Eur Radiol. 2007;17(2):347–62.

    Article  PubMed  Google Scholar 

  164. Jagust WJ, Zheng L, Harvey DJ, et al. Neuropathological basis of magnetic resonance images in aging and dementia. Ann Neurol. 2008;63(1):72–80.

    Article  PubMed  Google Scholar 

  165. Zamrini E, De Santi S, Tolar M. Imaging is superior to cognitive testing for early diagnosis of Alzheimer’s disease. Neurobiol Aging. 2004;25(5):685–91.

    Article  PubMed  Google Scholar 

  166. Gonzalez RG, Fischman AJ, Guimaraes AR, et al. Functional MR in the evaluation of dementia – correlation of abnormal dynamic cerebral blood-volume measurements with changes in cerebral metabolism on positron emission tomography with fludeoxyglucose F-18. Am J Neuroradiol. 1995;16(9):1763–70.

    PubMed  CAS  Google Scholar 

  167. Buckner RL, Snyder AZ, Shannon BJ, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: Evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25(34):7709–17.

    Article  PubMed  CAS  Google Scholar 

  168. Jack CR, Lowe VJ, Senjem ML, et al. C-II PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131:665–80.

    Article  PubMed  Google Scholar 

  169. Villain N, Desgranges B, Viader F, et al. Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer’s disease. J Neurosci. 2008;28(24):6174–81.

    Article  PubMed  CAS  Google Scholar 

  170. Teipel SJ, Meindl T, Grinberg L, Heinsen H, Hampel H. Novel MRI techniques in the assessment of dementia. Eur J Nucl Med Mol Imaging. 2008;35 Suppl 1:S58–69.

    Article  PubMed  Google Scholar 

  171. Ashburner J, Friston KJ. Voxel-based morphometry – the methods. Neuroimage. 2000;11(6):805–21.

    Article  PubMed  CAS  Google Scholar 

  172. Baron JC, Chetelat G, Desgranges B, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease. Neuroimage. 2001;14(2):298–309.

    Article  PubMed  CAS  Google Scholar 

  173. Karas GB, Burton EJ, Rombouts S, et al. A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage. 2003;18(4):895–907.

    Article  PubMed  CAS  Google Scholar 

  174. Hirata Y, Matsuda H, Nemoto K, et al. Voxel-based morphometry to discriminate early Alzheimer’s disease from controls. Neurosci Lett. 2005;382(3):269–74.

    Article  PubMed  CAS  Google Scholar 

  175. Chetelat G, Landeau B, Eustache F, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study. Neuroimage. 2005;27(4):934–46.

    Article  PubMed  CAS  Google Scholar 

  176. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis – I Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179–94.

    Article  PubMed  CAS  Google Scholar 

  177. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA. 2000;97(20):11050–5.

    Article  PubMed  CAS  Google Scholar 

  178. Du AT, Schuff N, Kramer JH, et al. Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain. 2007;130:1159–66.

    Article  PubMed  Google Scholar 

  179. Lerch JP, Pruessner J, Zijdenbos AP, et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol Aging. 2008;29(1):23–30.

    Article  PubMed  Google Scholar 

  180. Kantarci K. H-1 Magnetic resonance spectroscopy in dementia. Br J Radiol. 2007;80:S146–52.

    Article  PubMed  CAS  Google Scholar 

  181. Bozzali M, Falini A, Franceschi M, et al. White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry. 2002;72(6):742–6.

    Article  PubMed  CAS  Google Scholar 

  182. Chua TC, Wen W, Slavin MJ, Sachdev PS. Diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease: a review. Curr Opin Neurol. 2008;21(1):83–92.

    Article  PubMed  Google Scholar 

  183. Celone KA, Calhoun VD, Dickerson BC, et al. Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: An independent component analysis. J Neurosci. 2006;26(40):10222–31.

    Article  PubMed  CAS  Google Scholar 

  184. Dickerson BC, Sperling RA. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease: Insights from functional MRI studies. Neuropsychologia. 2008;46(6):1624–35.

    Article  PubMed  Google Scholar 

  185. Sperling RA, Dickerson BC, Pihlajamaki M, et al. Functional alterations in memory networks in early Alzheimer’s disease. Neuromolecular Med. 2010;12(1):27–43.

    Article  PubMed  CAS  Google Scholar 

  186. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA. 2001;98(2):676–82.

    Article  PubMed  CAS  Google Scholar 

  187. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network – anatomy, function, and relevance to disease. Year in Cognitive Neuroscience 2008. Vol 1124. Oxford: Blackwell Publishing; 2008:1-38

    Google Scholar 

  188. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proc Natl Acad Sci USA. 2004;101(13):4637–42.

    Article  PubMed  CAS  Google Scholar 

  189. Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–19.

    Article  PubMed  CAS  Google Scholar 

  190. De Santi S, de Leon MJ, Rusinek H, et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging. 2001;22(4):529–39.

    Article  PubMed  CAS  Google Scholar 

  191. Drzezga A, Lautenschlager N, Siebner H, et al. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging. 2003;30(8):1104–13.

    Article  PubMed  Google Scholar 

  192. Mosconi L, Tsui WH, De Santi S, et al. Reduced hippocampal metabolism in MCI and AD – automated FDG-PET image analysis. Neurology. 2005;64(11):1860–7.

    Article  PubMed  CAS  Google Scholar 

  193. Engler H, Forsberg A, Almkvist O, et al. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain. 2006;129:2856–66.

    Article  PubMed  Google Scholar 

  194. Summers WK, Majovski LV, Marsh GM, Tachiki K, Kling A. Oral tetrahydroaminoacridine in long-germ treatment of senile demenzia, Alzheimer type. N Engl J Med. 1986;315(20):1241–5.

    Article  PubMed  CAS  Google Scholar 

  195. Sano M, Ernesto C, Thomas RG, et al. A controlled trial of selegiline, alpha-tocopherol, or both as treatment for Alzheimer’s disease. N Engl J Med. 1997;336(17):1216–22.

    Article  PubMed  CAS  Google Scholar 

  196. Rogers SL, Farlow MR, Doody RS, et al. A 24-week, double-blind, placebo-controlled trial of donepezil in patients with Alzheimer’s disease. Neurology. 1998;50(1):136–45.

    PubMed  CAS  Google Scholar 

  197. Schenk D, Barbour R, Dunn W, et al. Immunization with amyloid-beta attenuates Alzheimer disease-like pathology in the PDAPP mouse. Nature. 1999;400(6740):173–7.

    Article  PubMed  CAS  Google Scholar 

  198. Raskind MA, Peskind ER, Wessel T, Yuan W, Galantamine USASG. Galantamine in AD – A 6-month randomized, placebo-controlled trial with a 6-month extension. Neurology. 2000;54(12):2261–8.

    PubMed  CAS  Google Scholar 

  199. Zandi PP, Carlson MC, Plassman BL, et al. Hormone replacement therapy and incidence of Alzheimer disease in older women – The Cache County Study. JAMA J Am Med Assoc. 2002;288(17):2123–9.

    Article  CAS  Google Scholar 

  200. Reisberg B, Doody R, Stoffler A, et al. Memantine in moderate-to-severe Alzheimer’s disease. N Engl J Med. 2003;348(14):1333–41.

    Article  PubMed  CAS  Google Scholar 

  201. Tabira T. Immunization therapy for Alzheimer disease: a comprehensive review of active immunization strategies. Tohoku J Exp Med. 2010;220(2):95–106.

    Article  PubMed  CAS  Google Scholar 

  202. Bonda DJ, Lee HP, Lee HG, et al. Novel therapeutics for Alzheimer’s disease: An update. Curr Opin Drug Discov Dev. 2010;13(2):235–46.

    CAS  Google Scholar 

  203. Galimberti D, Scarpini E. Treatment of Alzheimer’s disease: symptomatic and disease-modifying approaches. Curr Aging Sci. 2010;3(1):46–56.

    Article  PubMed  CAS  Google Scholar 

  204. Baron JC. Mapping the ischaemic penumbra with PET: Implications for acute stroke treatment. Cerebrovasc Dis. 1999;9(4):193–201.

    Article  PubMed  CAS  Google Scholar 

  205. Heiss WD. Ischemic penumbra: evidence from functional imaging in man. J Cereb Blood Flow Metab. 2000;20(9):1276–93.

    Article  PubMed  CAS  Google Scholar 

  206. Heiss WD, Kracht L, Grond M, et al. Early [C-11]flumazenil/H2O positron emission tomography predicts irreversible ischemic cortical damage in stroke patients receiving acute thrombolytic therapy. Stroke. 2000;31(2):366–9.

    Article  PubMed  CAS  Google Scholar 

  207. Heiss WD, Kracht LW, Thiel A, Grond M, Pawlik G. Penumbral probability thresholds of cortical flumazenil binding and blood flow predicting tissue outcome in patients with cerebral ischaemia. Brain. 2001;124:20–9.

    Article  PubMed  CAS  Google Scholar 

  208. Lovblad KO, Laubach HJ, Baird AE, et al. Clinical experience with diffusion-weighted MR in patients with acute stroke. Am J Neuroradiol. 1998;19(6):1061–6.

    PubMed  CAS  Google Scholar 

  209. Sorensen AG, Copen WA, Ostergaard L, et al. Hyperacute stroke: simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time. Radiology. 1999;210(2):519–27.

    PubMed  CAS  Google Scholar 

  210. Heiss WD. Best measure of ischemic penumbra: Positron emission tomography. Stroke. 2003;34(10):2534–5.

    Article  PubMed  Google Scholar 

  211. Warach S. Measurement of the ischemic penumbra with MRI: It’s about time. Stroke. 2003;34(10):2533–4.

    Article  PubMed  Google Scholar 

  212. Guadagno JV, Warburton EA, Jones PS, et al. How affected is oxygen metabolism in DWI lesions? A combined acute stroke PET-MR study. Neurology. 2006;67(5):824–9.

    Article  PubMed  CAS  Google Scholar 

  213. Takasawa M, Jones PS, Guadagno JV, et al. How reliable is perfusion MR in acute stroke? Validation and determination of the penumbra threshold against quantitative PET. Stroke. 2008;39(3):870–7.

    Article  PubMed  Google Scholar 

  214. Zaro-Weber O, Moeller-Hartmann W, Heiss WD, Sobesky J. The performance of MRI-based cerebral blood flow measurements in acute and subacute stroke compared with 15O-water positron emission tomography identification of penumbral flow. Stroke. 2009;40(7):2413–21.

    Article  PubMed  Google Scholar 

  215. Sobesky J, Weber OZ, Lehnhardt FG, et al. Which time-to-peak threshold best identifies penumbral flow? A comparison of perfusion-weighted magnetic resonance imaging and positron emission tomography in acute ischemic stroke. Stroke. 2004;35(12):2843–7.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Suite 2.301, Charlestown, MA, 02129, USA

    Ciprian Catana MD, PhD, A. Gregory Sorensen MD & Bruce R. Rosen MD, PhD

Authors
  1. Ciprian Catana MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Gregory Sorensen MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bruce R. Rosen MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ciprian Catana MD, PhD .

Editor information

Editors and Affiliations

  1. Temple University School of Medicine, 3401 Broad Street, Philadelphia, 19140-5189, Pennsylvania, USA

    Scott H. Faro

  2. Dept. of Radiology, Temple University Hospital, 3401 Broad Street, Philadelphia, 19140-5189, Pennsylvania, USA

    Feroze B. Mohamed

  3. , Radiology, University of Southern California School, Zonal Avenue 1975, Los Angeles, 90033, California, USA

    Meng Law

  4. , Department of Radiology, Medical College of Wisconsin, Watertown Plank Road 8701, Milwaukee, 53226, USA

    John T. Ulmer

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Catana, C., Sorensen, A.G., Rosen, B.R. (2011). Simultaneous PET and MR Imaging of the Human Brain. In: Faro, S., Mohamed, F., Law, M., Ulmer, J. (eds) Functional Neuroradiology. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0345-7_42

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-0345-7_42

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-0343-3

  • Online ISBN: 978-1-4419-0345-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation