Image Slices Research Articles

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1–3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400–1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.

Meta-learners for few-shot weakly-supervised medical image segmentation

Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of work in other tasks such as segmentation and detection. We propose a new generic Meta-Learning framework for few-shot weakly supervised segmentation in medical imaging domains. The proposed approach includes a meta-training phase that uses a meta-dataset. It is deployed on an out-of-distribution few-shot target task, where a single highly generalizable model, trained via a selective supervised loss function, is used as a predictor. The model can be trained in several distinct ways, such as second-order optimization, metric learning, and late fusion. Some relevant improvements of existing methods that are part of the proposed approach are presented. We conduct a comparative analysis of meta-learners from distinct paradigms adapted to few-shot image segmentation in different sparsely annotated radiological tasks. The imaging modalities include 2D chest, mammographic, and dental X-rays, as well as 2D slices of volumetric tomography and resonance images. Our experiments consider in total 9 meta-learners, 4 backbones, and multiple target organ segmentation tasks. We explore small-data scenarios in radiology with varying weak annotation styles and densities. Our analysis shows that metric-based meta-learning approaches achieve better segmentation results in tasks with smaller domain shifts compared to the meta-training datasets, while some gradient- and fusion-based meta-learners are more generalizable to larger domain shifts. Guidelines learned from the comparative performance assessment of the analyzed methods are summarized to support those readers interested in the field.

Refined Detection and Classification of Knee Ligament Injury Based on ResNet Convolutional Neural Networks.

Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to healthcare professionals who must analyze each case post-image acquisition. This process is characterized by its sluggishness and subjectivity, making it susceptible to errors. The anterior cruciate ligament (ACL), a frequently injured knee ligament, predominantly affects a youthful and sports-active demographic. ACL injuries often leave patients with substantial disabilities and alter knee mechanics. Since some of these cases necessitate surgery, it is crucial to accurately classify and detect ACL injury. This paper investigates the utilization of pre-trained convolutional neural networks featuring residual connections (ResNet) along with image-processing methods to identify ACL injury and differentiate between various tear levels. The ResNet employed in this study is not the standard ResNet but rather an adapted version capable of processing 3D volumes constructed from 2D image slices. Achieving a peak accuracy of 97.15% with a custom split, 96.32% through Monte-Carlo cross-validation, and 93.22% via five-fold cross-validation, our approach enhances the performance of three-class classifiers by over 7% in terms of raw accuracy. Moreover, we achieved an improvement of more than 1% across all types of evaluation. It is quite clear that the model's output can effectively serve as an initial diagnostic baseline for radiologists with minimal effort and nearly instantaneous results. This advancement underscores the paper's focus on harnessing deep learning for the nuanced detection and classification of ACL tears, demonstrating a significant leap toward automating and refining diagnostic accuracy in sports medicine and orthopedics.

Quantitative assessment of cement bridges and voids in cement-stabilized permeable base materials using a mask R-CNN-based CT image segmentation strategy

Accurate extraction of aggregates, cement bridges, and voids in permeable cement-stabilized base materials (PCBM) is a challenging task. This paper proposed a segmentation and reconstruction paradigm for raw CT slice images using a mask regional convolutional neural network (Mask R-CNN). The results demonstrate that the proposed method effectively segments and reconstructs unclear raw CT slice images, providing a convenient and advanced tool for accurately assessing the meso-scale structures of cement bridges and voids in PCBM prepared with different combinations of cement content and compaction force. An increased cement content or compaction force results in the migration of cement paste, leading to thicker localized cement bridges and blockage of permeable pore space. This phenomenon further disrupts the homogeneous distribution of cement bridges and voids. An increased compaction force tends to elongate and flatten pore throats, while an increased cement content may clog voids, rendering them impermeable. The vertical permeability of the PCBM is significantly greater than the horizontal permeability, which should be considered during material design optimization. The proposed method could serve as a reference for microscopic analysis and structural design optimization of PCBM.

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

Distortion Correction and Denoising of Light Sheet Fluorescence Images.

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson-Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

Observation of Renner-Teller and predissociation coupled vibronic intensity borrowing in dissociative electron attachment to OCS.

We use a time-of-flight-based velocity map imaging method to look into the dissociative electron attachment to a linear OCS molecule at electron beam energies ranging from 4.5 to 8.5eV. The conical time-gated wedge slice imaging method is utilized to extract fragments' slice images, kinetic energy (KE), and angular distributions, which provide a complete kinematic understanding of this experiment on the dissociative electron attachment process. We observe that the formation of S- is relatively higher than the O- product. Three distinct dissociative KE bands of S-/OCS have been observed for the 5.0 and 6.5eV resonance positions. We notice a prominent rovibrationally coupled bimodality for each KE band in the variation of the most probable KE values. When the electron energy is changed from 5.5 to 6.0eV, we observed vibronic intensity borrowing in the highest momentum band of S- via the Σ → Π symmetric dipole-forbidden transitions within the 1.5eV energy gap. Multiple peaks in the angular distributions of S- and their modeling indicate the presence of Renner-Teller vibronic splitting. Using Q-Chem's implemented complex absorbing potential-equation of motion-electron affinity coupled cluster singles and doubles aug-cc-pVDZ+4s3p level of multireference-based electronic structure theory, we confirm the presence of OCS temporary negative ion bending vibrations and Renner-Teller vibronic splittings for the Π symmetric states. Additionally, we notice the presence of a non-radiative predissociation continuum (bringing down the rotational spectrum) and speed-dependent angular anisotropy in the S- fragmentation. Our findings at the resonance of OCS at 6.5eV closely align with the prediction of vibronic intensity borrowing by Orlandi and Siebrand [J. Chem. Phys. 58, 4513 (1973)].

Racial differences in body composition and survival among older adults with gastrointestinal malignancies

IntroductionMuscle and adipose tissue measures can be quantified from routinely obtained computed tomography (CT) images and are predictors of chemotherapy-related toxicities and survival among patients with gastrointestinal (GI) malignancies. Most studies to date have consisted of predominantly White patients, and the role of body composition among minoritized racial groups is unknown. We examined racial differences in body composition and survival among patients with GI malignancies. Materials and MethodsThis was a prospective cohort study of patients with GI malignancies. Single slices of axial CT images from L3 segments were analyzed using Slice-O-Matic software. The skeletal muscle area (cm2) was divided by height to obtain the skeletal muscle index (SMI, cm2/m2). Skeletal muscle radiodensity (SMD) in Hounsfield units (HU) was used for muscle composition. We compared body composition parameters between non-Hispanic (NH)-White and NH-Black participants. Cox models were used to examine the impact of body composition on survival. We proposed new race-specific cutoffs for body composition using optimal stratification. ResultsFive hundred forty patients were included, of which 24% were NH-Black. In Cox models stratified by race, each 5 cm2/m2 decrease in SMI was associated with increase in risk of all-cause mortality in NH-Black patients (hazard ratio [HR] 1.25; 95% confidence interval [CI] 1.04–1.49 p = 0.02). With the existing cut points, neither sarcopenia nor myosteatosis was associated with worse survival. Using a new cutoff for sarcopenia in NH-Black patients, NH-Black patients with sarcopenia (HR 2.31 95%CI 1.10–4.88 p = 0.03) and myosteatosis (HR 2.63 95% CI 1.25–5.53 p = 0.01) had worse survival. DiscussionNH-Black older patients with GI cancers and sarcopenia or myosteatosis have worse overall survival.

Sarcopenia accompanied by systemic inflammation can predict clinical outcomes in patients with head and neck cancer undergoing curative therapy.

Evaluation of sarcopenia accompanied by systemic inflammation status is a more beneficial prognostic marker than sarcopenia alone in various cancers. However, few studies have focused on this combination in patients with head and neck squamous cell cancer (HNSCC). In this study, we investigated how the combination of sarcopenia and systemic inflammation could affect survival in patients with HNSCC. Moreover, we explored which systemic inflammation markers could be better prognostic indicators when accompanied by sarcopenia. We retrospectively reviewed the medical records of patients with HNSCC treated between 2012 and 2016. Sarcopenia was defined by the skeletal muscle area measured on a computed tomography image slice at the level of the third cervical vertebra. The neutrophil/lymphocyte, platelet/lymphocyte, and lymphocyte/monocyte ratios (NLR, PLR, and LMR, respectively) were used as systemic inflammation markers that were combined with sarcopenia to evaluate prognosis. A total of 100 patients were enrolled, and 71 patients were considered sarcopenia. Patients with sarcopenia had significantly lower LMR and higher NLR and PLR. They also showed worse overall survival (OS) and progression-free survival (PFS). The comparative assessment of multiple combination patterns of sarcopenia and systemic inflammation indices proved that sarcopenia plus LMR considered as most reliable indicator for prognosis in HNSCC patients. Sarcopenia plus low LMR was a significantly poor prognostic factor both for OS and PFS with greater HR values than sarcopenia alone. The combination of sarcopenia and LMR was considered the most sensitive prognostic factor in patients with HNSCC, suggesting it might be beneficial for identifying poor outcome risks.

Measuring CSF shunt flow with MRI using flow enhancement of signal intensity (FENSI).

To develop and validate a noninvasive imaging technique for accurately assessing very slow CSF flow within shunt tubes in pediatric patients with hydrocephalus, aiming to identify obstructions that might impede CSF drainage. A simulation of shunt flow enhancement of signal intensity (shunt-FENSI) signal is used to establish the relationship between signal change and flow rate. The quantification of flow enhancement of signal intensity data involves normalization, curve fitting, and calibration to match simulated data. Additionally, a phase sweep method is introduced to accommodate the impact of magnetic field inhomogeneity on the flow measurement. The method is tested in flow phantoms, healthy adults, intensive care unit patients with external ventricular drains (EVD), and shunt patients. EVDs enable shunt-flow measurements to be acquired with a ground truth measure of CSF drainage. The flow-rate-to-signal simulation establishes signal-flow relationships and takes into account the T1 of draining fluid. The phase sweep method accurately accounts for phase accumulation due to frequency offsets at the shunt. Results in phantom and healthy human participants reveal reliable quantification of flow rates using controlled flows and agreement with the flow simulation. EVD patients display reliable measures of flow rates. Shunt patient results demonstrate feasibility of the method and consistent flow rates for functional shunts. The results demonstrate the technique's applicability, accuracy, and potential for diagnosing and noninvasively monitoring hydrocephalus. Limitations of the current approach include a high sensitivity to motion and strict requirement of imaging slice prescription.

Adjacent object distorts the organ-of-interest in filtered back-projection tomographic image reconstruction: 'ramp filter' or 'reconstruction' artifact revisited.

To simulate the artifact caused by an adjacent object on organ-of-interest during filtered back-projection (FBP) tomographic reconstruction (the so-called "ramp filter" artifact) and to demonstrate the extent to which an organ-of-interest is influenced by such adjacent hot spot or attenuating object. Two simulations are conducted with two simplified phantoms: "hot spot" and "cardiac." First one is used to visualize effect of hot spot on its periphery. Second one is used to evaluate effect of nearby object (liver) on left ventricle (LV) as organ-of-interest. To generate sinograms, forward projection process is done with and without modeling radiation attenuation. FBP using windowed ramp filter is done. All slices are analyzed by plotting intensity profiles. In tomographic slices, there is a hypo-intense halo around presumed edge of object when compared to background intensity, more noticeable in phantoms with less blurring. Even with ramp filter applied, no halo is visible in FBP without attenuation for cardiac phantom. In contrast, in slices with considering attenuation, zones with different levels of count suppression on both sides of object are visualized instead. The most prominent one is between liver and LV in simulation with higher-attenuating object and higher activity. A single hot spot with sufficient amount of blurring does not distort its surroundings. Hot spots and attenuating objects near organ-of-interest, however, distort myocardial perfusion imaging. Artifactual defects are thus only created when attenuation is modeled during FBP, producing zones of count suppression between organ-of-interest and nearby object or hot spot.

A single crystal row–column-array for 3D ultrasound imaging

In vivo 3D ultrasound imaging with 2D-array transducers is of great importance for both clinical application and biomedical research, but it is complicated in fabrication and also very expensive in hardware due to thousands of electronic channels. In this work, we demonstrate a new fabrication process of 7-MHz 128 + 128 elements row-column-array (RCA) transducer with relaxor ferroelectric PMN-0.28PT single crystal. With piezoelectric single crystal and improved acoustic matching, the optimized performance of −6 dB bandwidth of ∼82 % and insertion loss of −44.6 dB is achieved. The axial and lateral imaging resolutions at different depth of the RCA transducer are quantified by the point spread function (PSF), and the results are respectively 0.20 mm and 0.41 mm at the depth of 7.7 mm, and 0.22 mm and 0.47 mm at the depth of 16.7 mm. The transducer is validated experimentally on a hyperechoic phantom, and 3D view and slices of B-mode images are obtained. The experimental results indicate that our developed RCA transducer can obtain high-quality 3D ultrasound images, demonstrating great potential on ultrafast 3D and functional imaging.

Effects of low-tube voltage coronary CT angiography on plaque and pericoronary fat assessment: intraindividual comparison.

To investigate the effects of low tube voltage on coronary plaques and pericoronary fat assessment, and to compare their extent among various levels of low voltage. Patients were recommended for high-pitch low-tube voltage coronary computed tomography angiography (CCTA), and they were included if they had poor image quality and were referred to a conventional CCTA. The patients were classified into a low-voltage group (with 70-kV, 80-kV, and 90-kV subgroups) and a conventional group (100/120 kV). Their total plaque and subcomponent volumes and pericoronary fat attenuation index (FAI) were measured. A total of 1002 image slices (from 65 patients and 74 plaques) were included, including 21, 31, 13, 4, and 61 patients in the 70-kV, 80-kV, 90-kV, 100-kV, and 120-kV groups respectively. The CT values of noncalcified plaques in the conventional and low-voltage groups were 54.6 ± 21.3 HU and 31.5 ± 22.6 HU, respectively (p < 0.05). Compared with the conventional group, the necrotic core and calcification volume were increased, and the fibrolipid volume, periplaque, and right coronary artery FAI were decreased in the low-voltage group and its subgroups (p < 0.001). The magnitude of changes in fibrous and calcification volumes increased in the 70-kV subgroup compared with that in the 90-kV subgroup (p < 0.05). Low tube voltages, particularly of 70 kV, have a significant effect on coronary plaque and FAI. The effect of low voltage on plaque composition is characterized by a polarization pattern, i.e., a decrease in fibrolipid (medium density) and an increase in necrotic core (low density) and calcification (high density). Our results highlight the comparability and repeatability of plaque and pericoronary fat assessments facilitated by the same or a similar tube voltage. It is necessary to carry out studies on the specificity threshold of low tube voltage at each level. • Low tube voltage had a significant effect on coronary plaque and pericoronary fat, particularly 70 kV. • The effect of low tube voltage on plaque composition shows the shift from medium-density mixed components to low- and high-density components. • It is necessary to correct the specificity threshold or attenuation difference for low tube voltage at each level.

Computerized Segmentation Method for Nonmasses on Breast DCE-MRI Images Using ResUNet++ with Slice Sequence Learning and Cross-Phase Convolution

The purpose of this study was to develop a computerized segmentation method for nonmasses using ResUNet++ with a slice sequence learning and cross-phase convolution to analyze temporal information in breast dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images. The dataset consisted of a series of DCE-MRI examinations from 54 patients, each containing three-phase images, which included one image that was acquired before contrast injection and two images that were acquired after contrast injection. In the proposed method, the region of interest (ROI) slice images are first extracted from each phase image. The slice images at the same position in each ROI are stacked to generate a three-dimensional (3D) tensor. A cross-phase convolution generates feature maps with the 3D tensor to incorporate the temporal information. Subsequently, the feature maps are used as the input layers for ResUNet++. New feature maps are extracted from the input data using the ResUNet++ encoders, following which the nonmass regions are segmented by a decoder. A convolutional long short-term memory layer is introduced into the decoder to analyze a sequence of slice images. When using the proposed method, the average detection accuracy of nonmasses, number of false positives, Jaccard coefficient, Dice similarity coefficient, positive predictive value, and sensitivity were 90.5%, 1.91, 0.563, 0.712, 0.714, and 0.727, respectively, larger than those obtained using 3D U-Net, V-Net, and nnFormer. The proposed method achieves high detection and shape accuracies and will be useful in differential diagnoses of nonmasses.

Total hip arthroplasty after failed transtrochanteric rotational osteotomy for osteonecrosis of the femoral head: analysis of three-dimensional morphological features

BackgroundIn total hip arthroplasty (THA) after failed transtrochanteric rotational osteotomy (TRO) for osteonecrosis of the femoral head (ONFH), deformity of the proximal femur has been reported to affect stem placement. The aims of this study were to evaluate the morphological changes in the proximal femur, muscle atrophy, and soft tissue thickening in THA after TRO and the clinical outcomes.MethodsThe TRO group included 17 patients (18 hips) who underwent THA after failed TRO. The control group included 21 patients (28 hips) who underwent primary THA for ONFH. To evaluate the deformity of the proximal femur before THA, we measured the anteroposterior and mediolateral diameters of the femur on computed tomographic slices 5 mm proximal to the lesser trochanter. To evaluate muscle atrophy and soft tissue thickening, we measured the thicknesses of the psoas major, iliac, and gluteus medius muscles and the anterior capsule of the hip joint.ResultsThe ratio of the anteroposterior to mediolateral diameters of the proximal femur was significantly greater in the TRO group (p < 0.01). The thicknesses of the muscles did not differ between the two groups, whereas the anterior capsule was significantly thicker in the TRO group (p < 0.05). Varus or valgus stem alignment (> 3°) was frequent in the TRO group (p < 0.01).ConclusionsThe round shape of the proximal femur was deformed after TRO compared with primary THA for ONFH, which may have caused malposition of the stem. In addition, we should pay attention to anterior protrusion of the proximal femur and thickening of the anterior capsule.

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.

Validation of three-dimensional thoracic electrical impedance tomography of horses during normal and increased tidal volumes

Objective. Data from two-plane electrical impedance tomography (EIT) can be reconstructed into various slices of functional lung images, allowing for more complete visualisation and assessment of lung physiology in health and disease. The aim of this study was to confirm the ability of 3D EIT to visualise normal lung anatomy and physiology at rest and during increased ventilation (represented by rebreathing). Approach. Two-plane EIT data, using two electrode planes 20 cm apart, were collected in 20 standing sedate horses at baseline (resting) conditions, and during rebreathing. EIT data were reconstructed into 3D EIT whereby tidal impedance variation (TIV), ventilated area, and right-left and ventral-dorsal centres of ventilation (CoVRL and CoVVD, respectively) were calculated in cranial, middle and caudal slices of lung, from data collected using the two planes of electrodes. Main results. There was a significant interaction of time and slice for TIV (p < 0.0001) with TIV increasing during rebreathing in both caudal and middle slices. The ratio of right to left ventilated area was higher in the cranial slice, in comparison to the caudal slice (p = 0.0002). There were significant effects of time and slice on CoVVD whereby the cranial slice was more ventrally distributed than the caudal slice (p < 0.0009 for the interaction). Significance. The distribution of ventilation in the three slices corresponds with topographical anatomy of the equine lung. This study confirms that 3D EIT can accurately represent lung anatomy and changes in ventilation distribution during rebreathing in standing sedate horses.

Marching Windows: Scalable Mesh Generation for Volumetric Data With Multiple Materials.

Volumetric data abounds in medical imaging and other fields. With the improved imaging quality and the increased resolution, volumetric datasets are getting so large that the existing tools have become inadequate for processing and analyzing the data. Here we consider the problem of computing tetrahedral meshes to represent large volumetric datasets with labeled multiple materials, which are often encountered in medical imaging or microscopy optical slice tomography. Such tetrahedral meshes are a more compact and expressive geometric representation so are in demand for efficient visualization and simulation of the data, which are impossible if the original large volumetric data are used directly due to the large memory requirement. Existing methods for meshing volumetric data are not scalable for handling large datasets due to their sheer demand on excessively large run-time memory or failure to produce a tet-mesh that preserves the multi-material structure of the original volumetric data. In this article we propose a novel approach, called Marching Windows, that uses a moving window and a disk-swap strategy to reduce the run-time memory footprint, devise a new scheme that guarantees to preserve the topological structure of the original dataset, and adopt an error-guided optimization technique to improve both geometric approximation error and mesh quality. Extensive experiments show that our method is capable of processing very large volumetric datasets beyond the capability of the existing methods and producing tetrahedral meshes of high quality.

The Science Performance of the Gemini High Resolution Optical Spectrograph

The Gemini High Resolution Optical Spectrograph (GHOST) is a fiber-fed spectrograph system on the Gemini South telescope that provides simultaneous wavelength coverage from 348 to 1061 nm, and is designed for optimal performance between 363 and 950 nm. It can observe up to two objects simultaneously in a 7.′5 diameter field of regard at R ≃ 56,000 or a single object at R ≃ 75,000. The spectral resolution modes are obtained by using integral field units to image slice a 1.″2 aperture by a factor of five in width using 19 fibers in the high resolution mode and by a factor of three in width using 7 fibers in the standard resolution mode. GHOST is equipped with hardware to allow for precision radial velocity measurements, expected to approach meters per second precision. Here, we describe the basic design and operational capabilities of GHOST, and proceed to derive and quantify the key aspects of its on-sky performance that are of most relevance to its science users.