Spatiotemporal Graph Neural Network Modelling Perfusion MRI

Ruodan Yan; Carola Bibiane Schönlieb; Chao Li

doi:10.1007/978-3-031-72069-7_39

Spatiotemporal Graph Neural Network Modelling Perfusion MRI

Ruodan Yan, Carola Bibiane Schönlieb, Chao Li (Lead / Corresponding author)

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model (PerfGAT), integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. In addition, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention
Subtitle of host publication	MICCAI 2024 - 27th International Conference, Proceedings
Editors	Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	411-421
Number of pages	11
ISBN (Electronic)	9783031720833
ISBN (Print)	9783031720826
DOIs	https://doi.org/10.1007/978-3-031-72069-7_39
Publication status	Published - 4 Oct 2024
Event	27th International Conference on Medical Image Computing and Computer-Assisted Intervention - Palmeraie Conference Centre, Marrakesh, Morocco Duration: 6 Oct 2024 → 10 Oct 2024 Conference number: 27th https://conferences.miccai.org/2024/en/ (Conference Website)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15002 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated title	MICCAI 2024
Country/Territory	Morocco
City	Marrakesh
Period	6/10/24 → 10/10/24
Internet address	https://conferences.miccai.org/2024/en/ (Conference Website)

Keywords

Dynamic Susceptibility Contrast MRI
Glioma
Graph Neural Network
Isocitrate Dehydrogenase
Spatiotemporal Learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-72069-7_39

Cite this

Yan, R., Schönlieb, C. B., & Li, C. (2024). Spatiotemporal Graph Neural Network Modelling Perfusion MRI. In M. G. Linguraru, Q. Dou, A. Feragen, S. Giannarou, B. Glocker, K. Lekadir, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention: MICCAI 2024 - 27th International Conference, Proceedings (pp. 411-421). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15002 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-72069-7_39

Yan, Ruodan ; Schönlieb, Carola Bibiane ; Li, Chao. / Spatiotemporal Graph Neural Network Modelling Perfusion MRI. Medical Image Computing and Computer Assisted Intervention: MICCAI 2024 - 27th International Conference, Proceedings. editor / Marius George Linguraru ; Qi Dou ; Aasa Feragen ; Stamatia Giannarou ; Ben Glocker ; Karim Lekadir ; Julia A. Schnabel. Springer Science and Business Media Deutschland GmbH, 2024. pp. 411-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Spatiotemporal Graph Neural Network Modelling Perfusion MRI",

abstract = "Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model (PerfGAT), integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. In addition, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.",

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Yan, R, Schönlieb, CB & Li, C 2024, Spatiotemporal Graph Neural Network Modelling Perfusion MRI. in MG Linguraru, Q Dou, A Feragen, S Giannarou, B Glocker, K Lekadir & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention: MICCAI 2024 - 27th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15002 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 411-421, 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, Marrakesh, Morocco, 6/10/24. https://doi.org/10.1007/978-3-031-72069-7_39

Spatiotemporal Graph Neural Network Modelling Perfusion MRI. / Yan, Ruodan; Schönlieb, Carola Bibiane; Li, Chao (Lead / Corresponding author).
Medical Image Computing and Computer Assisted Intervention: MICCAI 2024 - 27th International Conference, Proceedings. ed. / Marius George Linguraru; Qi Dou; Aasa Feragen; Stamatia Giannarou; Ben Glocker; Karim Lekadir; Julia A. Schnabel. Springer Science and Business Media Deutschland GmbH, 2024. p. 411-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15002 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Spatiotemporal Graph Neural Network Modelling Perfusion MRI

AU - Yan, Ruodan

AU - Schönlieb, Carola Bibiane

AU - Li, Chao

N1 - Conference code: 27th

PY - 2024/10/4

Y1 - 2024/10/4

N2 - Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model (PerfGAT), integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. In addition, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.

AB - Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model (PerfGAT), integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. In addition, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.

KW - Dynamic Susceptibility Contrast MRI

KW - Glioma

KW - Graph Neural Network

KW - Isocitrate Dehydrogenase

KW - Spatiotemporal Learning

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AN - SCOPUS:85206447349

SN - 9783031720826

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 411

EP - 421

BT - Medical Image Computing and Computer Assisted Intervention

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A2 - Dou, Qi

A2 - Feragen, Aasa

A2 - Giannarou, Stamatia

A2 - Glocker, Ben

A2 - Lekadir, Karim

A2 - Schnabel, Julia A.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention

Y2 - 6 October 2024 through 10 October 2024

ER -

Yan R, Schönlieb CB, Li C. Spatiotemporal Graph Neural Network Modelling Perfusion MRI. In Linguraru MG, Dou Q, Feragen A, Giannarou S, Glocker B, Lekadir K, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention: MICCAI 2024 - 27th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 411-421. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-72069-7_39

Spatiotemporal Graph Neural Network Modelling Perfusion MRI

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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