An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis

Bahadormanesh, Nikrouz; Tomka, Benjamin; Kadem, Mason; Khodaei, Seyedvahid; Keshavarz‐Motamed, Zahra

doi:10.1016/j.media.2023.102795

Cited by 7 publications

(3 citation statements)

References 166 publications

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“…We showed qualitative and quantitative patterns of calcific distribution that seem to closely match those demonstrated in ex-vivo studies by Thubrikar et al ( 23 ), and those posited in biomechanical simulated models ( 24 - 26 , 38 ). These experiments chiefly entail the following: calcification advances primarily from the high stress regions of the valve leaflets which form arcs and eventually rings covering the leaflet’s surface.…”

Section: Discussionsupporting

confidence: 88%

“…These markers could be especially important in the context of TAVR planning since different factors can influence the severity of various complications during and post-operation such as the location, quantity, and type of the calcific deposits ( 6 , 7 , 35 , 36 ). We have, therefore, developed a geometrical mapping framework that quantifies the topology of the calcification in the AV based on the common anatomical definitions of a tricuspid AV ( 37 , 38 ) and focused on the leaflet region of the valve to provide an in-vivo quantitative description of the pattern of calcific progression in AS.…”

Section: Discussionmentioning

confidence: 99%

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Regional assessment of aortic valve calcification using topographic maps in contrast-enhanced CT: in-vivo sex and severity-based differences in calcific presentation

Abdelkhalek,

Daeian,

Keshavarz-Motamed

2024

Quant Imaging Med Surg

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Background Procedural planning for transcatheter aortic valve replacement (TAVR) is routinely performed using contrast computed tomography (CT) in patients with severe aortic stenosis (AS). Despite its potential, little investigation has been done into the possibility of aortic valve calcification (AVC) scoring in contrast-enhanced CT. Contrast CT has superior spatial and contrast resolution compared to the non-contrast Agatston score protocol, which would allow for development of better pattern and distribution descriptors of calcific lesions in the aortic valve (AV). Methods We developed a new false positive rate (FPR) based method that can quantify leaflet calcification based on shape overlap metrics. We also introduce a novel regional scheme for quantifying the shape and structure of calcification using topographic maps. The study was designed to: (I) determine the feasibility of using a novel method based on FPR to detect AVC using contrast-enhanced CT images by assessing the volume scores measured using FPR versus non-contrast methods and alternative contrast methods for volume scoring based on fixed or dynamic HU thresholds. (II) Develop a new scheme for assessing calcific geometry and structure and evaluate patterns of calcification in the varied presentation of AS. Results Our results show a very strong correlation with non-contrast volume (r=0.919, P<0.001; n=178) and Agatston scores (r=0.913, P<0.001; n=178) that were evaluated using a standard calcium scoring technique. Finally, we analyzed the differences and similarities in the patterns of calcific deposition with respect to sex and degree of severity. Conclusions The FPR method demonstrates the best overall agreement with non-contrast scores across both low and high ends of calcific density compared to luminal attenuation methods. In addition, we showed that leaflet calcific deposition follows distinctive patterns across the belly of the leaflet, with the rate of calcific progression peaking at the non-coronary cusp (NCC) leaflet and lowest for the right-coronary cusp. Females experience significantly lower calcific deposition compared to males despite showing similar patterns and symptoms. Our findings suggest that precise regional assessment of calcific progression could be an important tool for monitoring AS development as well as predicting peri-procedural complications in TAVR.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Regional assessment of aortic valve calcification using topographic maps in contrast-enhanced CT: in-vivo sex and severity-based differences in calcific presentation

Abdelkhalek,

Daeian,

Keshavarz-Motamed

2024

Quant Imaging Med Surg

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“…The developed framework was validated against cardiac catheterization data with a considerable inter- and intra-patient variability with a broad range of diseases in a population of forty-nine patients (with AS). 20 Moreover, some of the sub-models of the patient-specific lumped parameter algorithm have been used previously, 20 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 with validation against in vivo cardiac catheterization 32 , 33 in patients with vascular diseases, in vivo magnetic resonance imaging data 34 in patients with AS, and in vivo magnetic resonance imaging data 35 , 36 , 37 in patients with mixed valvular diseases and coarctation.…”

Section: Methodsmentioning

confidence: 99%

Early Detection of Risk of Neo-Sinus Blood Stasis Post-Transcatheter Aortic Valve Replacement Using Personalized Hemodynamic Analysis

et al. 2023

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Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning

Khodaei,

Keshavarz-Motamed

2023

Studies in Computational Intelligence

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An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis

Cited by 7 publications

References 166 publications

Regional assessment of aortic valve calcification using topographic maps in contrast-enhanced CT: in-vivo sex and severity-based differences in calcific presentation

Regional assessment of aortic valve calcification using topographic maps in contrast-enhanced CT: in-vivo sex and severity-based differences in calcific presentation

Early Detection of Risk of Neo-Sinus Blood Stasis Post-Transcatheter Aortic Valve Replacement Using Personalized Hemodynamic Analysis

Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning

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