Direct Estimation of Tracer-Kinetic Parameter Maps from Highly Undersampled Brain DCE-MRI

By Yi Guo, Sajan Goud Lingala, Yinghua Zhu, R. Lebel, K. Nayak. 2016

41 citations recorded by the source corpus.

The purpose of this work was to develop and evaluate a T1‐weighted dynamic contrast enhanced (DCE) MRI methodology where tracer‐kinetic (TK) parameter maps are directly estimated from undersampled (k,t)‐space data.

Subfields: Genetics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “In this context of DCE-MRI, it’s tempting to speculate whether deep neural network approaches could be used for direct estimation of tracer-kinetic parameter maps from highly undersampled (k, t)-space data in dynamic recordings [183,184], a powerful way to by-pass 4D DCE-MRI reconstruction altogethe”