In Journal of medical imaging (Bellingham, Wash.)
PURPOSE : Scatter radiation in contrast-enhanced digital breast tomosynthesis (CEDBT) reduces the image quality and iodinated lesion contrast. Monte Carlo simulation can provide accurate scatter estimation at the cost of computational burden. A model-based convolutional method trades off accuracy for processing speed. The purpose of this study is to develop a fast and robust deep-learning (DL) convolutional neural network (CNN)-based scatter correction method for CEDBT.
APPROACH : Projection images and scatter maps of digital anthropomorphic breast phantoms were generated using Monte Carlo simulations. Experiments were conducted to validate the simulated scatter-to-primary ratio (SPR) at different locations of a breast phantom. Simulated projection images were used for CNN training and testing. Two separate U-Nets [low-energy (LE)-CNN and high-energy (HE)-CNN] were trained for LE and HE spectrum, respectively. CNN-based scatter correction was applied to a clinical case with a malignant iodinated mass to evaluate the influence on the lesion detection.
RESULTS : The average and standard deviation of mean absolute percentage error of LE-CNN and HE-CNN estimated scatter map are 2 % ± 0.4 % and 2.4 % ± 0.8 % , respectively. For clinical cases, the lesion signal difference to noise ratio average improvement was 190% after CNN-based scatter correction. To conduct scatter correction on clinical CEDBT images, the whole process of loading CNNs parameters and scatter correction for LE and HE images took < 4 s , with 9 GB GPU computational cost. The SPR variation across the breast agrees between experimental measurements and Monte Carlo simulations.
CONCLUSIONS : We developed a CNN-based scatter correction method for CEDBT in both CC view and mediolateral-oblique view with high accuracy and fast speed.
Duan Xiaoyu, Sahu Pranjal, Huang Hailiang, Zhao Wei
2023-Feb
contrast-enhanced digital breast tomosynthesis, convolutional neural network, scatter correction
