Yan Wu1, Yajun Ma2, Jiang Du2, and Lei Xing1
1Stanford University, Palo Alto, CA, United States, 2University of California San Diego, San Diego, CA, United States
1Stanford University, Palo Alto, CA, United States, 2University of California San Diego, San Diego, CA, United States
MR contrast can be retrospectively tuned
from a single T1-weighted image by combining deep learning-based quantitative parametric
mapping with Bloch
equations. High accuracy has been achieved in knee
MRI.
Scheme of retrospective tuning. From a single T1-weighted
image, tissue relaxation parametric maps (T1 map, proton density map, and B1
map) can be predicted using deep neural networks; which are subsequently used
to calculate signal intensity of other images (corresponding to different
imaging protocols) via the application of Bloch equations.
Retrospective tuning of tissue contrast in MRI. (a)
Given a single T1-weighted image acquired at 30°, images presumably acquired at
5°, 10°, and 20° are predicted and compared with the ground truth images, where
high image fidelity is achieved in the predicted images. (B) Quantitative
evaluation of variable contrast image predictions. Low L1 error (between 0.04
and 0.09) and high correlation coefficients (ranging from 0.97 to 0.99) are
consistently achieved.