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Quantification of Tracer Dilution Dynamics: An Exploration into the Mathematical Modeling of Medical Imaging

Ishmael N. Amartey, Andreas A. Linninger, Thomas Ventimiglia

TL;DR

The paper presents the spectral derivatives as a technique for recovering the impulse response function from the residue function, detailing the computational procedures involved and strategies for mitigating noise effects.

Abstract

Convolution and deconvolution are essential techniques in various fields, notably in medical imaging, where they play a crucial role in analyzing dynamic processes such as blood flow. This paper explores the convolution and deconvolution of arterial and microvascular signals for determining impulse and residue functions from in vivo or simulated data and the derivation of the relationship between the residue function and perfusion metrics such as the Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Transit Time to Heterogeneity (TTH). The paper presents the spectral derivatives as a technique for recovering the impulse response function from the residue function, detailing the computational procedures involved and strategies for mitigating noise effects.

Quantification of Tracer Dilution Dynamics: An Exploration into the Mathematical Modeling of Medical Imaging

TL;DR

The paper presents the spectral derivatives as a technique for recovering the impulse response function from the residue function, detailing the computational procedures involved and strategies for mitigating noise effects.

Abstract

Convolution and deconvolution are essential techniques in various fields, notably in medical imaging, where they play a crucial role in analyzing dynamic processes such as blood flow. This paper explores the convolution and deconvolution of arterial and microvascular signals for determining impulse and residue functions from in vivo or simulated data and the derivation of the relationship between the residue function and perfusion metrics such as the Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Transit Time to Heterogeneity (TTH). The paper presents the spectral derivatives as a technique for recovering the impulse response function from the residue function, detailing the computational procedures involved and strategies for mitigating noise effects.
Paper Structure (8 sections, 57 equations, 15 figures)

This paper contains 8 sections, 57 equations, 15 figures.

Table of Contents

  1. Introduction
  2. Deconvolution of two signals
  3. Convolution and Deconvolution Graphs
  4. Relation of key perfusion metrics: $CBF$, $MTT$, $TTH$ to weighted residue function $k(t)$
  5. Derivation of eq. (\ref{['eq54']})
  6. Spectral Derivatives
  7. Conclusion
  8. Future work

Figures (15)

  • Figure 1: Segmented blood vessel
  • Figure 2: Time concentration curves for artificial $c_art(t)$ and $h(t)$ for the construction of $c_{ven}(t)$ and $c_{voi}(t)$
  • Figure 3: A zoomed view of $c_{voi}(t)$
  • Figure 4: Gamma variate curve of $h(t)$
  • Figure 5: Residual curve of $r(t)$
  • ...and 10 more figures