Artificial Intelligence in Cardiovascular CT and MR Imaging

Lanzafame, Ludovica R. M.; Bucolo, Giuseppe M.; Muscogiuri, Giuseppe; Sironi, Sandro; Gaeta, Michele; Ascenti, Giorgio; Booz, Christian; Vogl, Thomas J.; Blandino, Alfredo; Mazziotti, Silvio; D’Angelo, Tommaso

doi:10.3390/life13020507

Cited by 17 publications

(5 citation statements)

References 109 publications

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“…A large amount of data in the range of thousands of images is beneficial for achieving a well-trained algorithm. AIaided analysis of multimodality carotid and coronary artery plaque tissue characterization and classification has already proven to be an accurate and robust path for facilitating clinical decision-making [59,60], but we only have limited data on using AI for PAD lesion characterization [61]. In our feasibility study, we developed an approach of obtaining several hundred images from one PAD lesion as described.…”

Section: Discussionmentioning

confidence: 99%

Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study

Csore,

Karmonik,

Wilhoit

et al. 2023

Diagnostics

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The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. hard plaque components in peripheral arterial disease (PAD). Five amputated lower extremities were imaged at a clinical ultra-high field 7 Tesla MRI. Ultrashort echo time (UTE), T1-weighted (T1w) and T2-weighted (T2w) datasets were acquired. Multiplanar reconstruction (MPR) images were obtained from one lesion per limb. Images were aligned to each other and pseudo-color red-green-blue images were created. Four areas in latent space were defined corresponding to the sorted images reconstructed by the VAE. Images were classified from their position in latent space and scored using tissue score (TS) as following: (1) lumen patent, TS:0; (2) partially patent, TS:1; (3) mostly occluded with soft tissue, TS:3; (4) mostly occluded with hard tissue, TS:5. Average and relative percentage of TS was calculated per lesion defined as the sum of the tissue score for each image divided by the total number of images. In total, 2390 MPR reconstructed images were included in the analysis. Relative percentage of average tissue score varied from only patent (lesion #1) to presence of all four classes. Lesions #2, #3 and #5 were classified to contain tissues except mostly occluded with hard tissue while lesion #4 contained all (ranges (I): 0.2–100%, (II): 46.3–75.9%, (III): 18–33.5%, (IV): 20%). Training the VAE was successful as images with soft/hard tissues in PAD lesions were satisfactory separated in latent space. Using VAE may assist in rapid classification of MRI histology images acquired in a clinical setup for facilitating endovascular procedures.

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Section: Discussionmentioning

confidence: 99%

Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study

Csore,

Karmonik,

Wilhoit

et al. 2023

Diagnostics

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“…In this context, another vast and thriving topic is that of artificial intelligence (AI), referring to the development of “trained” computerised models to perform tasks typically requiring human intelligence. AI is still in its early stages but has been showing tremendous promise in medical imaging and CMR [ 35 ], with new solutions for image acquisition, reconstruction, post-processing and analysis, along with biomarkers for a wide spectrum of cardiac conditions within the field of radiomics. However, the incredible number of AI models and their meaningful integration in clinical routine remains a challenge.…”

Section: Technical Hints In the Pediatric Populationmentioning

confidence: 99%

MR imaging of primary benign cardiac tumors in the pediatric population

Inserra,

Cannizzaro,

Passaniti

et al. 2023

Heliyon

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“…However, the pure anatomical assessment of coronary arteries obtained with CCTA, does not provide functional information on lesion-specific ischemia. Recent developments in hardware and software technology, particularly with the introduction of artificial intelligence (AI) tools, are improving image quality of CCTA, increasing the detectable features of CAD (i.e., evaluation and quantification of coronary stenosis, plaque characterization, assessment of myocardial ischemia) and expanding the prognostic role of CCTA with machine-learning (ML) algorithms [ 9 , 10 ]. Moreover, the use of dual energy CT (DECT) and the recent introduction of photon-counting detector scanners (PCD-CT) enabled the acquisition of ultra-high resolution images, with spectral information obtained along with each CT scan (material decomposition) and the reduction of blooming or movement artifacts together with the elimination of electronic noise [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

Cundari,

Marchitelli,

Pambianchi

et al. 2024

Radiol med

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Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.

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Artificial Intelligence in Cardiovascular CT and MR Imaging

Cited by 17 publications

References 109 publications

Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study

Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study

MR imaging of primary benign cardiac tumors in the pediatric population

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

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