Multiple Scans Research Articles

Comparing the test-retest reliability of resting-state functional magnetic resonance imaging metrics across single band and multiband acquisitions in the context of healthy aging.

The identification of meaningful functional magnetic resonance imaging (fMRI) biomarkers requires measures that reliably capture brain performance across different subjects and over multiple scanning sessions. Recent developments in fMRI acquisition, such as the introduction of multiband (MB) protocols and in-plane acceleration, allow for increased scanning speed and improved temporal resolution. However, they may also lead to reduced temporal signal to noise ratio and increased signal leakage between simultaneously excited slices. These methods have been adopted in several scanning modalities including diffusion weighted imaging and fMRI. To our knowledge, no study has formally compared the reliability of the same resting-state fMRI (rs-fMRI) metrics (amplitude of low-frequency fluctuations; seed-to-voxel and region of interest [ROI]-to-ROI connectivity) across conventional single-band fMRI and different MB acquisitions, with and without in-plane acceleration, across three sessions. In this study, 24 healthy older adults were scanned over three visits, on weeks 0, 1, and 4, and, on each occasion, underwent a conventional single band rs-fMRI scan and three different rs-fMRI scans with MB factors 4 and 6, with and without in-plane acceleration. Across all three rs-fMRI metrics, the reliability scores were highest with MB factor 4 with no in-plane acceleration for cortical areas and with conventional single band for subcortical areas. Recommendations for future research studies are discussed.

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

AbstractVegetation restoration is one of the effective measures to deal with land degradation, and shrubs, as the pioneer plants, can improve soil conditions and accelerate the recovery of desertification. In recent decades, single‐scan (SS) and multiple‐scan (MS) modes have been widely used for forest inventories by terrestrial laser scanner (TLS) but TLS‐based shrub observation, and data quality uncertainties are less studied, especially in eco‐fragile regions with high survey costs and harsh environmental conditions. In this study, two shrub sample plots with different topographic characteristics were selected as the study area in the southern Qinghai‐Tibetan Plateau. We compared the basic phenotypic parameters (BPPs) under SS and MS mode, including number (N), height (H) and crown width (CW), to evaluate the potential of SS mode in extracting vegetation attributes. Through the distribution of shrub information loss rate (L) with distance and shrub height, the limitations of SS mode in obtaining data were discussed. By combining the topographic features of scanning spots, topographic relief (T) and L, we provided reasonable opinions on TLS collection mode, setting of scanning spots, and other operational aspects. The results indicated that: (1) SS mode has good applicability in shrub observation under different terrain characteristics. (2) The height distribution of the shrub is the main factor affecting the quality of data collection, and the information loss bias caused by distance should be considered only in the shrub plots with uniform height distribution. (3) T has an impact on shrub growth but has no effect on the quality of data acquisition (no significant correlation between T and L). These findings will provide technical support for TLS data collection on ecological restoration of degraded grassland dunes.

286. Prevalence and Clinical Outcomes of COVID-Associated Pulmonary Fibrosis

Abstract Background Pulmonary fibrosis (PF) is a well-known consequence of severe lung disease and is associated with permanent changes as well as irreversible pulmonary dysfunction. The development of PF in patients infected with COVID-19 has been documented in multiple studies and case reports. However, prevalence and outcomes associated with PF have not been well established. Therefore, we sought to evaluate the prevalence and clinical outcomes of PF among patients infected with COVID-19. Methods This is an observational cohort study from January 2020 – January 2022. We collected data from adult patients diagnosed with COVID-19 who had at least two separate computerized tomography (CT) scans. Patients were grouped based on whether evidence of developing PF, defined by clinical radiological parameters, were seen on CT. The initial CT must be negative for PF to be included. We collected the following information: baseline characteristics, secondary infection, mortality, and treatments. The primary objective was to determine the prevalence of PF among COVID-19 patients. Secondary objectives were to evaluate the differences in 30-day all-cause mortality; intensive care unit (ICU) mortality; and prevalence of secondary infections. Results A total 161 patients were COVID-19 positive with multiple CT scans; 27 (16.8%) had signs of PF while 134 (83.2%) did not. Of the patients with signs of PF, 13 (48.1%) were male (mean age of 57 years) and 13 (48.1%) were admitted to the ICU. There was no difference in secondary bacterial infection between fibrotic and non-fibrotic patients (55.6% vs 38.5%, p=0.20). The most common bacterial infection among PF patients was caused by S. aureus (26.7%). The most common infection type among PF patients was nosocomial pneumonia (46.7%). Mortality at 30-day was higher in the fibrotic patients, 70% vs 24.2% (p< 0.01). The ICU mortality trended higher in the fibrotic group (69.2% vs 36.6%, p=0.124), although it was not statistically significant. Conclusion Pulmonary fibrosis can be fatal as a complication of COVID-19. Further investigation is warranted to evaluate the outcomes of PF in patients with COVID-19. Disclosures All Authors: No reported disclosures.

A multisensory-feedback tactile glove with dense coverage of sensing arrays for object recognition

Somatosensory networks that provide sophisticated sensory feedback and enable the dexterous manipulation of the human grasp remain difficult to replicate in robots, which is attributed to the grand challenge of densely covering the hand with tactile arrays. Here, a multisensory tactile glove is reported that is capable of object recognition with dense coverage of pressure and temperature sensing arrays. The synergistic effect of the multimodal configuration allows the tactile arrays to perceive contact pressure and thermal conductivity of an object involved in grasping motion, thus enhancing the accuracy via the combination of the mechanical features with thermal properties. By leveraging the multiple scanning technology and wireless transmission system, the tactile glove achieves a recognition accuracy of 94.2% in differentiating 20 types of objects with a modified deep learning algorithm. The large-area sensing arrays with high spatiotemporal resolution and multimodal sensing capabilities, which paves the way for the development of robot grasping tools, human–machine interfacing, and advanced prosthetics.

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.

Additive manufacturing of CMCs with bimodal microstructure

The present work deal with the fabrication of TiC-based composites with green Fe-based binder via additive manufacturing such as selective laser melting/laser fusion bed process. The as-built specimen exhibits a bimodal microstructure consisting of fine/ultrafine dendritic and coarser TiC phases. By adopting a novel multiple scan strategy, process defects such as cracks were eliminated by careful control of the melt pool temperature/cooling rates. Even though the mechanical properties may be improved, the anisotropic distribution of the dual phase leads to anisotropic distribution of hardness within the CMC surface.

Creep and fatigue properties of asphalt mastic with steel slag powder filler

Steel slag powder (SSP) was selected as an inorganic micro powder filler instead of limestone powder (LSP) to prepare asphalt mastic in this paper. Firstly, the microscopic characteristics of SSP and LSP such as particle size distribution, surface morphology, phase composition and chemical composition were characterized by laser particle size analyzer, scanning electron microscopy (SEM), X-ray diffraction (XRD) analyzer, and X-ray fluorescence (XRF) analyzer. Then, the composition and content of chemical functional groups of asphalt mastics were studied by fourier transform infrared spectrometer (FTIR). Finally, the effect of SSP on the creep and fatigue properties of asphalt mastics was investigated via multiple stress creep recovery (MSCR) and time scanning experiments using a dynamic shear rheometer (DSR). The results showed that the addition of SSP as mineral filler instead of LSP to asphalt binder enhanced the deformation resistance and the fatigue life of the asphalt mastics.The complex shear modulus of asphalt mastics show an increase trend with an increase in the SSP content. Compared with LSP asphalt mastic, the complex shear modulus of SSP asphalt mastic ( SSP content 30 %, 50 %, 100 %) increased by 84.9 %, 118.0 % and 256.1 %, respectively. From the perspective of structural design and material selection, the fatigue life NDER corresponding to the cumulative dissipative energy rate(DER) is recommended to analyze the fatigue life of asphalt mastics.The results of the MSCR and time scanning experiments demonstrated that the use of a combination of SSP and LSP as filler could be an effective approach for comprehensive utilization of steel slag in asphalt mixture.

Modeling of High-Pressure and High-Temperature Microemulsion Experiments using HLD-NAC-Based Equation of State

Summary Surfactant flooding is a promising technique that can reduce interfacial tension (IFT) between oil and water to ultralow values, mobilizing previously trapped oil. For reservoirs at moderate to high pressures, understanding and modeling how pressure affects the phase behavior of a surfactant-brine-oil system is important to the design and implementation of an efficient/cost-effective surfactant flooding project. Typically, however, phase behavior experiments and models of that phase behavior are made only at low pressures. The main objective of this paper is to show how to model experimental data in a unified way for a large range of pressure, temperature, and other parameters, using hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC)-based equation-of-state (EOS). Pressure and temperature scans show that pressure has a significant effect on the surfactant microemulsion phase behavior, shifting it from an optimal three-phase system at low pressure to a nonoptimal two-phase system at high pressure. Further, multiple scans at different water/oil ratios (WORs) show a shift in the optimum indicating that phase behavior partitioning of the various components is changing with oil saturation. We obtained good fits of all experimental data including all two- and three-phase regions using a single tuned HLD-NAC EOS for a wide range of simultaneous variations in pressure, temperature, salinity, and overall composition. Such a simultaneous match and prediction by a single set of model parameters has never been done before. We also demonstrate the type of data needed for an accurate EOS. When input into a numerical simulator, the tuned EOS improves the predictions of the resulting phase behavior (size and shape of the two-phase lobes and three-phase regions) and IFTs with changing pressure, temperature, salinity, WORs, and surfactant/alcohol concentrations.

Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics

Recent studies have used T1w contrast-enhanced (T1w-CE) magnetic resonance imaging (MRI) radiomic features and machine learning to predict post-stereotactic radiosurgery (SRS) brain metastasis (BM) progression, but have not examined the effects of combining clinical and radiomic features, BM primary cancer, BM volume effects, and using multiple scanner models. To investigate these effects, a dataset of n = 123 BMs from 99 SRS patients with 12 clinical features, 107 pre-treatment T1w-CE radiomic features, and BM progression determined by follow-up MRI was used with a random decision forest model and 250 bootstrapped repetitions. Repeat experiments assessed the relative accuracy across primary cancer sites, BM volume groups, and scanner model pairings. Correction for accuracy imbalances across volume groups was investigated by removing volume-correlated features. We found that using clinical and radiomic features together produced the most accurate model with a bootstrap-corrected area under the receiver operating characteristic curve of 0.77. Accuracy also varied by primary cancer site, BM volume, and scanner model pairings. The effect of BM volume was eliminated by removing features at a volume-correlation coefficient threshold of 0.25. These results show that feature type, primary cancer, volume, and scanner model are all critical factors in the accuracy of radiomics-based prognostic models for BM SRS that must be characterised and controlled for before clinical translation.

MNG-3 STRATIFICATION OF TUMOR GROWTH BY RISK FACTORS AND PREDICTED TUMOR VOLUME CURVES FOR SUPRATENTORIAL MENINGIOMAS

Abstract Background The natural history of meningiomas is still unclear, and no guidelines have been established based on objective indices regarding the necessity and timing of therapeutic intervention.Objective: We attempted to provide statistics on the characteristics of tumor volume change, stratify tumor growth by risk factors, and generate a predictive tumor volume curve based on the statistics, with the aim of describing the natural history of meningiomas. Methods 313 cases were included in the study, with the origin of meningioma being the circumflex and parasagittal sinus areas and the cerebral sickle region, and with multiple MRI scans performed at intervals of at least 3 months. Relative growth rate (RGR) and annual volume change (AVC) were calculated by measuring tumor volume, and the patients were classified into three groups according to the combination of gender, age, and MRI T2WI signal intensity, and compared. Results The median RGR and AVC of the entire cohort were 6.1% and 0.20 cm3/year, respectively, and there were significant differences between groups in gender (p=0.018) and MRI T2WI (p < 0.001) for RGR and tumor location (p=0.025) and initial tumor volume (p < 0.001)for AVC. The median RGR and AVC in the classification were 17.5% and 1.05 cm3/year for the very high growth group, 8.2% and 0.33 cm3/year for the high growth group, and 3.4% and 0.04 cm3/year for the low growth group, showing significant differences between the groups (p < 0.001). The predicted tumor volume curve showed an average 2.24-fold or 5.24 cm3 increase in volume over 5 years in the very high-growth group, while little tumor volume change was observed in the low-growth group. Conclusion The combination of growth risk factors allowed stratification of tumor growth and provided a predictive tumor volume curve for each county. The results may assist in the treatment of meningiomas.

Harmonizing machine learning imaging biomarkers – CDESH and the case for harmonization in both MR image and low‐dimensional (scalar) output space

AbstractBackgroundIn 2018 we presented the Computational DESH (CDESH) metric (DOI:10.1016/j.nicl.2018.11.015), a machine learning measure of abnormal CSF space patterns commonly observed on MRI in Disproportionately Enlarged Subarachnoid‐space Hydrocephalus (DESH‐type) NPH. We have shown that CDESH positive individuals often have discordant CSF and PET amyloid measures (DOI:10.1016/j.jalz.2019.06.4921). Positive CDESH scores are predictive of cognitive decline (DOI:10.1016/j.neuroimage.2021.117899, DOI: 10.1212/WNL.0000000000008616). The score is calculated from regional CSF volumes in a sulcal atlas and within the ventricles using a support vector machine (SVM). The Mayo Clinic Study of Aging (MCSA) changed scanner platforms (GE to Siemens) and T1‐weighted image resolution (1.2mm x 1.05mm x 1.05mm to 0.8mm isotropic) in 2017. We refer to these as “isotropic” or “anisotropic” scans. Our objective was to assess the dependence of a machine learning method (specifically CDESH) to image input variation and, if necessary, determine what needed corrections.MethodAll data considered is from the MCSA or Mayo Clinic ADRC. The SVM was trained on anisotropic images with laboriously labelled ground truth. We resampled the isotropic data from a crossover study (N=112, typically scanned same‐day with both platforms/resolutions) to match the anisotropic resolution. Two approaches to correction were evaluated (Figure 1). In Figure 2 we compare of the score with and without correction cross‐sectionally in 998 MCSA participants. In Figure 3 we present longitudinal series from 23 participants with multiple scans using the isotropic resolution protocol and at least one prior anisotropic image.ResultsComparisons of data from the crossover study are presented in Figure 1. Matching voxel resolution to the data used to train the SVM is critical and a subsequent mild linear score adjustment may be warranted. Cross‐sectional comparisons (Figure 2) confirm improvement matching voxel resolution with a possible mild linear score adjustment. After matching voxel resolution CDESH score trajectories (Figure 3) are markedly more stable across this scanner/protocol change.ConclusionThe SVM‐based CDESH score is sensitive to voxel resolution. Corrections are needed in image processing and possibly in the scalar output score to achieve stability across protocol and scanner changes. The resulting correction supports coherent long timeseries data and longitudinal extension of previous work.

Harmonizing machine learning imaging biomarkers – CDESH and the case for harmonization in both MR image and low‐dimensional (scalar) output space

AbstractBackgroundIn 2018 we presented the Computational DESH (CDESH) metric (DOI:10.1016/j.nicl.2018.11.015), a machine learning measure of abnormal CSF space patterns commonly observed on MRI in Disproportionately Enlarged Subarachnoid‐space Hydrocephalus (DESH‐type) NPH. We have shown that CDESH positive individuals often have discordant CSF and PET amyloid measures (DOI:10.1016/j.jalz.2019.06.4921). Positive CDESH scores are predictive of cognitive decline (DOI:10.1016/j.neuroimage.2021.117899, DOI: 10.1212/WNL.0000000000008616). The score is calculated from regional CSF volumes in a sulcal atlas and within the ventricles using a support vector machine (SVM). The Mayo Clinic Study of Aging (MCSA) changed scanner platforms (GE to Siemens) and T1‐weighted image resolution (1.2mm x 1.05mm x 1.05mm to 0.8mm isotropic) in 2017. We refer to these as “isotropic” or “anisotropic” scans. Our objective was to assess the dependence of a machine learning method (specifically CDESH) to image input variation and, if necessary, determine what needed corrections.MethodAll data considered is from the MCSA or Mayo Clinic ADRC. The SVM was trained on anisotropic images with laboriously labelled ground truth. We resampled the isotropic data from a crossover study (N=112, typically scanned same‐day with both platforms/resolutions) to match the anisotropic resolution. Two approaches to correction were evaluated (Figure 1). In Figure 2 we compare of the score with and without correction cross‐sectionally in 998 MCSA participants. In Figure 3 we present longitudinal series from 23 participants with multiple scans using the isotropic resolution protocol and at least one prior anisotropic image.ResultsComparisons of data from the crossover study are presented in Figure 1. Matching voxel resolution to the data used to train the SVM is critical and a subsequent mild linear score adjustment may be warranted. Cross‐sectional comparisons (Figure 2) confirm improvement matching voxel resolution with a possible mild linear score adjustment. After matching voxel resolution CDESH score trajectories (Figure 3) are markedly more stable across this scanner/protocol change.ConclusionThe SVM‐based CDESH score is sensitive to voxel resolution. Corrections are needed in image processing and possibly in the scalar output score to achieve stability across protocol and scanner changes. The resulting correction supports coherent long timeseries data and longitudinal extension of previous work.

CT Scans and Cancer Risks: A Systematic Review and Dose-response Meta-analysis

BackgroundThere is still uncertainty on whether ionizing radiation from CT scans can increase the risks of cancer. This study aimed to identify the association of cumulative ionizing radiation from CT scans with pertaining cancer risks in adults.MethodsFive databases were searched from their inception to November 15, 2020. Observational studies reporting cancer risks from CT scans in adults were included. The main outcome included quantified cancer risks as cancer case numbers in exposed/unexposed adult participants with unified converted measures to odds ratio (OR) for relative risk, hazard ratio. Global background radiation (2.4 mSv per year) was used as control for lifetime attribution risk (LAR), with the same period from incubation after exposure until survival to 100 years.Results25 studies were included with a sum of 111,649,943 participants (mean age: 45.37 years, 83.4% women), comprising 2,049,943 actual participants from 6 studies with an average follow-up period as 30.1 years (range, 5 to 80 years); 109,600,000 participants from 19 studies using LAR. The cancer risks for adults following CT scans were inordinately increased (LAR adults, OR, 10.00 [95% CI, 5.87 to 17.05]; actual adults, OR, 1.17 [95%CI, 0.89 to 1.55]; combined, OR, 5.89 [95%CI, 3.46 to 10.35]). Moreover, cancer risks elevated with increase of radiation dose (OR, 33.31 [95% CI, 21.33 to 52.02]), and multiple CT scan sites (OR, 14.08 [95% CI, 6.60 to 30.05]). The risk of solid malignancy was higher than leukemia. Notably, there were no significant differences for age, gender, country, continent, study quality and studying time phrases.ConclusionsBased on 111.6 million adult participants from 3 continents (Asia, Europe and America), this meta-analysis identifies an inordinately increase in cancer risks from CT scans for adults. Moreover, the cancer risks were positively correlated with radiation dose and CT sites. The meta-analysis highlights the awareness of potential cancer risks of CT scans as well as more reasonable methodology to quantify cancer risks in terms of life expectancy as 100 years for LAR.Prospero trial registration numberCRD42019133487.

Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning.

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model-namely, combine-all in semi-supervised learning (CqSL)-to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured similarity loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.

Platelet Membrane Cloaked Nanotubes to Accelerate Thrombolysis by Thrombus Clot-Targeting and Penetration.

Thrombotic diseases have a high rate of mortality and disability, and pose a serious threat to global public health. Currently, most thrombolytic drugs especially protein drugs have a short blood-circulation time, resulting in low thrombolytic efficiency. Therefore, a platelet membrane (Pm) cloaked nanotube (NT-RGD/Pm) biomimetic delivery system with enhanced thrombolytic efficiency is designed. Nanotubes (NT) with an excellent clot-penetration properties are used to load a protein thrombolytic drug urokinase (Uk). Platelet-targeting arginine glycine-aspartic peptide (RGD) is grafted onto the surface of the nanotubes (NT-RGD) prior to cloaking. Multiple particle tracking (MPT) technique and confocal laser scanning microscope (CLSM) analysis are applied and the results show that the nanotubes possess a strong penetration and diffusion capacity in thrombus clots. After the Pm cloaking on NT-RGD/Uk, it shows a thrombus microenvironmental responsive release property and the half-life of Uk is six times longer than that of free Uk. Most importantly, NT-RGD-Uk/Pm exhibits a 60% thrombolytic efficiency in the FeCl3 -induced thrombosis mouse model, and it is able to significantly reduce the bleeding side effects of Uk. This Pm-cloaked nanotube system is an effective and promising platform for the controlled and targeted delivery of drugs for the thrombus treatment.

Image quality and scan time optimisation for in situ phase contrast x-ray tomography of the intervertebral disc

In-line phase contrast synchrotron tomography combined with in situ mechanical loading enables the characterisation of soft tissue micromechanics via digital volume correlation (DVC) within whole organs. Optimising scan time is important for reducing radiation dose from multiple scans and to limit sample movement during acquisition. Also, although contrasted edges provided by in-line phase contrast tomography of soft tissues are useful for DVC, the effect of phase contrast imaging on its accuracy has yet to be investigated. Due to limited time at synchrotron facilities, scan parameters are often decided during imaging and their effect on DVC accuracy is not fully understood. Here, we used previously published data of intervertebral disc phase contrast tomography to evaluate the influence of i) fibrous image texture, ii) number of projections, iii) tomographic reconstruction method, and iv) phase contrast propagation distance on DVC results. A greater understanding of how image texture influences optimal DVC tracking was obtained by visualising objective function mapping, enabling tracking inaccuracies to be identified. When reducing the number of projections, DVC was minimally affected by image high frequency noise but with a compromise in accuracy. Iterative reconstruction methods improved image signal-to-noise and consequently significantly lowered DVC displacement uncertainty. Propagation distance was shown to affect DVC accuracy. Consistent DVC results were achieved within a propagation distance range which provided contrast to the smallest scale features, where; too short a distance provided insufficient features to track, whereas too long led to edge effect inconsistencies, particularly at greater deformations. Although limited to a single sample type and image setup, this study provides general guidelines for future investigations when optimising image quality and scan times for in situ phase contrast x-ray tomography of fibrous connective tissues.

Label-free proteome quantification and evaluation.

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom.

Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom. The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94HU, lung volume of 797-3785cm3 , and lung mass of -800 to 1751g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84HU, lung volume of 66-3785cm3 , and lung mass of 170-1751g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

Abstract 15814: Postoperative Surveillance Imaging Following Elective Aortic Hemiarch Replacement

Introduction: The optimal protocol for imaging following aortic hemiarch replacement remains a topic of debate with varied intervals at each institution. For the last 3 years, our institution has surveilled patients at 3 months, yearly for 3 years, and at 5 years. Hypothesis: We hypothesize surveillance CT scans performed at 3 months will identify any pathologies related to the ascending aortic repair that will require re-intervention; additional imaging may be superfluous. Methods: Using our single institution-maintained database, 348 patients who underwent an elective aortic hemiarch repair between February of 2010 and December of 2021 were identified. One patient with a restricted chart was excluded. The timing for and stability of surveillance imaging was reviewed, paying special attention to pathology associated with the repair. Results: Of the 347 patients, 274 completed surveillance imaging. 231 patients had their initial imaging around 3 months following the operation, 126 patients completed imaging around one year, and 72 patients were imaged 2-10 years post-operatively with some patients undergoing multiple scans during this period. Ten patients required re-intervention; of these 10, two patients demonstrated pathology, on CT scan, associated with the actual ascending aortic repair. These two patients developed a pseudoaneurysm (PSA), one at the distal aspect of the repair and one at the left ventricular outflow tract. Another two patients had degeneration of known distal aortic aneurysms. The two PSAs were intervened upon within the first 3 months and the degenerating aneurysms were intervened upon at 1 and 2.5 years post-operatively. Conclusions: These data support surveillance imaging completed at 3 months following hemiarch replacement is sufficient to identify pathology related to the repair. While other indications may dictate ongoing surveillance, for most patients, the need for further imaging is potentially zero.

Next Level in Computed Tomography-Guided Periradicular Infiltration Therapy: Same Efficiency with Less Radiation Exposure

This retrospective cohort study investigated the radiation exposure and clinical efficiency of a new institutional low-dose protocol for computed tomography (CT)-guided lumbar periradicular infiltration (PRI). This was a retrospective matched-pair comparison of patients undergoing single-level lumbar PRI therapy employing a new low-dose CT protocol consisting of a helical scan with reduced energy levels and tube current versus the institutional standard CT protocol. The following variables were collected: dose-length product for the planning step, interventional step, and total examination, number of CT guidance scans, examination time, and postprocedural improvement on the numerical rating scale for radicular pain. Forty-five patients were allocated to each group. A sufficient radiation dose reduction of 30% during PRI was achieved with the low-dose protocol with a median dose-length product of 9.8mGy∗cm compared to 32.9mGy∗cm with the standard protocol (P < 0.001). No need for additional multiple scans during the interventional mode was observed in the low-dose group, resulting in a comparable procedure time between the groups. Furthermore, the short-term pain-reducing effect of PRI was comparable between the low-dose and standard protocols (median delta numerical rating scale= 4 in both groups). Our low-dose protocol with less tube voltage and lower electric current leads to less radiation exposure with the same safety and efficiency. In conclusion, every facility that performs CT-based procedures should check whether a further dose reduction is applicable to avoid stochastic radiation damage to the patient.