Deep learning image reconstruction algorithm. impact on image quality in coronary computed tomography angiography

PurposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).Material and methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.ResultsDLIR algorithm did not impact vascular attenuation (P >= 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P <= 0.021).DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P >= 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P <= 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).ConclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD

Noise Reduction Using Singular Value Decomposition with Jensen–Shannon Divergence for Coronary Computed Tomography Angiography

Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart in a stationary state. However, reconstructed CT images, including those of the coronary arteries, contain insufficient X-ray photons and considerable noise. In this study, we introduce an image-processing technique for noise reduction using singular value decomposition (SVD) for CCTA images. The threshold of SVD was determined on the basis of minimization of Jensen–Shannon (JS) divergence. Experiments were performed with various numerical phantoms and varying levels of noise to reduce noise in clinical CCTA images using the determined threshold value. The numerical phantoms produced 10% higher-quality images than the conventional noise reduction method when compared on a quantitative SSIM basis. The threshold value determined by minimizing the JS–divergence was found to be useful for efficient noise reduction in actual clinical images, depending on the level of noise

Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study

Background: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. Methods: A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: ESRGAN-processed images improved the specificity and PPV at all three coronary arteries (LAD-left anterior descending, LCx-left circumflex and RCA-right coronary artery) compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0% (95% confidence interval [CI]: 30%, 52.7%) and 26.9% (95% CI: 22.9%, 31.4%) at LAD; 41.7% (95% CI: 22.1%, 63.4%) and 36.4% (95% CI: 28.9%, 44.5%) at LCx; 55% (95% CI: 38.5%, 70.7%) and 47.1% (95% CI: 38.7%, 55.6%) at RCA; while corresponding values for original CCTA were 21.8% (95% CI: 13.2%, 32.6%) and 22.8% (95% CI: 20.8%, 24.9%); 12.5% (95% CI: 2.6%, 32.4%) and 27.6% (95% CI: 24.7%, 30.7%); 17.5% (95% CI: 7.3%, 32.8%) and 32.7% (95% CI: 29.6%, 35.9%) at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 (95% CI: 0.64, 0.89), 0.81 (95% CI: 0.69, 0.93), 0.82 (95% CI: 0.71, 0.94) and 0.86 (95% CI: 0.76, 0.96) corresponding to original CCTA and ESRGAN-HR, average and median images, respectively. Conclusions: This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques

Quantitative cardiac dual source CT; from morphology to function

Cardiovascular diseases are a large contributor to the global mortality rate. Non-invasive imaging techniques, such as computed tomography (CT) imaging, have been playing a growing role in the risk assessment, diagnosis, and prognosis of coronary artery disease (CAD). One of the main challenges in the evaluation of CAD is the establishment of the optimal workflow to evaluate the anatomical as well as the functional aspects of CAD in all phases of the ischemic cascade.The research described in this thesis investigates the possibilities of CT to perform both morphological and functional evaluation of CAD and it is debated whether CT can be used clinically for the visualization of the entire ischemic cascade.Results show that the diagnostic and prognostic value of CT procedures for coronary artery disease evaluation can be improved by adding additional functional information to the anatomical evaluation. This was concluded from research done on two new technologies analyzing the blood flow through the coronaries and through the heart muscle. Besides that, important questions regarding protocol optimization and standardization have been investigated. Although CT shows great potential for the evaluation of CAD, the clinical workflow and combination of techniques to be used is yet to be optimized. Automating processes, for example with the use of Artificial Intelligence (AI), can enhance the clinical implementation and can help the field of cardiac radiology deal with the increased demand for cardiac imaging

The year in cardiology: imaging. The year in cardiology 2019.

No abstract available

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.</jats:p

Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography

Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinician's diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the coronary artery without motion (the ground-truth data required in supervised deep learning) is medically unattainable, we apply a style transfer method to 2D image patches cropped from full-phase 4D computed tomography (CT) to synthesize these images. We then train a convolutional neural network (CNN) for motion artifact correction using this synthetic ground-truth (SynGT). During testing, the output motion-corrected 2D image patches of the trained network are reinserted into the 3D CT volume with volumetric interpolation. The proposed method is evaluated using both phantom and clinical data. A phantom study demonstrates comparable results to other methods in quantitative performance and outperforms those methods in computation time. For clinical data, a quantitative analysis based on metric measurements is presented that confirms the correction of motion artifacts. Moreover, an observer study finds that by applying the proposed method, motion artifacts are markedly reduced, and boundaries of the coronary artery are much sharper, with a strong inter-observer agreement (κ = 0.78). Finally, evaluations using commercial software on the original and resulting CT volumes of the proposed method reveal a considerable increase in tracked coronary artery length.ope

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image