Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom

Abstract

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

“…The image quality and accuracy problem [113,114]: CS can utilize the characteristics and prior information of signals in the reconstruction process to restore high-quality signals through optimized algorithms. This can enhance the imaging quality and accuracy of PAI, making the imaging results more reliable.…”

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

“…With their strong feature learning capabilities and end-to-end data-driven advantages, deep-learning models can effectively and accurately perform reconstruction and segmentation tasks using optoacoustic image data, as well as develop complicated representations. Deep learning-based techniques have superior generalization and robustness over traditional techniques for handling noise, artifacts, and structural complexity in PAI [26][27][28]. However, despite the significant progress made by deep-learning methods in the field of PAI processing, there are still some challenges and limitations.…”