On impulse response functions computed from dynamic contrast-enhanced image data by algebraic deconvolution and compartmental modeling

被引:17
作者
Brix, Gunnar [1 ]
Ravesh, Mona Salehi [2 ]
Zwick, Stefan [2 ,3 ]
Griebel, Juergen [1 ]
Delorme, Stefan [4 ]
机构
[1] Fed Off Radiat Protect, Dept Med & Occupat Radiat Protect, D-85764 Oberschleissheim, Germany
[2] German Canc Res Ctr, Dept Med Phys Radiol, D-6900 Heidelberg, Germany
[3] Univ Med Ctr Freiburg, Dept Radiol, Freiburg, Germany
[4] German Canc Res Ctr, Dept Radiol, D-6900 Heidelberg, Germany
来源
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS | 2012年 / 28卷 / 02期
关键词
Contrast-enhanced dynamic imaging; Microcirculation; Indicator dilution theory; Compartmental modeling; Deconvolution; CEREBRAL-BLOOD-FLOW; PHARMACOKINETIC ANALYSIS; TRACER KINETICS; CT; MICROCIRCULATION; QUANTIFICATION; VOLUME; TUMORS; MRI;
D O I
10.1016/j.ejmp.2011.03.004
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Concentration-time courses measured by dynamic contrast-enhanced (DCE) imaging can be described by a convolution of the arterial input with an impulse response function, Q(T)(t), characterizing tissue microcirculation. Data analysis is based on two different approaches: computation of Q(T)(t) by algebraic deconvolution (AD) and subsequent evaluation according to the indicator dilution theory (IDT) or parameterization of Q(T)(t) by analytical expressions derived by compartmental modeling. Pitfalls of both strategies will be addressed in this study. Tissue data acquired by DCE-CT in patients with head-and-neck cancer and simulated by a reference model (MMID4) were analyzed by a two-compartment model (TCM), a permeability-limited two-compartment model (PL-TCM) and AD. Additionally, MMID4 was used to compute the 'true' response function that corresponds to the simulated tumor data. TCM and AD yielded accurate fits, whereas PL-TCM performed worse. Nevertheless, the corresponding response functions diverge markedly. The response curves obtained by TCM decrease exponentially in the early perfusion phase and overestimate the tissue perfusion, Q(T)(0). AD also resulted in response curves starting with a negative slope and not as the 'true' response function in accordance with the IDT - with a horizontal plateau. They are thus not valid responses in the sense of the IDT that can be used unconditionally for parameter estimation. Response functions differing considerably in shape can result in virtually identical tissue curves. This non-uniqueness makes a strong argument not to use algebraic but rather analytical deconvolution to reduce the class of solutions to representatives that are in accordance with a-priori knowledge. To avoid misinterpretations and systematic errors, users must be aware of the pitfalls inherent to the different concepts. (C) 2011 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:119 / 128
页数:10
相关论文
共 28 条
[1]   Head and Neck Squamous Cell Carcinoma: CT Perfusion Can Help Noninvasively Predict Intratumoral Microvessel Density [J].
Ash, Lorraine ;
Teknos, Theodoros N. ;
Gandhi, Dheerhaj ;
Patel, Samip ;
Mukherji, Suresh K. .
RADIOLOGY, 2009, 251 (02) :422-428
[3]   Dynamic contrast-enhanced CT of head and neck tumors: perfusion measurements using a distributed-parameter tracer kinetic model. Initial results and comparison with deconvolution-based analysis [J].
Bisdas, Sotirios ;
Konstantinou, George N. ;
Lee, Puor Sherng ;
Thng, Choon Hua ;
Wagenblast, Jens ;
Baghi, Mehran ;
Koh, Tong San .
PHYSICS IN MEDICINE AND BIOLOGY, 2007, 52 (20) :6181-6196
[4]   Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series [J].
Brix, G ;
Kiessling, F ;
Lucht, R ;
Darai, S ;
Wasser, K ;
Delorme, S ;
Griebel, J .
MAGNETIC RESONANCE IN MEDICINE, 2004, 52 (02) :420-429
[5]   Regional blood flow capillary permeability, and compartmental volumes: Measurement with dynamic CT - Initial experience [J].
Brix, G ;
Bahner, ML ;
Hoffmann, UJ ;
Horvath, A ;
Schreiber, W .
RADIOLOGY, 1999, 210 (01) :269-276
[6]   Dynamic Contrast-Enhanced CT Studies Balancing Patient Exposure and Image Noise [J].
Brix, Gunnar ;
Lechel, Ursula ;
Petersheim, Markus ;
Krissak, Radko ;
Fink, Christian .
INVESTIGATIVE RADIOLOGY, 2011, 46 (01) :64-70
[7]   Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements [J].
Brix, Gunnar ;
Griebel, Juergen ;
Kiessling, Fabian ;
Wenz, Frederik .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2010, 37 :S30-S51
[8]   Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method [J].
Brix, Gunnar ;
Zwick, Stefan ;
Griebel, Juergen ;
Fink, Christian ;
Kiessling, Fabian .
EUROPEAN RADIOLOGY, 2010, 20 (09) :2166-2175
[9]   Pharmacokinetic analysis of tissue microcirculation using nested models: Multimodel inference and parameter identifiability [J].
Brix, Gunnar ;
Zwick, Stefan ;
Kiessling, Fabian ;
Griebel, Juergen .
MEDICAL PHYSICS, 2009, 36 (07) :2923-2933
[10]   Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhanced T1-weighted MRI [J].
Buckley, DL .
MAGNETIC RESONANCE IN MEDICINE, 2002, 47 (03) :601-606