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
Synopsis
While versatile soft tissue contrasts are achievable
in MRI, contrast attainable from each scan is predetermined by the imaging
protocol. A retrospective tuning of contrast will provide an opportunity to normalize
MRI data for radiomics analysis. In this study, we present a new paradigm to obtain a spectrum of contrasts from a single T1-weighted
image. Using
deep learning, T1 map, proton density map, and B1 map are predicted from every T1-weighted
image, and new contrasts can be obtained with the application of Bloch
equations. The method has been validated in knee
MRI with high accuracy achieved.
Introduction
While versatile soft tissue contrasts
are achievable in MRI, contrast attainable from each scan is predetermined by
the imaging protocol. A retrospective tuning of contrast will provide an opportunity
to normalize MRI data for radiomics analysis. In this study, we present a new
paradigm to obtain a spectrum of tissue contrasts from a single
T1-weighted image obtained in clinical
practice. In this way, no additional scans
are required for generating quantitative T1 maps.Methods
We propose a novel
framework for retrospective tuning of MRI contrast, where deep learning based quantitative
MRI is combined with Bloch equations. In theory, retrospective change of tissue
contrast in MRI can be achieved by applying Bloch equations on tissue
relaxation parametric maps, which however are hard to obtain due to the long
scan time. Leveraging from the capability of deep learning,
we propose a quantitative MRI approach to extract parametric
maps from single MR images without conducting extra data acquisition. Using
deep convolutional neural networks, T1 map, proton density map, and B1 map are
predicted from every single T1-weighted image. Based on the predicted parametric/field
maps, a spectrum of soft tissue contrasts can be obtained, where Bloch
equations are applied with various imaging parameter values. The principle is
illustrated in Figure 1.
In the quantitative parametric mapping
step, deep convolutional neural networks are used to provide direct mapping
from single T1 weighted images to corresponding parametric/field maps. For
ground truth, every T1 map is obtained from four T1-weighted images acquired
with variable flip angle (5°, 10°, 20°, and 30°), proton
density map is calculated from T1-weighted image and the corresponding T1 map,
and B1 map is measured using the actual flip angle method
[1]. In these tasks, self-attention convolutional
neural network framework [2] is employed as shown in Figure 2, where the
hierarchical network architecture is adopted (enabling feature extraction at
various scales), global shortcuts and relatively dense local shortcuts are equipped
(leading to an improved network performance), and the attention mechanism is
integrated (to make efficient use of non-local information). A total of
1,224 slice images from 51 subjects are utilized for model training, and 120
images of 5 different subjects are employed for testing. In training, the Adam
algorithm is used to update the network parameters, and the iterative procedure
continues until convergence is reached. For a test image acquired using the
same imaging protocol, quantitative T1 map, proton density map and B1 map are
automatically generated from a single T1-weighted image by the established
network models. The predicted T1 maps are compared with ground truth maps with L1
error and correlation coefficient calculated. In addition, every T1 map is also
predicted from two T1-weighted images and gets compared with the maps predicted
from single input images.
After parametric/field maps are estimated from a T1-weighted image, Bloch
equations are used to calculate the signal intensity of MR images with the adoption
of different imaging parameter values (flip angle, TR, etc). While a wide spectrum
of contrasts can be obtained, the proposed method is only validated at certain
contrasts (corresponding to the flip angles specified in training data) due to
the availability of ground truth images.
Results
Using a large data set, deep learning models have been trained to
predict quantitative T1 map, proton density map and B1 map from a single T1-weighted
image. Images in two representative cases are shown in Figure 3(a), and the quantitative
results for all test sets are given in Figure 3(b). The predicted maps show
high image fidelity to the ground truth maps.
In addition to the single image prediction, map is predicted from two input -weighted images acquired
using 20° and 30° respectively (Figure 4). The difference between the resultant
map and the corresponding maps derived from single -weighted images (acquired
using 20° or 30°) is negligible. Quantitatively, the correlation coefficient
and L1 error for the multi-image prediction are 0.9836 and 0.0567 respectively,
as compared to 0.9728/0.9742 and 0.0689/0.0672 for the single image predictions.
After tissue relaxation
parametric maps are obtained from a single image, the signal intensity of MR
images presumably acquired using different flip angles are obtained with the
application of Bloch equations. From a specific T1-weighted image (acquired
using a flip angle of 30°), other T1-weighted images (corresponding to flip
angle of 5°, 10° and 20°) are predicted and compared to the ground truth images
as illustrated in Figure 5. High image fidelity is consistently achieved in the
predicted images with low L1 error (between 0.04 and 0.09) and high correlation
coefficients (ranging from 0.97 to 0.99).
Discussion
In the proposed method, quantitative maps are
extracted from a single T1-weighted image with the aid of a priori
knowledge. In the two-step
contrast
tuning strategy, deep neural networks are responsible for extracting
inherent
tissue relaxation property, and the use
of Bloch equations imposes an explicit control over the imaging protocol, gaining unlimited possibilities of
tissue
contrast.Conclusion
A new data-driven
strategy is proposed for retrospective MRI contrast tuning. Using deep
learning models, a spectrum of tissue contrast is obtained
from a single T1-weighted image without additional data acquisition, providing
an opportunity to normalize MRI data for radiomics analysis.Acknowledgements
The research
was supported by NIH/NCI (1R01 CA176553), NIH/NIAMS (1R01
AR068987), NIH/NINDS (1R01 NS092650). References
1.
Y. J. Ma, W. Zhao, L. Wan, T. Guo, A. Searleman,
H. Jang, et al., "Whole knee
joint T1 values measured in vivo at 3T by combined 3D ultrashort echo time
cones actual flip angle and variable flip angle methods," Magnetic resonance in medicine, vol. 81,
pp. 1634-1644, 2019.
2. Wu,
Y., Y. Ma, J. Liu, W. Zhao, J. Du et al., Self-attention
convolutional neural network for improved MR image reconstruction.
Information Sciences, 2019. 490: p. 317-328.