User menu

Analysis of contrast-enhanced MR images to assess renal function.

Bibliographic reference Michoux, Nicolas ; Vallée, J-P ; Pechère-Bertschi, A ; Montet, X ; Buehler, L ; et. al. Analysis of contrast-enhanced MR images to assess renal function.. In: Magma (New York, N.Y.), Vol. 19, no. 4, p. 167-79 (2006)
Permanent URL
  1. Knesplova L, Krestin GP (1998) Magnetic resonance in the assessment of renal function. Eur Radiol 8:201–211
  2. Grenier N, Basseau F, Ries M, Tyndal B, Jones R, Moonen C (2003) Functional MRI of the kidney. Abdom Imaging 28:164–175
  3. Zhang J, Pedrosa I, Rofsky NM (2003) MR techniques for renal imaging. Radiol Clin North Am 41:877–907
  4. Huang AJ, Lee VS, Rusinek H (2003) MR imaging of renal function. Radiol Clin North Am 41:1001–1017
  5. Rusinek H, Kaur M, Lee VS (2004) Renal magnetic resonance imaging. Curr Opin Nephrol Hypertens 13:667–673
  6. Grenier N, Hauger O, Cimpean A, Perot V (2006) Update of renal imaging. Semin Nucl Med 36:3–15
  7. Hermoye L, Annet L, Lemmerling P, Peeters F, Jamar F, Gianello P, Van Huffel S, Van Beers BE (2004) Calculation of the renal perfusion and glomerular filtration rate from the renal impulse response obtained with MRI. Magn Reson Med 51:1017–1025
  8. Prasad Pottumarthi V., Goldfarb James, Sundaram Chandru, Priatna Agus, Li Wei, Edelman Robert R., Captopril MR Renography in a Swine Model: Toward a Comprehensive Evaluation of Renal Arterial Stenosis, 10.1148/radiology.217.3.r00dc34813
  9. Lee VS, Rusinek H, Noz ME, Lee P, Raghavan M, Kramer EL (2003) Dynamic three-dimensional MR renography for the measurement of single kidney function: initial experience. Radiology 227:289–294
  10. Michoux N, Joannides R, Gouesbet G, Thuillez C, Maheu B, Le Sceller L (1999) Physical determinism in human arterial dynamics. Eur Phys J AP 8:265–268
  11. Holstein-Rathlou NH, Marsh DJ (1994) Renal blood flow regulation and arterial pressure fluctuations: a case study in nonlinear dynamics. Physiol Rev 74:637–681
  12. Toga AW (1999) Brain warping. Academic, San Diego
  13. Fitzpatrick J., Hill Derek, Maurer Calvin, Image Registration, Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis ISBN:9780819477606 p.447-513, 10.1117/3.831079.ch8
  14. Giele EL, de Priester JA, Blom JA, den Boer JA, van Engelshoven JM, Hasman A, Geerlings M (2001) Movement correction of the kidney in dynamic MRI scans using FFT phase difference movement detection. J Magn Reson Imaging 14:741–749
  15. Gerig G, Kikinis R, Kuoni W, von Schulthess GK, Kubler O (1991) Semiautomated ROI analysis in dynamic MR studies. Part I: image analysis tools for automatic correction of organ displacements. J Comput Assist Tomogr 15:725–732
  16. von Schulthess GK, Kuoni W, Gerig G, Wuthrich R, Duewell S, Krestin G (1991) Semiautomated ROI analysis in dynamic MR studies. Part II: application to renal function examination. J Comput Assist Tomogr 15:733–741
  17. Yim PJ, Marcos HB, Mcauliffe M, McGarry D, Heaton I, Choyke P (2001) Registration of time-series contrast enhanced magnetic resonance images for renography. In: Proceedings of the 14th IEEE Symposium on Computer Based Medical Systems, Bethesda, Maryland, USA:pp 516–520
  18. Woods RP, Cherry SR, Mazziotta JC (1992) Rapid automated algorithm for aligning and reslicing PET images. J Comput Assist Tomogr 16:620–633
  19. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000
  20. Sun Y, Jolly M, Moura JM (2004) Integrated registration of dynamic renal perfusion MR images. In: Proceedings of the International Conference on Image Processing (ICIP’04), Singapore, vol. 3, pp. 1923–1926
  21. Dornier C, Ivancevic MK, Thevenaz P, Vallee JP (2003) Improvement in the quantification of myocardial perfusion using an automatic spline-based registration algorithm. J Magn Reson Imaging 18:160–168
  22. Pal N, Pal S (1993) A review on image segmentation techniques. Pattern Recognit 26:1277–1294
  23. Coulam CH, Bouley DM, Sommer FG (2002) Measurement of renal volumes with contrast-enhanced MRI. J Magn Reson Imaging 15:174–179
  24. Palmer PL, Dabis H, Kittler J (1996) A performance measure for boundary detection algorithms. Comput Vis Image Underst 63:476–494
  25. Makowski P, Sorensen TS, Therkildsen SV, Materka A, Stodkilde-Jorgensen H, Pedersen EM (2002) Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization. Comput Med Imaging Graph 26:9–17
  26. Gleason SS, Sari-Sarraf H, Abidi MA, Karakashian O, Morandi F (2002) A new deformable model for analysis of X-ray CT images in preclinical studies of mice for polycystic kidney disease. IEEE Trans Med Imaging 21:1302–1309
  27. Martín-Fernández M, Alberola-López C (2005) An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours. Med Image Anal 9:1–23
  28. Xie J, Jiang YF, Tsui HT (2005) Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging 24:45–57
  29. Wang Y, Teoh EK (2005) Dynamic B-snake model for complex objects segmentation. Image Vis Comput 23:1029–1040
  30. Hojjatoleslami SA, Kittler J (1998) Region growing: a new approach. IEEE Trans Image Process 7:1079–1084
  31. Meyer F, Beucher S (1990) Morphological segmentation. J Vis Commun Image Represent 11:21–46
  32. Paragios N (2003) A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging 22:773–776
  33. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647
  34. Mehnert A, Jackway P (1997) An improved seeded region growing algorithm. Pattern Recognit Lett 18:1065–1071
  35. Boykov Y, Lee VS, Rusinek H, Bansal R (2001) Segmentation of dynamic N-D data sets via graph cuts using markov models. In: Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, Utrecht, The Netherlands, pp 1058–1066
  36. Huang AJ, Boykov Y, Rusinek H, Lee VS (2003) Validation of a graph cuts algorithm for semi-automated segmentation of magnetic resonance renographic images. In: Proceedings of the International Society for Magnetic Resonance in Medicine, 11th Scientific Meeting and Exhibition. Toronto, USA, p. 1059
  37. de Priester JA, Kessels AG, Giele EL, den Boer JA, Christiaans MH, Hasman A, van Engelshoven JM (2001) MR renography by semiautomated image analysis: performance in renal transplant recipients. J Magn Reson Imaging 14:134–140
  38. Yuksel SE, El-Baz A, Farag AA, Abo El-Ghar ME, Eldiasty TA, Ghoneim MA (2005) Automatic detection of renal rejection after kidney transplantation. In: Proceedings of the Computer Assisted Radiology and Surgery Conference, Berlin, Germany, pp, 773–778
  39. Song T, Lee VS, Rusinek H, Sajous BJ, Laine AF (2005) Registration and segmentation of dynamic three-dimensional MR renography based on Fourier representations and k-means clustering. In: Proceedings of the International Society for Magnetic Resonance in Medicine 13th Scientific Meeting, Miami Beach, Florida, USA, p442 (2266)
  40. Kybic J, Unser M (2003) Fast parametric elastic image registration. IEEE Trans Image Process 12:1427–1442
  41. Lee VS, Rusinek H, Johnson G, Rofsky NM, Krinsky GA, Weinreb JC (2001) MR renography with low-dose gadopentetate dimeglumine: feasibility. Radiology 221:371–379
  42. Agildere AM, Tarhan NC, Bozdagi G, Demirag A, Niron EA, Haberal M (1999) Correlation of quantitative dynamic magnetic resonance imaging findings with pathology results in renal transplants: a preliminary report. Transplant Proc 31:3312–3316
  43. Sharma RK, Gupta RK, Poptani H, Pandey CM, Gujral RB, Bhandari M (1995) The magnetic resonance renogram in renal transplant evaluation using dynamic contrast-enhanced MR imaging. Transplantation 59:1405–1409
  44. Michaely HJ, Schoenberg SO, Oesingmann N, Ittrich C, Buhlig C, Friedrich D, Struwe A, Rieger J, Reininger C, Samtleben W, Weiss M, Reiser MF (2006) Renal Artery Stenosis: Functional Assessment with Dynamic MR Perfusion Measurements – Feasibility Study. Radiology DOI: 10.1148/radiol.2382041553
  45. Peters AM, Brown J, Crossman D, Brady AJ, Hemingway AP, Roddie ME, Allison DJ (1990) Noninvasive measurement of renal blood flow with technetium-99m-DTPA in the evaluation of patients with suspected renovascular hypertension. J Nucl Med 31:1980–1985
  46. Wedeking P, Eaton S, Covell DG, Nair S, Tweedle MF, Eckelman WC (1990) Pharmacokinetic analysis of blood distribution of intravenously administered 153Gd-labeled Gd(DTPA)2- and 99mTc(DTPA) in rats. Magn Reson Imaging 8:567–575
  47. Vallee JP, Lazeyras F, Khan HG, Terrier F (2000) Absolute renal blood flow quantification by dynamic MRI and Gd-DTPA. Eur Radiol 10:1245–1252
  48. Montet X, Ivancevic MK, Belenger J, Jorge-Costa M, Pochon S, Pechere A, Terrier F, Vallee JP (2003) Noninvasive measurement of absolute renal perfusion by contrast medium-enhanced magnetic resonance imaging. Invest Radiol 38:584–592
  49. Michoux N, Montet X, Pechère A, Ivancevic MK, Martin P-Y, Keller A, Didier D, Terrier F, Vallée J-P (2005) Parametric and quantitative analysis of MR renographic curves for assessing the functional behaviour of the kidney. Eur J Radiol 54:124–135
  50. Pedersen M, Shi Y, Anderson P, Stodkilde-Jorgensen H, Christian Djurhuus J, Gordon I, Frokiaer J (2004) Quantitation of differential renal blood flow and renal function using dynamic contrast-enhanced MRI in rats. Magn Reson Med 51:510–517
  51. Frank JA, Choyke PL, Austin HA, Girton ME (1991) Functional MR of the kidney. Magn Reson Med 22:319–323
  52. Dumoulin CL, Buonocore MH, Opsahl LR, Katzberg RW, Darrow RD, Morris TW, Batey C (1994) Noninvasive measurement of renal hemodynamic functions using gadolinium enhanced magnetic resonance imaging. Magn Reson Med 32:370–378
  53. Myers BD, Sommer FG, Li K, Tomlanovich S, Pelc N, McDonnell C, Pagtalunan E, Newton L, Jamison R (1994) Determination of blood flow to the transplanted kidney. A novel application of phase-contrast, cine magnetic resonance imaging. Transplantation 57:1445–1450
  54. Rempp K A, Brix G, Wenz F, Becker C R, Gückel F, Lorenz W J, Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging., 10.1148/radiology.193.3.7972800
  55. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715–725
  56. Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn Reson Med 36:726–736
  57. Wirestam R, Andersson L, Ostergaard L, Bolling M, Aunola JP, Lindgren A, Geijer B, Holtas S, Stahlberg F (2000) Assessment of regional cerebral blood flow by dynamic susceptibility contrast MRI using different deconvolution techniques. Magn Reson Med 43:691–700
  58. Fine DR, Lurie RE, Candy GP (1994) An anatomical and physiological model of the renal parenchymal model development and parametric identification. Physiol Meas 15:407–428
  59. Koh TS, Hou Z (2002) A numerical method for estimating blood flow by dynamic functional imaging. Med Eng Phys 24:151–158
  60. Aumann S, Schoenberg SO, Just A, Briley-Saebo K, Bjornerud A, Bock M, Brix G (2003) Quantification of renal perfusion using an intravascular contrast agent (part 1): Results in a canine model. Magn Reson Med 49:276–287
  61. Schoenberg SO, Aumann S, Just A, Bock M, Knopp MV, Johansson LO, Ahlstrom H (2003) Quantification of renal perfusion abnormalities using an intravascular contrast agent (part 2): Results in animals and humans with renal artery stenosis. Magn Reson Med 49:288–298
  62. Sourbron S, Luyparte R, Van Schuerbeek P, Dujardin M, Stadnik T, Osteaux M (2004) Deconvolution of dynamic contrast-enhanced MRI data by linear inversion: choice of the regularization parameter. Magn Reson Med 52:209–213
  63. Bendat JS (1990) Nonlinear system analysis and identification from random data. Wiley, New York, Chichester, Brisbane
  64. Ljung L (1999) System identification – theory for the user 2nd edn. Prentice Hall, Upper Saddle River
  65. Yang D, Ye Q, Williams M, Sun Y, Hu TC, Williams DS, Moura JM, Ho C (2001) USPIO-enhanced dynamic MRI: evaluation of normal and transplanted rat kidneys. Magn Reson Med 46:1152–1163
  66. Wronski T., Seeliger E., Persson P. B., Forner C., Fichtner C., Scheller J., Flemming B., The step response: a method to characterize mechanisms of renal blood flow autoregulation, 10.1152/ajprenal.00420.2002
  67. Baumann D, Rudin M (2000) Quantitative assessment of rat kidney function by measuring the clearance of the contrast agent Gd(DOTA) using dynamic MRI. Magn Reson Imaging 18:587–595
  68. Laurent D, Poirier K, Wasvary J, Rudin M (2002) Effect of essential hypertension on kidney function as measured in rat by dynamic MRI. Magn Reson Med 47:127–134
  69. Patlak Clifford S., Blasberg Ronald G., Fenstermacher Joseph D., Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data, 10.1038/jcbfm.1983.1
  70. Hackstein N, Heckrodt J, Rau WS (2003) Measurement of single-kidney glomerular filtration rate using a contrast-enhanced dynamic gradient-echo sequence and the Rutland–Patlak plot technique. J Magn Reson Imaging 18:714–725
  71. Annet L, Hermoye L, Peeters F, Jamar F, Dehoux J-P, Van Beers BE (2004) Glomerular filtration rate: assessment with dynamic contrast-enhanced MRI and a cortical-compartment model in the rabbit kidney. J Magn Reson Imaging 20:843–849
  72. Shuter B, Tofts PS, Wang SC, Pope JM (1996) The relaxivity of Gd-EOB-DTPA and Gd-DTPA in liver and kidney of the Wistar rat. Magn Reson Imaging 14:243–253
  73. Thomas SR (1998) Cycles and separations in a model of the renal medulla. Am J Physiol 275:F671–F690
  74. Layton HE, Pitman EB, Moore LC (1997) Nonlinear filter properties of the thick ascending limb. Am J Physiol 273:F625–F634
  75. Chang H, Fujita T (1999) A numerical model of the renal distal tubule. Am J Physiol 276:F931–F951
  76. Layton AT, Layton HE (2002) A numerical method for renal models that represent tubules with abrupt changes in membrane properties. J Math Biol 45:549–567
  77. Gyenge CC, Bowen BD, Reed RK, Bert JL (2003) Mathematical model of renal elimination of fluid and small ions during hyper- and hypovolemic conditions. Acta Anaesthesiol Scand 47:122–137
  78. Uttamsingh RJ, Leaning MS, Bushman JA, Carson ER, Finkelstein L (1985) Mathematical model of the human renal system. Med Biol Eng Comput 23:525–535
  79. Russel FG, Wouterse AC, van Ginneken CA (1987) Physiologically based pharmacokinetic model for the renal clearance of phenolsulfonphthalein and the interaction with probenecid and salicyluric acid in the dog. J Pharmacokinet Biopharm 15:349–368
  80. Wu G (1998) Using three-, four-, and n-compartment closed models to estimate glomerular filtration rate during and after a constant rate intravenous infusion. Eur J Pharm Biopharm 46:397–402
  81. Curti G, DeMartini D, Santaniello B, Taddei G, Fresco GF (1998) A theoretical four-compartment model to evaluate separate kidney technetium-99m-MAG3 kinetics in humans. Kidney Int 54:2029–2036
  82. Zarzuelo A, Lanao JM, Lopez FG, Sanchez-Navarro A (2002) Influence of the infusion rate on disposition of netilmicin in the isolated rat perfused kidney. Eur J Pharm Sci 16:133–141
  83. Lee VS, Huang AJ, Kaur M, Rusinek H, Nazzaro CA, Kramer EL, Leonard E (2005) Single kidney GFR measurements derived from a multicompartmental model analysis of 3D MR renography. In: Proceedings of the International Society for Magnetic Resonance in Medicine, South Beach, Miami, Florida, USA, vol. 13, p. 552
  84. Brayton RK, Directo SW, Hatchel GD, Vidigal L (1979) A new algorithm for statistical circuit design based on quasi-Newton methods and function splitting. IEEE Trans Circuits Syst CAS-26:784–794
  85. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441
  86. Coleman TF (1996) An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6:418–445
  87. Nelder J. A., Mead R., A Simplex Method for Function Minimization, 10.1093/comjnl/7.4.308
  88. Peeters F, Annet L, Hermoye L, Van Beers BE (2004) Inflow correction of hepatic perfusion measurements using T1-weighted, fast gradient-echo, contrast-enhanced MRI. Magn Reson Med 51:710–717
  89. Murase K (2004) Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast- enhanced magnetic resonance imaging. Magn Reson Med 51:858–862
  90. Bevington PR, Robinson DK (2002) Data reduction and error analysis for the physical sciences, 3rd edn. McGraw–Hill, New York
  91. Ahearn TS, Staff RT, Redpath TW, Semple SIK (2005) The use of the Levenberg–Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data. Phys Med Biol 50:85–92
  92. Renken NS, Krestin GP (2005) Magnetic resonance imaging of the kidney. Semin Ultrasound CT MR 26:153–161
  93. Prasad P (2005) Magnetic resonance imaging: methods and biologic applications. Humana Press, NJ, pp. 197–224
  94. Choyke PL, Kobayashi H (2005) Functional magnetic resonance imaging of the kidney using macromolecular contrast agents. Abdom Imaging 31:224–231