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Multiparametrische Bildgebung mittels simultaner MR/PET

Methodische Aspekte und Möglichkeiten der klinischen Anwendungen

Multiparametric imaging with simultaneous MR/PET

Methodological aspects and possible clinical applications

Der Radiologe volume 53, pages 669–675 (2013)Cite this article

Zusammenfassung

Die MR/PET ermöglicht als Hybridverfahren die Akquisition einer Vielzahl von Parametern während einer einzelnen Untersuchung. Dazu gehören die Darstellung der Anatomie, aber auch funktioneller und metabolischer Informationen, etwa zu Perfusion, Diffusion und Stoffwechsel. Es wurde gezeigt, dass die Zusammenführung dieser Informationen v. a. bei onkologischen Fragestellungen in vielen Fällen zu einer Verbesserung der diagnostischen Genauigkeit führt. Aufgrund der Fülle und Komplexität der hierbei anfallenden Daten ist die Anwendung von Klassifikationsverfahren und Methoden der Parameterselektion sinnvoll. Die vorliegende Arbeit gibt einen Überblick über diese Methoden und deren Anwendungsmöglichkeiten in multiparametrischer Bildgebung mittels MR/PET.

Abstract

Combined magnetic resonance imaging-positron emission tomography (MR/PET) enables acquisition of a variety of imaging parameters during a single examination including anatomical as well as functional information, such as perfusion, diffusion and metabolism. Numerous studies have shown that the combination of these parameters can improve the diagnostic accuracy for many applications especially in oncological imaging. Due to the amount and the complexity of the acquired multiparametric data there is a need for advanced analytical tools, such as methods of parameter selection and data classification. The present article summarizes these methods and their applications in multiparametric imaging via MR/PET.

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Literatur

  1. Antoch G, Saoudi N, Kuehl H et al (2004) Accuracy of whole-body dual-modality fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (FDG-PET/CT) for tumor staging in solid tumors: comparison with CT and PET. J Clin Oncol 22:4357–4368

    PubMed  Article  Google Scholar 

  2. Arbizu J, Tejada S, Marti-Climent JM et al (2012) Quantitative volumetric analysis of gliomas with sequential MRI and (1)(1)C-methionine PET assessment: patterns of integration in therapy planning. Eur J Nucl Med Mol Imaging 39:771–781

    PubMed  Article  Google Scholar 

  3. Artan Y, Haider MA, Langer DL et al (2010) Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans Image Process 19:2444–2455

    PubMed  Article  Google Scholar 

  4. Awasthi R, Rathore RK, Soni P et al (2012) Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers. Neuroradiology 54:205–213

    PubMed  Article  Google Scholar 

  5. Bailey D, Barthel H, Beyer T et al (2013) Summary report of the First International Workshop on PET/MR Imaging, March 19–23, 2012, Tübingen, Germany. Mol Imaging Biol 1–11

  6. Chen JJ, Wieckowska M, Meyer E et al (2008) Cerebral blood flow measurement using fMRI and PET: a cross-validation study. Int J Biomed Imaging 2008:516359

    PubMed  Article  Google Scholar 

  7. Cheng HL, Stikov N, Ghugre NR et al (2012) Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging 36:805–824

    PubMed  Article  Google Scholar 

  8. Delso G, Furst S, Jakoby B et al (2011) Performance measurements of the Siemens mMR integrated whole-body PET/MR scanner. J Nucl Med 52:1914–1922

    PubMed  Article  Google Scholar 

  9. Dukart J, Mueller K, Horstmann A et al (2011) Combined evaluation of FDG-PET and MRI improves detection and differentiation of dementia. PLoS One 6:e18111

    PubMed  Article  CAS  Google Scholar 

  10. Feng CM, Narayana S, Lancaster JL et al (2004) CBF changes during brain activation: fMRI vs. PET. NeuroImage 22:443–446

    PubMed  Article  Google Scholar 

  11. Floeth FW, Sabel M, Stoffels G et al (2008) Prognostic value of 18F-fluoroethyl-L-tyrosine PET and MRI in small nonspecific incidental brain lesions. J Nucl Med 49:730–737

    PubMed  Article  Google Scholar 

  12. Gillings N (2013) Radiotracers for positron emission tomography imaging. MAGMA 26:149–158

    PubMed  Article  Google Scholar 

  13. Gunn RN, Gunn SR, Cunningham VJ (2001) Positron emission tomography compartmental models. J Cereb Blood Flow Metab 21:635–652

    PubMed  Article  CAS  Google Scholar 

  14. Hambrock T, Vos PC, Hulsbergen-Van De Kaa CA et al (2013) Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging – effect on observer performance. Radiology 266:521–530

    PubMed  Article  Google Scholar 

  15. Hu X, Wong KK, Young GS et al (2011) Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 33:296–305

    PubMed  Article  Google Scholar 

  16. Jacobs MA, Barker PB, Bluemke DA et al (2003) Benign and malignant breast lesions: diagnosis with multiparametric MR imaging. Radiology 229:225–232

    PubMed  Article  Google Scholar 

  17. Kim SG, Ogawa S (2012) Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J Cereb Blood Flow Metab 32(7):1188–1206

    PubMed  Article  CAS  Google Scholar 

  18. Kloppel S, Stonnington CM, Barnes J et al (2008) Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 131:2969–2974

    PubMed  Article  Google Scholar 

  19. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

    Google Scholar 

  20. Kumar R, Dhanpathi H, Basu S et al (2008) Oncologic PET tracers beyond [(18)F]FDG and the novel quantitative approaches in PET imaging. Q J Nucl Med Mol Imaging 52:50–65

    PubMed  CAS  Google Scholar 

  21. Malayeri AA, El Khouli RH, Zaheer A et al (2011) Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics 31:1773–1791

    PubMed  Article  Google Scholar 

  22. Martirosian P, Boss A, Schraml C et al (2010) Magnetic resonance perfusion imaging without contrast media. Eur J Nucl Med Mol Imaging 37(Suppl 1):S52–S64

    PubMed  Article  Google Scholar 

  23. Newberg AB, Wang J, Rao H et al (2005) Concurrent CBF and CMRGlc changes during human brain activation by combined fMRI-PET scanning. NeuroImage 28:500–506

    PubMed  Article  Google Scholar 

  24. Niaf E, Rouviere O, Mege-Lechevallier F et al (2012) Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys Med Biol 57:3833–3851

    PubMed  Article  Google Scholar 

  25. Park H, Wood D, Hussain H et al (2012) Introducing parametric fusion PET/MRI of primary prostate cancer. J Nucl Med 53:546–551

    PubMed  Article  CAS  Google Scholar 

  26. Pauleit D, Floeth F, Hamacher K et al (2005) O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain 128:678–687

    PubMed  Article  Google Scholar 

  27. Pinker K, Stadlbauer A, Bogner W et al (2012) Molecular imaging of cancer: MR spectroscopy and beyond. Eur J Radiol 81:566–577

    PubMed  Article  CAS  Google Scholar 

  28. Rui X, Wunsch D II (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16:645–678

    Article  Google Scholar 

  29. Schick F (2006) MRT sequences. Part I. Radiologe 46:615–627

    PubMed  Article  CAS  Google Scholar 

  30. Schlemmer HP, Pichler BJ, Schmand M et al (2008) Simultaneous MR/PET imaging of the human brain: feasibility study. Radiology 248:1028–1035

    PubMed  Article  Google Scholar 

  31. Schmidt H, Brendle C, Schraml C et al (2013) Correlation of simultaneously acquired diffusion-weighted imaging and 2-Deoxy-[18F] fluoro-2-D-glucose positron emission tomography of pulmonary lesions in a dedicated whole-body magnetic resonance/positron emission tomography system. Invest Radiol 48:247–255

    PubMed  Article  Google Scholar 

  32. Shah V, Turkbey B, Mani H et al (2012) Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging. Med Phys 39:4093–4103

    PubMed  Article  Google Scholar 

  33. Sinha S, Lucas-Quesada FA, Debruhl ND et al (1997) Multifeature analysis of Gd-enhanced MR images of breast lesions. J Magn Reson Imaging 7:1016–1026

    PubMed  Article  CAS  Google Scholar 

  34. Sourbron SP, Buckley DL (2012) Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 57:R1–R33

    PubMed  Article  CAS  Google Scholar 

  35. Turkbey B, Mani H, Shah V et al (2011) Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging based molds. J Urol 186:1818–1824

    PubMed  Article  Google Scholar 

  36. Turkbey B, Pinto PA, Mani H et al (2010) Prostate cancer: value of multiparametric MR imaging at 3 T for detection – histopathologic correlation. Radiology 255:89–99

    PubMed  Article  Google Scholar 

  37. Van Der Maaten LJP, Postma EO, Van Den Herik HJ (2009) Dimensionality reduction: a comparative review. TiCC Technical Report 2009–005

  38. Weinmann H-J, Ebert W, Misselwitz B et al (2003) Tissue-specific MR contrast agents. Eur J Radiol 46:33–44

    PubMed  Article  Google Scholar 

  39. Ye FQ, Berman KF, Ellmore T et al (2000) H(2)(15)O PET validation of steady-state arterial spin tagging cerebral blood flow measurements in humans. Magn Reson Med 44:450–456

    PubMed  Article  CAS  Google Scholar 

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Einhaltung der ethischen Richtlinien

Interessenkonflikt. Die korrespondierende Autorin N.F. Schwenzer gibt für sich und ihre Koautoren S. Gatidis, H. Schmidt, C.D. Claussen an, dass kein Interessenkonflikt besteht. Soweit der Beitrag personenbezogene Daten enthält, wurde von den Patienten eine zusätzliche Einwilligung nach erfolgter Aufklärung eingeholt. Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

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  1. Abteilung für Diagnostische und Interventionelle Radiologie, Radiologische Universitätsklinik, Klinikum der Eberhard-Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland

    S. Gatidis, H. Schmidt, C.D. Claussen & N.F. Schwenzer

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  1. S. Gatidis
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  2. H. Schmidt
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  3. C.D. Claussen
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  4. N.F. Schwenzer
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Correspondence to N.F. Schwenzer.

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Gatidis, S., Schmidt, H., Claussen, C. et al. Multiparametrische Bildgebung mittels simultaner MR/PET. Radiologe 53, 669–675 (2013). https://doi.org/10.1007/s00117-013-2496-3

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  • DOI: https://doi.org/10.1007/s00117-013-2496-3

Schlüsselwörter

  • Diagnostische Genauigkeit
  • Funktionelle Gewebeeigenschaften
  • Klassifikation
  • Parameterselektion
  • Maschinelles Lernen

Keywords

  • Diagnostic accuracy
  • Functional tissue characteristics
  • Classification
  • Parameter selection
  • Machine learning