Objective:

To investigate the feasibility of building the breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images deduction model by using deep learning algorithms

with the aim of obtaining the DCE-MRI sequences w

ithout actual scanning.

Methods:

Clinical data and MRI images of 54 subjects with breast MRI examinations were collected retrospectively in Xian International Medical Center Hospital from January 2020 to August 2021 and divided into a training set (38 subjects)

a test set 1 (8 subjects) and a test set 2 (8 subjects). Here

we built the breast DCE-MRI images deduction model using the Pix2Pix algorithm based on the T1-weighted imaging (T1WI) and the first post-contrast phase of DCE sequence images in the training set. Furthermore

the model performance for synthesizing images and sequences was evaluated in the test set using several metrics such as peak signal to noise ratio (PSNR)

structural similarity index measure (SSIM)

mean squared error (MSE)

and mean absolute error (MAE). In addition

the correlation between the synthesized DCE sequences and the original DCE sequences was evaluated.

Results:

In total

760 images of the first post-contrast phase of DCE were synthesized using the model. The synthesized images had good structural similarity and low information loss compared with the original images

where the mean SSIM was 0.710.01 and PSNR was 23.701.41. In addition

we found that the synthesized DCE sequences had small reconstruction errors

MAE was 0.0320.004

MSE was 0.0060.002

and the synthesized first post-contrast phase of DCE sequences had a significantly positive correlation with the original sequences

(

r

=0.8720.038

95% CI: 0.870-0.874

P

=0.000).

Conclusion:

The model which we built can automatically synthesize the first post-contrast phase of DCE sequence images

which provides a new idea for supplementing the missing information in non-enhanced sequences. At the same time

it avoids the application of contrast agents

shortens the scanning time

and lays a solid foundation for the popularization and application of breast MRI screening.