Use of dynamic contrast enhanced time intensity curve shape analysis in MRI: Theory and practice

Reports in Medical Imaging

Volumn 6, Issue 1, 2013, Pages 71-82

  Lavini, Cristina ^a   Buiter, Maarten S ^b   Maas, Mario ^a  

^a ACADEMIC MEDICAL CENTER   (Netherlands)

^b NETHERLANDS CANCER INSTITUTE   (Netherlands)

Author keywords

DCE MRI; Pattern recognition; Review; TIC shape

Indexed keywords

CLASSIFICATION ALGORITHM; CONTRAST ENHANCEMENT; DIAGNOSTIC IMAGING; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE ENHANCEMENT; IMAGING AND DISPLAY; INTERMETHOD COMPARISON; NUCLEAR MAGNETIC RESONANCE IMAGING; PARAMETRIC TEST; QUANTITATIVE ANALYSIS; REVIEW; TIME INTENSITY CURVE SHAPE;

EID: 84889771482     PISSN: None     EISSN: 11791586     Source Type: Journal    
DOI: 10.2147/RMI.S35088     Document Type: Review

References (57)

1. Evelhoch JL. Key factors in the acquisition of contrast kinetic data for oncology. J Magn Reson Imaging. 1999;10(3):254-259.
2. 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):223-232.
3. Padhani AR, Leach MO. Antivascular cancer treatments: functional assessments by dynamic contrast-enhanced magnetic resonance imaging. Abdom Imaging. 2005;30(3):324-341.
4. Leach MO, Brindle KM, Evelhoch JL, et al; Pharmacodynamic/Pharmacokinetic Technologies Advisory Committee, Drug Development Office, Cancer Research UK. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer. 2005;92(9):1599-1610.
5. Henderson E, Rutt BK, Lee TY. Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging. 1998;16(9):1057-1073.
6. Kaiser WA, Zeitler E. MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary observations. Radiology. 1989;170:681-686.
7. Hauth EA, Stockamp C, Maderwald S, et al. Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced MR mammography. Clin Imaging. 2006;30(3):160-165.
8. Kubassova O, Boesen M, Boyle RD, et al. Fast and robust analysis of dynamic contrast enhanced MRI datasets. Med Image Comput Comput Assist Interv. 2007;10(2):261-269.
9. van Rijswijk CS, Hogendoorn PC, Taminiau AH, Bloem JL. Synovial sarcoma: dynamic contrast-enhanced MR imaging features. Skeletal Radiol. 2001;30(1):25-30.
10. Van der Woude HJ, Verstraete KL, Hogendoorn PC, Taminiau AH, Hermans J, Bloem JL. Musculoskeletal tumors: does fast dynamic contrast-enhanced subtraction MR imaging contribute to the characterization? Radiology. 1998;208(3):821-828.
11. Verstraete KL, Lang P. Bone and soft tissue tumors: the role of contrast agents for MR imaging. Eur J Radiol. 2000;34(3):229-246.
12. Lavini C, de Jonge MC, van de Sande MG, Tak PP, Nederveen AJ, Maas M. Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system. Magn Reson Imaging. 2007;25:604-612.
13. Yuan J, Chow SK, Yeung DK, King AD. A five-colour colour-coded mapping method for DCE-MRI analysis of head and neck tumors. Clin Radiol. 2012;67(3):216-223.
14. Degani H, Gusis V, Weinstein D, Fields S, Strano S. Mapping pathophysiological features of breast tumors by MRI at high spatial resolution. Nat Med. 1997;3(7):780-782.
15. Massurakis S, Gibbs P, Horsman A. Primary breast abnormalities: selective pixel sampling on dynamic gadolinium-enhanced MR images. Radiology. 1998;206:465-473.
16. Rieber A, Brambs HJ, Gabelmann A, Helmann V, Kreienberg R, Kuhn T. Breast MRI for monitoring response of primary breast cancer to neo-adjuvant chemotherapy. Eur Radiol. 2002;12:1711-1719.
17. Preim U, Glaßer S, Preim B, Fischbach F, Ricke J. Computer-aided diagnosis in breast DCE-MRI-quantification of the heterogeneity of breast lesions. Eur J Radiol. 2012;81(7):1532-1538.
18. Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed. 2009;22(1):28-39.
19. Hawighorst H, Libicher M, Knopp MV, Moehler T, Kauffmann GW, Kaick GV. Evaluation of angiogenesis and perfusion of bone marrow lesions: role of semiquantitative and quantitative dynamic MRI. J Magn Reson Imaging. 1999;10(3):286-294.
20. van Rijswijk CS, Geirnaerdt MJ, Hogendoorn PC, et al. Soft-tissue tumors: value of static and dynamic gadopentetate dimeglumine-enhanced MR imaging in prediction of malignancy. Radiology. 2004;233:493-502.
21. Tokuda O, Hayashi N, Taguchi K, Matsunaga N. Dynamic contrast-enhanced perfusion MR imaging of diseased vertebrae: analysis of three parameters and the distribution of the time-intensity curve patterns. Skeletal Radiol. 2005;34(10):632-638.
22. Kawakami Y, Kunisada T, Sugihara S, et al. New approach for assessing vascular distribution within bone tumors using dynamic contrast-enhanced MRI. J Cancer Res Clin Oncol. 2007;133(10):697-703.
23. Lavini C, Pickaart BC, de Jonge MC, Schaap GR, Maas M. Region of interest and pixel-by-pixel analysis dynamic contrast enhanced magnetic resonance imaging parameters and time-intensity curve shapes: a comparison in chondroid tumors. Magn Reson Imaging. 2009;27:62-68.
24. Sasaki M, Sumi M, Eida S, et al. Multiparametric MR imaging of sinonasal diseases: time-signal intensity curve-and apparent diffusion coefficient-based differentiation between benign and malignant lesions. Am J Neuroradiol. 2011;32(11):2154-2159.
25. Lavini C, Verhoeff JJ, Majoie CB, Stalpers LJ, Richel DJ, Maas M. Model-based, semiquantitative and time intensity curve shape analysis of dynamic contrast-enhanced MRI: a comparison in patients undergoing antiangiogenic treatment for recurrent glioma. J Magn Reson Imaging. 2011;34(6):1303-1312.
26. Eida S, Ohki M, Sumi M, Yamada T, Nakamura T. MR factor analysis: improved technology for the assessment of 2D dynamic structures of benign and malignant salivary gland tumors. J Magn Reson Imaging. 2008;27(6):1256-1262.
27. Yabuuchi H, Matsuo Y, Kamitani T, et al. Parotid gland tumor: can addition of diffusion-weighted MR-imaging to dynamic contrast-enhanced MR imaging improve diagnostic accuracy in characterization? Radiology. 2008;249(3):909-916.
28. Schaefer JF, Schneider V, Vollmar J, et al. Solitary pulmonary nodules: association between signal characteristics in dynamic contrast enhanced MRI and tumor angiogenesis. Lung Cancer. 2006;53(1):39-49.
29. Donmez FY, Yekeler E, Saeidi V, Tunaci A, Tunaci M, Acunas G. Dynamic contrast enhancement patterns of solitary pulmonary nodules on 3D gradient-recalled echo MRI. AJR Am J Roentgenol. 2007;189(6):1380-1386.
30. Kono R, Fujimoto K, Terasaki H, et al. Dynamic MRI of solitary pulmonary nodules: comparison of enhancement patterns of malignant and benign small peripheral lung lesions. AJR Am J Roentgenol. 2007;188(1):26-36.
31. Yamashita Y, Fan ZM, Yamamoto H, et al. Spin-echo and dynamic gadolinium-enhanced FLASH MR imaging of hepatocellular carcinoma: correlation with histopathologic findings. J Magn Reson Imaging. 1994;4(1):83-90.
32. Wang J, Chen LT, Tsang YM, Liu TW, Shih TT. Dynamic contrast-enhanced MRI analysis of perfusion changes in advanced hepatocellular carcinoma treated with an antiangiogenic agent: a preliminary study. AJR Am J Roentgenol. 2004;183(3):713-719.
33. Tuncbilek N, Karakas HM, Altaner S. Dynamic MRI in indirect estimation of microvessel density, histologic grade, and prognosis in colorectal adenocarcinomas. Abdom Imaging. 2004;29(2):166-172.
34. Noworolski SM, Henry RG, Vigneron DB, Kurhanewicz J. Dynamic contrast-enhanced MRI in normal and abnormal prostate tissues as defined by biopsy, MRI, and 3D MRSI. Magn Reson Med. 2005;53(2): 249-255.
35. Kubassova O, Boyle R, Radjenovic A. Quantitative analysis of dynamic contrast enhanced MRI datasets of the metacarpophalangeal joints. Acad Radiol. 2007;14:1189-1200.
36. van der Leij C, van de Sande MG, Lavini C, Tak PP, Maas M. Rheumatoid synovial inflammation: pixel-by-pixel dynamic contrast-enhanced MR imaging time-intensity curve shape analysis-a feasibility study. Radiology. 2009;253(1):234-240.
37. van de Sande MG, van der Leij C, Lavini C, Wijbrandts CA, Maas M, Tak PP. Characteristics of synovial inflammation in early arthritis analysed by pixel-by-pixel time-intensity curve shape analysis. Rheumatology. 2012;51(7):1240-1245.
38. Kim EY, Kwack KS, Cho JH, Lee DH, Yoon SH. Usefulness of dynamic contrast-enhanced MRI in differentiating between septic arthritis and transient synovitis in the hip joint. AJR Am J Roentgenol. 2012;198(2): 428-433.
39. Giusti S, Faggioni L, Neri E, et al. Dynamic MRI of the small bowel: usefulness of quantitative contrast-enhancement parameters and time-signal intensity curves for differentiating between active and inactive Crohn's disease. Abdom Imaging. 2010;35(6):646-653.
40. Ziech ML, Lavini C, Caan MW, et al. Dynamic contrast-enhanced MRI in patients with luminal Crohn's disease. Eur J Radiol. 2012;81(11): 3019-3027.
41. Ziech ML, Lavini C, Bipat S, et al. Dynamic contrast-enhanced MRI in determining disease activity in perianal fistulizing Crohn's disease: a pilot study. AJR Am J Roentgenol. 2013;200(2):170-177.
42. Horsthuis K, Lavini C, Bipat S, Stokkers PC, Stoker J. Perianal Crohn's disease: evaluation of dynamic contrast-enhanced MR imaging as an indicator of disease activity. Radiology. 2009;251(2):380-387.
43. Eyal E, Degani H. Model based and model free parametric analysis of breast dynamic contrast enhanced MRI. NMR Biomed. 2009;22:40-53.
44. Kajihara M, Sugawara Y, Sakayama K, Kikuchi K, Mochizuki T, Murase K. Evaluation of tumor blood flow in musculoskeletal lesions: dynamic contrast-enhanced MR imaging and its possibility when monitoring the response to preoperative chemotherapy-work in progress. Radiat Med. 2007;25(3):94-105.
45. El Khouli RH, Macura KJ, Kamel IR, Jacobs MA, Bluemke DA. 3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis. AJR Am J Roentgenol. 2011;197(6):1498-1505.
46. Lavini C, Maas M. DCE-MRI analysis package comprising pixel-by-pixel classification of Time Intensity Curves shapes, permeability maps and Gd concentration calculation. Abstract from the ESMRMB 2008 Congress, October 2-4, 2008; Valencia, Spain. Magn Reson Materials Physics Biol Med. 2008:21 Suppl1, S1:486.
47. Kubassova O, Boesenb M, Cimmino M, Bliddal H. A computer-aided detection system for rheumatoid arthritis MRI data interpretation and quantification of synovial activity. Eur J Radiol. 2010;74(3):e67-e72.
48. Eyal E, Bloch BN, Rofsky NM, et al. Principal component analysis of dynamic contrast enhanced MRI in human prostate cancer. Invest Radiol. 2010;45:174-181.
49. Andersen C, Taagehøj JF, Mühler A, Rehling M. Approximation of arterial input curve data in MRI estimation of cerebral blood-tumor-barrier leakage: comparison between Gd-DTPA and 99 mTc-DTPA input curves. Magn Reson Imaging. 1996;14(3):235-241.
50. Parker GJ, Roberts C, Macdonald A, et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006;56(5):993-1000.
51. Dale BM, Jesberger JA, Lewin JS, Hillenbrand CM, Duerk JL. Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. J Magn Reson Imaging. 2003;18(5):575-584.
52. Guo J-Y, Reddick WE. DCE-MRI Pixel by Pixel quantitative Curve Pattern Analysis and its application to osteosarcoma. J Magn Res Imaging. 2009;30:177-184.
53. Kale MC, Clymer BD, Koch RM, et al. Multispectral co-occurrence with three random variables in dynamic contrast enhanced magnetic resonance imaging of breast cancer. IEEE Trans Med Imaging. 2008;27(10):1425-1431.
54. Lucht RE, Delorme S, Hei J, et al. Classification of signal-time curves obtained by dynamic magnetic resonance mammography: statistical comparison of quantitative methods. Invest Radiol. 2005;40(7):442-447.
55. Nattkemper TW, Arnrich B, Lichte O, et al; UK MARIBS Breast Screening Study. Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. Artif Intell Med. 2005;34(2):129-139.
56. Levman J, Leung T, Causer P, Plewes D, Martel AL. Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging. 2008;27(5):688-696.
57. Agner SC, Soman S, Libfeld E, et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging. 2011;24(3):446-463.