Validation of an AI-based algorithm for measurement of the thoracic aortic diameter in low-dose chest CT.

OBJECTIVES: To evaluate the performance of artificial intelligence (AI) software for automatic thoracic aortic diameter assessment in a heterogeneous cohort with low-dose, non-contrast chest computed tomography (CT). MATERIALS AND METHODS: Participants of the Imaging in Lifelines (ImaLife) study who underwent low-dose, non-contrast chest CT (August 2017-May 2022) were included using random samples of 80 participants <50y, ≥80y, and with thoracic aortic diameter ≥40 mm. AI-based aortic diameters at eight guideline compliant positions were compared with manual measurements. In 90 examinations (30 per group) diameters were reassessed for intra- and inter-reader variability, which was compared to discrepancy of the AI system using Bland-Altman analysis, paired samples t-testing and linear mixed models. RESULTS: We analyzed 240 participants (63 ± 16 years; 50 % men). AI evaluation failed in 11 cases due to incorrect segmentation (4.6 %), leaving 229 cases for analysis. No difference was found in aortic diameter between manual and automatic measurements (32.7 ± 6.4 mm vs 32.7 ± 6.0 mm, p = 0.70). Bland-Altman analysis yielded no systematic bias and a repeatability coefficient of 4.0 mm for AI. Mean discrepancy of AI (1.3 ± 1.6 mm) was comparable to inter-reader variability (1.4 ± 1.4 mm); only at the proximal aortic arch showed AI higher discrepancy (2.0 ± 1.8 mm vs 0.9 ± 0.9 mm, p < 0.001). No difference between AI discrepancy and inter-reader variability was found for any subgroup (all: p > 0.05). CONCLUSION: The AI software can accurately measure thoracic aortic diameters, with discrepancy to a human reader similar to inter-reader variability in a range from normal to dilated aortas.

Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT.

PURPOSE: Phantom studies in CT emphysema quantification show that iterative reconstruction and deep learning-based noise reduction (DLNR) allow lower radiation dose. We compared emphysema quantification on ultra-low-dose CT (ULDCT) with and without noise reduction, to standard-dose CT (SDCT) in chronic obstructive pulmonary disease (COPD). METHOD: Forty-nine COPD patients underwent ULDCT (third generation dual-source CT; 70ref-mAs, Sn-filter 100kVp; median CTDIvol 0.38 mGy) and SDCT (64-multidetector CT; 40mAs, 120kVp; CTDIvol 3.04 mGy). Scans were reconstructed with filtered backprojection (FBP) and soft kernel. For ULDCT, we also applied advanced modelled iterative reconstruction (ADMIRE), levels 1/3/5, and DLNR, levels 1/3/5/9. Emphysema was quantified as Low Attenuation Value percentage (LAV%, ≤-950HU). ULDCT measures were compared to SDCT as reference standard. RESULTS: For ULDCT, the median radiation dose was 84 % lower than for SDCT. Median extent of emphysema was 18.6 % for ULD-FBP and 15.4 % for SDCT (inter-quartile range: 11.8-28.4 % and 9.2 %-28.7 %, p = 0.002). Compared to SDCT, the range in limits of agreement of emphysema quantification as measure of variability was 14.4 for ULD-FBP, 11.0-13.1 for ULD-ADMIRE levels and 10.1-13.9 for ULD-DLNR levels. Optimal settings were ADMIRE 3 and DLNR 3, reducing variability of emphysema quantification by 24 % and 27 %, at slight underestimation of emphysema extent (-1.5 % and -2.9 %, respectively). CONCLUSIONS: Ultra-low-dose CT in COPD patients allows dose reduction by 84 %. State-of-the-art noise reduction methods in ULDCT resulted in slight underestimation of emphysema compared to SDCT. Noise reduction methods (especially ADMIRE 3 and DLNR 3) reduced variability of emphysema quantification in ULDCT by up to 27 % compared to FBP.

Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review.

This review provides an overview of the currently available computed tomography (CT) techniques for myocardial tissue characterization in ischemic heart disease, including CT perfusion and late iodine enhancement. CT myocardial perfusion imaging can be performed with static and dynamic protocols for the detection of ischemia and infarction using either single- or dual-energy CT modes. Late iodine enhancement may be used for the analysis of myocardial infarction. The accuracy of these CT techniques is highly dependent on the imaging protocol, including acquisition timing and contrast administration. Additionally, the options for qualitative and quantitative analysis and the accuracy of each technique are discussed.

Optimal timing of image acquisition for arterial first pass CT myocardial perfusion imaging.

PURPOSE: To determine the optimal timing of arterial first pass computed tomography (CT) myocardial perfusion imaging (CTMPI) based on dynamic CTMPI acquisitions. METHODS AND MATERIALS: Twenty-five patients (59±8.4years, 14 male)underwent adenosine-stress dynamic CTMPI on second-generation dual-source CT in shuttle mode (30s at 100kV and 300mAs). Stress perfusion magnetic resonance imaging (MRI) was used as reference standard for differentiation of non-ischemic and ischemic segments. The left ventricle (LV) wall was manually segmented according to the AHA 16-segment model. Hounsfield units (HU) in myocardial segments and ascending (AA) and descending aorta (AD) were monitored over time. Time difference between peak AA and peak AD and peak myocardial enhancement was calculated, as well as the, time delay from fixed HU thresholds of 150 and 250 HU in the AA and AD to a minimal difference of 15 HU between normal and ischemic segments. Furthermore, the duration of the 15 HU difference between ischemic and non-ischemic segments was calculated. RESULTS: Myocardial ischemia was observed by MRI in 10 patients (56.3±9.0years; 8 male). The delay between the maximum HU in the AA and AD and maximal HU in the non-ischemic segments was 2.8s [2.2-4.3] and 0.0s [0.0-2.8], respectively. Differentiation between ischemic and non-ischemic myocardial segments in CT was best during a time window of 8.6±3.8s. Time delays for AA triggering were 4.5s [2.2-5.6] and 2.2s [0-2.8] for the 150 HU and 250 HU thresholds, respectively. While for AD triggering, time delays were 2.4s [0.0-4.8] and 0.0s [-2.2-2.6] for the 150 HU and 250 HU thresholds, respectively. CONCLUSION: In CTMPI, the differentiation between normal and ischemic myocardium is best accomplished during a time interval of 8.6±3.8s. This time window can be utilized by a test bolus or bolus tracking in the AA or AD using the time delays identified here.

Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation.

Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.