Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that can realize high contrast imaging with a penetration depth of the acoustic. Recently, deep learning (DL) methods have also been successfully applied to PAT for improving the image reconstruction quality. However, the current DL-based PAT methods are implemented by the supervised learning strategy, and the imaging performance is dependent on the available ground-truth data. To overcome the limitation, this work introduces a new image domain transformation method based on cyclic generative adversarial network (CycleGAN), termed as PA-GAN, which is used to remove artifacts in PAT images caused by the use of the limited-view measurement data in an unsupervised learning way. A series of data from phantom and in vivo experiments are used to evaluate the performance of the proposed PA-GAN. The experimental results show that PA-GAN provides a good performance in removing artifacts existing in photoacoustic tomographic images. In particular, when dealing with extremely sparse measurement data (e.g., 8 projections in circle phantom experiments), higher imaging performance is achieved by the proposed unsupervised PA-GAN, with an improvement of ∼14% in structural similarity (SSIM) and ∼66% in peak signal to noise ratio (PSNR), compared with the supervised-learning U-Net method. With an increasing number of projections (e.g., 128 projections), U-Net, especially FD U-Net, shows a slight improvement in artifact removal capability, in terms of SSIM and PSNR. Furthermore, the computational time obtained by PA-GAN and U-Net is similar (∼60 ms/frame), once the network is trained. More importantly, PA-GAN is more flexible than U-Net that allows the model to be effectively trained with unpaired data. As a result, PA-GAN makes it possible to implement PAT with higher flexibility without compromising imaging performance.

The present study was designed to investigate the associations among ultrasonic backscatter, bone densities and microstructure in bovine cancellous bone. Ultrasonic backscatter measurements were performed on 33 bovine cancellous bone specimens with a 2.25-MHz transducer. Ultrasonic apparent backscatter parameters (‘apparent’ means not compensating for ultrasonic attenuation and diffraction) were calculated with optimal signal of interests (SOI). The results showed that ultrasonic backscatter was significantly related to bone densities and microstructure (R2=0.17-0.88, p<0.05). After adjusting the correlations by bone mineral density (BMD), the bone apparent density (BAD) and some trabecular structural features still contributed significantly to the adjusted correlations, with moderate additional variance explained (ΔR2=9.7% at best). Multiple linear regressions revealed that both BAD and trabecular structure contributed significantly and independently to the prediction of ultrasound backscatter (adjusted R2=0.75-0.89, p<0.05), explaining an additional 14.0% of the variance at most, compared with that of BMD measurements alone. The results proved that ultrasonic backscatter was primarily determined by BAD, not BMD, but the combination of bone structure and densities could achieve encouragingly better performances (89% of the variance explained at best) in predicting backscatter properties. This study demonstrated that ultrasonic apparent backscatter might provide additional density and structural features unrelated to current BMD measurement. Therefore, we suggest that ultrasonic backscatter measurement could play a more important role in cancellous bone evaluation.

Ultrasound focusing, with strong penetrability and superior biocompatibility, has attracted substantial interest in biomedical science. Conventional focusing techniques generally rely on an active transducer array or curved lens, and acoustic metamaterials have recently emerged as an alternative approach. However, the low operating frequency of an ultrasound focusing metasurface below 1 MHz and the narrow bandwidth hinder its broad applications in practice. Here, we experimentally realize broadband ultrasound focusing at megahertz frequencies based on a compact and passive metasurface. Three-dimensional spot focusing, ranging from 0.5 to 1.4 MHz, with high spatial resolution (Full width at half maximum of 0.63λ and full length at half maximum of 2.75λ at 1 MHz) and tunable focal length (18.5–71 mm), is demonstrated. The focusing pattern is further enriched by realizing a broadband ultrasound scalpel, which effectively converges sound energy within a sharp “needle” area. Additionally, accompanied by an enhancement of ultrasound intensity, a prominent temperature rise appears in the focal region, the dependence of which on the ultrasound frequency is investigated and the optimized operating frequency is revealed. Broadband and subwavelength ultrasound spot focusing and a scalpel at megahertz frequencies would promote applications in particle manipulation, nondestructive detection, diagnostic sonography, and ultrasound therapy.

Objective . This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. Approach . DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. Main results . Experiments were conducted at 2× , 4× , and 8× magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2× , 4× , and 8× ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR’s SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. Significance . This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.