Deep learning-based generation of DSC MRI parameter maps using DCE MRI data
Pei, Haoyang ; Lyu, Yixuan ; Lambrecht, Sebastian ; Lin, Doris ; Feng, Li ; Liu, Fang ; Nyquist, Paul ; van Zijl, Peter ; Knutsson, Linda LU
and Xu, Xiang
(2025)
In AJNR. American journal of neuroradiology
p.1-11
- Abstract
BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research. However, using both DSC and DCE MRI in the same scan session requires two doses of gadolinium contrast agent. The objective was to develop deep-learning based methods to synthesize DSC-derived parameter maps from DCE MRI data.
MATERIALS AND METHODS: Independent analysis of data collected in previous studies was performed. The database contained sixty-four participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed following DCE MRI. A... (More)
BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research. However, using both DSC and DCE MRI in the same scan session requires two doses of gadolinium contrast agent. The objective was to develop deep-learning based methods to synthesize DSC-derived parameter maps from DCE MRI data.
MATERIALS AND METHODS: Independent analysis of data collected in previous studies was performed. The database contained sixty-four participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed following DCE MRI. A conditional generative adversarial network (cGAN) was designed and trained to generate synthetic DSC-derived maps from DCE MRI data. The median parameter values and distributions between synthetic and real maps were compared using linear regression and Bland-Altman plots.
RESULTS: Using cGAN, realistic DSC parameter maps could be synthesized from DCE MRI data. For controls without brain tumors, the synthesized parameters had distributions similar to the ground truth values. For patients with brain tumors, the synthesized parameters in the tumor region correlated linearly with the ground truth values. In addition, areas not visible due to susceptibility artifacts in real DSC maps could be visualized using DCE-derived DSC maps.
CONCLUSIONS: DSC-derived parameter maps could be synthesized using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI using a single dose of contrast agent.
ABBREVIATIONS: cGAN=conditional generative adversarial network; K trans=volume transfer constant; rCBV=relative cerebral blood volume; rCBF=relative cerebral blood flow; V e=extravascular extracellular volume; V p=plasma volume.
(Less)
- author
- Pei, Haoyang
; Lyu, Yixuan
; Lambrecht, Sebastian
; Lin, Doris
; Feng, Li
; Liu, Fang
; Nyquist, Paul
; van Zijl, Peter
; Knutsson, Linda
LU
and Xu, Xiang
- organization
- publishing date
- 2025-04-07
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- AJNR. American journal of neuroradiology
- pages
- 1 - 11
- publisher
- American Society of Neuroradiology
- external identifiers
-
- pmid:40194853
- ISSN
- 1936-959X
- DOI
- 10.3174/ajnr.A8768
- language
- English
- LU publication?
- yes
- additional info
- © 2025 by American Journal of Neuroradiology.
- id
- c55d4f30-d692-4ec0-a08a-eb755b200d50
- date added to LUP
- 2025-04-12 19:46:58
- date last changed
- 2025-04-14 09:01:41
@article{c55d4f30-d692-4ec0-a08a-eb755b200d50,
abstract = {{<p>BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research. However, using both DSC and DCE MRI in the same scan session requires two doses of gadolinium contrast agent. The objective was to develop deep-learning based methods to synthesize DSC-derived parameter maps from DCE MRI data.</p><p>MATERIALS AND METHODS: Independent analysis of data collected in previous studies was performed. The database contained sixty-four participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed following DCE MRI. A conditional generative adversarial network (cGAN) was designed and trained to generate synthetic DSC-derived maps from DCE MRI data. The median parameter values and distributions between synthetic and real maps were compared using linear regression and Bland-Altman plots.</p><p>RESULTS: Using cGAN, realistic DSC parameter maps could be synthesized from DCE MRI data. For controls without brain tumors, the synthesized parameters had distributions similar to the ground truth values. For patients with brain tumors, the synthesized parameters in the tumor region correlated linearly with the ground truth values. In addition, areas not visible due to susceptibility artifacts in real DSC maps could be visualized using DCE-derived DSC maps.</p><p>CONCLUSIONS: DSC-derived parameter maps could be synthesized using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI using a single dose of contrast agent.</p><p>ABBREVIATIONS: cGAN=conditional generative adversarial network; K trans=volume transfer constant; rCBV=relative cerebral blood volume; rCBF=relative cerebral blood flow; V e=extravascular extracellular volume; V p=plasma volume. </p>}},
author = {{Pei, Haoyang and Lyu, Yixuan and Lambrecht, Sebastian and Lin, Doris and Feng, Li and Liu, Fang and Nyquist, Paul and van Zijl, Peter and Knutsson, Linda and Xu, Xiang}},
issn = {{1936-959X}},
language = {{eng}},
month = {{04}},
pages = {{1--11}},
publisher = {{American Society of Neuroradiology}},
series = {{AJNR. American journal of neuroradiology}},
title = {{Deep learning-based generation of DSC MRI parameter maps using DCE MRI data}},
url = {{http://dx.doi.org/10.3174/ajnr.A8768}},
doi = {{10.3174/ajnr.A8768}},
year = {{2025}},
}