Volume Estimation Research Articles

EBNA-1 and VCA-p18 immunoglobulin markers link Epstein-Barr virus immune response and brain’s myelin content to fatigue in a community-dwelling cohort

Given the association of Epstein-Barr virus (EBV) with subjective perception of fatigue and demyelination in clinical conditions, the question about potential subclinical effects in the adult general population remains open. We investigate the association between individuals’ EBV immune response and perceived fatigue in a community dwelling cohort (n=864, age 62 ± 10 years old; 49% women) while monitoring brain tissue properties. Fatigue levels are assessed with the established fatigue severity scale, the EBNA-1 and VCA p18 immunoglobulin G (IgG) chronic response – with multiplex serology and the estimates of local brain volume, myelin content, and axonal density - using relaxometry- and multi-shell diffusion-based magnetic resonance imaging (MRI). In our analysis we adjust for the effects of demographic and cardiovascular risk factors, sleep apnea, depression, and polygenic risk score for multiple sclerosis. We demonstrate that EBNA-1 IgG levels are positively associated with perceived levels of fatigue, whilst VCA p18 IgG levels show a positive correlation with myelin content and a negative one with an estimate of axonal g-ratio in male participants. In the context of EBVs immune response, the polygenic risk for multiple sclerosis is not associated with increased fatigue levels, brain myelination or atrophy. Our findings bring empirical evidence about the potential role of EBVs chronic immune response in perceived fatigue and hint towards a protective role of myelination specific for men. They underscore the added value of advanced assessment of brain tissue microstructure in uncovering the mechanisms behind frequent fatigue complaints associated with EBV infection and multiple sclerosis.

Cognitive Impairment, Associated Clinical Factors, and MR Volumetric Measures in Myelin Oligodendrocyte Glycoprotein-IgG-Associated Disease.

Cognitive impairment is a common and challenging symptom in multiple sclerosis and neuromyelitis optica spectrum disease; however, data in myelin oligodendrocyte glycoprotein-IgG-associated disease (MOGAD) remain scarce. In this cross-sectional study, we investigated the frequency of cognitive impairment, associated clinical factors, and MRI volumetric measures in MOGAD. Participants were investigated in a single center by certified psychologists and underwent a standardized 3-T-MRI protocol. MRI data were processed with FreeSurfer for gray and white matter volume estimation, presented as a fraction of total intracranial volume. Sera screening for antineural antibodies has been conducted using cell-based assays. The following clinical factors were included in the multivariate logistic regression analysis: sex, age, overall number of previous relapses, and specifically the history of acute disseminated encephalomyelitis (ADEM)/ADEM-like episodes and other brain relapses. Thirty-two patients with MOGAD (19 female, median age 29.4 years) after a median of 2 relapses with a median EDSS of 1.0 were recruited. Seven patients (21.9%) demonstrated cognitive impairment with the most prevalent deficits in mental flexibility (16.7%), attention (11.1%-14.8%), and verbal working memory (10.3%). 72.4% suffered from fatigue and 42.9% from signs of depression, moderate to severe in 28.6%. The overall number of previous relapses (odds ratio [OR] 1.789, 95% CI 1.041-3.074) and specifically ADEM/ADEM-like episodes (OR 16.929, 95% CI 1.228-233.427) were the only clinical factors associated with cognitive impairment in a multivariate logistic regression model. Screening for antineuronal antibodies remained negative. Cerebral white matter (WM) (0.300 vs 0.317, p = 0.003) and deep gray matter (DGM) (0.036 vs 0.038, p = 0.002) volumes were reduced in patients with MOGAD compared with healthy controls (n = 32). Both cognitive impairment (0.031 vs 0.036, p = 0.003) and history of ADEM/ADEM-like episodes (0.032 vs 0.036, p = 0.006) were associated with reduced DGM volume compared with unaffected patients with MOGAD. Despite a low overall disability, every 5th patient with MOGAD experiences cognitive impairment. Cognitive impairment is associated with a higher number of relapses and particularly ADEM/ADEM-like attacks. Although both WM and DGM atrophies are apparent in MOGAD, the latter only seems to have an association with cognitive impairment.

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.

To accurately capture an individual's food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community. This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Subscapularis Muscle Radiographic Integrity and Patient-Reported Outcomes Following Arthroscopic Anatomic Glenoid Reconstruction With Distal Tibial Allograft.

Shoulder stabilization surgery has evolved over time, and bony augmentation procedures on the glenoid side are being performed more often. The Latarjet procedure modifies subscapularis anatomy because the conjoined tendon divides the subscapularis muscle fibers through a split/takedown, which has structural and functional implications. Arthroscopic anatomic glenoid reconstruction (AAGR) re-creates anatomy. This technique uses the Halifax portal to deploy and fix a distal tibial allograft (DTA) through the rotator interval, thus preserving the subscapularis anatomy. The purpose was to analyze the radiographic properties of the subscapularis muscle after AAGR. It was hypothesized that the subscapularis muscle structure remains preserved postoperatively. Case series; Level of evidence, 4. A retrospective review was performed comprising a consecutive series of patients treated with AAGR with DTA between November 2012 and April 2021 for traumatic anterior shoulder instability with glenoid bone loss. Patients were excluded if they had posterior instability, glenoid fracture, missing pre- or postoperative computed tomography (CT) scans, or only CT arthrogram available. Radiographic variables measured on CT scans included estimates of subscapularis muscle volume, subscapularis/infraspinatus muscle ratio, and fatty infiltration according to the Goutallier classification. Pre- and postoperative Western Ontario Shoulder Instability index scores were collected as a secondary outcome of this study. Ninety-three patients were included in the study with a clinical follow-up of 2.3 ± 1.5 years (mean ± SD). The subscapularis volume increased from 185.91 ± 45.85 mL preoperatively to 194.1 ± 49.0 mL postoperatively (P = .006). The subscapularis to infraspinatus muscle ratio showed a significant increase from 0.96 ± 0.27 to 1.05 ± 0.30 after surgery (P = .002). All patients had a Goutallier stage of 0 before and after surgery. The Western Ontario Shoulder Instability scores showed a significant improvement from 64.8 ± 15.5 preoperatively to 28.2 ± 24.0 postoperatively (P < .001). Patients who undergo AAGR with DTA for traumatic shoulder instability with glenoid bone loss have a preserved subscapularis muscle volume with no fatty infiltration, while showing a significant improvement in clinical outcomes.

Using unmanned aerial vehicles to estimate body volume at scale for ecological monitoring

Abstract Demographic data are essential to construct mechanistic models to understand how populations change over time and in response to global threats like climate change. Existing demographic data are either lacking or insufficient for many species, particularly those that are challenging to obtain direct measurements from that can be used to estimate demographic rates, like marine mammals. A method for collecting accurate demographic data to construct robust demographic models at scale would fill this knowledge gap for difficult‐to‐access species. We introduce a novel, non‐invasive method to estimate the 3D body size (volume) of pinnipeds (seals, sea lions and walruses) that will allow monitoring at high spatial and temporal scales. Our method integrates 3D structure‐from‐motion photogrammetry data collected via planned flight missions using off‐the‐shelf, multirotor unmanned aerial vehicles (UAVs). We apply and validate this method on the grey seal Halichoerus grypus, a pinniped species that spends much of its time at sea but is predictably observable during its annual breeding season. We investigate the optimal ground sampling distance (GSD) for surveys by calculating the success rates and accuracy of volume estimates of individuals at different altitudes. Based on current technology, we establish an optimal GSD of at least 0.8 cm px−1 for animals similar in size to UK grey seals (~1.2–2.5 m length), making our method reproducible and applicable to other species. We found volume estimates were accurate and could be successfully estimated for up to 68% of hauled‐out seals in study areas. Our method accurately estimates individual body volume of pinnipeds in a time‐ and cost‐effective manner while minimising disturbance. While the approach is applied to pinnipeds here, the method could be adapted to further taxa that are otherwise challenging to obtain direct measurements from. Our proposed approach therefore has the potential to fill demographic research gaps, which will improve our ability to protect and conserve species into the future.

In-depth clinical characterization of intravenous iron infusion-induced hypophosphatemic osteomalacia and its resolution

Abstract Iron deficiency anemia is treated by iron supplementation. Increasing evidence has shown that the carbohydrate components in iron infusions can cause hypophosphatemia and subsequent osteomalacia due to excess iFGF23 (intact Fibroblast Growth Factor 23). We here undertook an in-depth characterization of 13 patients with iron infusion-induced osteomalacia (IIIO). Patients were characterized (monocentric institutional practice) by means of laboratory, bone density, HR-pQCT and virtual osteoid volume estimation. We additionally report a patient who was treated with burosumab because iron infusions had to be continued despite osteomalacia. All 13 patients received ferric carboxymaltose (FCM) infusions and presented with low phosphate levels in the laboratory. Stopping the FCM infusions and supportive treatment by substitution of phosphate, calcium, native and/or active vitamin D was the chosen therapeutic approach. Pain, mobility, biochemical data, such as serum phosphate levels, bone mineral density, bone microstructure and virtual osteoid volume were the main outcome measures. Our results indicate biochemical normalization (e. g. phosphate levels pre: 0.50 mmol/l ± 0.23 mmol/l, post: 0.93 mmol/l ± 0.32 mmol/l, P&amp;lt;.001) after stopping the FCM infusion and establishing supportive treatment. Additionally, pain levels on the visual analogue scale (VAS) decreased (VASpre 7.31 ± 1.22, VASpost 2.73 ± 1.27, P&amp;lt;.0001) and areal bone mineral density (expressed as T-score) improved significantly (T-scorepre: -1.85 ± 1.84, T-scorepost: -0.91 ± 2.13, P&amp;lt;.05). One patient requiring ongoing FCM infusions experienced significant additional benefits from burosumab treatment. In conclusion, our results highlight the importance of monitoring phosphate in patients treated with FCM infusions. Stopping FCM infusions is effective in addressing the excess of iFGF23 and thereby phosphate wasting. Supportive therapy enables quick recovery of the musculoskeletal system and improves pain levels in these patients.

Association Between Finger Plethysmographic Features and Impedance-Based Thoracic Fluid Content Measurement in a Lower Body Negative Pressure Model of Hemorrhagic Shock.

Timely identification of the need for lifesaving intervention in battlefield conditions may be improved through automated monitoring of the injured warfighter. Technologies that combine maximal noninvasive insight with minimal equipment footprint give the greatest opportunity for deployment at scale with inexperienced providers in forward areas. Finger photoplethysmography (PPG) signatures are associated with impending hemorrhagic shock but may be insufficient alone. Transthoracic impedance (TTI) monitoring is a complementary modality to PPG and able to identify volume loss and estimate functional cardiovascular parameters. We sought to understand how PPG features correlate with volume loss estimation from TTI during lower body negative pressure (LBNP) challenge. We hypothesized that features of the PPG waveform would correlate with thoracic fluid content (TFC) as measured by TTI. We obtained physiologic monitoring data from healthy adult subjects in LBNP hemorrhagic shock models after local Institutional Review Board and DoD Human Research Protection Office approval. Subjects were excluded for pregnancy, age >45 years, and conditions prohibitive of LBNP exposure. Subjects were instrumented with noninvasive sensors, including a finger PPG sensor and a TTI monitor. Subjects underwent a stepwise LBNP exposure program of -10 mmHg every 10 minutes and notified laboratory staff at first sign of near syncope, terminating the sequential program. TTI data were continuously streamed to a custom program written in MATLAB and time synchronized. To calculate PPG measures, we downsampled data to 250 Hz, screened, and parsed each beat. We featurized each beat to include a systolic, diastolic, and dicrotic notch peak, beat length and area under the curve (AUC), peak-to-peak systolic/diastolic interval, and leading/trailing slopes, all normalized to instantaneous heart rate. Thoracic fluid content was normalized to subjects' pre-LBNP baselines. We summarized all PPG features and the TFC using means (SD) generated as a subject average for each step. We used generalized estimating equation models to examine the relationship between TFC and PPG features while controlling for LBNP stage and subject. Thirty-two subjects were enrolled; 4 participants were excluded because of sensor malfunction. Twenty-eight subjects had a mean (SD) age of 25.11 (6.66) years. A total of 35.7% of subjects were female. Photoplethysmography analysis demonstrated a decreased systolic-diastolic peak interval, diastolic peak height, and beat AUC with decreased LBNP pressure. End-stage baseline normalized TFC showed an average decrease of 14.68% (±4.98%) (range: 7.54% to 27.69%). The strongest average correlations between stage TFC and PPG occurred in beat length (0.68) and normalized AUC (0.69). In generalized estimating equation models incorporating all stages, beat length, normalized AUC, and the systolic-diastolic interval were all significantly associated with time as a function of LBNP level (P < .001). Thoracic fluid content began decreasing at 12.8 (4.7) minutes, the normalized AUC decreased at 20.7 (7.2) minutes, the beat length decreased at 20.9 (7.0) minutes, and the systolic-diastolic time interval decreased at 30.6 (16.7) minutes. While both PPG features and impedance-based TFC trend congruently in the perishock state following LBNP exposure, peripheral pulse wave signals lag redistribution of thoracic fluid volume. Photoplethysmography features of beat length and normalized AUC may serve as a surrogate for TFC when direct thoracic sensing is not available.

The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.

Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review's purview. Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.

Stereo bathymetry to monitor small seasonal agriculture water ponds in ungauged areas

Abstract. Small reservoirs represent a critical water supply to farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Over 200,000 ancient rainwater harvesting reservoirs (“tanks”) exist across South India, but with their complex history, considerable size variation, and widespread distribution, understanding the hydrological role of these tanks has been difficult. Fortunately, the last decade has seen improvements in sensors and technologies that enhance our ability to assess the hydrological role of these tanks. In particular, high-resolution Digital Elevation Model (DEMs), now much easier to produce, can be used to improve the characterization of tanks and their surrounding watersheds. Here, a high-resolution DEM is created during the lowest reservoir conditions using Pléiades stereoscopy, and along with two global DEMs, compared with volume estimates from a field-derived UAV reference DEM for a set of tanks in South India. This study demonstrates that a Pléiades-derived DEM can capture accurate reservoir geometry and, when simulating water volume, achieves volumetric differences of 2–8% compared to our UAV reference data. The Pléiades-derived-DEM produced an equivalent height bias less than the expected bias for the recently launched Surface Water and Ocean Topography (SWOT) mission. Deriving high-resolution tank bathymetry from space during low-water conditions provides an opportunity to systematically and repeatably measure tank volume. These results are encouraging in efforts to utilize very high resolution DEMs chosen at an appropriate hydrological time, particularly in regions where water management and security is paramount.

Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis

The analysis of geometrical cell shape is fundamental to understand motility, development, and responses to external stimuli. The moment invariants framework quantifies cellular shape and size, although its applicability has not been explored for rod-shaped bacteria. In this work, we use moment invariants to evaluate the extent of cell shape change (projected area and volume) during plasmolysis, as Escherichia coli cells are subjected to hyperosmotic shock. The characteristic cell size descriptors width, length and area show systematic decrease as external salt (NaCl) conditions increase—except for high salt, where a small population of cells shows evidence of membrane rupture. We use these two-dimensional results to estimate cell volume during plasmolysis, finding a minimum volume that is not reduced further with increase in salt concentration. Next, we computed elongation and dispersion, metrics that quantify how cell shape is stretched out or differs from an ellipse, respectively. For dispersion, we observe the development of a long tail for the distribution at high salt. Moreover, the use of elongation-dispersion plots enables distinction of plasmolyzed and normal cells despite the presence of broad distributions. Altogether, a protocol is provided to evaluate bacterial shape, highlighting a set of metrics that help distinguish among bacterial populations.•Moment invariants enable quantitative description of bacterial morphology in two dimensions, and estimation of volume•We apply the moment invariants framework to describe changes in bacterial shape during plasmolysis•The proposed methodology shows suitability to distinguish among cellular populations.

Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson ARTEMIS trial (ClinicalTrials.gov, NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient (CCC), and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 [range, 29-78] years). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the CCCs were 0.95 (95% CI: 0.90-0.98) and 0.94 (95% CI: 0.87-0.97), respectively. CNN-predicted TTC and TTV had an AUC of 0.72 (95% CI: 0.34-0.94) and 0.72 (95% CI: 0.40-0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data. ©RSNA, 2024.

Exploration of Bituminous Materials Using 2D Electrical Resistivity Imaging, Kurdistan Region, NE Iraq

Solid bituminous materials are widely spread in the Iraqi Kurdistan Region, they are filled so many locations, such as fracture zones, fault planes, and bedding planes. These materials can be used in several industries like pavement road, roofing, bituminous paint and waterproof, etc. The study aims to find an economical quantity of these materials by using 2D resistivity imaging in three locations that are different in geology and tectonic settings. These locations are Alla Syaw, Mera De, and Nasalih villages. The data are collected using Syscal R1 plus resistivity meter, for the sake of following up horizontal and vertical extension of these materials Wenner-Schlumberger array is used with 72 electrodes and a spacing interval of 5 m. Three profiles were conducted for each location, which have lengths equal to 360 m. The data processing was carried out by using the RES2DINV, and the smoothness was performed using finite difference forward modeling. The results show that all the bituminous materials are detected in shallow depths and only in the sandstone layers of the Tanjero, Gercus, and Injana formations filling the fracture zones and fault planes. This is also attributed to the more brittle behavior of the sandstone layer than other layers of these formations which deforms easily by applying external stresses. In the Tanjero Formation bituminous body is detected at a depth of about 10 m, which has an extension of more than 50 m from profile-1 to profile-2 (this is a lateral extension). The average width and height of the body are about 8m and 20 m, so it forms an approximate total volume equal to 8000 m3. The study concludes that the preliminary estimation of the total volume of the detected bituminous bodies is about 20000 m3, which has great importance as raw material for different industries. The bituminous material has a high resistivity value in most of the profiles reaching more than 200 ohm.m.

Multi-level digital-twin models of pulmonary mechanics: correlation analysis of 3D CT lung volume and 2D Chest motion

Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation ofRsquared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities.

Comparison of Multiple Methods for Supraglacial Melt-Lake Volume Estimation in Western Greenland During the 2021 Summer Melt Season

Supraglacial melt-lakes form and evolve along the western edge of the Greenland Ice Sheet and have proven to play a significant role in ice sheet surface hydrology and mass balance. Prior methods to quantify melt-lake volume have relied upon Landsat-8 optical imagery, available at 30 m spatial resolution but with temporal resolution limited by satellite overpass times and cloud cover. We propose two novel methods to quantify the volume of meltwater stored in these lakes, including a high-resolution surface DEM (ArcticDEM) and an ablation model using daily averaged automated weather station data. We compare our methods to the depth-reflectance method for five supraglacial melt-lakes during the 2021 summer melt season. We find agreement between the depth-reflectance and DEM lake infilling methods, within +/−15% for most cases, but our ablation model underproduces by 0.5–2 orders of magnitude the volumetric melt needed to match our other methods, and with a significant lag in meltwater onset for routing into the lake basin. Further information regarding energy balance parameters, including insolation and liquid precipitation amounts, is needed for adequate ablation modelling. Despite the differences in melt-lake volume estimates, our approach in combining remote sensing and meteorological methods provides a framework for analysis of seasonal melt-lake evolution at significantly higher spatial and temporal scales, to understand the drivers of meltwater production and its influence on the spatial distribution and extent of meltwater volume stored on the ice sheet surface.

Improving airborne laser scanning-based species-specific forest volume estimation using sentinel-2 time series

ABSTRACT Species-specific timber volume estimates are required to support forest planning and conservation. We evaluated whether additional predictors from a Sentinel-2 time series can improve airborne laser scanning (ALS)-based estimation of species-specific timber volumes. Furthermore, we determined the satellite image dates that provided the greatest improvement in accuracy. The Sentinel-2 time series was constructed to cover 1 March–30 November time-period, with a focus on late spring and early summer. The estimation was done using the k Most Similar Neighbor method and predictors extracted from the ALS data and Sentinel-2 images. Our best model included both ALS and Sentinel-2 time-series predictors, and the relative root mean square error (RMSE) values for pine, spruce and deciduous timber volumes were 40.8%, 57.0% and 51.3%, respectively (mean 49.7%). All deciduous trees were treated as one species. When bands from an individual image were used instead of the time series, the best result was obtained with an image from September where the respective relative RMSE values were 42.2% (deciduous), 58.4% (pine) and 60.6% (spruce) with a mean value of 53.7%. A fusion of a Sentinel-2 time-series and ALS data can improve species-specific estimation results compared to the use of individual Sentinel-2 images or ALS only.

Echocardiographic estimates of stroke volume in healthy dogs: Comparability, reference intervals, and reproducibility

Introduction/Objectives: To compare estimates of stroke volume (SV) from different anatomic sites and to generate reference intervals for indices such as shunt volume (ShuntVol) or regurgitant volume (RegVol) in a large sample of healthy dogs. Animals, Materials and MethodsNinety healthy dogs underwent an echocardiogram where SV was assessed at the level of the pulmonary valve (SVPV), aortic valve (SVAV), mitral valve (SVMV), and left ventricle using the difference in end-diastolic volume and end-systolic volume from a right parasternal long-axis four-chamber view (SVLV_RPLx) and left apical four-chamber view (SVLV_Ap4Ch). Eight dogs underwent repeated echocardiograms by the same operator on three different days and by three different operators on the same day. Bland-Altman plots and 95% reference intervals were generated. Reproducibility was described using coefficients of variation (CV) and reproducibility coefficients. ResultsMean difference (95% limits of agreement) for ShuntVol (SVPV-SVAV), RegVolLV_RPLx (SVLV_RPLx-SVAV), RegVolLV_Ap4Ch (SVLV_Ap4Ch-SVAV), and RegVolMV (SVMV-SVAV) were: -0.14 (-0.72, 0.44), -0.05 (-0.59, 0.48), -0.16 (-0.71, 0.39), and 0.12 (-0.76, 1.00) mL/kg, respectively. All but RegVolLV_RPLx showed significant (P<0.01) fixed bias. Reference intervals for ShuntVol, RegVolLV_RPLx, RegVolLV_Ap4Ch, and RegVolMV were: -0.85-0.64, -0.65-0.58, -0.77-0.52, and -0.91-1.06 mL/kg, respectively. Intra-operator and inter-operator CVs were lowest for SVAV, and highest for SVMV and SVLV_AP4Ch, respectively. ConclusionsEchocardiographic estimates of SV are not interchangeable and can exhibit wide limits of agreement. Reference intervals help provide a frame of reference to assess disease severity in dogs with a shunting lesion (ShuntVol) and mitral regurgitation (RegVol).

Design and dispensing volume estimation of a single piezo-driven dispenser with a rigid–flexible combined transmission mechanism

Design and dispensing volume estimation of a single piezo-driven dispenser with a rigid–flexible combined transmission mechanism

Agricultural spray drone deposition, Part 1: Methods for high throughput spray pattern analysis

Abstract Effective pesticide application is dependent on precise and sufficient delivery of active ingredients to targeted pests. Water-sensitive papers (WSP) have been used to estimate the stain coverage, droplet density, droplet size, total spray volume, and other spray quality metrics by analyzing deposit stains using image analysis software. However, due to their expensive cost, WSPs are typically distributed along unidimensional transects at intervals of 0.5 m or more, which comprises 0.5% or less of the total treated area. This might limit the ability to accurately represent the deposition of agricultural sprayers with irregular patterns, such as agricultural drone sprayers in their early development stage. This study introduces a novel approach utilizing white Kraft paper and a blue colorant proxy for assessing spray deposition. A custom Python-based image analysis tool, SprayDAT (Spray Droplet Analysis Tool), was developed and compared to traditional image analysis software, DepositScan. Both models showed increased accuracy in detecting larger objects, with SprayDAT generally performing better for smaller droplets. DepositScan underestimated the total deposited spray volume by up to 2.7 times less compared to the colorant extraction assessed via spectrophotometry and the predicted output based on flow rate, coverage, and speed. Accuracy of software-estimated spray volume declined with increasing total stain coverage, likely due to overlapping stain objects. Droplet density exhibited a Gaussian trend with peak density at approximately 22% stain cover, offering evidence for overlapped stains for both DepositScan and SprayDAT as stain cover increased. Both models showed exponential growth in volumetric median diameter (VMD) with increasing stain cover. SprayDAT is freely accessible through an online repository. It features a user-friendly interface for batch-processing large sets of scanned images and offers versatility for customization to meet individual needs, such as adjusting spread factor, updating the standard curve for spray volume estimation, or modifying the stain detection threshold.

Point of care ultrasound measurement of paralumbar caudal vena cava diameter and caudal vena cava to aortic ratio in hypovolemic dogs.

Accurate assessment of intravascular volume is critical for precise fluid prescription. In people, bedside or point of care ultrasound is used to measure the inferior vena cava, with or without paired aortic measurement, to estimate intravascular volume. To determine if point of care ultrasound measurement of the caudal vena cava (CVC) diameter or the CVC diameter to the abdominal aorta (Ao) diameter (CVC:Ao) at the paralumbar view are associated with changes in intravascular volume, mean arterial pressure (MAP), or cardiac output in normovolemic and hypovolemic dogs. 8 purpose-bred dogs. Pressure-targeted hemorrhagic shock was induced in purpose-bred dogs under general anesthesia. Dogs were exsanguinated to a mean arterial pressure of 40 mmHg, or a maximum 60% blood volume lost, then auto-transfused shed blood. At a left paralumbar view, longitudinal plane measurements of the abdominal CVC diameter and aortic diameter were obtained. Measurements were performed at 4 timepoints: baseline under anesthesia (TP1), after hemorrhagic shock was induced (TP2), after ½ of shed blood had been re-transfused (TP3), and post-resuscitation with completed re-transfusion (TP4). Additional variables collected included cardiac output using thermodilution and arterial blood pressure. CVC:Ao was not significantly different between timepoints and was not associated with changes in CO (p = 0.28) or MAP (p = 0.50). CVC diameter was significantly different between baseline (TP1) and hemorrhagic shock (TP2). CVC diameter was significantly different at TP2 compared to TP1 after controlling for the effect of CO (p = 0.03) and MAP (p = 0.001). Aortic diameter was also significantly different at TP2 (p = 0.002, p = 0.001) and TP3 (p = 0.023, p = 0.017) compared to TP1 after controlling for CO and MAP. Obtaining point of care ultrasound images for CVC:Ao measurement was feasible. With a marked decrease in intravascular volume, both CVC and Ao diameter decreased, resulting in an unchanged CVC:Ao. Despite changes in CVC and Ao diameters, these changes were not associated with measured changes in CO, emphasizing that CO is not a direct estimate of intravascular volume and is affected by many compensatory mechanisms. Additional studies are needed to determine the most accurate method for bedside measurement of intravascular volume status.