In vitro pharmacokinetic phantom for two-compartment modeling in DCE-MRI

被引:0
作者
Wahyulaksana, Geraldi [1 ]
Saporito, Salvatore [1 ]
den Boer, Jacques A. [1 ]
Herold, Ingeborg H. F. [2 ]
Mischi, Massimo [1 ]
机构
[1] Eindhoven Univ Technol, Dept Elect Engn, NL-5612 AZ Eindhoven, Netherlands
[2] Catharina Hosp, Dept Anesthesiol, NL-5623 EJ Eindhoven, Netherlands
关键词
DCE-MRI; in vitro phantom; pharmacokinetic modeling; two-compartmental model; time-intensity curve; CONTRAST-ENHANCED MRI; TREATMENT RESPONSE; BREAST-CANCER; PARAMETERS; BLOOD; FLOW; PERFUSION; TRACER; UNCERTAINTY; CIRCULATION;
D O I
10.1088/1361-6560/aae33b
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established minimally-invasive method for assessment of extravascular leakage, hemodynamics, and tissue viability. However, differences in acquisition protocols, variety of pharmacokinetic models, and uncertainty on physical sources of MR signal hamper the reliability and widespread use of DCE-MRI in clinical practice. Measurements performed in a controlled in vitro setup could be used as a basis for standardization of the acquisition procedure, as well as objective evaluation and comparison of pharmacokinetic models. In this paper, we present a novel flow phantom that mimics a two-compartmental (blood plasma and extravascular extracellular space/EES) vascular bed, enabling systemic validation of acquisition protocols. The phantom consisted of a hemodialysis filter with two compartments, separated by hollow fiber membranes. The aim of this phantom was to vary the extravasation rate by adjusting the flow in the two compartments. Contrast agent transport kinetics within the phantom was interpreted using two-compartmental pharmacokinetic models. Boluses of gadolinium-based contrast-agent were injected in a tube network connected to the hollow fiber phantom; time-intensity curves (TICs) were obtained from image series, acquired using a T1-weighted DCE-MRI sequence. Under the assumption of a linear dilution system, the TICs obtained from the input and output of the system were then analyzed by a system identification approach to estimate the trans-membrane extravasation rates in different flow conditions. To this end, model-based deconvolution was employed to determine (identify) the impulse response of the investigated dilution system. The flow rates in the EES compartment significantly and consistently influenced the estimated extravasation rates, in line with the expected trends based on simulation results. The proposed phantom can therefore be used to model a two-compartmental vascular bed and can be employed to test and optimize DCE-MRI acquisition sequences in order to determine a standardized acquisition procedure leading to consistent quantification results.
引用
收藏
页数:13
相关论文
共 50 条
[1]   Fitting the Two-Compartment Model in DCE-MRI by Linear Inversion [J].
Flouri, Dimitra ;
Lesnic, Daniel ;
Sourbron, Steven P. .
MAGNETIC RESONANCE IN MEDICINE, 2016, 76 (03) :998-1006
[2]   Renal perfusion in acute kidney injury with DCE-MRI: Deconvolution analysis versus two-compartment filtration model [J].
Zoellner, Frank G. ;
Zimmer, Fabian ;
Klotz, Sarah ;
Hoeger, Simone ;
Schad, Lothar R. .
MAGNETIC RESONANCE IMAGING, 2014, 32 (06) :781-785
[3]   Functional imaging of acute kidney injury at 3 Tesla: Investigating multiple parameters using DCE-MRI and a two-compartment filtration model [J].
Zoellner, Frank G. ;
Zimmer, Fabian ;
Klotz, Sarah ;
Hoeger, Simone ;
Schad, Lothar R. .
ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK, 2015, 25 (01) :58-65
[4]   PHARMACOKINETIC MODELS IN CLINICAL PRACTICE: WHAT MODEL TO USE FOR DCE-MRI OF THE BREAST? [J].
Litjens, G. J. S. ;
Heisen, M. ;
Buurman, J. ;
Romeny, B. M. ter Haar .
2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, 2010, :185-188
[5]   Portable perfusion phantom for quantitative DCE-MRI of the abdomen [J].
Kim, Harrison ;
Mousa, Mina ;
Schexnailder, Patrick ;
Hergenrother, Robert ;
Bolding, Mark ;
Ntsikoussalabongui, Bernard ;
Thomas, Vinoy ;
Morgan, Desiree E. .
MEDICAL PHYSICS, 2017, 44 (10) :5198-5209
[6]   Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data [J].
Kontopodis, Eleftherios ;
Kanli, Georgia ;
Manikis, Georgios C. ;
Van Cauter, Sofie ;
Marias, Kostas .
CANCER INFORMATICS, 2015, 14 :41-51
[7]   A novel anthropomorphic flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition techniques [J].
Knight, Silvin P. ;
Browne, Jacinta E. ;
Meaney, James F. ;
Smith, David S. ;
Fagan, Andrew J. .
PHYSICS IN MEDICINE AND BIOLOGY, 2016, 61 (20) :7466-7483
[8]   Renal compartment segmentation in DCE-MRI images [J].
Yang, Xin ;
Minh, Hung Le ;
Cheng, Kwang-Ting ;
Sung, Kyung Hyun ;
Liu, Wenyu .
MEDICAL IMAGE ANALYSIS, 2016, 32 :269-280
[9]   Quality-based pharmacokinetic model selection on DCE-MRI for characterizing orbital lesions [J].
Lecler, Augustin ;
Balvay, Daniel ;
Cuenod, Charles-Andre ;
Marais, Louise ;
Zmuda, Mathieu ;
Sadik, Jean-Claude ;
Galatoire, Olivier ;
Farah, Edgar ;
El Methni, Jonathan ;
Zuber, Kevin ;
Berges, Olivier ;
Savatovsky, Julien ;
Fournier, Laure .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2019, 50 (05) :1514-1525
[10]   Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI [J].
Holland, Martin D. ;
Morales, Andres ;
Simmons, Sean ;
Smith, Brandon ;
Misko, Samuel R. ;
Jiang, Xiaoyu ;
Hormuth, David A. ;
Christenson, Chase ;
Koomullil, Roy P. ;
Morgan, Desiree E. ;
Li, Yufeng ;
Xu, Junzhong ;
Yankeelov, Thomas E. ;
Kim, Harrison .
MEDICAL PHYSICS, 2022, 49 (01) :271-281