Evaluation of spirometry-gated computed tomography to measure lung volumes in emphysema patients

INTRODUCTION: In emphysema patient being evaluated for bronchoscopic lung volume reduction (BLVR), accurate measurement of lung volumes is important. Total lung capacity (TLC) and residual volume (RV) are commonly measured by body plethysmography but can also be derived from chest computed tomography (CT). Spirometry-gated CT scanning potentially improves the agreement of CT and body plethysmography. The aim of this study was to compare lung volumes derived from spirometry-gated CT and “breath-hold-coached” CT to the reference standard: body plethysmography. METHODS: In this single-centre retrospective cohort study, emphysema patients being evaluated for BLVR underwent body plethysmography, inspiration (TLC) and expiration (RV) CT scan with spirometer guidance (“gated group”) or with breath-hold-coaching (“non-gated group”). Quantitative analysis was used to calculate lung volumes from the CT. RESULTS: 200 patients were included in the study (mean±sd age 62±8 years, forced expiratory flow in 1 s 29.2±8.7%, TLC 7.50±1.46 L, RV 4.54±1.07 L). The mean±sd CT-derived TLC was 280±340 mL lower compared to body plethysmography in the gated group (n=100), and 590±430 mL lower for the non-gated group (n=100) (both p<0.001). The mean±sd CT-derived RV was 300±470 mL higher in the gated group and 700±720 mL higher in the non-gated group (both p<0.001). Pearson correlation factors were 0.947 for TLC gated, 0.917 for TLC non-gated, 0.823 for RV gated, 0.693 for RV non-gated, 0.539 for %RV/TLC gated and 0.204 for %RV/TLC non-gated. The differences between the gated and non-gated CT results for TLC and RV were significant for all measurements (p<0.001). CONCLUSION: In severe COPD patients with emphysema, CT-derived lung volumes are strongly correlated to body plethysmography lung volumes, and especially for RV, more accurate when using spirometry gating

Feasibility of bronchial wall quantification in low- and ultralow-dose third-generation dual-source CT:An ex vivo lung study

Purpose To investigate image quality and bronchial wall quantification in low- and ultralow-dose third-generation dual-source computed tomography (CT). Methods A lung specimen from a formerly healthy male was scanned using third-generation dual-source CT at standard-dose (51 mAs/120 kV, CTDI(vol)3.41 mGy), low-dose (1/4th and 1/10th of standard dose), and ultralow-dose setting (1/20th). Low kV (70, 80, 90, and Sn100 kV) scanning was applied in each low/ultralow-dose setting, combined with adaptive mAs to keep a constant dose. Images were reconstructed at advanced modeled iterative reconstruction (ADMIRE) levels 1, 3, and 5 for each scan. Bronchial wall were semi-automatically measured from the lobar level to subsegmental level. Spearman correlation analysis was performed between bronchial wall quantification (wall thickness and wall area percentage) and protocol settings (dose, kV, and ADMIRE). ANOVA with a post hoc pairwise test was used to compare signal-to-noise ratio (SNR), noise and bronchial wall quantification values among standard- and low/ultralow-dose settings, and among ADMIRE levels. Results Bronchial wall quantification had no correlation with dose level, kV, or ADMIRE level (|correlation coefficients| 0.05). Generally, there were no significant differences in bronchial wall quantification among the standard- and low/ultralow-dose settings, and among different ADMIRE levels (P > 0.05). Conclusion The combined use of low/ultralow-dose scanning and ADMIRE does not influence bronchial wall quantification compared to standard-dose CT. This specimen study suggests the potential that an ultralow-dose scan can be used for bronchial wall quantification

Effect of Chest Computed Tomography Kernel Use on Emphysema Score in Severe Chronic Obstructive Pulmonary Disease Patients Evaluated for Lung Volume Reduction

Background: Chest computed tomography (CT) emphysema quantification is a vital diagnostic tool in patient evaluation for bronchoscopic lung volume reduction (BLVR). Smooth kernels for CT image reconstruction are generally recommended for quantitative analyses. This recommendation is not always followed, which may affect quantification of emphysema extent and eventually, treatment decisions. Objective: The main goal is to demonstrate the influence of CT reconstruction kernels on emphysema quantification in patients with severe COPD, considered for BLVR. Methods: Chest CT scans were acquired with one multi-detector CT system and reconstructed using three different kernels: smooth, medium smooth, and sharp. Other parameters were kept constant. Emphysema scores (ESs), meaning the percentage of voxels below-950 Hounsfield units, were calculated and compared to the smooth reference kernel using paired t tests. Bland-Altman plots were made to assess the biases and limits of agreement between kernels. Results: Ninety-eight COPD patient CT scans were analyzed. The sharp kernel had a systematic bias of 6.2% and limits of agreement of 16.6% to-4.2% compared to the smooth kernel. The medium smooth kernel had a systematic bias of 5.7% and limits of agreement of 9.2% and 2.2% compared to the smooth kernel. The ES differed, for a single patient, up to 18% for different kernels. Conclusions: Chest CT kernel reconstruction can lead to a significant difference in emphysema severity quantification. This may cause invalid treatment selection in COPD patients evaluated for BLVR. Standardization of a smooth CT kernel setting and/or normalization to a standard kernel is strongly recommended

Low CT temporal sampling rates result in a substantial underestimation of myocardial blood flow measurements

The purpose of this study was to evaluate the effect of temporal sampling rate in dynamic CT myocardial perfusion imaging (CTMPI) on myocardial blood flow (MBF). Dynamic perfusion CT underestimates myocardial blood flow compared to PET and SPECT values. For accurate quantitative analysis of myocardial perfusion with dynamic perfusion CT a stable calibrated HU measurement of MBF is essential. Three porcine hearts were perfused using an ex-vivo Langendorff model. Hemodynamic parameters were monitored. Dynamic CTMPI was performed using third generation dual source CT at 70 kVp and 230-350 mAs/rot in electrocardiography(ECG)-triggered shuttle-mode (sampling rate, 1 acquisition every 2-3 s; z-range, 10.2 cm), ECG-triggered non-shuttle mode (fixed table position) with stationary tube rotation (1 acquisition every 0.5-1 s, 5.8 cm), and non-ECG-triggered continuous mode (1 acquisition every 0.06 s, 5.8 cm). Stenosis was created in the circumflex artery, inducing different fractional flow reserve values. Volume perfusion CT Myocardium software was used to analyze ECG-triggered scans. For the non-ECG triggered scans MASS research version was used combined with an in-house Matlab script. MBF (mL/g/min) was calculated for non-ischemic segments. True MBF was calculated using input flow and heart weight. Significant differences in MBF between shuttle, non-shuttle and continuous mode were found, with median MBF of 0.87 [interquartile range 0.72-1.00], 1.20 (1.07-1.30) and 1.65 (1.40-1.88), respectively. The median MBF in shuttle mode was 56% lower than the true MBF. In non-shuttle and continuous mode, the underestimation was 41% and 18%. Limited temporal sampling rate in standard dynamic CTMPI techniques contributes to substantial underestimation of true MBF

Preparing CT imaging datasets for deep learning in lung nodule analysis:Insights from four well-known datasets

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.</p

Improved precision of noise estimation in CT with a volume-based approach

Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm(2) ROIs and 27 0.75-cm(3) VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (−40%); 4.7 versus 9.9 HU for ULD-CT (−53%). Mean systematic bias barely changed: −1.6 versus −0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time

Assessment of Dynamic Change of Coronary Artery Geometry and Its Relationship to Coronary Artery Disease, Based on Coronary CT Angiography

To investigate the relationship between dynamic changes of coronary artery geometry and coronary artery disease (CAD) using computed tomography (CT). Seventy-one patients underwent coronary CT angiography with retrospective electrocardiographic gating. End-systolic (ES) and end-diastolic (ED) phases were automatically determined by dedicated software. Centerlines were extracted for the right and left coronary artery. Differences between ES and ED curvature and tortuosity were determined. Associations of change in geometrical parameters with plaque types and degree of stenosis were investigated using linear mixed models. The differences in number of inflection points were analyzed using Wilcoxon signed-rank tests. Tests were done on artery and segment level. One hundred thirty-seven arteries (64.3%) and 456 (71.4%) segments were included. Curvature was significantly higher in ES than in ED phase for arteries (p = 0.002) and segments (p < 0.001). The difference was significant only at segment level for tortuosity (p = 0.005). Number of inflection points was significantly higher in ES phase on both artery and segment level (p < 0.001). No significant relationships were found between degree of stenosis and plaque types and dynamic change in geometrical parameters. Non-invasive imaging by cardiac CT can quantify change in geometrical parameters of the coronary arteries during the cardiac cycle. Dynamic change of vessel geometry through the cardiac cycle was not found to be related to the presence of CAD

Evaluation of the Performance of Coordinate Measuring Machines in the Industry, Using Calibrated Artefacts

AbstractThe coordinate measuring machines (CMM's) has given a new impulse in the field of geometrical and dimensional metrology. The CMM's in industrial environments have become an important resource for the quality systems, monitoring manufacturing processes, reduction errors during the manufacturing process, inspection of product specifications and in continuous quality improvement. However, there is a need to evaluate, through practical, fast, effective and low cost methods, the CMM metrological specifications. Using calibrated artefacts, able to reproduce the geometric elements frequently measured, it seeks to ensure stability of the functional and metrological characteristics between calibrations and simultaneously knowing the errors. With better monitoring of the control parameters it is possible evaluate and optimize the calibration set deadlines, timely detection of faults and failures, detect structural changes and changes in environmental conditions of the laboratories, thus seeking to conduct a more detailed assessment of the stability of metrological characteristics of a CMM in industrial environments

Relationships of pericoronary and epicardial fat measurements in male and female patients with and without coronary artery disease

INTRODUCTION: Although pericoronary adipose tissue (PCAT) is a component of the epicardial adipose tissue (EAT) depot, they may have different associations to coronary artery disease (CAD). We explored relationships between pericoronary adipose tissue mean attenuation (PCAT MA) and EAT measurements in coronary CT angiography (CCTA) in patients with and without CAD. MATERIAL AND METHODS: CCTA scans of 185 non-CAD and 81 CAD patients (86.4% &gt;50% stenosis) were included and retrospectively analyzed. PCAT MA and EAT density/volume were measured and analyzed by sex, including associations with age, risk factors and tube voltage using linear regression models. RESULTS: In non-CAD and CAD, mean PCAT MA and EAT volume were higher in men than in women (non-CAD: -92.5 ± 10.6HU vs -96.2 ± 8.4HU, and 174.4 ± 69.1 cm 3 vs 124.1 ± 57.3 cm 3; CAD: -92.2 ± 9.0HU vs -97.4 ± 9.7HU, and 193.6 ± 62.5 cm 3 vs 148.5 ± 50.5 cm 3 (p &lt; 0.05)). EAT density was slightly lower in men than women in non-CAD (-96.4 ± 6.3HU vs -94.4 ± 5.5HU (p &lt; 0.05)), and similar in CAD (-98.2 ± 5.2HU vs 98.2 ± 6.4HU). There was strong correlation between PCAT MA and EAT density (non-CAD: r = 0.725, p &lt; 0.001, CAD: r = 0.686, p &lt; 0.001) but no correlation between PCAT MA and EAT volume (non-CAD: r = 0.018, p = 0.81, CAD: r = -0.055, p = 0.63). A weak inverse association was found between EAT density and EAT volume (non-CAD: r = -0.244, p &lt; 0.001, CAD: r = -0.263, p = 0.02). In linear regression models, EAT density was significantly associated with PCAT MA in both non-CAD and CAD patients independent of risk factors and tube voltage. CONCLUSION: In CAD and non-CAD patients, EAT density, but not EAT volume, showed significant associations with PCAT MA. Compared to women, men had higher PCAT MA and EAT volume independently of disease status, but similar or slightly lower EAT density. Differences in trends and relations of PCAT MA and EAT by sex could indicate that personalized interpretation and thresholding is needed. </p