Ultrasound imaging using a matrix array allows real-time multi-planar volumetric imaging. To enhance image quality, the matrix array should provide fast volumetric ultrasound imaging with spatially consistent focusing in the lateral and elevational directions. However, because of the significantly increased data size, dealing with massive and continuous data acquisition is a significant challenge. We have designed an imaging acquisition sequence that handles volumetric data efficiently using a single 256-channel Verasonics ultrasound research platform multiplexed with a 1024-element matrix array. The developed sequence has been applied for building an ultrasonic pupilometer. Our results demonstrate the capability of the developed approach for structural visualization of an ex vivo porcine eye and the temporal response of the modeled eye pupil with moving iris at the volume rate of 30 Hz. Our study provides a fundamental ground for researchers to establish their own volumetric ultrasound imaging platform and could stimulate the development of new volumetric ultrasound approaches and applications.

Abnormal changes of the microvasculature are reported to be key evidence of the development of several critical diseases, including cancer, progressive kidney disease, and atherosclerotic plaque. Super-resolution ultrasound imaging is an emerging technology that can identify the microvasculature noninvasively, with unprecedented spatial resolution beyond the acoustic diffraction limit. Therefore, it is a promising approach for diagnosing and monitoring the development of diseases. In this review, we introduce current super-resolution ultrasound imaging approaches and their preclinical applications on different animals and disease models. Future directions and challenges to overcome for clinical translations are also discussed.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images.Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually.Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work.Conclusion: S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.

Photoacoustic (PA) imaging detects acoustic signals generated by thermal expansion of a light-excited tissue or contrast agents. PA signal amplitude and image quality directly depend on the light fluence at the target depth. With conventional PA imaging systems, approximately 30% energy of incident light at the near-infrared region would be lost due to reflection on the skin surface. Such light loss directly leads to a reduction of PA signal and image quality. A new light delivery scheme that collects and redistributes reflected light energy was recently suggested, which is called the light catcher. In our previous study, proof of concept using a finite-element simulation model was shown and a laboratory-built prototype of the light catcher was applied on tissue-mimicking phantoms. In this paper, we present an elaborate prototype of a high-frequency PA probe with the light catcher fabricated using 3-D printing technology, which is conformal to a subcutaneous tumor in mice. The in vivo usefulness of the developed prototype was evaluated in a mouse tumor model. Equipped with the light catcher, PA signal amplitude from the clinical photosensitizer injected into the mouse tumor was enhanced by 33.7%, which is approximately equivalent to the percent light loss due to reflection on the skin.