Post-processing Workflow Research Articles

Going with the flow: Implementing a 4D flow MRI program at a children's hospital.

Four-dimensional phase contrast MRI (4D flow) has emerged as a versatile imaging technique for comprehensive visualization and both qualitative and quantitative assessment of cardiovascular blood flow. 4D flow is a three-dimensional, time-resolved acquisition that is gated to the cardiac cycle. 4D flow provides cardiovascular velocity and flow assessment across the volume of acquisition and yields a multitude of advanced hemodynamic parameters that help to assess the impact of cardiovascular disease on flow and vice versa, guiding the clinical and surgical management of patients with congenital and acquired heart disease. In the past, lengthy scan acquisition and complex post-processing workflows hindered 4D flow adoption into routine clinical practice. Decreasing image acquisition times and improvements in post-processing techniques have made 4D flow a clinically useful tool. The purpose of this communication is to facilitate more widespread adoption of 4D flow by describing its clinical utility, technical acquisition, optimization, and post-processing in pediatric cardiovascular imaging at our center.

Parameter optimization for proton density fat fraction quantification in skeletal muscle tissue at 7T.

To establish an image acquisition and post-processing workflow for the determination of the proton density fat fraction (PDFF) in calf muscle tissue at 7T. Echo times (TEs) of the applied vendor-provided multi-echo gradient echo sequence were optimized based on simulations of the effective number of signal averages (NSA*). The resulting parameters were validated by measurements in phantom and in healthy calf muscle tissue (n = 12). Additionally, methods to reduce phase errors arising at 7T were evaluated. Finally, PDFF values measured at 7T in calf muscle tissue of healthy subjects (n = 9) and patients with fatty replacement of muscle tissue (n = 3) were compared to 3T results. Simulations, phantom and in vivo measurements showed the importance of using optimized TEs for the fat-water separation at 7T. Fat-water swaps could be mitigated using a phase demodulation with an additional B0 map, or by shifting the TEs to longer values. Muscular PDFF values measured at 7T were comparable to measurements at 3T in both healthy subjects and patients with increased fatty replacement. PDFF determination in calf muscle tissue is feasible at 7T using a chemical shift-based approach with optimized acquisition and post-processing parameters.

Assessing Intra-Bundle Impregnation in Partially Impregnated Glass Fiber-Reinforced Polypropylene Composites Using a 2D Extended-Field and Multimodal Imaging Approach.

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-field images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production.

Automated molecular structure segmentation from documents using ChemSAM

Chemical structure segmentation constitutes a pivotal task in cheminformatics, involving the extraction and abstraction of structural information of chemical compounds from text-based sources, including patents and scientific articles. This study introduces a deep learning approach to chemical structure segmentation, employing a Vision Transformer (ViT) to discern the structural patterns of chemical compounds from their graphical representations. The Chemistry-Segment Anything Model (ChemSAM) achieves state-of-the-art results on publicly available benchmark datasets and real-world tasks, underscoring its effectiveness in accurately segmenting chemical structures from text-based sources. Moreover, this deep learning-based approach obviates the need for handcrafted features and demonstrates robustness against variations in image quality and style. During the detection phase, a ViT-based encoder-decoder model is used to identify and locate chemical structure depictions on the input page. This model generates masks to ascertain whether each pixel belongs to a chemical structure, thereby offering a pixel-level classification and indicating the presence or absence of chemical structures at each position. Subsequently, the generated masks are clustered based on their connectivity, and each mask cluster is updated to encapsulate a single structure in the post-processing workflow. This two-step process facilitates the effective automatic extraction of chemical structure depictions from documents. By utilizing the deep learning approach described herein, it is demonstrated that effective performance on low-resolution and densely arranged molecular structural layouts in journal articles and patents is achievable.

Plasma electrolytic polishing of additively manufactured metal parts: Optimizing the post processing workflow

Additive manufacturing of metal parts is gaining acceptance in industry due to its unique manufacturing capabilities. Higher surface roughness of printed parts is one of the major challenges of this advanced manufacturing technique, which requires a crucial post-processing stage before the components can be used in practice. This study investigates the influence of plasma electrolytic polishing (PeP) and the combination of PeP and particle blasting as a post processing technique for additively manufactured stainless steel samples. The results show a 70% improvement in surface roughness after 10 minutes of particle blasting and 3 minutes of PeP in sequence and presents the possibility of optimizing processing time to achieve better surface qualities with minimal post processing time.

Recording the SS Thistlegorm: Rapid Multi-Image Underwater Photogrammetric Survey of a Large Second World War Wreck

This paper describes the workflow followed to survey and monitor the interior and exterior of a large and complex metal wreck, the SS Thistlegorm in the Red Sea, using digital photogrammetry. Utilizing a simple single-camera rig and off-the-shelf software, this study presents a cost-effective and easily replicable method for monitoring change in metal wrecks that meets professional standards and can involve non-specialists in data collection. This paper details the background of the SS Thistlegorm, the equipment used, and the photogrammetric surveys conducted in 2017 and 2022. It outlines the importance of having a clearly thought-out data-management system when working on a large target recorded over a limited series of dives. In particular, this paper considers testing the accuracy of the data obtained and describes the post-processing workflow in detail. The conclusion underscores the feasibility of achieving an accurate geo-referenced baseline 3D survey with readily available equipment and how these data can contribute to historical research and ongoing monitoring efforts.

Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

Machine learning (ML) methods are commonly applied in the fields of extraterrestrial physics, space science, and plasma physics. In a prior publication, an ML classification technique, the Random Forest (RF) algorithm, was utilized to automatically identify and categorize erroneous signals, including instrument errors, noisy signals, outlier data points, and the impact of solar flares (SFs) on the ionosphere. This data communication includes the pre-processed dataset used in the aforementioned research, along with a workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of ML and ionospheric physics science, as well as being useful to other researchers for diverse objectives.

Plasma electrolytic polishing of additively manufactured metal parts: Optimizing the post processing workflow

Plasma electrolytic polishing of additively manufactured metal parts: Optimizing the post processing workflow

Noninvasive radiomics model reveals macrophage infiltration in glioma

Preoperative MRI is an essential diagnostic and therapeutic reference for gliomas. This study aims to evaluate the prognostic aspect of a radiomics biomarker for glioma and further investigate its relationship with tumor microenvironment and macrophage infiltration. We covered preoperative MRI of 664 glioma patients from three independent datasets: Jiangsu Province Hospital (JSPH, n = 338), The Cancer Genome Atlas dataset (TCGA, n = 252), and Repository of Molecular Brain Neoplasia Data (REMBRANDT, n = 74). Incorporating a multistep post-processing workflow, 20 radiomics features (Rads) were selected and a radiomics survival biomarker (RadSurv) was developed, proving highly efficient in risk stratification of gliomas (cut-off = 1.06), as well as lower-grade gliomas (cut-off = 0.64) and glioblastomas (cut-off = 1.80) through three fixed cut-off values. Through immune infiltration analysis, we found a positive correlation between RadSurv and macrophage infiltration (RMΦ = 0.297, p < 0.001; RM2Φ = 0.241, p < 0.001), further confirmed by immunohistochemical-staining (glioblastomas, n = 32) and single-cell sequencing (multifocal glioblastomas, n = 2). In conclusion, RadSurv acts as a strong prognostic biomarker for gliomas, exhibiting a non-negligible positive correlation with macrophage infiltration, especially with M2 macrophage, which strongly suggests the promise of radiomics-based models as a preoperative alternative to conventional genomics for predicting tumor macrophage infiltration and provides clinical guidance for immunotherapy.

Development of a moving vehicle identification framework using structural vibration response and deep learning algorithms

The accurate identification of vehicle position, velocity, and mass on bridges is crucial in the field of structural health monitoring (SHM). Existing methods for moving vehicle identification primarily rely on staged approaches, which may not adequately address multiple vehicle scenarios. This study is the first to propose a framework that simultaneously considers vehicle velocity, mass, and location as supervised learning problems using structural vibration response. The framework comprises three phases: data generation and preparation, network building and training, and prediction. In Phase 1, a labeling strategy is introduced to convert arbitrary moving vehicle scenarios into a unified, trainable format. Phase 2 presents a novel loss function specifically designed to measure the discrepancy between the network output and the label. Lastly, Phase 3 introduces a data post-processing workflow that converts the network output into moving vehicle information. To demonstrate the effectiveness of the proposed method, the identification accuracy is quantified and analyzed using five different indexes under different vehicle numbers, velocity intervals, and mass intervals. Furthermore, the influences of some key variables, the modeling error and model simplification are discussed in detail. The proposed approach surpasses conventional methods by overcoming staged identification limitations and accommodating multiple vehicles, while efficiently identifying their velocity, mass, and moving range on the bridge without requiring initial conditions.

Chasing the bird: 3D acoustic tracking of aerial flight displays with a minimal planar microphone array

ABSTRACT Tracking the flight patterns of birds and bats in three-dimensional space is central to key questions in evolutionary ecology but remains a difficult technical challenge. For example, complex aerial flight displays are common among birds breeding in open habitats, but information on flight performance is limited. Here, we demonstrate the feasibility of using a large ground-based 4-microphone planar array to track the aerial flight displays of the cryptic Jack Snipe Lymnocryptes minimus. The main element of male display flights resembles a galloping horse at a distance. Under conditions of sufficient signal-to-noise ratio and of vertical alignment with the microphone array, we successfully tracked male snipe in 3D space for up to 25 seconds with a total flight path of 280 m. The ’gallop’ phase of male snipe dropped from ca. 141 to 64 m above ground at an average velocity of 77 km/h and up to 92 km/h. Our project is one of the first applications of bioacoustics to measure 3D flight paths of birds under field conditions, and our results were consistent with our visual observations. Our microphone array and post-processing workflow provides a standardised protocol that could be used to collect comparative data on birds with complex aerial flight displays.

Vector Angular Continuity in the Fusion of Coseismic Deformations at Multiple Optical Correlation Scales.

As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results; the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the correlation window size. This study regards coseismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the correlation window. This paper takes the coseismic deformations in both the east-west and north-south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different correlation window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity.

Wear behaviour of lithography ceramic manufactured dental zirconia

ObjectiveThe study aims to evaluate the wear surface using 3D surface roughness and other material characterization of zirconia fabricated using photopolymerization based Lithography-based Ceramic Manufacturing (LCM).MethodLCM technology was used to fabricate zirconia specimens of size 10 × 10 × 2mm3. Scanning Electron Microscope, 3D–profilometer, X-ray Diffraction, and hardness test characterized the samples before and after wear and Coefficient of friction (COF) was monitored.ResultThe COF was around 0.7 and did not differ much between the horizontally and vertically printed specimens. However, the surface roughness after wear for horizontally printed specimen was 0.567 ± 0.139 μm, while that for vertically printed specimen was 0.379 ± 0.080 μm. The reduced valley depth and the dale void volume were low for the vertically printed zirconia specimen, indicating lesser voids and low fluid retention. In addition, it was observed that the hardness value of the vertically printed sample was better. The scanning electron microscopic images and 3D surface profiles of the zirconia specimens depicted the surface topography and revealed the wear track.ConclusionThe study shows that zirconia fabricated using LCM technology possesses surface roughness of about 0.5 μm with no machining scars that are usually associated with CAD/CAM dentistry and also indicating agreement with clinically acceptable values for minimal surface roughness of dental restorations. Dental restorations using LCM fabricated zirconia redues the requirement of post-processing work flow that is part of CAD/CAM dentistry.

Integration and Comparison Methods for Multitemporal Image-Based 2D Annotations in Linked 3D Building Documentation

Data acquisition systems and methods to capture high-resolution images or reconstruct 3D point clouds of existing structures are an effective way to document their as-is condition. These methods enable a detailed analysis of building surfaces, providing precise 3D representations. However, for the condition assessment and documentation, damages are mainly annotated in 2D representations, such as images, orthophotos, or technical drawings, which do not allow for the application of a 3D workflow or automated comparisons of multitemporal datasets. In the available software for building heritage data management and analysis, a wide range of annotation and evaluation functions are available, but they also lack integrated post-processing methods and systematic workflows. The article presents novel methods developed to facilitate such automated 3D workflows and validates them on a small historic church building in Thuringia, Germany. Post-processing steps using photogrammetric 3D reconstruction data along with imagery were implemented, which show the possibilities of integrating 2D annotations into 3D documentations. Further, the application of voxel-based methods on the dataset enables the evaluation of geometrical changes of multitemporal annotations in different states and the assignment to elements of scans or building models. The proposed workflow also highlights the potential of these methods for condition assessment and planning of restoration work, as well as the possibility to represent the analysis results in standardised building model formats.

Numerical Simulation of Hydrate Formation in the LArge-Scale Reservoir Simulator (LARS)

The LArge-scale Reservoir Simulator (LARS) has been previously developed to study hydrate dissociation in hydrate-bearing systems under in-situ conditions. In the present study, a numerical framework of equations of state describing hydrate formation at equilibrium conditions has been elaborated and integrated with a numerical flow and transport simulator to investigate a multi-stage hydrate formation experiment undertaken in LARS. A verification of the implemented modeling framework has been carried out by benchmarking it against another established numerical code. Three-dimensional (3D) model calibration has been performed based on laboratory data available from temperature sensors, fluid sampling, and electrical resistivity tomography. The simulation results demonstrate that temperature profiles, spatial hydrate distribution, and bulk hydrate saturation are consistent with the observations. Furthermore, our numerical framework can be applied to calibrate geophysical measurements, optimize post-processing workflows for monitoring data, improve the design of hydrate formation experiments, and investigate the temporal evolution of sub-permafrost methane hydrate reservoirs.

Automated CT reformations reduce time and variability in trauma panscan exam completion

To test the hypothesis that an automated post-processing workflow reduces trauma panscan exam completion times and variability. One-hundred-fifty consecutive trauma panscans performed between June 2018 and December 2019 were included, half before and half after implementation of an automated software-driven post-processing workflow. Acquisition and reconstruction timestamps were used to calculate total examination time (first acquisition to last reformation), setup time (between the non-contrast and contrast-enhanced acquisitions), and reconstruction time (for the contrast-enhanced reconstructions and reformations). The performing technologist was recorded and accounted for in analyses using linear mixed models to assess differences between the pre- and post-intervention groups. Exam, setup, and recon times were (mean ± standard deviation) 33.5 ± 4.6, 9.2 ± 2.4, and 23.6 ±4.7min before and 27.8 ± 1.5, 8.9 ± 1.4, and 18.9 ±1.7min after intervention. These reductions of 5.7 and 4.7min in the mean exam and recon times were statistically significant (p< 0.001) while the setup time was not (p= 0.49). The reductions in standard deviation were statistically significant for exam and recon times (p< 0.0001) but not for setup time (p= 0.13). All automated panscans were completed within 36min, versus 65% with the traditional workflow. Automation of image reconstruction workflow significantly decreased mean exam and reconstruction times as well as variability between exams, thus facilitating a consistently rapid imaging assessment, and potentially reducing delays in critical management decisions.

Generalized fluid carving with fast lattice-guided seam computation

In this paper, we introduce a novel method for intelligently resizing a wide range of volumetric data including fluids. Fluid carving, the technique we build upon, only supported particle-based liquid data, and because it was based on image-based techniques, it was constrained to rectangular boundaries. We address these limitations to allow a much more versatile method for volumetric post-processing. By enclosing a region of interest in our lattice structure, users can retarget regions of a volume with non-rectangular boundaries and non-axis-aligned motion. Our approach generalizes to images, videos, liquids, meshes, and even previously unexplored domains such as fire and smoke. We also present a seam computation method that is significantly faster than the previous approach while maintaining the same level of quality, thus making our method more viable for production settings where post-processing workflows are vital.

Automated Processing of Head CT Perfusion Imaging for Ischemic Stroke Triage: A Practical Guide to Quality Assurance and Interpretation.

Recent successful trials of thrombectomy launched a shift to imaging-based patient selection for stroke intervention. Many centers have adopted CT perfusion imaging (CTP) as a routine part of stroke workflow, and the demand for emergent CTP interpretation is growing. Fully automated CTP postprocessing software that rapidly generates standardized color-coded CTP summary maps with minimal user input and with easy accessibility of the software output is increasingly being adopted. Such automated postprocessing greatly streamlines clinical workflow and CTP interpretation for radiologists and other frontline physicians. However, the straightforward interface overshadows the computational complexity of the underlying postprocessing workflow, which, if not carefully examined, predisposes the interpreting physician to diagnostic errors. Using case examples, this article aims to familiarize the general radiologist with interpreting automated CTP software data output in the context of contemporary stroke management, providing a discussion of CTP acquisition and postprocessing, a stepwise guide for CTP quality assurance and troubleshooting, and a framework for avoiding clinically significant pitfalls of CTP interpretation in commonly encountered clinical scenarios. Interpreting radiologists should apply the outlined approach for quality assurance and develop a comprehensive search pattern for the identified pitfalls, to ensure accurate CTP interpretation and optimize patient selection for reperfusion.

Deep Learning Approaches to Earth Observation Change Detection

The interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task; therefore, a standard approach with manual detection of the elements of interest by experts in the domain of Earth Observation needs to be replaced by innovative methods that can guarantee optimal results with unquestionable value and within reasonable time. In this paper, we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to address these particular needs, which can be further refined and used in post-processing workflows for a large variety of applications.

Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data

BackgroundEarly fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations.ObjectiveThe aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies.MethodsTo this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization.ResultsA workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding.ConclusionsThe proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials.