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.

We demonstrate a flexible wearable optical fiber strain sensor using side-polished fiber Bragg grating (FBG) with a D-shaped polished zone of ~54 μm depth and ~15 mm length. The opto-mechanical response of the sensor embedded in a biocompatible polydimethylsiloxane elastomer is firstly investigated through various subtle wrist pulse tests. In comparison to a bare FBG, the developed sensor exhibits about two times of magnitude larger in time domain due to the more sensitive light-strain interaction with fiber gratings. Then along with the sound wave profiles of original audios, the extracted throat vibration signals validate the great potential of the sensor for wearable human speech detection.

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.

Ultrasound computed tomography (USCT) has emerged as a promising tool for quantitative assessment of musculoskeletal (MSK) diseases. However, the accuracy of echo intensity—a key imaging biomarker—is often compromised by non-optimal imaging conditions, such as probe tilt and limb eccentricity. In this study, we propose a novel echo intensity correction method for USCT that quantitatively compensates for these two major sources of error. The method integrates finite element simulation and phantom experiments to establish correction functions for each influencing factor. These functions are then applied to USCT images from volunteers through automated parameter extraction and intensity adjustment. Validation on both phantom and in vivo data showed that the proposed method significantly improved the uniformity and diagnostic accuracy of echo intensity measurements, leading to a clear improvement in the diagnostic accuracy of MSK diseases. This method enhances the reliability of USCT-based quantitative diagnosis and holds strong potential for broader clinical adoption.

Focused ultrasound (FU) technology is extensively employed in clinical applications such as tumor ablation, Parkinson’s disease treatment, and neuropathic pain management. The safety and efficacy of FU therapy critically depend on the accurate quantification of the acoustic field, particularly the high-pressure distribution in focal region. To address the limitations of existing acoustic measurement techniques—including invasiveness, inability to measure high sound pressure, and system complexity—this study proposes a non-invasive method termed Laser Deflection Acoustic Field Quantification (LDAQ), based on the laser deflection principle. An experimental system was constructed utilizing the acousto-optic deflection effect, which incorporates precision displacement control, rotational scanning, and synchronized triggering. Through tomographic scanning, laser deflection images of the acoustic field were acquired at multiple orientations. An inversion algorithm using Radon transforms was proposed to reconstruct the refractive index gradient distributions from the variations of light intensity and spot displacement. An adaptive weighted fusion strategy was then employed to map these optical signals to the sound pressure field. To validate the LDAQ technique, an acoustic field generated by an FU transducer operating at 0.84 MHz was measured. The reconstructed results were compared with both hydrophone measurements and numerical simulations. The findings demonstrated high consistency among all three results within the focal zone. Full-field analysis yielded a root mean square error (RMSE) of 0.1102 between LDAQ and simulation, and an RMSE of 0.1422 between LDAQ and hydrophone measurements. These results confirm that LDAQ enables non-invasive and high-precision quantification of megapascal-level focused acoustic fields, offering a reliable methodology for acoustic field characterization to support FU treatment optimization and device standardization.

Full-waveform inversion (FWI) typically employs finite-difference time-domain (FDTD) methods to solve the wave equation under the point-source assumption. For computational efficiency, the coarsest grid that satisfies dispersion and stability requirements is generally preferred. However, this grid may not resolve the finite aperture and spatial phase characteristics of a transducer, thereby introducing imaging artifacts and errors. This paper proposes an Operator-consistent reduced source that maps a local fine-grid radiation field to node-specific forcing histories for a prescribed coarse-grid operator. These histories are reused in repeated simulations without online fine-grid computation. On a 0.50 mm grid, the method reduced root-mean-square (RMS) field relative error by 96.4% compared with the Direct coarse-grid aperture source. In controlled FWI with independent observations, the sound-speed root-mean-square error was 2.14 m/s, compared with 4.94 and 5.17 m/s for the Point source and the Direct coarse-grid aperture source, respectively. Hydrophone measurements, blind ring-water validation, and three repeated phantom acquisitions further assessed field reproduction and reconstruction performance. The method enables coarse-grid FDTD simulations to represent the radiated fields of finite-aperture transducers whose device-scale features are not directly resolved by the target grid.