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Overview of Functional MR, CT, and US Imaging Techniques in Clinical Use

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Functional Imaging in Oncology

Abstract

Functional and molecular imaging have potential to improve the diagnostic performance of conventional morphological imaging, by providing insights into the tumor cellularity, vascular function, metabolism, and molecular biology. In this chapter, we describe the leading functional imaging strategies, i.e., vascular imaging methods, which include the dynamic contrast-enhanced MRI, perfusion CT, and contrast-enhanced ultrasonography, as well as methods that probe tumor cellularity and metabolism, i.e., diffusion-weighted MRI and magnetic resonance spectroscopy, respectively. We address their technical aspects, including the data acquisition and analysis, and outline their possible applications in clinical practice.

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Abbreviations

1H:

Proton

1H-MRS:

Proton magnetic resonance spectroscopy

2D:

Two-dimensional

3D:

Three-dimensional

A:

Initial amplitude of MRI signal enhancement

ADC:

Apparent diffusion coefficient

AIF:

Arterial input function

ASL:

Arterial spin labeling

ATP:

Adenosine-5′-triphosphate

C:

Carbon-13

CEUS:

Contrast-enhanced ultrasonography

CNS:

Central nervous system

CT:

Computed tomography

DCE-MRI:

Dynamic contrast-enhanced

DCUS:

Double contrast-enhanced ultrasound

DSC:

Dynamic-susceptibility contrast

DWI:

Diffusion-weighted magnetic resonance imaging

EES:

Extracellular extravascular space

Gd:

Gadolinium

Gd-DOTA:

Gadolinium tetraazacyclododecanetetraacetate

Gd-DTPA:

Gadolinium diethylenetriaminepentaacetate

Gd-HP-DO3A:

Gadolinium hydroxypropyltetraazacyclododecane triacetate

IAUC:

Initial area under the contrast agent concentration–time curve

IRF:

Impulse residue function

k ep :

Exchange rate constant

K trans :

Endothelial transfer coefficient

mM:

Millimolar

mm2 :

Square millimeter

MMCAs:

Macromolecular contrast agents

MRI:

Magnetic resonance imaging

MRS:

Magnetic resonance spectroscopy

MRSI:

Magnetic resonance spectroscopic imaging

MTT:

Mean transit time

MVD:

Microvessel density

Na:

Sodium

NTP:

Nucleoside triphosphates

P:

Phosphorus

P MRS:

Phosphor magnetic resonance spectroscopy

Pi :

Inorganic phosphor

rBF:

Relative blood flow

rBV:

Relative blood volume

s:

Second

T1:

Longitudinal relaxation time

T2:

Transverse relaxation time

T2*:

Relaxation time caused by spin–spin interactions and local magnetic field inhomogeneities

tCho:

Choline compounds (total choline)

tCr:

Creatine compounds (total creatine)

TTP:

Time to peak

USPIOs:

Ultrasmall iron oxide particle s

UTP:

Uridine-5′-triphosphate

v e :

Extravascular extracellular space fractional volume

References

  1. Kuhl CK, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101–10.

    Article  PubMed  CAS  Google Scholar 

  2. Kovar DA, et al. A new method for imaging perfusion and contrast extraction fraction: input functions derived from reference tissues. J Magn Reson Imaging. 1998;8:1126–34.

    Article  PubMed  CAS  Google Scholar 

  3. Leach MO, et al. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer. 2005;92:1599–610.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  4. Tofts PS, et al. Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusible tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–32.

    Article  PubMed  CAS  Google Scholar 

  5. Jackson A, et al. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging. Clin Cancer Res. 2007;13:3449–59.

    Article  PubMed  Google Scholar 

  6. Chang YC, et al. Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI. Phys Med Biol. 2004;49:3593–602.

    Article  PubMed  Google Scholar 

  7. Mayr NA, et al. Pixel analysis of MR perfusion imaging in predicting radiation therapy outcome in cervical cancer. J Magn Reson Imaging. 2000;12:1027–33.

    Article  PubMed  CAS  Google Scholar 

  8. Daldrup-Link HE, Brasch RC. Macromolecular contrast agents for MR mammography: current status. Eur Radiol. 2003;13:354–65.

    PubMed  Google Scholar 

  9. Michel SCA, et al. Preoperative breast cancer staging: MR imaging of the axilla with ultrasmall superparamagnetic iron oxide enhancement. Radiology. 2002;225:527–36.

    Article  PubMed  Google Scholar 

  10. Hudgins PA, et al. Ferumoxtran-10, a superparamagnetic iron oxide as a magnetic resonance enhancement agent for imaging lymph nodes: a phase 2 dose study. AJNR Am J Neuroradiol. 2002;23:649–56.

    PubMed  Google Scholar 

  11. Tropres I, et al. Vessel size imaging. Magn Reson Med. 2001;45:397–408.

    Article  PubMed  CAS  Google Scholar 

  12. Edelman RR, et al. Cerebral blood flow: assessment with dynamic contrast-enhanced T2*-weighted MR imaging at 1.5 T. Radiology. 1990;176:211–20.

    PubMed  CAS  Google Scholar 

  13. Rempp KA, et al. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology. 1994;193:637–41.

    PubMed  CAS  Google Scholar 

  14. Wenz F, et al. Effect of radiation on blood volume in low-grade astrocytomas and normal brain tissue: quantification with dynamic susceptibility contrast MR imaging. Am J Roentgenol. 1996;166:187–93.

    Article  CAS  Google Scholar 

  15. Padhani AR. Functional MRI for anticancer therapy assessment. Eur J Cancer. 2002;38:2116–27.

    Article  PubMed  CAS  Google Scholar 

  16. Ichikawa T, et al. Characterization of hepatic lesions by perfusion-weighted MR imaging with an echoplanar sequence. Am J Roentgenol. 1998;170:1029–34.

    Article  CAS  Google Scholar 

  17. Kuhl CK, et al. Breast neoplasms: T2* susceptibility-contrast, first-pass perfusion MR imaging. Radiology. 1997;202:87–95.

    PubMed  CAS  Google Scholar 

  18. Kvistad KA, et al. Differentiating benign and malignant breast lesions with T2*-weighted first pass perfusion imaging. Acta Radiol. 1999;40:45–51.

    PubMed  CAS  Google Scholar 

  19. Borogovac A, Asllani I. Arterial Spin Labeling (ASL) fMRI: advantages, theoretical constraints, and experimental challenges in neurosciences. Int J Biomed Imaging. 2012;2012:818456.

    PubMed  PubMed Central  Google Scholar 

  20. Warmuth C, et al. Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology. 2003;228:523–32.

    Article  PubMed  Google Scholar 

  21. Wolf RL, et al. Grading of CNS neoplasms using continuous arterial spin labeled perfusion MR imaging at 3 Tesla. J Magn Reson Imaging. 2005;22:475–82.

    Article  PubMed  Google Scholar 

  22. Barrett T, et al. MRI of tumor angiogenesis. J Magn Reson Imaging. 2007;26:235–49.

    Article  PubMed  Google Scholar 

  23. Schor-Bardach R, et al. Does arterial spin-labeling MR imaging-measured tumor perfusion correlate with renal cell cancer response to antiangiogenic therapy in a mouse model? Radiology. 2009;251:731–42.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Schmitt P, et al. Quantitative tissue perfusion measurements in head and neck carcinoma patients before and during radiation therapy with a non-invasive MR imaging spin-labeling technique. Radiother Oncol. 2003;67:27–34.

    Article  PubMed  Google Scholar 

  25. D’Ippolito G, et al. CT and MRI in monitoring response: state-of-the-art and future developments. Q J Nucl Med Mol Imaging. 2011;55:603–19.

    PubMed  Google Scholar 

  26. Miles KA, Griffiths MR. Perfusion CT: a worthwhile enhancement? Br J Radiol. 2003;76:220–31.

    Article  PubMed  CAS  Google Scholar 

  27. Miles KA, et al. Application of CT in the investigation of angiogenesis in oncology. Acad Radiol. 2000;7:840–50.

    Article  PubMed  CAS  Google Scholar 

  28. Swensen SJ, et al. Lung nodule enhancement at CT: prospective findings. Radiology. 1996;201:447–55.

    PubMed  CAS  Google Scholar 

  29. Jinzaki M, et al. Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr. 2000;24:835–42.

    Article  PubMed  CAS  Google Scholar 

  30. Cenic A, et al. A CT method to measure hemodynamics in brain tumors: validation and application of cerebral blood flow maps. AJNR Am J Neuroradiol. 2000;21:462–70.

    PubMed  CAS  Google Scholar 

  31. Hattori H, et al. Tumor blood flow measured using dynamic computed tomography. Invest Radiol. 1994;29:873–6.

    Article  PubMed  CAS  Google Scholar 

  32. Wintermark M, et al. Simultaneous measurement of regional cerebral blood flow by perfusion CT and stable xenon CT: a validation study. AJNR Am J Neuroradiol. 2001;22:905–14.

    PubMed  CAS  Google Scholar 

  33. Claudon M, et al. Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) – update 2008. Ultraschall Med. 2008;29:28–44.

    Article  PubMed  CAS  Google Scholar 

  34. Postema M, Gilja OH. Contrast-enhanced and targeted ultrasound. World J Gastroenterol. 2011;17:28–41.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Huang P, et al. Double contrast-enhanced ultrasonography evaluation of preoperative Lauren classification of advanced gastric carcinoma. Arch Med Sci. 2011;7:287–93.

    Article  PubMed  PubMed Central  Google Scholar 

  36. van der Woude HJ, et al. Treatment of high-grade bone sarcomas with neoadjuvant chemotherapy: the utility of sequential color Doppler sonography in predicting histopathologic response. Am J Roentgenol. 1995;165:125–33.

    Article  Google Scholar 

  37. Singh S, et al. Color Doppler ultrasound as an objective assessment tool for chemotherapeutic response in advanced breast cancer. Breast Cancer. 2005;12:45–51.

    Article  PubMed  Google Scholar 

  38. Lassau N, et al. Prognostic value of angiogenesis evaluated with high-frequency and colour Doppler sonography for preoperative assessment of primary cutaneous melanomas: correlation with recurrence after a 5 year follow-up period. Cancer Imaging. 2006;6:24–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ross BD, et al. Evaluation of cancer therapy using diffusion magnetic resonance imaging. Mol Cancer Ther. 2003;2:581–7.

    PubMed  CAS  Google Scholar 

  40. Coenegrachts K, et al. Improved focal liver lesion detection: comparison of single-shot diffusion-weighted echoplanar and single-shot T2 weighted turbo spin echo techniques. Br J Radiol. 2007;80:524–31.

    Article  PubMed  CAS  Google Scholar 

  41. Katahira K, et al. Ultra-high-b-value diffusion-weighted MR imaging for the detection of prostate cancer: evaluation in 201 cases with histopathological correlation. Eur Radiol. 2011;21:188–96.

    Article  PubMed  Google Scholar 

  42. Kwee TC, et al. Whole-body MRI, including diffusion-weighted imaging, for the initial staging of malignant lymphoma: comparison to computed tomography. Invest Radiol. 2009;44:683–90.

    Article  PubMed  Google Scholar 

  43. Gutzeit A, et al. Comparison of diffusion-weighted whole body MRI and skeletal scintigraphy for the detection of bone metastases in patients with prostate or breast carcinoma. Skeletal Radiol. 2010;39(4):333–43.

    Article  PubMed  Google Scholar 

  44. Bruegel M, et al. Characterization of focal liver lesions by ADC measurements using a respiratory triggered diffusion-weighted single-shot echo-planar MR imaging technique. Eur Radiol. 2008;18:477–85.

    Article  PubMed  Google Scholar 

  45. Cui Y, et al. Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology. 2008;248:894–900.

    Article  PubMed  Google Scholar 

  46. Sun YS, et al. Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology. 2010;254:170–8.

    Article  PubMed  Google Scholar 

  47. Lin C, et al. Whole-body diffusion-weighted imaging with apparent diffusion coefficient mapping for treatment response assessment in patients with diffuse large B-cell lymphoma: pilot study. Invest Radiol. 2011;46:341–9.

    PubMed  Google Scholar 

  48. Horger M, et al. Whole-body diffusion-weighted MRI with apparent diffusion coefficient mapping for early response monitoring in multiple myeloma: preliminary results. AJR Am J Roentgenol. 2011;196:W790–5.

    Article  PubMed  Google Scholar 

  49. Heerschap A. In vivo MRS in clinical oncology. In: Shields AF, Price P, editors. In vivo imaging of cancer therapy. Totowa: Humana Press Inc.; 2007.

    Google Scholar 

  50. Kurhanewicz J, et al. Analysis of cancer metabolism by imaging hyperpolarized nuclei: prospects for translation to clinical research. Neoplasia. 2011;13:81–97.

    PubMed  PubMed Central  Google Scholar 

  51. van der Graaf M. In vivo magnetic resonance spectroscopy: basic methodology and clinical applications. Eur Biophys J. 2010;39:527–40.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kurhanewicz J, Vigneron DB. Advances in MR spectroscopy of the prostate. Magn Reson Imaging Clin N Am. 2008;16:697–710, ix–x.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chu DZ, et al. Proton NMR of human breast tumors: correlation with clinical prognostic parameters. J Surg Oncol. 1987;36:1–4.

    Article  PubMed  CAS  Google Scholar 

  54. Sijens PE, et al. Human breast cancer in vivo: H-1 and P-31 MR spectroscopy at 1.5 T. Radiology. 1988;169:615–20.

    PubMed  CAS  Google Scholar 

  55. Bradamante S, et al. High-resolution 1H NMR spectroscopy in the diagnosis of breast cancer. Magn Reson Med. 1988;8:440–9.

    Article  PubMed  CAS  Google Scholar 

  56. Katz-Brull R, et al. Clinical utility of proton magnetic resonance spectroscopy in characterizing breast lesions. J Natl Cancer Inst. 2002;94:1197–203.

    Article  PubMed  CAS  Google Scholar 

  57. King AD, et al. Monitoring of treatment response after chemoradiotherapy for head and neck cancer using in vivo 1H MR spectroscopy. Eur Radiol. 2010;20:165–72.

    Article  PubMed  Google Scholar 

  58. Steen RG. Response of solid tumors to chemotherapy monitored by in vivo 31P nuclear magnetic resonance spectroscopy: a review. Cancer Res. 1989;49:4075–85.

    PubMed  CAS  Google Scholar 

  59. Leach MO, et al. Measurements of human breast cancer using magnetic resonance spectroscopy: a review of clinical measurements and a report of localized 31P measurements of response to treatment. NMR Biomed. 1998;11:314–40.

    Article  PubMed  CAS  Google Scholar 

  60. Segebarth CM, et al. MR image-guided P-31 MR spectroscopy in the evaluation of brain tumor treatment. Radiology. 1987;165:215–9.

    PubMed  CAS  Google Scholar 

  61. Shinkwin MA, et al. Integrated magnetic resonance imaging and phosphorus spectroscopy of soft tissue tumors. Cancer. 1991;67:1849–58.

    Article  PubMed  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Radiology, GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, 5800, 6202 AZ, Maastricht, The Netherlands

    Ewelina Kluza PhD, Doenja M. J. Lambregts MD, PhD & Regina G. H. Beets-Tan MD, PhD

Authors
  1. Ewelina Kluza PhD
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  2. Doenja M. J. Lambregts MD, PhD
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  3. Regina G. H. Beets-Tan MD, PhD
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Corresponding author

Correspondence to Regina G. H. Beets-Tan MD, PhD .

Editor information

Editors and Affiliations

  1. Health Time Group, Jaén, Spain

    Antonio Luna

  2. Dept. of MRI, Clínica Girona, Girona, Spain

    Joan C. Vilanova

  3. Botafogo - Rio de Janeiro/RJ, Brazil

    L. Celso Hygino da Cruz Jr.

  4. Centro de Diagnóstico Dr. Enrique Rossi, Buenos Aires, Argentina

    Santiago E. Rossi

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Kluza, E., Lambregts, D.M.J., Beets-Tan, R.G.H. (2014). Overview of Functional MR, CT, and US Imaging Techniques in Clinical Use. In: Luna, A., Vilanova, J., Hygino da Cruz Jr., L., Rossi, S. (eds) Functional Imaging in Oncology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40412-2_13

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  • DOI: https://doi.org/10.1007/978-3-642-40412-2_13

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