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

Coronary Artery Fly-Through Using Electron Beam Computed Tomography

BACKGROUND: Virtual reality techniques have recently been introduced into clinical medicine. This study examines the possibility of coronary artery fly-through using a dataset obtained by noninvasive coronary angiography with contrast-enhanced electron-beam computed tomography. METHODS AND RESULT

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

In vivo assessment of three dimensional coronary anatomy using electron beam computed tomography after intravenous contrast administration

Intravenous coronary angiography with electron beam computed tomography (EBCT) allows for the non-invasive visualisation of coronary arteries. With dedicated computer hardware and software, three dimensional renderings of the coronary arteries can be constructed, starting from the individual transaxial tomograms. This article describes image acquisition, postprocessing techniques, and the results of clinical studies. EBCT coronary angiography is a promising coronary artery imaging technique. Currently it is a reasonably robust technique for the visualisation and assessment of the left main and left anterior descending coronary artery. The right and circumflex coronary arteries can be visualised less consistently. Improvements in image acquisition and postprocessing techniques are expected to improve visualisation and diagnostic accuracy of the technique

Intravenous coronary angiography by electron beam computed tomography: a clinical evaluation

BACKGROUND:-Noninvasive detection of coronary stenoses with electron beam CT (EBCT) after intravenous injection of contrast medium has recently emerged. We sought to determine the diagnostic accuracy of EBCT angiography in the clinical setting using conventional coronary angiography as the "gold standard." METHODS AND RESULTS: Thirty-seven patients (30 men) were investigated. After intravenous injection of 150 mL of contrast medium, 40 to 60 consecutive transaxial tomograms, covering the proximal and middle parts of the coronary arteries, were obtained with ECG triggering at end diastole during breath-holding. Three-dimensional reconstructions of the proximal and middle parts of the arteries were compared with the conventional angiograms. Of the 259 proximal and middle coronary segments, 211 (81%) were analyzable by EBCT. Of the left anterior descending coronary artery (LAD) segments, 95% were assessable. Right coronary artery (RCA) and left circumflex artery (LCx) segments were assessable in 66% and 76%, respectively. Overall sensitivity and specificity to detect a >50% diameter stenosis were 77% and 94%, respectively. This was 82% and 92% for the LAD, 60% and 97% for the RCA, and 83% and 89% for the LCx (all figures based on assessable lesions). CONCLUSIONS: Intravenous EBCT coronary angiography is a promising coronary imaging technique. The technique is not yet robust enough to be an alternative to conventional coronary angiography. It can detect and rule out significant coronary artery disease of the left main proximal and mid portions of the LAD with good accuracy

Towards reference values of pericoronary adipose tissue attenuation:impact of coronary artery and tube voltage in coronary computed tomography angiography

Objectives: To determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence. Methods: This retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19–79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups. Results: The overall mean ± standard deviation (SD) PCATMA was − 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: − 88.5 ± 10.5 HU versus − 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was − 92.4 ± 11.6 HU, − 88.4 ± 9.9 HU, and − 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was − 95.6 ± 9.6 HU, − 90.2 ± 11.5 HU, − 87.3 ± 9.9 HU, − 82.7 ± 6.2 HU, and − 79.3 ± 6.8 HU, respectively (p < 0.001). Conclusions: In patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting. Key Points: • In patients without plaque on cCTA, PCATMAdiffers slightly by coronary artery (LAD, LCX, RCA). • Tube voltage of cCTA affects PCATMAmeasurement, with mean PCATMAincreasing linearly with increasing kV. • For longitudinal cCTA analysis of PCATMA, the use of equal kV setting is strongly recommended

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Objectives To investigate the association of pericoronary adipose tissue mean attenuation (PCAT(MA)) with coronary artery disease (CAD) characteristics on coronary computed tomography angiography (CCTA). Methods We retrospectively investigated 165 symptomatic patients who underwent third-generation dual-source CCTA at 70kVp: 93 with and 72 without CAD (204 arteries with plaque, 291 without plaque). CCTA was evaluated for presence and characteristics of CAD per artery. PCAT(MA) was measured proximally and across the most severe stenosis. Patient-level, proximal PCAT(MA) was defined as the mean of the proximal PCAT(MA) of the three main coronary arteries. Analyses were performed on patient and vessel level. Results Mean proximal PCAT(MA) was -96.2 +/- 7.1 HU and -95.6 +/- 7.8HU for patients with and without CAD (p = 0.644). In arteries with plaque, proximal and lesion-specific PCAT(MA) was similar (-96.1 +/- 9.6 HU, -95.9 +/- 11.2 HU, p = 0.608). Lesion-specific PCAT(MA) of arteries with plaque (-94.7 HU) differed from proximal PCAT(MA) of arteries without plaque (-97.2 HU, p = 0.015). Minimal stenosis showed higher lesion-specific PCAT(MA) (-94.0 HU) than severe stenosis (-98.5 HU, p = 0.030). Lesion-specific PCAT(MA) of non-calcified, mixed, and calcified plaque was -96.5 HU, -94.6 HU, and -89.9 HU (p = 0.004). Vessel-based total plaque, lipid-rich necrotic core, and calcified plaque burden showed a very weak to moderate correlation with proximal PCAT(MA). Conclusions Lesion-specific PCAT(MA) was higher in arteries with plaque than proximal PCAT(MA) in arteries without plaque. Lesion-specific PCAT(MA) was higher in non-calcified and mixed plaques compared to calcified plaques, and in minimal stenosis compared to severe; proximal PCAT(MA) did not show these relationships. This suggests that lesion-specific PCAT(MA) is related to plaque development and vulnerability