Deep Learning–Based Generation of DSC MRI Parameter Maps Using Dynamic Contrast-Enhanced MRI Data
Year: 2025
Type: article
Abstract: BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained by using 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 2 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 64 participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed after 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 by 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 by using DCE-derived DSC maps. CONCLUSIONS: DSC-derived parameter maps could be synthesized by using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI by using a single dose of contrast agent.
DOI: 10.3174/ajnr.a8768
Institutions: Center for Advanced Imaging Innovation and Research, New York University, Icahn School of Medicine at Mount Sinai, Beihang University, Johns Hopkins University, Institut für Diagnostische und Interventionelle Neuroradiologie, Technical University of Munich, Ludwig-Maximilians-Universität München, Harvard University, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Kennedy Krieger Institute, Lund University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: Vascular Contributors to Blood Brain Barrier Permeability and Regional White Matter Hyperintensity Progression in Young Asymptomatic People, G1806908935, G2965162781, Ultra-Fast High-Resolution Multi-Parametric MRI for Characterizing Cartilage Extracellular Matrix, G4695054747, G5798971052, Development and Translation of D-glucose as a Diagnostic Agent for MRI of Cancer, Non-invasive Magnetic Resonance Imaging of Glycogen in the Human Liver, MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers, Cerebral Energy Metabolism in ME/CFS with and without PASC, Center for Advanced Imaging Innovation and Research (CAI2R), Deep Learning Technology for Rapid Morphological and Quantitative Imaging of Knee Pathology, Rapid Three-dimensional Simultaneous Knee Multi-Relaxation Mapping
OpenAlex ID: W4409224141