Direct Imaging Research Articles

Accuracy and reliability of webceph on posteroanterior cephalogram- A retrospective study

Lateral cephalogram plays a vital role in orthodontic diagnosis and treatment planning, from the advent of cephalometric radiography, whereas limited attention was given to posteroanterior cephalogram which evaluates skeletal discrepancies in transverse dimension. Thus, our study focuses on comparing the accuracy and reliability of fully automated AI driven software ‘WebCeph’, a semi-automated software ‘EasyCeph’ and manual tracing on posteroanterior cephalogram.A sample of 30 pretreatment posteroanterior cephalometric radiographic digital images were collected and analyzed using manual tracing, WebCeph and EasyCeph with 20 landmarks (16 linear and 2 angular measurements). For manual tracing, hard copies of digital images were obtained, whereas for EasyCeph and WebCeph, direct digital images used for analysis. ANOVA Test was done to compare the measurements of each parameter among WebCeph, EasyCeph and manual tracing. Bonferroni post hoc test was performed for individual comparison among 3 groups. The intraoperator reliability were evaluated by correlation coefficient test after 1 month by retracing 5 randomly selected cephalograms. On comparing three methods, 6 out of 18 parameters showed statistically significant differences. On individual comparison, EasyCeph shows significant differences with its counterparts, whereas WebCeph and manual tracing values shows no statistically significant difference. The intraclass correlation coefficient shows strong correlation for manual tracing (0.84 to 0.95) and EasyCeph (0.78 to 0.89)The automated cephalometric measurements from WebCeph are reasonably consistent, accurate and reliable when compared with Manual tracing and EasyCeph.

Small-molecule inhibitors of the CD40–CD40L costimulatory interaction are effective in pancreatic islet transplantation and prevention of type 1 diabetes models

As part of our work to develop small-molecule inhibitors (SMIs) of the CD40-CD40L(CD154) costimulatory protein-protein interaction, here, we describe the ability of two of our most promising SMIs, DRI-C21041 and DRI-C21095, to prolong the survival and function of islet allografts in two murine models of islet transplantation (under the kidney capsule and in the anterior chamber of the eye) and to prevent autoimmune type 1 diabetes (T1D) onset in NOD mice. In both transplant models, a significant portion of islet allografts (50%-80%) remained intact and functional long after terminating treatment, suggesting the possibility of inducing operational immune tolerance via inhibition of the CD40-CD40L axis. SMI-treated mice maintained the structural integrity and function of their islet allografts with concomitant reduction in immune cell infiltration as evidenced by direct longitudinal imaging in situ. Furthermore, in female NODs, three-month SMI treatment reduced the incidence of diabetes from 80% to 60% (DRI-C21041) and 25% (DRI-C21095). These results (i) demonstrate the susceptibility of this TNF superfamily protein-protein interaction to small-molecule inhibition, (ii) confirm the in vivo therapeutic potential of these SMIs of a critical immune checkpoint, and (iii) reaffirm the therapeutic promise of CD40-CD40L blockade in islet transplantation and T1D prevention. Thus, CD40L-targeting SMIs could ultimately lead to alternative immunomodulatory therapeutics for transplant recipients and prevention of autoimmune diseases that are safer, less immunogenic, more controllable (shorter half-lives), and more patient-friendly (i.e., suitable for oral administration, which makes them easier to administer) than corresponding antibody-based interventions.

Spontaneously Blinkogenic Probe for Wash‐Free Single‐Molecule Localization‐Based Super‐Resolution Imaging in Living Cells

Single‐molecule localization super‐resolution fluorescence imaging relies on the fluorescence ON/OFF switching of fluorescent probes to break the diffraction limit. However, the unreacted or nonspecifically bound probes cause non‐targeted ON/OFF switching, resulting in substantial fluorescence background that significantly reduces localization precision and accuracy. Here, we report a blinkogenic probe HM‐DS655‐Halo that remains blinking OFF until it binds to HaloTag, thereby triggering its self‐blinking activity and enabling its application in direct SMLM imaging in living cells without wash‐out steps. We employed the ratio of the duty cycle before and after self‐blinking activation, termed as the parameter "RDC" to characterize blinkogenicity. The covalent binding to HaloTag induces HM‐DS655‐Halo to transition from a fluorescent OFF state to a fluorescence blinking state. This transition also leads to a change in the RDC value, which is calculated to be 12, ensuring super blinkogenicity to effectively suppress background signals in living cells. HM‐DS655‐Halo was successfully applied in dynamic SMLM imaging of diverse intracellular sub‐structures with minimal background noise, including mitochondrial fission and contact, cell migration, and pseudopod growth.

Spontaneously Blinkogenic Probe for Wash-Free Single-Molecule Localization-Based Super-Resolution Imaging in Living Cells.

Single-molecule localization super-resolution fluorescence imaging relies on the fluorescence ON/OFF switching of fluorescent probes to break the diffraction limit. However, the unreacted or nonspecifically bound probes cause non-targeted ON/OFF switching, resulting in substantial fluorescence background that significantly reduces localization precision and accuracy. Here, we report a blinkogenic probe HM-DS655-Halo that remains blinking OFF until it binds to HaloTag, thereby triggering its self-blinking activity and enabling its application in direct SMLM imaging in living cells without wash-out steps. We employed the ratio of the duty cycle before and after self-blinking activation, termed as the parameter "RDC" to characterize blinkogenicity. The covalent binding to HaloTag induces HM-DS655-Halo to transition from a fluorescent OFF state to a fluorescence blinking state. This transition also leads to a change in the RDC value, which is calculated to be 12, ensuring superblinkogenicity to effectively suppress background signals in living cells. HM-DS655-Halo was successfully applied in dynamic SMLM imaging of diverse intracellular sub-structures with minimal background noise, including mitochondrial fission and contact, cell migration, and pseudopod growth.

An alternating minimization algorithm with trajectory for direct exoplanet detection. The AMAT algorithm

Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard point spread function (PSF) subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles and rely on signal-to-noise ratio maps to detect the planet. These steps do not interact or feed each other, leading to potential limitations in the accuracy and efficiency of exoplanet detection. We aim to develop a novel approach that iteratively finds the flux of the planet and the low-rank approximation of quasi-static signals in an attempt to improve upon current PSF subtraction techniques. In this study, we extend the standard L2 norm minimization paradigm to an L1 norm minimization framework in order to better account for noise statistics in the high contrast images. Then, we propose a new method, referred to as the alternating minimization algorithm with trajectory (AMAT), that makes more advanced use of estimating the low-rank approximation of the speckle field and the planet flux by alternating between them and utilizing both L1 and L2 norms. For the L1 norm minimization, we propose using L1 norm low-rank approximation (L1-LRA), a low-rank approximation computed using an exact block-cyclic coordinate descent method, while we use randomized singular value decomposition for the L2 norm minimization. Additionally, we enhance the visibility of the planet signal using a likelihood ratio as a post-processing step. Numerical experiments performed on a VLT/SPHERE-IRDIS dataset show the potential of AMAT to improve upon the existing approaches in terms of higher S/N, sensitivity limits (contrast curves), and receiver operating characteristic curves. Moreover, for a systematic comparison, we used datasets from the exoplanet data challenge to compare our algorithm with other algorithms in the challenge, and we find AMAT with a likelihood ratio map performs better than most algorithms tested on the exoplanet data challenge.

Population of giant planets around B stars from the first part of the BEAST survey

Exoplanets form from circumstellar protoplanetary disks whose fundamental properties (notably their extent, composition, mass, temperature, and lifetime) depend on the host star properties, such as their mass and luminosity. B stars are among the most massive stars and their protoplanetary disks test extreme conditions for exoplanet formation. This paper investigates the frequency of giant planet companions around young B stars (median age of 16 Myr) in the Scorpius-Centaurus (Sco-Cen) association, the closest association containing a large population of B stars. We systematically searched for massive exoplanets with the high-contrast direct imaging instrument SPHERE using the data from the BEAST survey, which targets a homogeneous sample of young B stars from the wide Sco-Cen association. We derived accurate detection limits in the case of non-detections. We found evidence in previous papers for two substellar companions around 42 stars. The masses of these companions are straddling the sim 13 Jupiter mass deuterium burning limit, but their mass ratio with respect to their host star is close to that of Jupiter. We derived a frequency of such massive planetary-mass companions around B stars of $11_ $<!PCT!>, accounting for the survey sensitivity. The discoveries of substellar companions b and B happened after only a few stars in the survey had been observed, raising the possibility that massive Jovian planets might be common around B stars. However, our statistical analysis shows that the occurrence rate of such planets is similar around B stars and around solar-type stars of a similar age, while B-star companions exhibit low mass ratios and a larger semi-major axis.

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation

Objective.Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.Approach.A novel network (TransSin-UNet) was proposed to continuously estimate the final-position image of the target, corresponding to end of the frame acquisition. Within the radial k-space reconstruction window, the spatial-temporal dependencies among the sinogram representation of the spokes were modeled by a transformer encoder subnetwork, followed by a UNet subnetwork operating in the spatial domain for pixel-level fine-tuning. By simulating motion-dependent radial sampling with (tiny) golden angles, we generated datasets from 25 4D digital anthropomorphic lung cancer phantoms. The network was then trained and extensively evaluated across datasets characterized by varying azimuthal radial profile increments.Main Results.The method required additional 4.8 ms per frame over the conventional approach involving direct image reconstruction with motion-corrupted spokes. TransSin-UNet outperformed architectures relying solely on transformer encoders or UNets across all the comparative evaluations, leading to a noticeable enhancement in image quality and target positioning accuracy. The normalized root mean-squared error decreased by 50% from the initial value of 0.188 on average, whereas the mean Dice similarity coefficient of the gross tumor volume increased from 85.1% to 96.2% in the investigated cases. Furthermore, the final-positions of anatomical structures undergoing substantial intra-frame deformations were precisely derived.Significance.The proposed approach enables an effective intra-frame motion compensation, offering an opportunity to reduce errors in radial cine-MR imaging for real-time motion management.

The Search for Exoplanets: Methods and History

The discovery of exoplanets, planets beyond the solar system, has raised key questions about extraterrestrial life and the technology needed to find it. While considerable progress in exoplanet detection has been achieved over the past three decades, the methods currently in use still face notable limitations. Indeed, the detection of exoplanets has come a long way since its first confirmation in 1992. At first, these celestial objects were considered as figment of movies and science fiction, but it is credible today that Radial Velocity, Transit, Gravitational Microlensing, Direct Imaging, and Astrometry approaches have found these planetary bodies. While each of the methods works well, all techniques possess specific systematic errors depending on orbital radius, planetary size, and so on. For the past few decades, missions like Hubble, Spitzer as well as Kepler have delivered the fundamental catalogs and enormously improved the knowledge in the field of exoplanets. Recent developments, specifically with the JWST, have provided new ideas with indications of biosignatures on planet exoplanet K2-18b. The future prospects for exoplanet discovery also look good, for example, with the help of the machine learning for analysis and planned instruments like Nancy Grace Roman Space Telescope and the concept of Habitable Worlds Observatory. These observatories are oriented towards the search of potentially habitable exoplanets and for exploration of other universal enigmatic processes. In this paper, the advancement of technique in identifying exoplanets is outlined with specific focus on significant accomplishments, trends, technologies, and future outlook, which shows a fairly promising field that holds great potential for finding more habitable planets.

Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging.

In clinical ultrasound, current 2-D strain imaging faces challenges in quantifying three orthogonal normal strain components. This requires separate image acquisitions based on the pixel-dependent cardiac coordinate system, leading to additional computations and estimation discrepancies due to probe orientation. Most systems lack shear strain information, as displaying all components is challenging to interpret. This paper presents a 3-D high-spatial-resolution, coordinate-independent strain imaging approach based on principal stretch and axis estimation. All strain components are transformed into three principal stretches along three normal principal axes, enabling direct visualization of the primary deformation. We devised an efficient 3-D speckle tracking method with tilt filtering, incorporating randomized searching in a two-pass tracking framework and rotating the phase of the 3-D correlation function for robust filtering. The proposed speckle tracking approach significantly improves estimates of displacement gradients related to the axial displacement component. Non-axial displacement gradient estimates are enhanced using a correlation-weighted least-squares method constrained by tissue incompressibility. Simulated and in vivo canine cardiac datasets were evaluated to estimate Lagrangian strains from end-diastole to end-systole. The proposed speckle tracking method improves displacement estimation by a factor of 4.3 to 10.5 over conventional 1-pass processing. Maximum principal axis/direction imaging enables better detection of local disease regions than conventional strain imaging. Coordinate-independent tracking can identify myocardial abnormalities with high accuracy. This study offers enhanced accuracy and robustness in strain imaging compared to current methods.

Adaptive Threshold Wave Front Sensing for Portable Adaptive Optics System Driven by Hybrid Parallel Technique

Our Portable Adaptive Optics (PAO) system designed for high-contrast imaging of exoplanets with current 2–4 m class telescopes achieves a correction speed of nearly 1000 Hz, utilizing a Shack–Hartmann Wave Front Sensor (WFS) in a 9 × 9 sub-aperture configuration. As we look towards adapting the PAO system for larger telescopes, an increase in the number of sub-apertures in the WFS and enhanced precision in wave front detection are imperative. Originally programmed in LabVIEW, our initial PAO software is based on a traditional centroid calculation module for nighttime wave front sensing and lacks adaptive processing of background noise. To address these limitations and to boost the PAO system's performance and accuracy in wave front detection, we propose a compressive neural network (Th-Net) combined with a specialized hybrid parallel programming approach for wave front detection. Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system's operational speed and wave front detection precision under uneven background noise. This work paves the way so that a duplicable and low-cost PAO system can be used for direct imaging of exoplanets with large telescopes.

Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.

Measuring Black Hole Light Echoes with Very Long Baseline Interferometry

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this Letter, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission.

Comparisons for Different State-of-art Planet Searching Scenarios: Transit, Radial Velocity and Direct-imaging

The vastness of the universe has inspired mankind’s infinite imagination of the exoplanets with living. The exploration of them reflects human beings’ yearning for possible life and the pursuit of a possible future home. From the first detection of an exoplanet in 1992 to the detection of 5,747 exoplanets on August 16, 2024, scholars have worked hard to explore. This study presents typical results of exoplanet detection in recent years, describing in general terms which planet retrieval methods have been used, what results are obtained by each method and which parameters of the planet can be detected. To be specific, three main methods for detecting exoplanets are detailly evaluated, namely the transit, the radial velocity and the direct imaging. By comparing these three methods, detection methods suitable for different types of exoplanets are summarized. According to the analysis, the limitations of current exoplanet detection are summarized based on the results, and future countermeasures are outlined. These results offer a guideline for exoplanet exploration and shed light on understanding the three main methods of detecting exoplanets.

Object Localization in Highly Cluttered Environments Using Neural Network Learning on Microwave Scattering Data

ABSTRACTBifocusing‐based microwave imaging is a promising direct imaging method for object localization in various applications. However, the method has the limitation that it only works well in environments with a homogenous background. In this paper, we present a direct microwave imaging method using a neural network (NN) model that can localize a small object even in highly cluttered environments with strong scatterers. The NN model is trained on some data sets obtained through multistatic measurements in a nonhomogeneous environment. To verify the approach, we prepared an experimental testbed equipped with a water tank containing 3 strong scatterers and 16 antennas to obtain 920 MHz multistatic scattering data. This experiment shows good localization performance, with an average localization error of approximately 4 mm (1/9 of a wavelength) over the entire experimental area, even in a strong scattering background.

Properties of generalized polyhedral convex multifunctions

This paper presents a study of generalized polyhedral convexity under basic operations on multifunctions. We address the preservation of generalized polyhedral convexity under sums and compositions of multifunctions, the domains and ranges of generalized polyhedral convex multifunctions, and the direct and inverse images of sets under such mappings. Then we explore the class of optimal value functions defined by a generalized polyhedral convex objective function and a generalized polyhedral convex constrained mapping. The new results provide a framework for representing the relative interior of the graph of a generalized polyhedral convex multifunction in terms of the relative interiors of its domain and mapping values in locally convex topological vector spaces. Among the new results in this paper is a significant extension of a result by Bonnans and Shapiro on the domain of generalized polyhedral convex multifunctions from Banach spaces to locally convex topological vector spaces.

Characterization of Defocused Coherent Imaging Systems with Periodic Objects.

Recent advancements in quantum and quantum-inspired imaging techniques have enabled high-resolution 3D imaging through photon correlations. These techniques exhibit reduced degradation of image resolution for out-of-focus samples compared to conventional methods (i.e., intensity-based incoherent imaging). A key advantage of these correlation-based approaches is their independence from the system numerical aperture (NA). Interestingly, both improved resolution of defocused images and NA-independent scaling are linked to the spatial coherence of light. This suggests that while correlation measurements exploit spatial coherence, they are not essential for achieving this imaging advantage. This discovery has led to the development of optical systems that achieve similar performance by using spatially coherent illumination and relying on intensity measurements: direct 3D imaging with NA-independent resolution was recently demonstrated in a correlation-free setup using LED light. Here, we explore the physics behind the enhanced performance of defocused coherent imaging, showing that it arises from the modification of the sample's spatial harmonic content due to diffraction, unlike the blurring seen in conventional imaging. The results we present are crucial for understanding the implications of the physical differences between coherent and incoherent imaging, and are expected to pave the way for the practical application of the discovered phenomena.

Exoplanet detection techniques for direct imaging dark zone maintenance datasets

Exoplanet detection techniques for direct imaging dark zone maintenance datasets

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

Emerging strategies to investigate the biology of early cancer.

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

First in-vivo magic angle directional imaging using dedicated low-field MRI.

To report the first in-vivo results from exploiting the magic angle effect, using a dedicated low-field MRI scanner that can be rotated about two axes. The magic angle directional imaging (MADI) method is used to depict collagen microstructures with 3D collagen tractography of knee ligaments and the meniscus. A novel low-field MRI system was developed, based on a transverse field open magnet, where the magnet can be rotated about two orthogonal axes. Sets of volume scans at various orientations were obtained in healthy volunteers. The experiments focused on the anterior cruciate ligament (ACL) and the meniscus of the knee. The images were co-registered, anatomical regions of interest (ROIs) were selected and the collagen fiber orientations in each voxel were estimated from the observed image intensity variations. The 3D collagen tractography was superimposed on conventional volume images. The MADI method was successfully employed for the first time producing in-vivo results comparable to those previously reported for excised animal specimens using conventional MRI. Tractography plots were generated for the ACL and the menisci. These results are consistent with the known microstructure of collagen fibers in these tissues. Images obtained using low-field MRI with 1 mm3 resolution were of sufficient quality for the MADI method, which was shown to produce high quality in-vivo information of collagen microstructures. This was achieved using a cost effective and sustainable low-field magnet making the technique potentially accessible and scalable, potentially changing the way we image injuries or disease in joints.