Expediting Image Acquisition in Reflection Ultrasound Computed Tomography

Reconstruction of reflection ultrasound computed tomography with sparse transmissions using conditional generative adversarial network

Ultrasound computed tomography (UCT) has attracted increasing attention due to its potential for early breast cancer diagnosis and screening. Synthetic aperture imaging is a widely used means for reflection UCT image reconstruction, due to its ability to produce isotropic and high-resolution anatomical images. However, obtaining fully sampled UCT data from all directions over multiple transmissions is a time-consuming scanning process. Even though sparse transmission strategy could mitigate the data acquisition complication, image quality reconstructed by traditional Delay and Sum (DAS) methods may degrade substantially. This study presents a deep learning framework based on a conditional generative adversarial network, UCT-GAN, to efficiently reconstruct reflection UCT image from sparse transmission data. The evaluation experiments using breast imaging data in vivo show that the proposed UCT-GAN is able to generate high-quality reflection UCT images when using 8 transmissions only, which are comparable to that reconstructed from the data acquired by 512 transmissions. Quantitative assessment in terms of peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measurement (SSIM) show that the proposed UCT-GAN is able to efficiently reconstruct high-quality reflection UCT images from sparsely available transmission data, outperforming several other methods, such as RED-GAN, DnCNN-GAN, BM3D. In the experiment of 8-transmission sparse data, the PSNR is 29.52 dB, and the SSIM is 0.7619. The proposed method has the potential of being integrated into the UCT imaging system for clinical usage.

Real-time assessment of high-intensity focused ultrasound heating and cavitation with hybrid optoacoustic ultrasound imaging

High-intensity focused ultrasound (HIFU) enables localized ablation of biological tissues by capitalizing on the synergistic effects of heating and cavitation. Monitoring of those effects is essential for improving the efficacy and safety of HIFU interventions. Herein, we suggest a hybrid optoacoustic-ultrasound (OPUS) approach for real-time assessment of heating and cavitation processes while providing an essential anatomical reference for accurate localization of the HIFU-induced lesion. Both effects could clearly be observed by exploiting the temperature dependence of optoacoustic (OA) signals and the strong contrast of gas bubbles in pulse-echo ultrasound (US) images. The differences in temperature increase and its rate, as recorded with a thermal camera for different HIFU pressures, evinced the onset of cavitation at the expected pressure threshold. The estimated temperatures based on OA signal variations were also within 10-20 % agreement with the camera readings for temperatures below the coagulation threshold (∼50 °C). Experiments performed in excised tissues as well as in a post-mortem mouse demonstrate that both heating and cavitation effects can be effectively visualized and tracked using the OPUS approach. The good sensitivity of the suggested method for HIFU monitoring purposes was manifested by a significant increase in contrast-to-noise ratio within the ablated region by > 10 dB and > 5 dB for the OA and US images, respectively. The hybrid OPUS-based monitoring approach offers the ease of handheld operation thus can readily be implemented in a bedside setting to benefit several types of HIFU treatments used in the clinics.

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.