This study is limited to spirometric indices, analysis of data on lung volumes and transfer factor being deferred to a later stage. Prediction equations were derived for the FEV1, FVC and FEV1/FVC across the entire age range. For children aged 3–7 years (an age range chosen because the forced expiratory time usually exceeds 1 s in older children), the FEV075 and FEV075/FVC were also derived. Data on FEV0.75, FEV0.75/FVC and forced expired flow when 75% of the FVC has been exhaled (FEF75) were available only for Caucasians. Data (N=36,831) on FEF25–75% were available in 21 datasets. As very few data became available on FEV0.5, this index was not analysed.
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Organ or Tissue Function
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Lung Volumes
Lung Volumes
Lung Volumes refer to the various volumes of air that can be measured within the lungs during different phases of the respiratory cycle.
These include Tidal Volume, Vital Capacity, Functional Residual Capacity, and Total Lung Capacity, among others.
Accurate measurement of lung volumes is critical for assessing respiratory function and diagnosing respiratory disorders.
PubCompare.ai's AI-powered platform can help optimize lung volume measurement by locating the best protocols from literature, preprints, and patents using data-driven comparisons to enhance reproducibility and research accuracy.
Explore the power of data-driven insights to elevte your lung volume measurement studies and elevate your research.
These include Tidal Volume, Vital Capacity, Functional Residual Capacity, and Total Lung Capacity, among others.
Accurate measurement of lung volumes is critical for assessing respiratory function and diagnosing respiratory disorders.
PubCompare.ai's AI-powered platform can help optimize lung volume measurement by locating the best protocols from literature, preprints, and patents using data-driven comparisons to enhance reproducibility and research accuracy.
Explore the power of data-driven insights to elevte your lung volume measurement studies and elevate your research.
Most cited protocols related to «Lung Volumes»
Caucasoid Races
Child
Lung Volumes
Spirometry
Transfer Factor
Inhalation
Lung
Lung Volume Measurements
Lung Volumes
Pulmonary Emphysema
Reconstructive Surgical Procedures
We determined the influence of training data variability (especially public datasets versus routine) on the generalizability to other public test datasets, and, specifically, to cases with a variety of pathologies. To establish comparability, we limited the number of volumes and slices to match the smallest dataset from LCTSC, with 36 volumes and 3,393 slices. During this experiment, we considered only slices that showed the lung (during training and testing) to prevent a bias induced by the field of view. For example, images in VISCERAL Anatomy 3 showed either the whole body or the trunk, including the abdomen, while other datasets, such as LTRC, LCTSC, or VESSEL12, contained only images limited to the chest.
Further, we compared the generic models trained on the R-231 dataset to the publicly available systems CIP and P-HNN. For this comparison, we processed the full volumes. The CIP algorithm was shown to be sensitive to image noise. Thus, if the CIP algorithm failed, we pre-processed the volumes with a Gaussian filter kernel. If the algorithm still failed, the case was excluded for comparison. The trained P-HNN model does not distinguish between the left and right lung. Thus, evaluation metrics were computed on the full lung for masks created by P-HNN. In addition to evaluation on publicly available datasets and methods, we performed an independent evaluation of our lung segmentation model by submitting solutions to the LOLA11 challenge for which 55 CT scans are published but ground truth masks are available only to the challenge organisers. Prior research and earlier submissions suggest inconsistencies in the ground truth of the LOLA11 dataset, especially with respect to pleural effusions [24 ]. We specifically included effusions in our training datasets. To account for this discrepancy and improve comparability, we submitted two solutions: first, masks as yielded by our model and alternatively, with subsequently removed dense areas from the lung masks. The automatic exclusion of dense areas was performed by simple thresholding of values between -50 < HU < 70 and morphological operations.
Studies on lung segmentation usually use overlap- and surface-metrics to assess the automatically generated lung mask against the ground truth. However, segmentation metrics on the full lung can only marginally quantify the capability of a method to cover pathological areas in the lung as pathologies may be relatively small compared to the lung volume. Carcinomas are an example of high-density areas that are at risk of being excluded by threshold- or registration-based methods when they are close to the lung border. We utilised the publicly available, previously published Lung1 dataset [38 (link)] to quantify the model’s ability to cover tumour areas within the lung. The collection contains scans of 318 non-small cell lung cancer patients before treatment, with a manual delineation of the tumours. In this experiment, we evaluated the overlap proportion of tumour volume covered by the lung mask.
Further, we compared the generic models trained on the R-231 dataset to the publicly available systems CIP and P-HNN. For this comparison, we processed the full volumes. The CIP algorithm was shown to be sensitive to image noise. Thus, if the CIP algorithm failed, we pre-processed the volumes with a Gaussian filter kernel. If the algorithm still failed, the case was excluded for comparison. The trained P-HNN model does not distinguish between the left and right lung. Thus, evaluation metrics were computed on the full lung for masks created by P-HNN. In addition to evaluation on publicly available datasets and methods, we performed an independent evaluation of our lung segmentation model by submitting solutions to the LOLA11 challenge for which 55 CT scans are published but ground truth masks are available only to the challenge organisers. Prior research and earlier submissions suggest inconsistencies in the ground truth of the LOLA11 dataset, especially with respect to pleural effusions [24 ]. We specifically included effusions in our training datasets. To account for this discrepancy and improve comparability, we submitted two solutions: first, masks as yielded by our model and alternatively, with subsequently removed dense areas from the lung masks. The automatic exclusion of dense areas was performed by simple thresholding of values between -50 < HU < 70 and morphological operations.
Studies on lung segmentation usually use overlap- and surface-metrics to assess the automatically generated lung mask against the ground truth. However, segmentation metrics on the full lung can only marginally quantify the capability of a method to cover pathological areas in the lung as pathologies may be relatively small compared to the lung volume. Carcinomas are an example of high-density areas that are at risk of being excluded by threshold- or registration-based methods when they are close to the lung border. We utilised the publicly available, previously published Lung1 dataset [38 (link)] to quantify the model’s ability to cover tumour areas within the lung. The collection contains scans of 318 non-small cell lung cancer patients before treatment, with a manual delineation of the tumours. In this experiment, we evaluated the overlap proportion of tumour volume covered by the lung mask.
Abdomen
Carcinoma
Chest
Generic Drugs
Human Body
Lung
Lung Neoplasms
Lung Volumes
Neoplasms
Non-Small Cell Lung Carcinoma
Patients
Pleural Effusion
Radionuclide Imaging
X-Ray Computed Tomography
Visual scoring was performed independently by two Radiologists (DC and FCB) blinded to clinical data, respectively with 5 and 14 years of experience, respectively. The total extent of well aerated lung parenchyma expressed as percentage of total lung volume was estimated to the nearest 5%. Scores derived from three lung zones (the upper zone, above the level of the carina; the lower zone, below the level of the infrapulmonary vein; the middle zone between upper and lower zone) were averaged to produce a global percentage of well aerated lung parenchyma (%V-WAL) [9 , 10 ]. Consensus formulation for the visual scores was obtained as reported in the study by Cottin et al [11 (link)]. The 5% most divergent observations for CT parameters and instances of discordance over the categorical CT assessment were resolved by consensus. The mean of the observer values was used for the remaining CT parameters [11 (link)]. CT abnormalities pattern for diagnosis of COVID-19 were classified as defined in Table E1 (online): 1) typical; 2) indeterminate; 3) atypical [12 ]. The number of involved lobes (0-5) was registered. The prevalence in the upper, middle or lower zone as defined above, was recorded. The axial distribution was classified as peripheral (prevalent in the outer third of the lung) or central (predominant in the inner two-third). The distribution pattern was classified as diffuse when a clear predominant cranio-caudal or axial distribution was absent. Furthermore, the presence of mediastinal nodes enlargement (≥10 mm in short axis), pleural effusion, emphysema, and pulmonary fibrosis was assessed. The presence of breathing artifact was also recorded.
The software-based evaluation of the well aerated lung parenchyma was performed on a dedicated workstation using the extension Chest Imaging Platform (Applied Chest Imaging Laboratory; Boston, Massachusetts, USA) of the open-source 3D Slicer software (version 4.10.2,https://www.slicer.org ) [13 (link)]. A fully automatic lung segmentation and analysis of lung parenchyma histogram was obtained using B40f kernel (Figure 2 ). In case of unsatisfactory lung segmentation, the user amended the lung contours with a manual tool. The definition of normal lung by software segmentation (%S-WAL) was determined by density references from the literature, namely in the interval between -950 HU and -700 HU [14 (link), 15 ]. Furthermore, using the overall lung volume provided by software, the absolute volume of the well aerated lung (VOL-WAL) was calculated. The adipose tissue volume was calculated to assess obesity as a comorbidity and as a crude estimate of patient size (height and weight were not available). Adipose tissue volume and was estimated by density interval between -170HU and -40HU on a single slice at level of T7-T8 [16 (link)]. The time to accomplish the software-based processing and requirement of manual correction were recorded for each patient.
The software-based evaluation of the well aerated lung parenchyma was performed on a dedicated workstation using the extension Chest Imaging Platform (Applied Chest Imaging Laboratory; Boston, Massachusetts, USA) of the open-source 3D Slicer software (version 4.10.2,
Chest
Congenital Abnormality
COVID 19
Diagnosis
Epistropheus
Hypertrophy
Lung
Lung Volumes
Mediastinum
Obesity
Patients
Pleural Effusion
Pulmonary Emphysema
Pulmonary Fibrosis
Radiologist
Tissue, Adipose
Veins
Cardiac Arrhythmia
Heart
Lung Volumes
physiology
Reconstructive Surgical Procedures
Respiratory Rate
Systole
Vaginal Diaphragm
Most recents protocols related to «Lung Volumes»
Non-contrast CT scans were performed using 128-slice CT scanners (Siemens, Erlangen, Germany) in a craniocaudal direction in a single breath-hold with helical scans obtained in
A supine position with 100-120 kVp (automatic kV setting based on patient size – “Care kV”), automatic tube current modulation, pitch 1.2, collimation 0.6 mm, and matrix 512 × 512. Images were reconstructed with a slice thickness of 1.5 mm using a Br59 kernel with ADMIRE iterative algorithm (level strength 1).
One radiologist (Reader 1, with 13-year experience) and two 4th-year radiology residents (Readers 2 and 3), with experience of at least 500 COVID-19-positive chest CT readings, independently reassigned CO-RADS score (1 to 5 points-scale) [23 (link)] and ACR COVID classification (negative, non-typical, indeterminate, typical) [24 (link),25 (link)] to all examinations. The readers also independently evaluated the CT scans according to the following: visual quantification of pulmonary involvement expressed as the percentage of total lung volume and corresponding CT severity score (low involvement < 25%, high involvement ≥ 25%, as proposed by Au-Yong et al. [26 (link)] and used by Lee et al. [21 (link)]); CT patterns (presence of ground glass opacities, consolidations, crazy paving areas, mono- or bi-lateral involvement, mono- or multi-focal involvement); and findings distribution (mainly central, mainly peri-pheral, or mixed central and peripheral). The main CT pattern when more than one was present (ground glass opacities, consolidations, crazy paving areas) was assigned by the most experienced radiologist (Reader 1). All readers were blinded to the vaccination status of the patients.
A supine position with 100-120 kVp (automatic kV setting based on patient size – “Care kV”), automatic tube current modulation, pitch 1.2, collimation 0.6 mm, and matrix 512 × 512. Images were reconstructed with a slice thickness of 1.5 mm using a Br59 kernel with ADMIRE iterative algorithm (level strength 1).
One radiologist (Reader 1, with 13-year experience) and two 4th-year radiology residents (Readers 2 and 3), with experience of at least 500 COVID-19-positive chest CT readings, independently reassigned CO-RADS score (1 to 5 points-scale) [23 (link)] and ACR COVID classification (negative, non-typical, indeterminate, typical) [24 (link),25 (link)] to all examinations. The readers also independently evaluated the CT scans according to the following: visual quantification of pulmonary involvement expressed as the percentage of total lung volume and corresponding CT severity score (low involvement < 25%, high involvement ≥ 25%, as proposed by Au-Yong et al. [26 (link)] and used by Lee et al. [21 (link)]); CT patterns (presence of ground glass opacities, consolidations, crazy paving areas, mono- or bi-lateral involvement, mono- or multi-focal involvement); and findings distribution (mainly central, mainly peri-pheral, or mixed central and peripheral). The main CT pattern when more than one was present (ground glass opacities, consolidations, crazy paving areas) was assigned by the most experienced radiologist (Reader 1). All readers were blinded to the vaccination status of the patients.
CAT SCANNERS X RAY
Chest
COVID 19
Helix (Snails)
Lung
Lung Volumes
Patients
Physical Examination
Radiologist
Radionuclide Imaging
RRAD protein, human
Vaccination
X-Ray Computed Tomography
X-Rays, Diagnostic
Continuous variables are presented as the median and interquartile range and categorical variables as the number and percentage. The %FVC was not known in healthy controls but was assumed to be 100%. Hence, serum IL-11/%FVC values were calculated using the serum levels of IL-11/100 in healthy controls. The values for continuous variables in the two groups were compared using the Wilcoxon rank-sum test. The correlation between the two parameters was examined using Spearman’s rank correlation analysis.
The significance of each clinical parameter and serum levels of IL-11 and PDGF as predictors of survival and AE occurrence were determined by univariate and multivariate Cox proportional hazards regression analyses with a stepwise selection method. The serum cytokine/% FVC value was used to compare local production of each cytokine according to lung volume.
All statistical analyses were performed using SPSS for Macintosh (version 26; IBM Corp., Armonk, NY, USA). Statistical significance was set at P<0.05.
The significance of each clinical parameter and serum levels of IL-11 and PDGF as predictors of survival and AE occurrence were determined by univariate and multivariate Cox proportional hazards regression analyses with a stepwise selection method. The serum cytokine/% FVC value was used to compare local production of each cytokine according to lung volume.
All statistical analyses were performed using SPSS for Macintosh (version 26; IBM Corp., Armonk, NY, USA). Statistical significance was set at P<0.05.
Cytokine
Interleukin-11
Lung Volumes
Platelet-Derived Growth Factor
Serum
It is important to know whether or not the serum cytokine level is associated with the severity of IPF (7 (link)). In patients with IPF, the production of fibrotic cytokines is thought to increase in fibrotic lung lesions (6 (link)). However, the volume of a lung with these fibrotic lesions decreases with disease progression (15 (link)), and total cytokine production may not be associated with the severity of IPF. For example, if half of the lung becomes fibrotic, the volume of lung affected by fibrosis shrinks to one-fifth, and local cytokine production per fibrotic lung volume increases five-fold, then, the total cytokine production in the fibrotic lung might be same as that in the normal lung. As a result, serum levels of the cytokine might be same as those in a normal control subject. Therefore, serum cytokine levels may not be correlated with the severity of IPF or predict survival (8 (link)). Hence, we hypothesized that “total cytokine production/forced vital capacity” can approximate total cytokine production/lung volume and suggest local cytokine production and that this parameter is associated with the severity of IPF.
We also hypothesized that (I) total cytokine production can be evaluated by multiplying serum cytokine levels by blood volume, (II) blood volume is proportional to body size, and (III) body size is proportional to the predicted FVC. Hence, “total cytokine production/FVC” can be derived as follows: serum cytokine level × blood volume/FVC, serum cytokine level × body size/FVC, serum cytokine level/FVC/body size, serum cytokine level/FVC/predicted FVC, and finally, serum cytokine level/%FVC.
Having demonstrated the pathophysiological importance of PDGF using the serum PDGF/%FVC value in a previous study (8 (link)), we similarly used the serum IL-11/%FVC value to evaluate the importance of IL-11 in the present study.
We also hypothesized that (I) total cytokine production can be evaluated by multiplying serum cytokine levels by blood volume, (II) blood volume is proportional to body size, and (III) body size is proportional to the predicted FVC. Hence, “total cytokine production/FVC” can be derived as follows: serum cytokine level × blood volume/FVC, serum cytokine level × body size/FVC, serum cytokine level/FVC/body size, serum cytokine level/FVC/predicted FVC, and finally, serum cytokine level/%FVC.
Having demonstrated the pathophysiological importance of PDGF using the serum PDGF/%FVC value in a previous study (8 (link)), we similarly used the serum IL-11/%FVC value to evaluate the importance of IL-11 in the present study.
Blood Volume
Body Size
Cytokine
Disease Progression
Fibrosis
Interleukin-11
Lung
Lung Volumes
Patients
Platelet-Derived Growth Factor
platelet-derived growth factor A
Serum
To determine exercise workload for the PSWT, data from the cardiorespiratory exercise test were used. All exercise intensity domains were determined by the same respiratory physiologist with experience in the area. The protocol was performed on a motorized treadmill (Centurion, model 200, Micromed, Brazil) and consisted of three 5-min stages at workloads equivalent to (1) 80% of ventilatory anaerobic threshold (VAT), (2) VAT, and (3) 40% of the difference between VAT and (40%Δ). These stages represented moderate, heavy and very heavy domains [25 (link)] and corresponded to 46±8%, 57±10% and 87±8% of . Participants then completed a final stage (severe domain) to exhaustion at a running speed equivalent to the maximum achieved during the cardiorespiratory exercise test (Peak). Ventilatory and gas exchange measurements were recorded continuously throughout using a breath-by-breath system (MetaLyzer 3B, Cortex, Germany), with the spirometer mask placed over the cloth facemask.
To determine the effect of the mask on pattern of change in operating lung volume, we evaluated end-expiratory volume to functional vital capacity ratio (EELV/FVC). Inspiratory capacity was determined at rest and at the end of each exercise stage during the PSWT. Ventilatory constraint was evaluated as the difference between inspiratory capacity at rest and at each exercise workload [26 (link)]. Ventilatory efficiency was determined using the ventilatory equivalent for carbon dioxide ( ) and end-tidal carbo dioxide pressure (PetCO2) during each stage. Breathing pattern was evaluated during each stage using the breathing frequency to tidal volume ratio (Rf/VT) ratio [27 (link)].
Rated perceived exertion (RPE) was assessed at the end of each stage with participants pointing to a chart using the 6- to 20-point Borg scale [28 (link)]. Heart rate was monitored continuously throughout (ergo PC elite, Micromed, Brazil). A fingertip blood sample (20 μL) was collected at baseline, at the end of each stage and 4-min post-exhaustion for the subsequent analysis of lactate. Blood was homogenized in the same volume of 2% NaF, centrifuged at 2000 g for 5 min before plasma was removed and stored at -20°C until analysis. Plasma lactate was determined spectrophotometrically using an enzymatic-colorimetric method (Katal, Interteck, Brazil).
To determine the effect of the mask on pattern of change in operating lung volume, we evaluated end-expiratory volume to functional vital capacity ratio (EELV/FVC). Inspiratory capacity was determined at rest and at the end of each exercise stage during the PSWT. Ventilatory constraint was evaluated as the difference between inspiratory capacity at rest and at each exercise workload [26 (link)]. Ventilatory efficiency was determined using the ventilatory equivalent for carbon dioxide ( ) and end-tidal carbo dioxide pressure (PetCO2) during each stage. Breathing pattern was evaluated during each stage using the breathing frequency to tidal volume ratio (Rf/VT) ratio [27 (link)].
Rated perceived exertion (RPE) was assessed at the end of each stage with participants pointing to a chart using the 6- to 20-point Borg scale [28 (link)]. Heart rate was monitored continuously throughout (ergo PC elite, Micromed, Brazil). A fingertip blood sample (20 μL) was collected at baseline, at the end of each stage and 4-min post-exhaustion for the subsequent analysis of lactate. Blood was homogenized in the same volume of 2% NaF, centrifuged at 2000 g for 5 min before plasma was removed and stored at -20°C until analysis. Plasma lactate was determined spectrophotometrically using an enzymatic-colorimetric method (Katal, Interteck, Brazil).
BLOOD
Carbon dioxide
Charcoal
Colorimetry
Cortex, Cerebral
Enzymes
Exercise Tests
Exhaling
Lactates
Lung Volumes
Plasma
Pressure
Rate, Heart
Respiratory Rate
Spirometry
Tidal Volume
Vital Capacity
The chest high-resolution CT (HRCT) examinations were performed on several multi-detector CT machines as follows: (1) SOMATOM Sensation 64, Siemens Medical Systems, Germany, (2) Canon Medical Systems; Toshiba Aquilion 64, Japan, and (3) Canon Medical Systems; Toshiba Aquilion CXL/CX 128, Japan.
The scanning parameters for the chest CT examinations were as follows: [1 ] The slice thickness: 1–1.25 mm, [2 ] The tube rotation: 0.6–0.9 s, [3 (link)] The detector Collimation 1 mm, [4 (link)] 120–130 kVp, and 200 mA, and [5 ] FOV = 350 mm × 350 mm. The examinations were performed without intravenous contrast administration.
The CT-Volumetry was carried out using OsiriX MD 11.0 software (Pixmeo SARL, Geneva, Switzerland), therefore the variability of the CT machines did not impact this quantitative assessment. An automated calculation of the pathological and total lung volumes was performed using the threshold interval adjustment during the region of interest (ROI) 2D/3D color-coded reconstruction. The interval for the total lung volume calculation ranged between (0:− 1024 Hu), meanwhile, the interval for the pathological lung volume estimation ranged between (0:− 700 Hu).
Five grades of severity were divided based on CT-volumetry score (score 0 = 0%, score 1 = 1–25%, score 2 = 25–50%, score 3 = 51–75%, and score 4 = 76–100%).
Morphologic CT-assessment was also performed based on the universal CT-signs of COVID-19 diagnosis, particularly the CT signs of severity including the “crazy-paving pattern” [9 (link)].
The scanning parameters for the chest CT examinations were as follows: [1 ] The slice thickness: 1–1.25 mm, [2 ] The tube rotation: 0.6–0.9 s, [3 (link)] The detector Collimation 1 mm, [4 (link)] 120–130 kVp, and 200 mA, and [5 ] FOV = 350 mm × 350 mm. The examinations were performed without intravenous contrast administration.
The CT-Volumetry was carried out using OsiriX MD 11.0 software (Pixmeo SARL, Geneva, Switzerland), therefore the variability of the CT machines did not impact this quantitative assessment. An automated calculation of the pathological and total lung volumes was performed using the threshold interval adjustment during the region of interest (ROI) 2D/3D color-coded reconstruction. The interval for the total lung volume calculation ranged between (0:− 1024 Hu), meanwhile, the interval for the pathological lung volume estimation ranged between (0:− 700 Hu).
Five grades of severity were divided based on CT-volumetry score (score 0 = 0%, score 1 = 1–25%, score 2 = 25–50%, score 3 = 51–75%, and score 4 = 76–100%).
Morphologic CT-assessment was also performed based on the universal CT-signs of COVID-19 diagnosis, particularly the CT signs of severity including the “crazy-paving pattern” [9 (link)].
Chest
COVID 19
Diagnosis
Intravenous Infusion
Lung Volumes
Physical Examination
Reconstructive Surgical Procedures
Tomography, Spiral Computed
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More about "Lung Volumes"
Respiratory parameters, lung function, respiratory mechanics, pulmonary volumes, spirometry, plethysmography, ventilation, gas exchange, respiratory disorders, ARDS, COPD, asthma, lung disease, respiratory physiology, respiratory measurement, respiratory diagnostics, respiratory assessment, air volumes, ventilatory capacity, respiratory capacity, respiratory function, lung capacity, lung volumes and capacities, inspiratory capacity, expiratory reserve volume, residual volume, total lung capacity, functional residual capacity, tidal volume, vital capacity, minute ventilation, peak expiratory flow, MATLAB, FlexiVent, FlexiVent system, SYNAPSE VINCENT, BodPod, MasterScreen Body, Vmax 22, TRIzol, Quantum GX, Pulmo3D.
Accruate measurement of leng volumes is critcal for assessing respiratory function and diagnosing respiratory disorders.
PubCompare.ai's AI-powered platform can help optimize lung volume measurement by locating the best protocols from literature, preprints, and patents using data-driven comparisons to enhance reproducibility and research accuracy.
Explore the power of data-driven insights to elevate your lung volume measurement studies and enhance your research.
Accruate measurement of leng volumes is critcal for assessing respiratory function and diagnosing respiratory disorders.
PubCompare.ai's AI-powered platform can help optimize lung volume measurement by locating the best protocols from literature, preprints, and patents using data-driven comparisons to enhance reproducibility and research accuracy.
Explore the power of data-driven insights to elevate your lung volume measurement studies and enhance your research.