X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. Single-mask differential phase contrastnimaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a novel model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask X-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as in retrieved images. The model validity as well as the proposed retrieval method is demonstrated via both experimental results on a system developed in-house as well as with Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences（中国科学院上海光学精密机械研究所）
Ghost imaging radar is a new system of gaze imaging radar with high detection sensitivity, super-resolution and better anti-interference performance, but the relative motion between the radar system and the target will make the target imaging deteriorate. This paper proposes to perform absolute position localisation of a single target in the field of view by weighting the correlation strength of a single frame image of rough target, and to compensate translation of the reference arm speckle according to the localisation and tracking trajectory to accumulate the rough image into a high quality …
Despite the recent advances of deep learning algorithms in medical imaging, the automatic segmentation algorithms for kidneys in MRI exams are still scarce. Automated segmentation of kidneys in Magnetic Resonance Imaging (MRI) exams are important for enabling radiomics and machine learning analysis of renal disease. In this work, we propose to use the popular Mask R-CNN for the automatic segmentation of kidneys in coronal T2-weighted Fast Spin Eco slices of 100 MRI exams. We propose the morphological operations as post-processing to further improve the performance of Mask R-CNN for this task. …
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generalizability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we prop…
University of California Riverside（加州大学河滨分校）；Carnegie Mellon University（卡内基梅隆大学）
Lensless cameras provide a framework to build thin imaging systems by replacing the lens in a conventional camera with an amplitude or phase mask near the sensor. Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems. Furthermore, existing methods struggle to recover dense scenes with large depth variations. In this paper, we propose a lensless imaging system that captures a small number of measurements using different patterns on a programmable mask. In this context, we make three contributio…
As a promising lensless imaging method for distance objects, intensity interferometry imaging (III) had been suffering from the unreliable phase retrieval process, hindering the development of III for decades. Recently, the introduction of the ptychographic detection in III overcame this challenge, and a method called ptychographic III (PIII) was proposed. We here experimentally demonstrate that PIII can image a dynamic distance object. A reasonable image for the moving object can be retrieved with only two speckle patterns for each probe, and only 10 to 20 iterations are needed. Meanwhile, PI…
Ghost imaging has recently been successfully achieved in the X-ray regime; due to the penetrating power of X-rays this immediately opens up the possibility of X-ray ghost tomography. No research into this topic currently exists in the literature. Here we present adaptations of conventional tomography techniques to this new ghost imaging scheme. Several numerical implementations for tomography through X-ray ghost imaging are considered. Specific attention is paid to schemes for denoising of the resulting tomographic reconstruction, issues related to dose fractionation, and considerations regard…
The development of single-photon counting detectors and arrays has made tremendous steps in recent years, not the least because of various new applications in, e.g., LIDAR devices. In this work, a 3D imaging device based on real thermal light intensity interferometry is presented. By using gated SPAD technology, a basic 3D scene is imaged in reasonable measurement time. Compared to conventional approaches, the proposed synchronized photon counting allows using more light modes to enhance 3D ranging performance. Advantages like robustness to atmospheric scattering or autonomy by exploiting exte…
Night vision imaging is a technology that converts non-visible object to human eyes into visible image in night and other low light environments. However, the conventional night vision imaging can only directly produce grayscale image. Here, we propose a novel night vision imaging method based on intensity correlation of light. The object's information detected by infrared non-visible light is expressed by visible light via the spatial intensity correlation of light. With simple data processing, a color night vision image can be directly produced by this approach without any pseudo-color image…
Intensity interferometry (II) exploits the second-order correlation to acquire the spatial frequency information of an object, which has been used to observe distant stars since 1950s. However, due to unreliability of employed imaging reconstruction algorithms, II can only image simple and sparse objects such as double stars. We here develop a method that overcomes this unreliability problem and enables imaging complex objects by combing II and a ptychography iterative algorithm. Different from previous ptychography iterative-type algorithms that work only for diffractive objects using coheren…
We report a simple single-pixel imaging system with a low mean squared error in the entire terahertz frequency region (3-13 THz) that employs a thin metallic ring with a series of directly perforated random masks and a subpixel mask digitization technique. This imaging system produces high pixel resolution reconstructed images, up to 1200 x 1200 pixels, and imaging area of 32 mm x 32 mm. It can be extended to develop advanced imaging systems in the near-ultraviolet to terahertz region.
Princeton University（普林斯顿大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
We introduce Mask-ToF, a method to reduce flying pixels (FP) in time-of-flight (ToF) depth captures. FPs are pervasive artifacts which occur around depth edges, where light paths from both an object and its background are integrated over the aperture. This light mixes at a sensor pixel to produce erroneous depth estimates, which can adversely affect downstream 3D vision tasks. Mask-ToF starts at the source of these FPs, learning a microlens-level occlusion mask which effectively creates a custom-shaped sub-aperture for each sensor pixel. This modulates the selection of foreground and backgroun…
University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）
Mask-based lensless imagers are smaller and lighter than traditional lensed cameras. In these imagers, the sensor does not directly record an image of the scene; rather, a computational algorithm reconstructs it. Typically, mask-based lensless imagers use a model-based reconstruction approach that suffers from long compute times and a heavy reliance on both system calibration and heuristically chosen denoisers. In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image. We leverage our knowledge of the physical system by unrolling a tr…
Metasurface optics have demonstrated vast potential for implementing traditional optical components in an ultra-compact and lightweight form factor. Metasurface lenses, also called metalenses, however, suffer from severe chromatic aberrations, posing serious limitations on their practical use. Existing approaches for circumventing such aberrations via dispersion engineering are limited to small apertures and often entails multiple scatterers per unit cell with small feature sizes. Here, we present an alternative technique to mitigate chromatic aberration and demonstrate high-quality, full-colo…
Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model is the masked autoencoder (MAE) which was developed on natural imaging data. The MAE is masking out a high fraction of visual transformer (ViT) input patches, to then recover the uncorrupted images as a pretraining task. In this work, we extend MAE to perform anomaly detection on breast magnetic resonance imaging (MRI). This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contras…
We present a novel adaptive multi-modal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource constrained device acquiring high resolution intensity frames and events, while the host is capable of performing computationally expensive tasks. We develop a joint intensity-neuromorphic event rate-distortion compression framework with a quadtree (QT) based compression of intensity and events scheme. The data acquisition on the chip is driven by the presence of objects of interest …
There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems---and the associated optimum aperture coding---under simple but meaningful propagation and sensor models. Among other results, we show that when thermal noise dominates, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates, randomly generated masks with lower transmissivity offer greater performance. We also provide comparisons to classical…
A novel phase retrieval algorithm for broadband hyperspectral phase imaging from noisy intensity observations is proposed. It utilizes advantages of the Fourier Transform spectroscopy in the self-referencing optical setup and provides, additionally beyond spectral intensity distribution, reconstruction of the investigated object's phase. The noise amplification Fellgett's disadvantage is relaxed by the application of sparse wavefront noise filtering embedded in the proposed algorithm. The algorithm reliability is proved by simulation tests and results of physical experiments on transparent obj…
We present a technique called single-pixel imaging camera based on complementary detection and optimized Bernoulli modulation (CSPI camera), which can significantly reduce the influence of the disturbance light to single-pixel imaging (SPI). The numerical simulation demonstrates that when the probability of the value "1" is P=0.5 for each coded pattern, CSPI camera is disturbance-free is still disturbance-free even if the intensity fluctuation of the disturbance light is much larger than the signal's intensity. The reconstruction results of both SPI and differential SPI are also compared. This…
We propose a novel concept of differential coded aperture snapshot spectral imaging (D-CASSI) technique exploiting the benefits of using {-1,+1} random mask, which is demonstrated by a broadband single-snapshot hyperspectral camera using compressed sensing. To double the information, we encode the image by two complementary random masks, which proved to be superior to two independent patterns. We utilize dispersed and non-dispersed encoded images captured in parallel onto a single detector. We explored several different approaches to processing the measured data, which demonstrates significant…
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging
University of Helsinki（赫尔辛基大学）；IMEC（微电子中心）；Universiteit Gent（根特大学）；Northwestern Polytechnical University（西北工业大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
California Institute of Technology（加州理工学院）；Brazilian Synchrotron Light Laboratory (LNLS)（巴西同步辐射光实验室（LNLS））；Brazilian Center for Research in Energy and Materials (CNPEM)（巴西能源与材料研究中心（CNPEM））
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level. Even though the imaging speed and quality is steadily improving, fully-automated segmentation and analysis methods are often not accurate enough. This is particularly true while imaging large samples (100um - 1mm) and deep inside the specimen. Drosophila embryogenesis, widely used as a developmental paradigm, presents an example for such a challenge, especially where cell outlines need to imaged - a general challenge in other system…
Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alte…
Reflection phase imaging provides label-free, high-resolution characterization of biological samples, typically using interferometric-based techniques. Here, we investigate reflection phase microscopy from intensity-only measurements under diverse illumination. We evaluate the forward and inverse scattering model based on the first Born approximation for imaging scattering objects above a glass slide. Under this design, the measured field combines linear forward-scattering and height-dependent nonlinear back-scattering from the object that complicates object phase recovery. Using only the forw…
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables. Inspired by the extended depth of field cameras, we propose a novel end-to-end learning-based technique to overcome this limitation, by introducing a phase mask at the aperture plane of the cameras in a stereo imaging system. The phase mask creates a depth-dependent point spread function, allowing us to recover sharp image texture and stereo correspondence over a significantly extended depth of field (EDOF) th…
Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods
Florida Institute of Technology（佛罗里达理工学院）；Massachusetts General Hospital, Harvard Medical School, USA（麻省总医院、哈佛医学院、美国）；A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, USA（A.A. Martinos生物医学成像中心、放射科、麻省总医院、哈佛医学院、美国）；Department of Mathematics and Systems Engineering, Florida Institute of Technology, USA（数学与系统工程系、佛罗里达理工学院、美国）；Department of Mechanical and Civil Engineering, Florida Institute of Technology, USA（机械与土木工程系、佛罗里达理工学院、美国）
