Three dimensional (3D) information of the microstructure organization of various relevant materials in industry and nature is fundamental to master the understanding of their macroscopic properties. X-ray scattering tensor tomography provides 3D directional information on unresolved microstructures in large volumes, facilitating the investigation of the microstructural organization in statistically large enough sample portions. However, established acquisition protocols such as scanning small angle x-ray scattering and x-ray grating interferometry require long measurement time. In this Letter, a rapid x-ray scattering tensor tomography acquisition method is proposed. It is based on circular gratings, which provide single-shot 2D-omnidirectional information of the sample scattering properties. The main advantage of the presented method is the reduced data acquisition time compared to the existing protocols for x-ray scattering tensor tomography, paving the way toward rapid time-resolved studies.

/pdf/x-ray-scattering-tensor-tomography-with-circular-gratings-2cbdh97kxx.pdf

X-ray scattering tensor tomography with circular gratings

Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field. 

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges.

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X Rays In Atomic And Nuclear Physics

Quantitative coherence analysis of dual phase grating x-ray interferometry with source grating

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization. 

pdf/ultrafast-radiographic-imaging-and-tracking-an-overview-of-2e0rdfudhk.pdf

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

PURPOSE X-ray grating interferometry (XGI) provides substantially increased contrast over conventional absorption-based imaging methods and therefore shows great potential for future biomedical applications. In this work, we propose a single-shot phase retrieval method for synchrotron-based high-energy x-ray grating interferometry. Contrary to existing retrieval methods, the presented novel approach enables direct retrieval of the object's phase map quantitatively from a single projection image, thus significantly simplifying the experimental procedure and reducing data acquisition times. METHODS The phase retrieval method is analytically derived, based on the phase-attenuation duality of soft tissues when being imaged with high-energy x rays. The sensitivity of the retrieved phase map, quantified by the standard deviation, is evaluated as a function of the photon number. Numerical experiments are performed to validate the proposed method and provide some quantitative insight. RESULTS The numerical results show that the method can provide high-quality phase images, where the well-known streak artifacts are significantly suppressed. Moreover, the retrieved phase maps confirm that the method is highly stable with respect to statistical noise. CONCLUSIONS Thanks to simplified experimental procedure and reduced acquisition time and dose deposition to the sample, we believe that this new method can find its potential in biomedical imaging and in vivo studies. Future work will focus on the adaptation of the method to polychromatic x ray from tube source and to computed tomography.

Technical Note: Single-shot phase retrieval method for synchrotron-based high-energy x-ray grating interferometry.

A three-image algorithm is proposed to retrieve the sample's transmission, refraction and dark-field information in hard X-ray grating interferometry. Analytical formulae of the three-image algorithm are theoretically derived and presented, and evaluated by proof-of-principle synchrotron radiation experiments. The results confirm the feasibility of the proposed algorithm. The novelty of the proposed algorithm is that it allows versatile and tunable multimodal X-ray imaging by substantially relaxing the existing limitations on the lateral grating position. Furthermore, this algorithm can also be adapted for samples with negligible refraction, reducing the number of required sample measurements to two. Furthermore, the noise properties of the retrieved images are investigated in terms of the standard deviations. Theoretical models are presented and verified by synchrotron radiation measurements. It is shown that the noise standard deviations exhibit strong dependence on the lateral grating position, especially in the case of refraction and dark-field images. Further noise reduction and dose reduction can thus be possible by optimizing the lateral grating position for a selected region of interest. Those results can serve as general guidelines to optimize the data acquisition scheme for specific applications and problems.

Transmission, refraction and dark-field retrieval in hard X-ray grating interferometry.

Inspired by recent experimental results, we report on a revised noise model of dark-field imaging using a grating interferometer. The accurate knowledge of the noise of the dark-field image is essential for noise reduction and image quality improvement. A revised noise model is theoretically derived in terms of the standard deviation of the dark-field image. While a low visibility assumption was made in previous models, the revised noise model makes no assumption on the visibility. It is​ shown by synchrotron radiation experimental results that, with a high visibility, the revised noise model agreed quantitatively with experimentally measured values, while previous models overestimated the noise present in the dark-field image significantly. Therefore, the revised model provides an accurate description of the noise behavior of grating-based dark-field imaging, and can be of use for energy-resolved dark-field imaging and potential dose optimizations.

Revised noise model of dark-field imaging using a grating interferometer

Retrieval of multiple scattering contrast from x-ray analyzer-based imaging

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