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Ausgewählte klinisch etablierte und wissenschaftliche Techniken der diffusionsgewichteten MRT

Im Kontext der onkologischen Bildgebung

Selected clinically established and scientific techniques of diffusion-weighted MRI

In the context of imaging in oncology

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Zusammenfassung

Hintergrund

Die diffusionsgewichtete Bildgebung („diffusion-weighted imaging“, DWI), ein Verfahren aus der Magnetresonanztomographie (MRT), wurde in der klinischen Routine primär für die Detektion von Schlaganfällen etabliert. Der Einsatz dieser Methode hat in den letzten 15 Jahren auch für die onkologische Diagnostik stark zugenommen, da Tumoren und Metastasen aufgrund ihrer hochzellulären Zusammensetzung in der DWI sehr gut sichtbar gemacht werden können.

Grundlage

Basis der diffusionsgewichteten Bildgebung ist das Experiment nach Stejskal-Tanner. Hier werden durch Schaltung einer speziellen Gradientenabfolge mithilfe echoplanarer Auslesetechnik mehrere, mindestens jedoch 2 Bilderserien akquiriert, die die Diffusionseigenschaften des Gewebes wiedergeben.

Klinische Anwendungen

Eine der ersten klinisch etablierten Anwendungen ist die DWI bei der Prostata-MRT, die in den international anerkannten PI-RADS- und ESUR-Leitlinien bereits fest integriert ist. In naher Zukunft wird die klinische Etablierung der DWI auch für die Mamma-MRT erwartet, hier wurden Spezifitätswerte und negative prädiktive Werte von 94 bzw. 92 % berichtet. Die DWI kann auch als Ganzkörperbildgebung für Patienten mit systemischen Knochenmarkerkrankungen wie dem multiplen Myelom oder zur Frage nach dem Ausmaß der Skelettmetastasierung zuverlässig eingesetzt werden.

Ausblick

Neue Techniken der DWI, wie die „intravoxel incoherent motion DWI“, die Kurtosis-Bildgebung oder histogrammbasierte Auswertungen stellen vielversprechende und innovative Verfahren dar, um Tumordetektion oder Therapieansprechen zunehmend quantitativer durchführen zu können.

Abstract

Background

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that was established in the clinical routine primarily for the detection of brain ischemia. In the past 15 years its clinical use has been extended to oncological radiology, as tumor and metastases can be depicted in DWI due to their hypercellular nature.

Principles

The basis of DWI is the Stejskal-Tanner experiment. The diffusion properties of tissue can be visualized after acquisition of at least two diffusion-weighted series using echo planar imaging and a specific sequence of gradient pulses.

Clinical applications

The use of DWI in prostate MRI was reported to be one of the first established applications that found its way into internationally recognized clinical guidelines of the European Society of Urological Radiology (ESUR) and the prostate imaging reporting and data system (PI-RADS) scale. Due to recently reported high specificity and negative predictive values of 94 % and 92 %, respectively, its regular use for breast MRI is expected in the near future. Furthermore, DWI can also reliably be used for whole-body imaging in patients with multiple myeloma or for measuring the extent of bone metastases.

Outlook

New techniques in DWI, such as intravoxel incoherent motion imaging, diffusion kurtosis imaging and histogram-based analyses represent promising approaches to achieve a more quantitative evaluation for tumor detection and therapy response.

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Abbreviations

DWI:

„Diffusion-weighted imaging“

ADC:

„Apparent diffusion coefficient“

IVIM:

„Intravoxel incoherent motion“

MRT:

Magnetresonanztomographie

GK-DWI:

Ganzkörper-DWI

Literatur

  1. Laun FB, Fritzsche KH, Kuder TA, Stieltjes DB (2011) Einführung in die Grundlagen und Techniken der Diffusionsbildgebung. Radiologe 51:170–179

    Article  CAS  PubMed  Google Scholar 

  2. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505

    Article  PubMed  Google Scholar 

  3. Sasaki M, Yamada K, Watanabe Y, Matsui M, Ida M, Fujiwara S et al (2008) Variability in absolute apparent diffusion coefficient values across different platforms may be substantial: a multivendor, multi-institutional comparison study 1. Radiology 249:624–630

    Article  PubMed  Google Scholar 

  4. Burdette JH, Elster AD, Ricci PE (1999) Acute cerebral infarction: Quantification of spin-density and T2 shine-through phenomena on diffusion-weighted MR images 1. Radiology 212:333–339

    Article  CAS  PubMed  Google Scholar 

  5. Padhani AR, Koh D-M, Collins DJ (2011) Whole-body diffusion-weighted MR imaging in cancer: current status and research directions. Radiology 261:700–718

    Article  PubMed  Google Scholar 

  6. Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G et al (2012) ESUR prostate MR guidelines. Eur Radiol 22:746–757

    Article  PubMed Central  PubMed  Google Scholar 

  7. Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ et al (2015) PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. Eur Urol 69(1):16–40

    Article  PubMed  Google Scholar 

  8. Hambrock T, Hoeks C, Hulsbergen-van de Kaa C, Scheenen T, Fütterer J, Bouwense S et al (2012) Prospective assessment of prostate cancer aggressiveness using 3-T diffusion-weighted magnetic resonance imaging–guided biopsies versus a systematic 10-core Transrectal ultrasound prostate biopsy cohort. Eur Urol 61:177–184

    Article  PubMed  Google Scholar 

  9. Gupta RT, Kauffman CR, Garcia-Reyes K, Palmeri ML, Madden JF, Polascik TJ et al (2015) Apparent Diffusion Coefficient Values of the Benign Central Zone of the Prostate: Comparison With Low- and High-Grade Prostate Cancer. Am J Roentgenol 205:331–336

    Article  Google Scholar 

  10. Somford DM, Hambrock T, Hulsbergen-van de Kaa CA, Fütterer JJ, van Oort IM, van Basten J-P et al (2012) Initial experience with identifying high-grade prostate cancer using diffusion-weighted MR imaging (DWI) in patients with a Gleason score ≤3 + 3 = 6 upon schematic TRUS-guided biopsy: a radical prostatectomy correlated series. Invest Radiol 47(3):153–158

    PubMed  Google Scholar 

  11. Katahira K, Takahara T, Kwee TC, Oda S, Suzuki Y, Morishita S et al (2010) Ultra-high-b-value diffusion-weighted MR imaging for the detection of prostate cancer: evaluation in 201 cases with histopathological correlation. Eur Radiol 21:188–196

    Article  PubMed  Google Scholar 

  12. Bittencourt LK, Attenberger UI, Lima D, Strecker R, de Oliveira A, Schoenberg SO et al (2014) Feasibility study of computed vs measured high b-value (1400 s/mm2) diffusion-weighted MR images of the prostate. World J Radiol 6(6):374–380

    Article  PubMed Central  PubMed  Google Scholar 

  13. Kaiser WA, Zeitler E (1989) MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary observations. Radiology 170:681–686

    Article  CAS  PubMed  Google Scholar 

  14. Orel SG, Schnall MD (2001) MR imaging of the breast for the detection, diagnosis, and staging of breast cancer 1. Radiology 220(1):13–30

    Article  CAS  PubMed  Google Scholar 

  15. Heywang-Köbrunner SH, Viehweg P, Heinig A, Küchler C (1997) Contrast-enhanced MRI of the breast: accuracy, value, controversies, solutions. Eur J Radiol 24:94–108

    Article  PubMed  Google Scholar 

  16. Thomassin-Naggara I, De Bazelaire C, Chopier J, Bazot M, Marsault C, Trop I (2013) Diffusion-weighted MR imaging of the breast: advantages and pitfalls. Eur J Radiol 82:435–443

    Article  CAS  PubMed  Google Scholar 

  17. Guo Y, Cai Y-Q, Cai Z-L, Gao Y-G, An N-Y, Ma L et al (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178

    Article  PubMed  Google Scholar 

  18. Partridge SC, Demartini WB, Kurland BF, Eby PR, White SW, Lehman CD (2010) Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging 31:562–570

    Article  PubMed  Google Scholar 

  19. Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M et al (2011) Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics 31(4):1059–1084

    Article  PubMed  Google Scholar 

  20. Bickelhaupt S, Laun FB, Tesdorff J, Lederer W, Daniel H, Stieber A et al (2015) Fast and Noninvasive characterization of suspicious lesions detected at breast cancer X-ray screening: capability of diffusion-weighted MR imaging with MIPs. Radiology doi: 10.1148/radiol.2015150425 (Epub ahead of print)

    PubMed  Google Scholar 

  21. Narquin S, Ingrand P, Azais I, Delwail V, Vialle R, Boucecbi S et al (2013) Comparison of whole-body diffusion MRI and conventional radiological assessment in the staging of myeloma. Diagn Interv Imaging 94:629–636

    Article  CAS  PubMed  Google Scholar 

  22. Pearce T, Philip S, Brown J, Koh DM, Burn PR (2012) Bone metastases from prostate, breast and multiple myeloma: differences in lesion conspicuity at short-tau inversion recovery and diffusion-weighted MRI. Br J Radiol 85:1102–1106

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Bonaffini PA, Ippolito D, Casiraghi A, Besostri V, Franzesi CT, Sironi S (2015) Apparent diffusion coefficient maps integrated in whole-body MRI examination for the evaluation of tumor response to chemotherapy in patients with multiple myeloma. Acad Radiol 22:1163–1171

    Article  PubMed  Google Scholar 

  24. Klenk C, Gawande R, Uslu L, Khurana A, Qiu D, Quon A et al (2014) Ionising radiation-free whole-body MRI versus 18 F-fluorodeoxyglucose PET/CT scans for children and young adults with cancer: a prospective, non-randomised, single-centre study. Lancet Oncol 15:275–285

    Article  PubMed  Google Scholar 

  25. Eiber M, Holzapfel K, Ganter C, Epple K, Metz S, Geinitz H et al (2011) Whole-body MRI including diffusion-weighted imaging (DWI) for patients with recurring prostate cancer: Technical feasibility and assessment of lesion conspicuity in DWI. J Magn Reson Imaging 33:1160–1170

    Article  PubMed  Google Scholar 

  26. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161:401–407

    Article  PubMed  Google Scholar 

  27. Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S et al (2009) Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Invest Radiol 44:769–775

    Article  PubMed  Google Scholar 

  28. Bäuerle T, Seyler L, Münter M, Jensen A, Brand K, Fritzsche KH et al (2013) Diffusion-weighted imaging in rectal carcinoma patients without and after chemoradiotherapy: A comparative study with histology. Eur J Radiol 82:444–452

    Article  PubMed  Google Scholar 

  29. Shah R, Stieltjes B, Andrulis M, Pfeiffer R, Sumkauskaite M, Delorme S et al (2013) Intravoxel incoherent motion imaging for assessment of bone marrow infiltration of monoclonal plasma cell diseases. Ann Hematol 92:1553–1557

    Article  PubMed  Google Scholar 

  30. Hauser T, Essig M, Jensen A, Gerigk L, Laun FB, Munter M et al (2013) Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters-preliminary results. Neuroradiology 55(5):527–536

    Article  PubMed  Google Scholar 

  31. Iima M, Yano K, Kataoka M, Umehana M, Murata K, Kanao S et al (2015) Quantitative non-gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol 50:205–211

    Article  PubMed  Google Scholar 

  32. Lemke A, Stieltjes B, Schad LR, Laun FB (2011) Toward an optimal distribution of b values for intravoxel incoherent motion imaging. Magn Reson Imaging 29:766–776

    Article  PubMed  Google Scholar 

  33. Wetscherek A, Stieltjes B, Laun FB (2015) Flow-compensated intravoxel incoherent motion diffusion imaging. Magn Reson Med 74:410–419

    Article  PubMed  Google Scholar 

  34. Fritzsche KH, Neher PF, Reicht I, van Bruggen T, Goch C, Reisert M et al (2012) MITK diffusion imaging. Methods Inf Med 51:441–448

    Article  CAS  PubMed  Google Scholar 

  35. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K (2005) Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440

    Article  PubMed  Google Scholar 

  36. Rosenkrantz AB, Sigmund EE, Johnson G, Babb JS, Mussi TC, Melamed J et al (2012) Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 264:126–135

    Article  PubMed  Google Scholar 

  37. Roethke MC, Kuder TA, Kuru TH, Fenchel M, Hadaschik BA, Laun FB et al (2015) Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate Cancer. Invest Radiol 50(8):483–489

    Article  CAS  PubMed  Google Scholar 

  38. Sun K, Chen X, Chai W, Fei X, Fu C, Yan X et al (2015) Breast cancer: diffusion Kurtosis MR imaging – diagnostic accuracy and correlation with clinical-pathologic factors. Radiology 277(1):46–55

    Article  PubMed  Google Scholar 

  39. Chen Y, Ren W, Zheng D, Zhong J, Liu X, Yue Q et al (2015) Diffusion kurtosis imaging predicts neoadjuvant chemotherapy responses within 4 days in advanced nasopharyngeal carcinoma patients. J Magn Reson Imaging 42(5):1354–1361

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Abteilung für Radiologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland

    M. T. Freitag, S. Bickelhaupt, C. Ziener, J. P. Radtke, J. Mosebach & H.-P. Schlemmer

  2. Abteilung für medizinische Informatik, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland

    K. Meier-Hein

  3. Abteilung für Urologie, Universitätsklinik Heidelberg, Heidelberg, Deutschland

    J. P. Radtke

  4. Abteilung für Medizinische Physik in der Radiologie, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland

    T.-A. Kuder & F. B. Laun

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Correspondence to M. T. Freitag.

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M.T. Freitag, S. Bickelhaupt, C. Ziener, K. Meier-Hein, J.P. Radtke, J. Mosebach, T.‑A. Kuder, H.-P. Schlemmer und F. B. Laun geben an, dass kein Interessenkonflikt besteht.

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Freitag, M.T., Bickelhaupt, S., Ziener, C. et al. Ausgewählte klinisch etablierte und wissenschaftliche Techniken der diffusionsgewichteten MRT. Radiologe 56, 137–147 (2016). https://doi.org/10.1007/s00117-015-0066-6

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