Separation of perfusion phases in angiographies

Guillaume Herpe; Julien Dambrine; Inès Bennis; Clément Thomas; Stéphane Velasco; Rémy Guillevin; Guillaume Herpe; Julien Dambrine; Inès Bennis; Clément Thomas; Stéphane Velasco; Rémy Guillevin

doi:10.3934/math.2021056

AIMS Mathematics

2021, Volume 6, Issue 1: 938-951. doi: 10.3934/math.2021056

Previous Article Next Article

Research article Special Issues

Separation of perfusion phases in angiographies

1.
LR COM CNRS I3M, DACTIM-MIS team, Laboratoire de Mathématiques et Applications (CNRS UMR7348)
2.
CHU de Poitiers. Service de Radiologie
3.
Université de Poitiers

Received: 15 July 2020 Accepted: 26 October 2020 Published: 05 November 2020
MSC : 92C55

The analysis of Cerebral Angiographies are an essential tool for the assessment of the future of patients that underwent thrombolysis after a stroke event. Many semi-qualitative visual diagnostic scales have been developed for this purpose. Perfusion angiographies show essentially three phases: the arterial (early), the capillary (intermediate), and venous (late) phase. We call parenchymogram the image sequence corresponding to the capillary phase only. Unfortunately the parenchymogram is often under exploited in practice, despite containing many pertinent hints on the quality of reperfusion. In this paper we propose a set of methods for the extraction of the parenchymogram from raw Cerebral Angiographies. These methods rely on basis pursuit and on the representation of images with an over-complete basis arising from an redundant wavelet transform. We will show that the extraction of the parenchymogram by applying the aforementioned methods on real clinical data allows us to recover essential information for the comparison of blood flow before and after thrombolysis.
- stroke,
- angiographies,
- parenchymogram,
- basis pursuit,
- undecimated wavelet transform
Citation: Guillaume Herpe, Julien Dambrine, Inès Bennis, Clément Thomas, Stéphane Velasco, Rémy Guillevin. Separation of perfusion phases in angiographies[J]. AIMS Mathematics, 2021, 6(1): 938-951. doi: 10.3934/math.2021056

Related Papers:

Abstract

The analysis of Cerebral Angiographies are an essential tool for the assessment of the future of patients that underwent thrombolysis after a stroke event. Many semi-qualitative visual diagnostic scales have been developed for this purpose. Perfusion angiographies show essentially three phases: the arterial (early), the capillary (intermediate), and venous (late) phase. We call parenchymogram the image sequence corresponding to the capillary phase only. Unfortunately the parenchymogram is often under exploited in practice, despite containing many pertinent hints on the quality of reperfusion. In this paper we propose a set of methods for the extraction of the parenchymogram from raw Cerebral Angiographies. These methods rely on basis pursuit and on the representation of images with an over-complete basis arising from an redundant wavelet transform. We will show that the extraction of the parenchymogram by applying the aforementioned methods on real clinical data allows us to recover essential information for the comparison of blood flow before and after thrombolysis.

References

[1]	F. Al-Ali, O. A. Berkhemer, W. P. Yousman, J. J. Elias, E. N. Bender, H. F. Lingsma, et al., The Capillary Index Score as a Marker of Viable Cerebral Tissue: Proof of Concept-The Capillary Index Score in the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Trial, Stroke, 47 (2016), 2286-2291.
[2]	P. Bankhead, C. N. Scholfield, J. G. McGeown, T. M. Curtis, Fast retinal vessel detection and measurement using wavelets and edge location refinement. PloS one, 7 (2012), e32435.
[3]	Z. Csaba, T. Vitalis, C. Charriaut-Marlangue, I. Margaill, B. Coqueran, P. L. Leger, et al., A simple novel approach for detecting blood-brain barrier permeability using GPCR internalization, Neuropathol Appl Neurobiol, 2020.
[4]	P. Goswami, M. K. Markey, S. J. Warach, A. N. Dula, Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography, Sci. Rep., 10 (2020), 1-10.
[5]	G. R. Lee, R. Gommers, F. Wasilewski, K. Wohlfahrt, A. O'Leary, PyWavelets: A Python package for wavelet analysis, Journal of Open Source Software, 4 (2019), 1237.
[6]	S. Mallat, A theory for multiresolution signal decomposition: The wavelet representation IEEE T. Pattern Anal., 11 (1989), 674-693.
[7]	S. Mallat, A Wavelet Tour of Signal Processing: The sparse Way, Third Edition, Academic Press, 2009.
[8]	B. S. Reddy, B. N. Chatterji, An FFT-based Technique for Translation, Rotation, and Scale-Invariant Image Registration, IEEE T. Image Process., 5 (1996), 1266-1271.
[9]	M. J. Shensa, Discrete wavelet transforms: Wedding the à trous and Mallat algorithms, IEEE T. Signal Proces., 40 (1992), 2464-2482.
[10]	J. Starck, J. Fadili, F. Murtagh, The Undecimated Wavelet Decomposition and its Reconstruction, IEEE T. Image Proces., 16 (2007), 297-309.
[11]	J.-L. Starck, F. Murtagh, Astronomical Image and Data Analysis, New York: Springer-Verlag, 2002.
[12]	J.-L. Starck, F. Murtagh, J. Fadili, Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis, Second Edition, Cambridge: Cambridge University Press, 2015.

Reader Comments

your name:*

Email:*
© 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)