Image Stacks Research Articles

Dynamic Mode Decomposition of Multiphoton and Stimulated Emission Depletion Microscopy Data for Analysis of Fluorescent Probes in Cellular Membranes.

An analysis of the membrane organization and intracellular trafficking of lipids often relies on multiphoton (MP) and super-resolution microscopy of fluorescent lipid probes. A disadvantage of particularly intrinsically fluorescent lipid probes, such as the cholesterol and ergosterol analogue, dehydroergosterol (DHE), is their low MP absorption cross-section, resulting in a low signal-to-noise ratio (SNR) in live-cell imaging. Stimulated emission depletion (STED) microscopy of membrane probes like Nile Red enables one to resolve membrane features beyond the diffraction limit but exposes the sample to a lot of excitation light and suffers from a low SNR and photobleaching. Here, dynamic mode decomposition (DMD) and its variant, higher-order DMD (HoDMD), are applied to efficiently reconstruct and denoise the MP and STED microscopy data of lipid probes, allowing for an improved visualization of the membranes in cells. HoDMD also allows us to decompose and reconstruct two-photon polarimetry images of TopFluor-cholesterol in model and cellular membranes. Finally, DMD is shown to not only reconstruct and denoise 3D-STED image stacks of Nile Red-labeled cells but also to predict unseen image frames, thereby allowing for interpolation images along the optical axis. This important feature of DMD can be used to reduce the number of image acquisitions, thereby minimizing the light exposure of biological samples without compromising image quality. Thus, DMD as a computational tool enables gentler live-cell imaging of fluorescent probes in cellular membranes by MP and STED microscopy.

Electron Microscopy Images as Set of Fragments for Mitochondrial Segmentation

Automatic mitochondrial segmentation enjoys great popularity with the development of deep learning. However, the coarse prediction raised by the presence of regular 3D grids in previous methods regardless of 3D CNN or the vision transformers suggest a possibly sub-optimal feature arrangement. To mitigate this limitation, we attempt to interpret the 3D EM image stacks as a set of interrelated 3D fragments for a better solution. However, it is non-trivial to model the 3D fragments without introducing excessive computational overhead. In this paper, we design a coherent fragment vision transformer (FragViT) combined with affinity learning to manipulate features on 3D fragments yet explore mutual relationships to model fragment-wise context, enjoying locality prior without sacrificing global reception. The proposed FragViT includes a fragment encoder and a hierarchical fragment aggregation module. The fragment encoder is equipped with affinity heads to transform the tokens into fragments with homogeneous semantics, and the multi-layer self-attention is used to explicitly learn inter-fragment relations with long-range dependencies. The hierarchical fragment aggregation module is responsible for hierarchically aggregating fragment-wise prediction back to the final voxel-wise prediction in a progressive manner. Extensive experimental results on the challenging MitoEM, Lucchi, and AC3/AC4 benchmarks demonstrate the effectiveness of the proposed method.

SAR sensing of the atmosphere: stack-based processing for tropospheric and ionospheric phase retrieval

ABSTRACT This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management.

Abstract 2306: A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics

Abstract Invasion of neighboring tissues is a cardinal feature of malignancy, notably observed in aggressive cancers like breast cancer, where it can lead to significant morbidity. To explore cancer invasion dynamics, the implementation of a three-dimensional (3D) tumor spheroid invasion assay offers a swift approach to mimicking a tumor micro-region or micro-metastasis. Differential Interference Contrast (DIC) time-lapse imaging was chosen for its fluorescence-free and non-destructive advantages in capturing live spheroid movement. However, the subsequent analysis posed challenges, including out-of-focus cells and a voluminous dataset with diverse microscope planes and time points. Our study introduces a segmentation framework featuring in-focus spatial stacking algorithm and novel training architecture that enabling in-depth automated analysis of 3D spheroid invasion behaviors within a microenvironment. Firstly, the MDA-MB-231 breast cancer spheroids were cultured in a microwell dish and then incorporated into a collagen type I matrix for the 3D spheroid invasion assay. Capturing spheroid invasion dynamics involved acquiring 15 z-plane images (spaced at 20 µm intervals) per timepoint for each spheroid using DIC microscopy at 15-minute intervals over a 28-hour period, employing a 20X objective lens. We refined the Focus Stacking framework into Multiple Slices Blurry Stacking (MSB-Stack) by pre-filtering blurred areas before stacking images. This process produced 6390 slices consolidated into 426 labeled stacked images, highlighting Invasive area, Spheroid core, and Invasive cells post-invasion. We then enhanced Mask R-CNN (Region based-Convolutional Neural Networks) with a weak-to-strong augmentation mechanism, leveraging well-segmented samples from MSB-Stack for training on both fully stacked and blurred images. Blurry Consistency Mask R-CNN (BCMask R-CNN) demonstrates overall improvement across feature extractors, outperforming Mask R-CNN. Using Mean average precision (mAP) as the main metric for instance segmentation and detection, our proposed model achieves 73.0% and 65.2% on mAP @[0.5, 0.95], showcasing robustness. Notably, our best results for mAP @0.5 reach 96.6% and 96.7% on both tasks, underscoring the effectiveness of consistency training in mitigating unclear boundary issues during preprocessing. In conclusion, our proposed method, MSB-Stack, effectively addresses the challenge of unclear boundaries in 3D breast cancer cell DIC microscopy datasets. The integration of weak-to-strong consistency training in our model, BCMask R-CNN, not only mitigates biases but also enhances performance across different backbones, setting the stage for future advancements in cell-tracking tasks and broader migratory analyses. Citation Format: Thi Kim Ngan Ngo, Thanh-Huy Nguyen, Fang-Yu Lin, Ting-Yuan Tu. A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2306.

Abstract 231: Live cell painting of drug responses in high grade serous cancer organoids

Abstract While most patients with high grade serous ovarian cancer (HGSC) respond to platinum-based chemotherapy, the response is rarely durable and recurrence almost inevitable. A characteristic of HGSC is defective DNA repair. A class of drugs called PARP inhibitors (PARPi) exploit this vulnerability and have proven useful in delaying recurrence. However, resistance is inevitable. In models of HGSC a protein that frequently confers resistance is called Bcl-xL, a member of the Bcl-2 family of proteins that prevent apoptosis. If treating the patients with a PARPi makes cancer cells dependent on Bcl-xL then adding an inhibitor of Bcl-xL to their treatment would be sufficient to cause the cancer cells to die. To test this, we have initiated companion studies for a clinical trial in which patients that have had a recurrence after receiving platinum-based therapy will first be treated with the PARPi Olaparib and then, an inhibitor of Bcl-xL, Navitoclax, will be added to their course of treatment. The idea is that for some women this will provide the one-two-punch needed to eliminate the cancer or at least dramatically prolong response. However, Bcl-xL is only one of the five known inhibitors of apoptosis. To identify which women will benefit most from adding Navitoclax to their treatment we need a biomarker(s). An ideal biomarker would also let us determine for other women which inhibitor of Bcl-2 proteins would be best combined with a PARPi. Our hypothesis is that patient derived organoids can be used as a pragmatic way to identify for individual patients which Bcl-2 protein inhibitor will synergize with a PARPi to optimize treatment. We are using organoids to develop biomarkers for High Grade Serous Cancer (HGSC) treatment response. Organoids generated from biopsy samples acquired prior to treatment for HGSC promise to be efficient and reliable experimental models that recapitulate in vitro patient tumors faithfully enough to facilitate translation to therapeutic decisions for patients. By adapting the relatively new technique of conditional reprogramming and combining it with novel methods for cell aggregation and hydrogel based synthetic ECM supports we can reproducibly generate HGSC patient-specific tumor organoids models in weeks with greater than 90% success. Organoids grown 384 well format are stained with novel non-toxic dyes enabling live cell painting of chemoresponses to drugs alone and in combination with drugs targeting anti-apoptotic proteins. Our data suggest that this approach captures the inherent heterogeneity of the disease, albeit local to the sampled site. We are now employing deep learning AI algorithms to enable automated analyses of 3D confocal image stacks of organoids to infer drug responses that will be compared to patient responses in ongoing clinical trials. Citation Format: David W. Andrews, Betty Li, Wiebke Schormann, Alla Buzina, Lilian Gien, Helen MacKay. Live cell painting of drug responses in high grade serous cancer organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 231.

Three-dimensional object geometry of mitochondria-associated signal: 3-D analysis pipeline for two-photon image stacks of cerebrovascular endothelial mitochondria.

Increasing evidence indicates the role of mitochondrial and vascular dysfunction in aging and aging-associated pathologies; however, the exact mechanisms and chronological processes remain enigmatic. High-energy demand organs, such as the brain, depend on the health of their mitochondria and vasculature for the maintenance of normal functions, therefore representing vulnerable targets for aging. This methodology article describes an analysis pipeline for three-dimensional (3-D) mitochondria-associated signal geometry of two-photon image stacks of brain vasculature. The analysis methods allow the quantification of mitochondria-associated signals obtained in real time in their physiological environment. In addition, signal geometry results will allow the extrapolation of fission and fusion events under normal conditions, during aging, or in the presence of different pathological conditions, therefore contributing to our understanding of the role mitochondria play in a variety of aging-associated diseases with vascular etiology.NEW & NOTEWORTHY Analysis pipeline for 3-D mitochondria-associated signal geometry of two-photon image stacks of brain vasculature.

Assessment of the axial resolution of a compact gamma camera with coded aperture collimator

PurposeHandheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods.MethodsAn experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12–100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM).ResultsWhile taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively.ConclusionOur results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Sleep deprivation drives brain-wide changes in cholinergic presynapse abundance in Drosophila melanogaster

Sleep is an evolutionarily conserved state that supports brain functions, including synaptic plasticity, in species across the animal kingdom. Here, we examine the neuroanatomical and cell-type distribution of presynaptic scaling in the fly brain after sleep loss. We previously found that sleep loss drives accumulation of the active zone scaffolding protein Bruchpilot (BRP) within cholinergic Kenyon cells of the Drosophila melanogaster mushroom body (MB), but not in other classes of MB neurons. To test whether similar cell type-specific trends in plasticity occur broadly across the brain, we used a flp-based genetic reporter to label presynaptic BRP in cholinergic, dopaminergic, GABAergic, or glutamatergic neurons. We then collected whole-brain confocal image stacks of BRP intensity to systematically quantify BRP, a marker of presynapse abundance, across 37 neuropil regions of the central fly brain. Our results indicate that sleep loss, either by overnight (12-h) mechanical stimulation or chronic sleep disruption in insomniac mutants, broadly elevates cholinergic synapse abundance across the brain, while synapse abundance in neurons that produce other neurotransmitters undergoes weaker, if any, changes. Extending sleep deprivation to 24 h drives brain-wide upscaling in glutamatergic, but not other, synapses. Finally, overnight male-male social pairings induce increased BRP in excitatory synapses despite male-female pairings eliciting more waking activity, suggesting experience-specific plasticity. Within neurotransmitter class and waking context, BRP changes are similar across the 37 neuropil domains, indicating that similar synaptic scaling rules may apply across the brain during acute sleep loss and that sleep need may broadly alter excitatory-inhibitory balance in the central brain.

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells.

Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers-all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.

Characterisation of pore structure of bulk wheat mixed with dockage using X-ray micro-computed tomography and deep learning

Characterisation of pore structure inside grain bulks is essential for predicting the airflow resistance during grain drying and aeration. Herein, the 3D pore network of wheat mixed with different percentages of canola and dockage was characterised. The characterised parameters include pore size, throat length, coordination number, airpath length, and tortuosity. To simulate the grain storage condition, the loaded wheat mixtures were cured using wax at 110 °C. The images of the waxed wheat mixtures were produced by using a high-resolution X-ray micro-computed tomography (μCT) system at a resolution of 50 μm per pixel. The computed space distribution was based on the 3D medial axis analysis of each image stack using Dragonfly 4.1 software. The pore space was segmented using a deep-learning model with 90.1–98.0% accuracy. A watershed-based image algorithm was used to generate the pore network from the segmented pore space. The pore structure parameters were extracted from the pore network of each wheat mixture. Most pores were classified as mesopores (>99%) in a range from 62.5 to 4000 μm, with less than 1% classified as micropores (1–62.5 μm). The mean pore size of clean bulk wheat was 648 ± 403 μm with a mean throat length of 1115 ± 611 μm. In the mixtures with 10% canola, 5 and 10% wheat dockage, the mean pore size was reduced by 10%, 29%, and 17%, respectively, from clean wheat. Adding canola and dockage generally influenced the connectivity in the pore microstructure with changes in bulk porosity, tortuosity, airpath number, and length.

Pore-scale physics of ice melting within unconsolidated porous media revealed by non-destructive magnetic resonance characterization

Melting of ice in porous media widely exists in energy and environment applications as well as extraterrestrial water resource utilization. In order to characterize the ice-water phase transition within complicated opaque porous media, we employ the nuclear magnetic resonance (NMR) and imaging (MRI) approaches. Transient distributions of transverse relaxation time T2 from NMR enable us to reveal the substantial role of inherent throat and pore confinements in ice melting among porous media. More importantly, the increase in minimum T2 provides new findings on how the confinement between ice crystal and particle surface evolves inside the pore. For porous media with negligible gravity effect, both the changes in NMR-determined melting rate and our theoretical analysis of melting front confirm that conduction is the dominant heat transfer mode. The evolution of mushy melting front and 3D spatial distribution of water content are directly visualized by a stack of temporal cross-section images from MRI, in consistency with the corresponding NMR results. For heterogeneous porous media like lunar regolith simulant, the T2 distribution shows two distinct pore size distributions with different pore-scale melting dynamics, and its maximum T2 keeps increasing till the end of melting process instead of reaching steady in homogeneous porous media.

Fast topographic optical imaging using encoded search focal scan

A central quest in optics is to rapidly extract quantitative information from a sample. Existing topographical imaging tools allow non-contact and three-dimensional measurements at the micro and nanoscales and are essential in applications including precision engineering and optical quality control. However, these techniques involve acquiring a focal stack of images, a time-consuming process that prevents measurement of moving samples. Here, we propose a method for increasing the speed of topographic imaging by orders of magnitude. Our approach involves collecting a reduced set of images, each integrated during the full focal scan, whilst the illumination is synchronously modulated during exposure. By properly designing the modulation sequence for each image, unambiguous reconstruction of the object height map is achieved using far fewer images than conventional methods. We describe the theoretical foundations of our technique, characterise its performance, and demonstrate sub-micrometric topographic imaging over 100 µm range of static and dynamic systems at rates as high as 67 topographies per second, limited by the camera frame rate. The high speed of the technique and its ease of implementation could enable a paradigm shift in optical metrology, allowing the real-time characterisation of large or rapidly moving samples.

Implicit neural representations in light microscopy.

Three-dimensional stacks acquired with confocal or two-photon microscopy are crucial for studying neuroanatomy. However, high-resolution image stacks acquired at multiple depths are time-consuming and susceptible to photobleaching. In vivo microscopy is further prone to motion artifacts. In this work, we suggest that deep neural networks with sine activation functions encoding implicit neural representations (SIRENs) are suitable for predicting intermediate planes and correcting motion artifacts, addressing the aforementioned shortcomings. We show that we can accurately estimate intermediate planes across multiple micrometers and fully automatically and unsupervised estimate a motion-corrected denoised picture. We show that noise statistics can be affected by SIRENs, however, rescued by a downstream denoising neural network, shown exemplarily with the recovery of dendritic spines. We believe that the application of these technologies will facilitate more efficient acquisition and superior post-processing in the future.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM).

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.

DWI scrolling artery sign for the diagnosis of giant cell arteritis: a pattern recognition approach

ObjectivesTo investigate the diagnostic accuracy of a pattern recognition approach for the evaluation of MRI scans of the head with diffusion-weighted imaging (DWI) in suspected giant cell arteritis (GCA).MethodsRetrospectively, 156...

Near-Earth Object Observations using Synthetic Tracking

Synthetic tracking (ST) has emerged as a potent technique for observing fast-moving near-Earth objects (NEOs), offering enhanced detection sensitivity and astrometric accuracy by avoiding trailing loss. This approach also empowers small telescopes to use prolonged integration times to achieve high sensitivity for NEO surveys and follow-up observations. In this study, we present the outcomes of ST observations conducted with Pomona College’s 1 m telescope at the Table Mountain Facility and JPL’s robotic telescopes at the Sierra Remote Observatory. The results showcase astrometric accuracy statistics comparable to stellar astrometry, irrespective of an object’s rate of motion, and the capability to detect faint asteroids beyond 20.5th magnitude using 11 inch telescopes. Furthermore, we detail the technical aspects of data processing, including the correction of differential chromatic refraction in the atmosphere and accurate timing for image stacking, which contribute to achieving precise astrometry. We also provide compelling examples that showcase the robustness of ST even when asteroids closely approach stars or bright satellites cause disturbances. Moreover, we illustrate the proficiency of ST in recovering NEO candidates with highly uncertain ephemerides. As a glimpse of the potential of NEO surveys utilizing small robotic telescopes with ST, we present significant statistics from our NEO survey conducted for testing purposes. These findings underscore the promise and effectiveness of ST as a powerful tool for observing fast-moving NEOs, offering valuable insights into their trajectories and characteristics. Overall, the adoption of ST stands to revolutionize fast-moving NEO observations for planetary defense and studying these celestial bodies.

Improved 3D DESS MR neurography of the lumbosacral plexus with deep learning and geometric image combination reconstruction.

To evaluate the impact of deep learning (DL) reconstruction in enhancing image quality and nerve conspicuity in LSP MRN using DESS sequences. Additionally, a geometric image combination (GIC) method to improve DESS signals' combination was proposed. Adult patients undergoing 3.0 Tesla LSP MRN with DESS were prospectively enrolled. The 3D DESS echoes were separately reconstructed with and without DL and DL-GIC combined reconstructions. In a subset of patients, 3D T2-weighted short tau inversion recovery (STIR-T2w) sequences were also acquired. Three radiologists rated 4 image stacks ('DESS S2', 'DESS S2 DL', 'DESS GIC DL' and 'STIR-T2w DL') for bulk motion, vascular suppression, nerve fascicular architecture, and overall nerve conspicuity. Relative SNR, nerve-to-muscle, -fat, and -vessel contrast ratios were measured. Statistical analysis included ANOVA and Wilcoxon signed-rank tests. p < 0.05 was considered statistically significant. Forty patients (22 females; mean age = 48.6 ± 18.5years) were enrolled. Quantitatively, 'DESS GIC DL' demonstrated superior relative SNR (p < 0.001), while 'DESS S2 DL' exhibited superior nerve-to-background contrast ratio (p value range: 0.002 to < 0.001). Qualitatively, DESS provided superior vascular suppression and depiction of sciatic nerve fascicular architecture but more bulk motion as compared to 'STIR-T2w DL'. 'DESS GIC DL' demonstrated better nerve visualization for several smaller, distal nerve segments than 'DESS S2 DL' and 'STIR-T2w DL'. Application of a DL reconstruction with geometric image combination in DESS MRN improves nerve conspicuity of the LSP, especially for its smaller branch nerves.

Tracking element-mineral associations with unsupervised learning and dimensionality reduction in chemical and optical image stacks of thin sections

In-situ chemical analysis on thin sections is a cornerstone of geochemical research. Despite massive advances in digital image analysis, the interpretation of such data within the optical petrological context of the thin section has largely remained an analogue task in geochemistry. In this contribution, we registered optical and micro-chemical images from large thin section areas. Chemical datasets from scanning electron microscopy energy dispersive spectrometry (SEM-EDX) and Synchrotron X-ray fluorescence microscopy (S-XFM) were exported from proprietary software. We then evaluated two dimensionality reduction techniques against false-colour images and phase maps to test whether previously unnoticed features became discoverable. Principal component analysis (PCA) and deep sparse autoencoder (DSA) neural networks were used to summarise the multi-channel SEM-EDX and S-XFM into one single red-green-blue (RGB) representation each.Applied to an oceanic gabbro, a cratonic peridotite, and a pelagic limestone, the PCA-based dimensionality reduction was found to produce crisp RGB maps of phases with less noise than false-colour three-element RGB maps. They were registered with optical images to form a simple multi-channel input for pixel-based classification into classes (i.e., semantic segmentation). This was fast and worked well for typical igneous and metamorphic rocks (1–3 h routine). In the chemically quite homogenous limestone, the combined optical and S-XFM PCA input was successfully classified into many phases that were unclassifiable on the conventional SEM-EDX phase map. From the segmented optical and S-XFM PCA map, we exported pixel locations of classified accessory phases back into the original proprietary S-XFM software. This allowed quantification of trace elements whose X-ray peaks were invisible in the original S-XFM quantification.The DSA-based approach yielded phase maps with greater noise, but the technique was very strong at detecting subtle features. In the gabbro sample, the DSA image identified cryptic relict cores in clinopyroxene more clearly than in the previously used Cr concentration map. A DSA training set can be obtained on a small ROI to generate a consistent large area DSA image from which more fragmental cores were observed. For a ROI on the gabbro thin section, the DSA representation map and the blended optical image were segmented and inspected with a loupe tool. This allowed us to generate interactive histograms and bin-scatter concentration plots of selected phases and to search for co-variations of element concentrations. This approach can be expanded to include data from any in situ geochemical acquisition tools and may help discover previously unseen features in complex elemental imagery.

Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction.

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.

Near-Infrared photometry of Neptune’s small regular satellites with Keck

We present data and images that provide insight on the infrared photometry and reflectivity of five of Neptune’s small, regular moons: Proteus, Despina, Galatea, Larissa, and Naiad. Data were taken in the H-band (1.485–1.781μm) on October 07, 2021 from the Keck telescope on Maunakea, Hawaii using the NIRC2 instrument with adaptive optics. We applied a shift-and-stack procedure that allowed us to enhance the signal to noise ratio on each moon as it moved across the detector frame-by-frame. We applied an aperture photometry method to these stacked images in order to extract the flux density and reflectivity of each moon. We find that the H-band albedo of Proteus, Larissa, Galatea, and Despina is the same within one standard deviation, with values near 0.13. The brightness of Naiad is somewhat lower, with a geometric albedo of 0.072±0.013. Comparing these findings with previous observations at visible and infrared wavelengths, we find that four of the five observed moons display a slight spectral reddening from visible to near-infrared wavelengths.