Quantitative imaging represents a pivotal advantage of ultrasound computed tomography (USCT), significantly distinguishing it from conventional ultrasound imaging techniques. Especially, Sound speed imaging in USTC has been documented to play a crucial role in the clinical diagnosis of breast cancer. Due to the high demand for resolution in medical applications, full-waveform inversion (FWI) has gradually become a research hotspot compared to time-of-flight methods due to its potential for high-resolution imaging. However, the main challenge of FWI is its sensitivity to initial values due to its inherent non-convex and non-linear properties. An inappropriate initial value can lead it to fall into a local optimal solution, which ultimately produces a sound speed image with no physiological significance, a phenomenon also known as ’cycle-skipping’, severely limiting the application of the method to USCT. In this paper, we propose a method based on an untrained neural network (UNN), which is embedded into the framework of the conventional iteration-based FWI as a sound speed image generator. The implicit regularization provided by this method can effectively alleviate the ill-posed nature of FWI. Significantly, the proposed UNN method allows for unsupervised training, which is essential in medical imaging due to the lack of ground truth paired data. Evaluations on numerical simulations, phantom experiments, and in-vivo breast experiments demonstrate the effectiveness of the proposed UNN in enhancing imaging robustness and reducing image artifacts compared to the traditional FWI. To the best of our knowledge, this study represents the first to investigate the UNN based FWI for USCT breast sound speed imaging and validate it on in-vivo breast experiments.

Ultrasound Computed Tomography (USCT) represents a paradigm shift in medical imaging, offering quantitative, high-resolution tissue characterization for diverse anatomical regions including breast, musculoskeletal system, brain, and lungs. By capturing the full ultrasonic wavefield through dedicated transducer arrays, USCT enables reconstruction of intrinsic tissue properties such as sound speed, attenuation, and acoustic impedance. Unlike conventional ultrasound, USCT provides three fundamental advantages: full-angle tomographic reconstruction, quantitative multi-parameter imaging capabilities, and operator-independent standardized acquisition—all while maintaining ultrasound’s inherent safety and cost-effectiveness. While clinical adoption is still evolving, the technology has achieved significant milestones with several commercially available systems receiving regulatory approval for breast imaging. This review synthesizes recent advances across five critical domains: system hardware design, reflection imaging, quantitative multi-parameter reconstruction (particularly through full-waveform inversion), precision calibration methodologies, and expanding clinical applications. Additionally, we have offered a comprehensive review of the application of deep learning-related technologies in USCT. By comprehensively analyzing current challenges and emerging trends, this work provides researchers and clinicians with an essential reference for understanding the state of the art and identifying pivotal pathways toward widespread clinical implementation of USCT technology.

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 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 block (SSM-SRP), 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). Additionally, 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.

Ultrasound Computed Tomography (USCT) is a promising radiation-free quantitative imaging technique for soft tissue characterization, yet the absence of standardized validation phantoms hinders its clinical standardization and algorithm benchmarking. This study developed and fabricated a suite of agar-based tissue-mimicking phantoms for speed-of-sound (SoS) validation of ring array USCT system. The phantoms featured a stable background medium (1540 m/s SoS) and embedded cylindrical inclusions of 5 mm and 10 mm diameters with three clinically relevant SoS contrasts, achieving precisely calibrated and long-term stable acoustic properties. A clinical 2048-element ring-array USCT system was adopted for data acquisition, and three reconstruction methods (ray-based method, L2-full waveform inversion (L2-FWI), and UNN-FWI) were quantitatively compared. Experimental results demonstrated that implicit-regularized UNN-FWI outperformed conventional methods, exhibiting the lowest area and SoS reconstruction errors across all contrast and size conditions. L2-FWI suffered from cycle-skipping artifacts and obvious quantitative underestimation, while ray-based methods showed severe spatial resolution limitations. This work provides a standardized, reusable phantom-based validation framework for USCT SoS imaging. The publicly released datasets further support global algorithm benchmarking, filling the critical gap in USCT quantitative quality assurance and facilitating the clinical translation and standardization of USCT technology.