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Abstract: Electron ptychography is a powerful computational method for atomic-resolution imaging with high contrast for weakly and strongly scattering elements. Modern algorithms coupled with fast and efficient detectors allow imaging specimens with tens of nanometers thicknesses with sub-0.5 Ångstrom lateral resolution. However, the axial resolution in these approaches is currently limited to a few nanometers, limiting their ability to solve novel atomic structures ab initio. Here, we experimentally demonstrate multi-slice ptychographic electron tomography, which allows atomic resolution three-dimensional phase-contrast imaging in a volume surpassing the depth of field limits. We reconstruct tilt-series 4D-STEM measurements of a Co 3 O 4 nanocube, yielding 2 Å axial and 0.7 Å transverse resolution in a reconstructed volume of ( 18.2 nm ) 3 . Our results demonstrate a 13.5-fold improvement in axial resolution compared to multi-slice ptychography while retaining the atomic lateral resolution and the capability to image volumes beyond the depth of field limit. Multi-slice ptychographic electron tomography significantly expands the volume of materials accessible using high-resolution electron microscopy. We discuss further experimental and algorithmic improvements necessary to also resolve single weakly scattering atoms in 3D.

Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments. [ABSTRACT FROM AUTHOR]

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC. [ABSTRACT FROM AUTHOR]

Optical encryption is a potential scheme for information security that exploits abundant degrees of freedom of light to encode information. However, conventional encryption based on fluorescent materials faces challenges in handling complex secret information. Alternatively, single‐pixel imaging (SPI) provides a computational modality to solve these problems. In this study, a high‐capacity fluorescence encryption scheme, achieved by introducing lanthanide materials and steganography into the encoding and decoding processes of SPI is proposed. Two types of well‐designed lanthanide luminescent materials are utilized and excited to generate fluorescence images (fluo‐images), which are crucial in this scheme. Various practical experiments using fluo‐images as secret keys demonstrate the robustness, effectiveness, and repeatability of this scheme. Furthermore, multi‐image experiments indicate the potential of this method to increase secret information capacity. Thus, the proposed fluorescence encryption scheme does provide an efficient computational encryption strategy based on lanthanide luminescent materials for information security, which can improve the security of traditional optical encryption and simultaneously enhance the flexibility of SPI computational decryption. [ABSTRACT FROM AUTHOR]

With the advancement of computational imaging, a large number of spectral imaging systems based on encoding–decoding have emerged, among which phase‐encoding spectral imaging systems have attracted widespread interest. Conventional phase‐encoding systems suffer from severe image degradation and limited light throughput. To address these challenges and achieve video‐rate spectral imaging with high spatial resolution and spectral accuracy, a novel optical system based on diffractive‐refractive hybrid optics is proposed. Here, a diffractive optical element is employed to perform imaging and dispersion functions, while a rear lens is used to shorten the system's back focal length and reduce the size of the point spread function. Meanwhile, convolutional neural network‐based spectral reconstruction algorithms are employed to reconstruct the spectral data cubes from diffraction blurred images. A compact, cost‐effective, and portable prototype has been constructed, demonstrating the capability to acquire and reconstruct 30 spectral data cubes per second, each with dimensions of 1080×1280×43${1080 \times 1280 \times 43}$ in the spectral range of 480–900 nm with a 10 nm spectral interval. The optical system has the potential to broaden the application scope of phase‐encoding spectral imaging systems in various scenarios. [ABSTRACT FROM AUTHOR]

Learned optics, which incorporate lightweight diffractive optics, coded-aperture modulation, and specialized image-processing neural networks, have recently garnered attention in the field of snapshot hyperspectral imaging (HSI). While conventional methods typically rely on a single lens element paired with an off-the-shelf color sensor, these setups, despite their widespread availability, present inherent limitations. First, the Bayer sensor's spectral response curves are not optimized for HSI applications, limiting spectral fidelity of the reconstruction. Second, single lens designs rely on a single diffractive optical element (DOE) to simultaneously encode spectral information and maintain spatial resolution across all wavelengths, which constrains spectral encoding capabilities. This work investigates a multi-channel lens array combined with aperture-wise color filters, all co-optimized alongside an image reconstruction network. This configuration enables independent spatial encoding and spectral response for each channel, improving optical encoding across both spatial and spectral dimensions. Specifically, we validate that the method achieves over a 5dB improvement in PSNR for spectral reconstruction compared to existing single-diffractive lens and coded-aperture techniques. Experimental validation further confirmed that the method is capable of recovering up to 31 spectral bands within the 429--700 nm range in diverse indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Significance: Imaging changes in subcellular structure is critical to understanding cell behavior but labeling can be impractical for some specimens and may induce artifacts. Although darkfield microscopy can reveal internal cell structures, it often produces strong signals at cell edges that obscure intracellular details. By optically eliminating the edge signal from darkfield images, we can resolve and quantify changes to cell structure without labeling. Aim: We introduce a computational darkfield imaging approach named quadrant darkfield (QDF) to separate smaller cellular features from large structures, enabling label-free imaging of cell organelles and structures in living cells. Approach: Using a programmable LED array as the illumination source, we vary the direction of illumination to encode additional information about the feature size within cells. This is possible due to the varying levels of directional scattering produced by features based on their sizes relative to the wavelength of light used. Results: QDF successfully resolved small cellular features without interference from larger structures. QDF signal is more consistent during cell shape changes than traditional darkfield. QDF signals correlate with flow cytometry side scatter measurements, effectively differentiating cells by organelle content. Conclusions: QDF imaging enhances the study of subcellular structures in living cells, offering improved quantification of organelle content compared with darkfield without labels. This method can be simultaneously performed with other techniques such as quantitative phase imaging to generate a multidimensional picture of living cells in real-time. [ABSTRACT FROM AUTHOR]

Pixel binning-based Quad sensors (mega-pixel resolution camera sensor) offer a promising solution to address the hardware limitations of compact cameras for low-light imaging. However, the binning process leads to reduced spatial resolution and introduces non-Bayer CFA artifacts. In this paper, we propose a Quad CFA-driven remosaicing model that effectively converts noisy Quad Bayer and standard Bayer patterns compatible to existing Image Signal Processor (ISP) without any loss in resolution. To enhance the practicality of the remosaicing model for real-world images affected by mixed noise, we introduce a novel dual-head joint remosaicing and denoising network (DJRD), which addresses the order of denoising and remosaicing by performing them in parallel. In DJRD, we customize two denoising branches for Quad Bayer and Bayer inputs. These branches model non-local and local dependencies, CFA location, and frequency information using residual convolutional layers, Swin Transformer, and wavelet transform-based CNN. Furthermore, to improve the model's performance on challenging cases, we fine-tune DJRD to handle difficult scenarios by identifying problematic patches through Moire and zipper detection metrics. This post-training phase allows the model to focus on resolving complex image regions. Extensive experiments conducted on simulated and real images in both Bayer and sRGB domains demonstrate that DJRD outperforms competing models by approximately 3 dB, while maintaining the simplicity of implementation without adding any hardware. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) particle image velocimetry (PIV) system typically consists of multiple cameras. However, micro-PIV systems for measuring microscale velocity fields lack sufficient space to accommodate them. In this work we propose an alternative approach based on computational imaging, enabling monocular micro-PIV systems to perform 3D flow field measurements without additional hardware or complex structure. The microscopic objective is designed to satisfy the required parameters, and the point spread function (PSF) responses of the system to different depths of the object surface are obtained. Additionally, a particle dataset generation method based on the PSFs of the optical system is proposed, and a deep-learning network is constructed for training. To validate the feasibility, particle images are captured in experiments and inputted into the network to reconstruct depth images and build three-dimensional flow fields. Simulation and experimental results demonstrate that the measurement deviation is within 13.2%, indicating the practicality of the proposed model.

Abstract Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.

Depth measurement and three-dimensional (3D) imaging under complex reflection and transmission conditions are challenging and even impossible for traditional structured light techniques, owing to the precondition of point-to-point triangulation. Despite recent progress in addressing this problem, there is still no efficient and general solution. Herein, a Fourier dual-slice projection with depth-constrained localization is presented to separate and utilize different illumination and reflection components efficiently, which can significantly decrease the number of projection patterns in each sequence from thousands to fifteen. Subsequently, multi-scale parallel single-pixel imaging (MS-PSI) is proposed based on the established and proven position-invariant theorem, which breaks the local regional assumption and enables dynamic 3D reconstruction. Our methodology successfully unveils unseen-before capabilities such as (1) accurate depth measurement under interreflection and subsurface scattering conditions, (2) dynamic measurement of the time-varying high-dynamic-range scene and through thin volumetric scattering media at a rate of 333 frames per second; (3) two-layer 3D imaging of the semitransparent surface and the object hidden behind it. The experimental results confirm that the proposed method paves the way for dynamic 3D reconstruction under complex optical field reflection and transmission conditions, benefiting imaging and sensing applications in advanced manufacturing, autonomous driving, and biomedical imaging.

Fourier ptychographic microscopy (FPM) breaks through the resolution limitations of conventional optical systems, which offer a full‐field view and high resolution without additional mechanical scanning. However, conventional image‐domain optimizations require trade‐offs between correction efficacy, data redundancy, and reconstruction accuracy. Furthermore, the existing linear time‐invariant model for actual nonlinear, time‐varying optical systems leads to forward model mismatch, complicating the corrections of the vignetting effect. To overcome these challenges and achieve stitching‐free FPM, a family of forward wavelet‐transform models (WL‐FPM) is proposed. WL‐FPM employs the reversibility of the wavelet transform for high‐fidelity reconstruction in the multiscale feature domain. The wavelet loss function is updated in each iteration, and non‐convex optimization is solved by complex back diffraction. WL‐FPM offers stitching‐free, high‐resolution, and robust reconstruction under various challenging conditions, including vignetting effects, LED position mismatch, intensity fluctuations, and high‐level noise environments, which outperform conventional FPM methods. Under a 4X objective with NA 0.1, WL‐FPM achieves a 435‐nm resolution and stitching‐free full‐field reconstruction of a 3.328 × 3.328 mm2 pathological section with distinct subcellular organelles. In live cell imaging, it provides a full‐field observation with distinct lipids in a single cell. A large number of simulation and experimental results demonstrate its potential for biomedical applications. [ABSTRACT FROM AUTHOR]

Super‐resolution structured‐illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out‐of‐focus fluorescence. Moreover, we show that FlexSIM achieves state‐of‐the‐art performance over a variety of open SIM datasets. [ABSTRACT FROM AUTHOR]

Video snapshot compressive imaging (SCI) uses a low-speed 2D detector to capture high-speed scene, where the dynamic scene is modulated by different masks and then compressed into a snapshot measurement. Following this, a reconstruction algorithm is needed to reconstruct the high-speed video frames. Although state-of-the-art (SOTA) deep learning-based reconstruction algorithms have achieved impressive results, they still face the following challenges due to excessive model complexity and GPU memory limitations: (1) These models need high computational cost, and (2) They are usually unable to reconstruct large-scale video frames at high compression ratios. To address these issues, we develop an efficient network for video SCI by using hierarchical residual-like connections and hybrid CNN-Transformer structure within a single residual block, dubbed EfficientSCI++. The EfficientSCI++ network can well explore spatial-temporal correlation using convolution in the spatial domain and Transformer in the temporal domain, respectively. We are the first time to demonstrate that a UHD color video ( 1644 × 3840 × 3 ) with high compression ratio (40) can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 34 dB. Moreover, a mixed-precision model is trained to further accelerate the video SCI reconstruction process and save memory footprint. Extensive results on both simulation and real data demonstrate that, compared with precious SOTA methods, our proposed EfficientSCI++ and EfficientSCI can achieve comparable reconstruction quality with much cheaper computational cost and better real-time performance. Code is available at https://github.com/mcao92/EfficientSCI-plus-plus. [ABSTRACT FROM AUTHOR]

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications. [ABSTRACT FROM AUTHOR]

Advances in imaging tools have always been a pivotal driver for new discoveries in neuroscience. An ability to visualize neurons and subcellular structures deep within the brain of a freely behaving animal is integral to our understanding of the relationship between neural activity and higher cognitive functions. However, fast highresolution imaging is limited to sub-surface brain regions and generally requires head fixation of the animal under the microscope. Developing new approaches to address these challenges is critical. The last decades have seen rapid progress in minimally invasive endo-microscopy techniques based on bare optical fibers. A single multimode fiber can be used to penetrate deep into the brain without causing significant damage to the overlying structures and provide high-resolution imaging. Here, we discuss how the full potential of high-speed super-resolution fiber endoscopy can be realized by a holistic approach that combines fiber optics, light shaping, and advanced computational algorithms. The recent progress opens up new avenues for minimally invasive deep brain studies in freely behaving mice. [ABSTRACT FROM AUTHOR]

Abstract We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across >110 cm2 areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis translation to capture, for each sample of interest, a multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640 9.4-megapixel images. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. The memory-efficient, compressed differentiable representation offered by the neural network effectively enables joint participation of the entire multi-TB dataset during the reconstruction process. Validation experiments on gauge blocks demonstrate a profilometric precision and accuracy of 10 µm or better. To demonstrate the broad utility of our new computational microscope, we applied STARCAM to a variety of decimeter-scale objects, with applications ranging from cultural heritage to industrial inspection.

Fourier ptychographic microscopy (FPM) is a computational imaging technology that can acquire high-resolution large-area images for applications ranging from biology to microelectronics. In this study, we utilize multifocal plane imaging to enhance the existing FPM technology. Using an RGB light emitting diode (LED) array to illuminate the sample, raw images are captured using a color camera. Then, exploiting the basic optical principle of wavelength-dependent focal length variation, three focal plane images are extracted from the raw image through simple R, G, and B channel separation. Herein, a single aspherical lens with a numerical aperture (NA) of 0.15 was used as the objective lens, and the illumination NA used for FPM image reconstruction was 0.08. Therefore, simultaneous multifocal plane FPM with a synthetic NA of 0.23 was achieved. The multifocal imaging performance of the enhanced FPM system was then evaluated by inspecting a transparent organic light-emitting diode (OLED) sample. The FPM system was able to simultaneously inspect the individual OLED pixels as well as the surface of the encapsulating glass substrate by separating R, G, and B channel images from the raw image, which was taken in one shot. [ABSTRACT FROM AUTHOR]

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. [ABSTRACT FROM AUTHOR]

Magnetic induction tomography (MIT) is an emerging imaging technology holding significant promise in the field of cerebral hemorrhage monitoring. The commonly employed imaging method in MIT is time-difference imaging. However, this approach relies on magnetic field signals preceding cerebral hemorrhage, which are often challenging to obtain. Multiple bioelectrical impedance information with different frequencies is added to this study on the basis of single-frequency information, and the collected signals with different frequencies are identified to obtain the magnetic field signal generated by single-layer heterogeneous tissue. The Stacked Autoencoder (SAE) neural network algorithm is used to reconstruct the images of head multi-layer tissues. Both numerical simulation and phantom experiments are carried out. The results indicate that the relative error of the multi-frequency SAE reconstruction is only 7. 82°/o, outperforming traditional algorithms. Moreover, under a noise level of 40 dB, the anti-interference capability of the MIT algorithm based on frequency identification and SAE is superior to traditional algorithms. This research explores a novel approach for the dynamic monitoring of cerebral hemorrhage and demonstrates the potential advantages of MIT in non-invasive monitoring. [ABSTRACT FROM AUTHOR]

Significance: Quantitative phase imaging (QPI) offers a label-free approach to noninvasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. Aim: This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. Approach: We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI’s applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. Results: QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. Conclusions: QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI’s capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care. [ABSTRACT FROM AUTHOR]

Imaging is a longstanding research topic in optics and photonics and is an important tool for a wide range of scientific and engineering fields. Computational imaging is a powerful framework for designing innovative imaging systems by incorporating signal processing into optics. Conventional approaches involve individually designed optical and signal processing systems, which unnecessarily increased costs. Computational imaging, on the other hand, enhances the imaging performance of optical systems, visualizes invisible targets, and minimizes optical hardware. Digital holography and computer-generated holography are the roots of this field. Recent advances in information science, such as deep learning, and increasing computational power have rapidly driven computational imaging and have resulted in the reinvention these imaging technologies. In this paper, I survey recent research topics in computational imaging, where optical randomness is key. Imaging through scattering media, non-interferometric quantitative phase imaging, and real-time computer-generated holography are representative examples. These recent optical sensing and control technologies will serve as the foundations of next-generation imaging systems in various fields, such as biomedicine, security, and astronomy. [ABSTRACT FROM AUTHOR]

Compressed sampling allows to accurately reconstruct a sparse signal even in case of incomplete signal measurements. In this paper, we apply this method to single-pixel imaging and explore the possibilities of image reconstruction by sampling it with an incomplete set of binary light patterns. Using computer simulation, we optimize the image sampling process and find parameters of light patterns such that single-pixel imaging works best. [ABSTRACT FROM AUTHOR]

Single-pixel imaging allows to obtain images without the use of photosensors with spatial resolution. In this method, an image is calculated by measuring the image conformity to a given set of light patterns by a single-pixel detector. However, when implementing single-pixel imaging in practice, one has to deal with various imperfections, which lead to the difference between the experiment and the idealized theoretical model. In this work, we analyze the effect of detector noise on the ability to compute an image using a compressed sampling algorithm. By conducting computer simulations of single-pixel imaging, we investigate methods for suppressing the effects of detector noise and find optimum parameters of the measurement process. As a result, we demonstrate the ability to obtain images with a realistic model of the detector noise. [ABSTRACT FROM AUTHOR]

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational virtual staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.