Synthetic generation of DSC‐MRI‐derived relative CBV maps from DCE MRI of brain tumors

• Published in: Magnetic resonance in medicine Vol. 85; no. 1; pp. 469 - 479
• Main Authors: Sanders, Jeremiah W., Chen, Henry Szu‐Meng, Johnson, Jason M., Schomer, Donald F., Jimenez, Jorge E., Ma, Jingfei, Liu, Ho‐Ling
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2021
• Subjects: Blood plasma; Blood volume; Brain; Brain cancer; Brain mapping; Brain Neoplasms - diagnostic imaging; Brain tumors; Cerebral blood flow; Cerebral Blood Volume; Cerebrovascular Circulation; Computation; Confidence intervals; Contrast agents; Contrast Media; Correlation analysis; deep learning; dynamic contrast enhanced; dynamic susceptibility contrast; Gadolinium; Humans; Imaging techniques; Magnetic Resonance Imaging; Medical imaging; Neuroimaging; Perfusion; perfusion MRI; Retrospective Studies; Substantia alba; Substantia grisea; Synthesis; Tumors; deep learning; perfusion MRI; brain tumors; dynamic contrast enhanced; dynamic susceptibility contrast
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.28432

Purpose Perfusion MRI with gadolinium‐based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium‐based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium‐based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. Methods One hundred nine brain‐tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor–to–white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. Results Pearson correlation analysis showed that both the tumor rCBV and tumor–to–white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland‐Altman analysis showed a mean difference between the synthetic and real rCBV tumor–to–white matter ratios of 0.20 with a 95% confidence interval of ±0.47. Conclusion Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain‐tumor perfusion imaging of DSC and DCE parameters with a single gadolinium‐based contrast agent administration.