Abstract
ObjectiveTo develop a deep learning model for synthesizing the first phases of dynamic (FP-Dyn) sequences to supplement the lack of information in unenhanced breast MRI examinations.MethodsIn total, 97 patients with breast MRI images were collected as the training set (n = 45), the validation set (n = 31), and the test set (n = 21), respectively. An enhance border lifelike synthesize (EDLS) model was developed in the training set and used to synthesize the FP-Dyn images from the T1WI images in the validation set. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the synthesized images were measured. Moreover, three radiologists subjectively assessed image quality, respectively. The diagnostic value of the synthesized FP-Dyn sequences was further evaluated in the test set.ResultsThe image synthesis performance in the EDLS model was superior to that in conventional models from the results of PSNR, SSIM, MSE, and MAE. Subjective results displayed a remarkable visual consistency between the synthesized and original FP-Dyn images. Moreover, by using a combination of synthesized FP-Dyn sequence and an unenhanced protocol, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI were 100%, 72.73%, 76.92%, and 100%, respectively, which had a similar diagnostic value to full MRI protocols.ConclusionsThe EDLS model could synthesize the realistic FP-Dyn sequence to supplement the lack of enhanced images. Compared with full MRI examinations, it thus provides a new approach for reducing examination time and cost, and avoids the use of contrast agents without influencing diagnostic accuracy.
Highlights
Breast cancer has become the most frequently-occurring tumor in women with an increasing incidence [1, 2]
We quantitatively compared the 1226 first phases of dynamic (FP-Dyn) images which were respectively synthesized by the enhance border lifelike synthesize (EDLS) model and the conventional
Compared with the Pix2Pix model, the EDLS model produced improvements of more than 2% in structural similarity (SSIM), which played an important role in improving the quality of the synthesized images
Summary
Breast cancer has become the most frequently-occurring tumor in women with an increasing incidence [1, 2]. A traditional full MRI examination protocol includes plain scanning and diffusion-weighted image (DWI), and dynamic-contrast enhanced (DCE) sequences. The long image acquisition times, high cost, and the risk of contrast agent allergy have limited its widespread application for breast cancer screening [8, 9]. Thereafter, Baltzer PA et al [11, 12] proposed an unenhanced abbreviated breast MRI (u-AB-MRI) protocol, including plain scanning and DWI. This protocol can significantly reduce the scanning time and is free of the contrast agent, but the diagnostic performance may be reduced due to the missing DCE sequence images. It is urgent to obtain DCE sequence images without actual scanning to compensate for the limitations in the u-AB-MRI protocol
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