DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI
Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T
Institutions
Authors
Funding
PathologyDevelopment of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain MetastasesDevelopment of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain MetastasesRapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancersPathologyRapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancersPathologyRapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancersDevelopment of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain Metastases