Speaker
Description
Reflection ultrasound computed tomography (RUCT) is emerging as an essential tool for clinical breast cancer screening. However, a persistent challenge in ultrasonic imaging lies in the degradation of image quality due to local sound speed variations in breast tissue and random noise in the circuitry. RUCT imaging is based on the classical delay and sum (DAS) algorithm. Its pixel value is directly determined by the Time of Flight (TOF), i.e., the RF data delay time from the transmitting element to the target and then to the receiving element under unrealistic uniform sound speed model. On the other hand, only a bandpass filter is manually designed to alleviate electrical noise. In this paper, we propose a deep learning method, termed the Self-Supervised Breast RUCT Reconstruction (SSBRR) framework, tailored specifically for improving RUCT through RF data processing. Our framework focuses on the challenge of accurately locate RF data delay from its subsequences and explore potential benefits of mitigating destruction through the slackness. Then, the estimation of RF data delay is significantly contributed by capturing spatial latent consistency across the receiving arrays. To evaluate the effectiveness of our proposed method, we employ well-established metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Our experiments demonstrate promising results, with our method achieving an average PSNR of 20.892 and an average SSIM of 0.792, particularly under conditions of extreme sparsity in transmission. The experimental analyses conducted attest to the superior performance of our framework compared to competing enhancement strategies.
