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Related Concept Videos

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Three-Compartment Open Model01:06

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
Impulse Response01:17

Impulse Response

The impulse response is the system's reaction to an input impulse. In an RC circuit, the voltage source is the input, and the capacitor's voltage is the output. The system's state and output response before and after input excitation are distinctly defined.
Kirchhoff's law forms an input signal equation, with the capacitor's current and voltage providing the output. Substituting the current and dividing by RC yields a differential equation. The output for an impulse input is the impulse...
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug is...

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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
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Published on: April 18, 2015

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

Gunnar Brix1, Mona Salehi Ravesh, Stefan Zwick

  • 1Department of Medical and Occupational Radiation Protection, Federal Office for Radiation Protection, Oberschleissheim, Germany. gbrix@bfs.de

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|April 19, 2011
PubMed
Summary

Dynamic contrast-enhanced (DCE) imaging analysis using algebraic deconvolution (AD) or compartmental modeling reveals significant pitfalls. Analytical deconvolution is preferred over AD for accurate tissue microcirculation parameter estimation in DCE-CT studies.

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Pharmacokinetics

Background:

  • Dynamic contrast-enhanced (DCE) imaging quantifies tissue microcirculation via concentration-time courses.
  • Analysis relies on deconvolution of arterial input with tissue impulse response functions (Q(T)(t)).
  • Two main analysis approaches exist: algebraic deconvolution (AD) and compartmental modeling.

Purpose of the Study:

  • To investigate the pitfalls of AD and compartmental modeling in DCE imaging data analysis.
  • To compare the accuracy and validity of different deconvolution methods for tissue microcirculation assessment.
  • To provide guidance on selecting appropriate analysis strategies to avoid misinterpretations.

Main Methods:

  • Analysis of DCE-CT data from head-and-neck cancer patients and simulated data using a reference model (MMID4).
  • Application of a two-compartment model (TCM), a permeability-limited two-compartment model (PL-TCM), and algebraic deconvolution (AD).
  • Computation of the 'true' response function using the MMID4 reference model for comparison.

Main Results:

  • TCM and AD provided accurate fits to tissue data, while PL-TCM performed poorly.
  • Response functions derived from TCM and AD diverged significantly from the 'true' response function.
  • AD and TCM generated response curves inconsistent with indicator dilution theory (IDT) principles, potentially leading to overestimation of perfusion.
  • Different response function shapes can yield similar tissue concentration-time curves, highlighting non-uniqueness.

Conclusions:

  • Algebraic deconvolution (AD) is not unconditionally valid for parameter estimation in DCE imaging due to inconsistencies with indicator dilution theory.
  • Analytical deconvolution, derived from compartmental modeling, is recommended to constrain solutions and align with a priori knowledge.
  • Awareness of the inherent pitfalls in different DCE analysis concepts is crucial to prevent misinterpretations and systematic errors.