Ex-vivo atherosclerotic plaque characterization using spectral photon-counting CT: Comparing material quantification to histology

Healy, Joe; Searle, Emily; Panta, Raj Kumar; Chernoglazov, Alex; Roake, Justin; Butler, Phil; Butler, Anthony; Gieseg, Steven P.; Adebileje, Sikiru A.; Alexander, Steven D.; Amma, Maya R.; Anjomrouz, Marzieh; Asghariomabad, Fatemeh; Atharifard, Ali; Atlas, James; Bell, Stephen T.; Butler, Anthony P.H.; Butler, Philip H.; Carbonez, Pierre; Chambers, Claire; Chapagain, Krishna M.; Chernoglazov, Alexander I.; Clark, Jennifer A.; Colgan, Frances; Crighton, Jonathan S.; Dahal, Shishir; Damet, Jérôme; Dapamede, Theodorus; de Ruiter, Niels J.A.; Dixit, Devyani; Doesburg, Robert M.N.; Dombroski, Karen; Duncan, Neryda; Gieseg, Steven P.; Gopinathan, Anish; Healy, Joseph L.; Holmes, Luke; Jonker, Kevin; Kirkbride, Tracy; Lowe, Chiara; Mandalika, V.B.H.; Matanaghi, Aysouda; Moghiseh, Mahdieh; Paladugu, Manoj; Palmer, David; Panta, Raj K.; Renaud, Peter; Sayous, Yann; Schleich, Nanette; Searle, Emily; Sheeja, Jereena S.; Smith, Aaron; Broeke, Leza Vanden; Vivek, V.S.; Peter Walker, E.; Wijesooriya, Manoj; Ross Younger, W.

doi:10.1016/j.atherosclerosis.2023.06.007

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Their findings showed that using tungsten as a contrast agent significantly reduced the image noise at both high and low radiation dose settings compared to iodine agents [ 34 ]. Similarly, the intrinsic spectral capability was reported by Healey et al, who demonstrated that PCCT could identify plaque vulnerability features such as LRNC, spotty calcification, and plaque ulceration in surgically obtained specimens from the carotid artery without the administration of contrast media, in comparison with histological imaging [ 42 ]. Another ex vivo study demonstrated that PCCT could distinguish the components of vulnerable atherosclerotic carotid plaque based on its characteristic energy-dependent attenuation characteristics related to differences in the photoelectric effect Compton scattering [ 43 ].…”

Section: Clinical Application Of Pcct In Carotid Plaquementioning

confidence: 74%

“…Plaque reconstructions were performed at different energy levels (45, 70, 120, and 190 keV). PCCT was able to distinguish among well-known features of plaque vulnerability, such as an intraplaque hemorrhage, as well as other plaque components including the FC, lipid, and necrosis [42]. Figures 2-5 demonstrate some examples of carotid atherosclerotic plaques using PCCT.…”

Section: Clinical Application Of Pcct In Carotid Plaquementioning

confidence: 99%

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Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization

Cau,

Saba,

Balestrieri

et al. 2024

Diagnostics

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Atherosclerotic plaque buildup in the coronary and carotid arteries is pivotal in the onset of acute myocardial infarctions or cerebrovascular events, leading to heightened levels of illness and death. Atherosclerosis is a complex and multistep disease, beginning with the deposition of low-density lipoproteins in the arterial intima and culminating in plaque rupture. Modern technology favors non-invasive imaging techniques to assess atherosclerotic plaque and offer insights beyond mere artery stenosis. Among these, computed tomography stands out for its widespread clinical adoption and is prized for its speed and accessibility. Nonetheless, some limitations persist. The introduction of photon-counting computed tomography (PCCT), with its multi-energy capabilities, enhanced spatial resolution, and superior soft tissue contrast with minimal electronic noise, brings significant advantages to carotid and coronary artery imaging, enabling a more comprehensive examination of atherosclerotic plaque composition. This narrative review aims to provide a comprehensive overview of the main concepts related to PCCT. Additionally, we aim to explore the existing literature on the clinical application of PCCT in assessing atherosclerotic plaque. Finally, we will examine the advantages and limitations of this recently introduced technology.

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Section: Clinical Application Of Pcct In Carotid Plaquementioning

confidence: 74%

Section: Clinical Application Of Pcct In Carotid Plaquementioning

confidence: 99%

Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization

Cau,

Saba,

Balestrieri

et al. 2024

Diagnostics

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Lung nodule segmentation via semi-residual multi-resolution neural networks

Wang,

Dai

2023

Open Life Sciences

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The integration of deep neural networks and cloud computing has become increasingly prevalent within the domain of medical image processing, facilitated by the recent strides in neural network theory and the advent of the internet of things (IoTs). This juncture has led to the emergence of numerous image segmentation networks and innovative solutions that facilitate medical practitioners in diagnosing lung cancer. Within the contours of this study, we present an end-to-end neural network model, christened as the “semi-residual Multi-resolution Convolutional Neural Network” (semi-residual MCNN), devised to engender precise lung nodule segmentation maps within the milieu of cloud computing. Central to the architecture are three pivotal features, each coalescing to effectuate a notable enhancement in predictive accuracy: the incorporation of semi-residual building blocks, the deployment of group normalization techniques, and the orchestration of multi-resolution output heads. This innovative model is systematically subjected to rigorous training and testing regimes, using the LIDC-IDRI dataset – a widely embraced and accessible repository – comprising a diverse ensemble of 1,018 distinct lung CT images tailored to the realm of lung nodule segmentation.

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Ex-vivo atherosclerotic plaque characterization using spectral photon-counting CT: Comparing material quantification to histology

Cited by 2 publications

References 44 publications

Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization

Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization

Lung nodule segmentation via semi-residual multi-resolution neural networks

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