Dynamic contrast-enhanced computed tomography (DCE-CT) is an emerging radiological technique, which consists in acquiring a rapid sequence of CT images, shortly after the injection of an intravenous contrast agent. The passage of the contrast agent in a tissue results in a varying CT intensity over time, recorded in time-attenuation curves (TACs), which can be related to the contrast supplied to that tissue via the supplying artery to estimate the local perfusion and permeability characteristics. The time delay between the arrival of the contrast bolus in the feeding artery and the tissue of interest, called the bolus arrival time (BAT), needs to be determined accurately to enable reliable perfusion analysis. Its automated identification is however highly sensitive to noise. We propose an accurate and efficient method for estimating the BAT from DCE-CT images. The method relies on a piecewise linear TAC model with four segments and suitable parameter constraints for limiting the range of possible values. The model is fitted to the acquired TACs in a multiresolution fashion using an iterative optimization approach. The performance of the method was evaluated on simulated and real perfusion data of lung and rectum tumours. In both cases, the method was found to be stable, leading to average accuracies in the order of the temporal resolution of the dynamic sequence. For reasonable levels of noise, the results were found to be comparable to those obtained using a previously proposed method, employing a full search algorithm, but requiring an order of magnitude more computation time.
Abdou, E, de Mey, J, De Ridder, M & Vandemeulebroucke, J 2017, Accurate Bolus Arrival Time Estimation using Piecewise Linear Model Fitting. in ED Angelini, MA Styner & ED Angelini (eds), Medical Imaging 2017: Medical Imaging 2017: Image Processing. vol. 10133, 101332F , Proceedings of SPIE, SPIE, 11/02/17. https://doi.org/10.1117/12.2254499
Abdou, E., de Mey, J., De Ridder, M., & Vandemeulebroucke, J. (2017). Accurate Bolus Arrival Time Estimation using Piecewise Linear Model Fitting. In E. D. Angelini, M. A. Styner, & E. D. Angelini (Eds.), Medical Imaging 2017: Medical Imaging 2017: Image Processing (Vol. 10133). Article 101332F (Proceedings of SPIE). https://doi.org/10.1117/12.2254499
@inproceedings{3be005d0764249c58114b436778a644d,
title = "Accurate Bolus Arrival Time Estimation using Piecewise Linear Model Fitting",
abstract = "Dynamic contrast-enhanced computed tomography (DCE-CT) is an emerging radiological technique, which consists in acquiring a rapid sequence of CT images, shortly after the injection of an intravenous contrast agent. The passage of the contrast agent in a tissue results in a varying CT intensity over time, recorded in time-attenuation curves (TACs), which can be related to the contrast supplied to that tissue via the supplying artery to estimate the local perfusion and permeability characteristics. The time delay between the arrival of the contrast bolus in the feeding artery and the tissue of interest, called the bolus arrival time (BAT), needs to be determined accurately to enable reliable perfusion analysis. Its automated identification is however highly sensitive to noise. We propose an accurate and efficient method for estimating the BAT from DCE-CT images. The method relies on a piecewise linear TAC model with four segments and suitable parameter constraints for limiting the range of possible values. The model is fitted to the acquired TACs in a multiresolution fashion using an iterative optimization approach. The performance of the method was evaluated on simulated and real perfusion data of lung and rectum tumours. In both cases, the method was found to be stable, leading to average accuracies in the order of the temporal resolution of the dynamic sequence. For reasonable levels of noise, the results were found to be comparable to those obtained using a previously proposed method, employing a full search algorithm, but requiring an order of magnitude more computation time.",
keywords = "Bolus arrival time, CT perfusion, Perfusion analysis, Piecewise linear model",
author = "Elhassan Abdou and {de Mey}, Johan and {De Ridder}, Mark and Jef Vandemeulebroucke",
year = "2017",
doi = "10.1117/12.2254499",
language = "English",
isbn = "978-1-5106-0712-5",
volume = "10133",
series = "Proceedings of SPIE",
editor = "Angelini, {Elsa D.} and Styner, {Martin A.} and Angelini, {Elsa D.}",
booktitle = "Medical Imaging 2017",
note = "SPIE : Medical Imaging 2017 ; Conference date: 11-02-2017 Through 16-03-2017",
}