DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Journal: Magma (New York, N.Y.)

PMID: 39112813

Abstract

INTRODUCTION: 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.

Authors

Ouri Cohen

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
Soudabeh Kargar

Animal Imaging Shared Resources, University of Colorado Anschutz Medical Campus, Cancer Center, Aurora, Colorado, USA.
Sungmin Woo

Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA. woos@mskcc.org.
Alberto Vargas

Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ricardo Otazo

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Keywords

Adult Algorithms Cervix Uteri Contrast Media Deep Learning Female Healthy Volunteers Humans Image Enhancement Image Processing, Computer-Assisted Least-Squares Analysis Magnetic Resonance Imaging Neural Networks, Computer Phantoms, Imaging Reproducibility of Results Uterine Cervical Neoplasms

External Resources

View on PubMed Access via DOI PubMed (39112813)

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Abstract

Authors

Keywords

External Resources

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