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Acousto-optic tomography (AOT) enables optical-contrast imaging deep inside scattering samples via localized ultrasound modulation of scattered light. However, the resolution of AOT is inherently limited by the ultrasound focus size, prohibiting microscopic investigations. In the last few years, advances in the field of digital wavefront-shaping have allowed the development of novel approaches for overcoming the acoustic resolution limit of AOT. However, these novel approaches require the execution of thousands of wavefront measurements within the sample speckle decorrelation time, limiting their application to static samples. Here, we show that it is possible to surpass the acoustic resolution limit with a conventional AOT system by exploiting the natural dynamics of speckle decorrelations rather than trying to overcome them. We achieve this by adapting the principles of super-resolution optical fluctuations imaging (SOFI), originally developed for imaging blinking fluorophores, to AOT. We show that naturally fluctuating optical speckle grains can serve as the analogues of blinking fluorophores, enabling super-resolution by statistical analysis of fluctuating acousto-optic signals.

Acousto-optic imaging (AOI) enables optical-contrast imaging deep inside scattering samples via localized ultrasound-modulation of scattered light. While AOI allows optical investigations at depths, its imaging resolution is inherently limited by the ultrasound wavelength, prohibiting microscopic investigations. Here, we propose a computational imaging approach that allows optical diffraction-limited imaging using a conventional AOI system. We achieve this by extracting diffraction-limited imaging information from speckle correlations in the conventionally detected ultrasound-modulated scattered-light fields. Specifically, we identify that since “memory-effect” speckle correlations allow estimation of the Fourier magnitude of the field inside the ultrasound focus, scanning the ultrasound focus enables robust diffraction-limited reconstruction of extended objects using ptychography (i.e., we exploit the ultrasound focus as the scanned spatial-gate probe required for ptychographic phase retrieval). Moreover, we exploit the short speckle decorrelation-time in dynamic media, which is usually considered a hurdle for wavefront-shaping- based approaches, for improved ptychographic reconstruction. We experimentally demonstrate noninvasive imaging of targets that extend well beyond the memory-effect range, with a 40-times resolution improvement over conventional AOI.

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We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images. Clinically, capturing the 3D models of the bones is crucial for surgical planning, implant fitting, and postoperative evaluation. X-ray imaging significantly reduces the exposure of patients to ionizing radiation compared to Computer Tomography (CT) imaging, and is much more common and inexpensive compared to Magnetic Resonance Imaging (MRI) scanners. However, retrieving 3D models from such 2D scans is extremely challenging. In contrast to the common approach of statistically modeling the shape of each bone, our deep network learns the distribution of the bones' shapes directly from the training images. We train our model with both supervised and unsupervised losses using Digitally Reconstructed Radiograph (DRR) images generated from CT scans. To apply our model to X-Ray data, we use style transfer to transform between X-Ray and DRR modalities. As a result, at test time, without further optimization, our solution directly outputs a 3D reconstruction from a pair of bi-planar X-ray images, while preserving geometric constraints. Our results indicate that our deep learning model is very efficient, generalizes well and produces high quality reconstructions.