Dual-wavelength in-line digital holography (DIDH) is one of the popular methods for quantitative phase imaging of objects with non-contact and high-accuracy features. Two technical challenges in the reconstruction of these objects include suppressing the amplified noise and the twin-image that respectively originate from the phase difference and the phase-conjugated wavefronts. In contrast to the conventional methods, the deep learning network has become a powerful tool for estimating phase information in DIDH with the assistance of noise suppressing or twin-image removing ability. However, most of the current deep learning-based methods rely on supervised learning and training instances, thereby resulting in weakness when it comes to applying this training to practical imaging settings. In this paper, a new DIDH network (DIDH-Net) is proposed, which encapsulates the prior image information and the physical imaging process in an untrained deep neural network. The DIDH-Net can effectively suppress the amplified noise and the twin-image of the DIDH simultaneously by automatically adjusting the weights of the network. The obtained results demonstrate that the proposed method with robust phase reconstruction is well suited to improve the imaging performance of DIDH.

Digital in-line holographic microscopy (DIHM) is a widely used interference technique for real-time reconstruction of living cells’ morphological information with large space-bandwidth product and compact setup. However, the need for a larger pixel size of detector to improve imaging photosensitivity, field-of-view, and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution. Additionally, the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image. The deep learning (DL) approach has emerged as a powerful tool for phase retrieval in DIHM, effectively addressing these challenges. However, most DL-based strategies are data-driven or end-to-end net approaches, suffering from excessive data dependency and limited generalization ability. Herein, a novel multi-prior physics-enhanced neural network with pixel super-resolution (MPPN-PSR) for phase retrieval of DIHM is proposed. It encapsulates the physical model prior, sparsity prior and deep image prior in an untrained deep neural network. The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods. With the capabilities of pixel super-resolution, twin-image elimination and high-throughput jointly from a single-shot intensity measurement, the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Light-sheet fluorescence microscopy (LSFM) facilitates high temporal-spatial resolution, low photobleaching and phototoxicity for long-term volumetric imaging. However, when a high axial resolution or optical sectioning capability is required, the field of view (FOV) is limited. Here, we propose to generate a large FOV of light-sheet by scanning multiple focus-shifted Gaussian beam arrays (MGBA) while keeping the high axial resolution. The positions of the beam waists of the multiple Gaussian beam arrays are shifted in both axial and lateral directions in an optimized arranged pattern, and then scanned along the direction perpendicular to the propagation axis to form an extended FOV of light-sheet. Complementary beam subtraction method is also adopted to further improve axial resolution. Compared with the single Gaussian light-sheet method, the proposed method extends the FOV from 12 μm to 200 μm while sustaining the axial resolution of 0.73 μm. Both numerical simulation and experiment on samples are performed to verify the effectiveness of the method.

By exploiting the total variation (TV) regularization scheme and the contrast transfer function (CTF), a phase map can be retrieved from single-distance coherent diffraction images via the sparsity of the investigated object. However, the CTF-TV phase retrieval algorithm often struggles in the presence of strong noise, since it is based on the traditional compressive sensing optimization problem. Here, convolutional neural networks, a powerful tool from machine learning, are used to regularize the CTF-based phase retrieval problems and improve the recovery performance. This proposed method, the CTF-Deep phase retrieval algorithm, was tested both via simulations and experiments. The results show that it is robust to noise and fast enough for high-resolution applications, such as in optical, x-ray, or terahertz imaging.

Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM, i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an extended optical sectioning capability directly from the FC-WFM z-stack data. Case studies of different specimens with a specific imaging system are used to illustrate this method. Consequently, the image quality achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of 3D information and spatial resolution, while the reconstruction data size is 21-fold smaller and the in-focus depth is doubled. This technique significantly reduces the 3D data acquisition requirements without losing detail and improves the 3D imaging speed by extracting the optical sectioning in the depth-of-field. This cost-effective and convenient method offers a promising tool to observe high-precision color 3D spatial distributions of biological samples.

Background: Ultrasound is ideal for displaying intracranial great vessels but not intracranial microvessels and terminal vessels. Even with contrast agents, the imaging effect is still unsatisfactory. In recent years, significant theoretical advances have been achieved in super-resolution imaging. The latest commonly used ultrafast plane-wave ultrasound Doppler imaging of the brain and microbubble-based super-resolution ultrasound imaging have been applied to the imaging of cerebral microvessels and blood flow in small animals such as mice but have not been applied to in vivo imaging of the cerebral microvessels in monkeys and larger animals. In China, preliminary research results have been obtained using super-resolution imaging in certain fields but rarely in fundamental and clinical experiments on large animals. In recent years, we have conducted a joint study with the Xi'an Jiaotong University to explore the application and performance of this new technique in the diagnosis of cerebrovascular diseases in large animals.Objective: To explore the characteristics and advantages of microbubble-based super-resolution ultrasound imaging of intracranial vessels in rhesus monkeys compared with conventional transcranial ultrasound.Methods: First, the effectiveness and feasibility of the super-resolution imaging technique were verified by modular simulation experiments. Then, the imaging parameters were adjusted based on in vitro experiments. Finally, two rhesus monkeys were used for in vivo experiments of intracranial microvessel imaging.Results: Compared with conventional plane-wave imaging, super-resolution imaging could measure the inner diameters of cerebral microvessels at a resolution of 1 mm or even 0.7 mm and extract blood flow information. In addition, it has a better signal-to-noise ratio (5.625 dB higher) and higher resolution (~30-fold higher). The results of the experiments with rhesus monkeys showed that microbubble-based super-resolution ultrasound imaging can achieve an optimal resolution at the micron level and an imaging depth >35 mm.Conclusion: Super-resolution imaging can realize the monitoring imaging of high-resolution and fast calculation of microbubbles in the process of tissue damage, providing an important experimental basis for the clinical application of non-invasive transcranial ultrasound.