Self-Supervised Optimization of RF Data Coherence for Improving Breast Reflection UCT Reconstruction

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control Vol. 72; no. 8; pp. 1147 - 1159
• Main Authors: He, Lei, Liu, Zhaohui, Cai, Yuxin, Zhang, Qiude, Zhou, Liang, Yuan, Jing, Xu, Yang, Ding, Mingyue, Yuchi, Ming, Qiu, Wu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Algorithms; Beamforming; Blind segment network; Breast - diagnostic imaging; Breast Neoplasms - diagnostic imaging; Coherence; Computed tomography; Delays; Effectiveness; Female; Frequency control; Humans; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Modules; Noise; Performance enhancement; Performance evaluation; Phantoms, Imaging; Radio frequency; radio frequency (RF) segment; Radio Waves; Reflection; reflection image reconstruction; Root-mean-square errors; self-supervised learning; Sidelobe reduction; Sidelobes; Signal quality; Signal to noise ratio; State space models; Tomography, X-Ray Computed - methods; Ultrasonic imaging; Ultrasonography, Mammary - methods; ultrasound computer tomography
• ISSN: 0885-3010; 1525-8955; 1525-8955
• DOI: 10.1109/TUFFC.2025.3581915

The reflection ultrasound computed tomography (UCT) is gaining prominence as an essential instrument for breast cancer screening. However, reflection UCT quality is often compromised by the variability in sound speed across breast tissue. Traditionally, reflection UCT utilizes the delay-and-sum (DAS) algorithm, where the time of flight (TOF) significantly affects the coherence of the reflected radio frequency (RF) data, based on an oversimplified assumption of uniform sound speed. This study introduces three meticulously engineered modules that leverage the spatial correlation of receiving arrays to improve the coherence of RF data and enable more effective summation. These modules include the self-supervised blind RF data segment block (BSegB) and the state-space model-based strong reflection prediction (SSM-SRP) block, followed by a polarity-based adaptive replacing refinement (PARR) strategy to suppress sidelobe noise caused by aperture narrowing. To assess the effectiveness of our method, we utilized standard image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean squared error (RMSE). In addition, coherence factor (CF) and variance (Var) were employed to verify the method's ability to enhance signal coherence at the RF data level. The findings reveal that our approach greatly improves performance, achieving an average PSNR of 19.64 dB, an average SSIM of 0.71, and an average RMSE of 0.10, notably under conditions of sparse transmission. The conducted experimental analyses affirm the superior performance of our framework compared to alternative enhancement strategies, including adaptive beamforming methods and deep learning-based beamforming approaches.