An Adaptive SCG-ECG Multimodal Gating Framework for Cardiac CTA.
Cardiovascular disease (CVD) is the leading cause of death worldwide. Coronary artery disease (CAD), a prevalent form of CVD, is typically assessed using catheter coronary angiography (CCA), an invasive, costly procedure with associated risks. While cardiac computed tomography angiography (CTA) pres...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1669 - 1681 |
|---|---|
| Main Authors: | , , , , |
| Format: | equations & formulas research tables/charts tracings Journal Article |
| Published: |
Springer Nature
Jun2025
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280527&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280527 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280527 185280527 189894379 185280527 10.1007/s10278-024-01289-2 185280527 ppf: 1669 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Adaptive SCG-ECG Multimodal Gating Framework for Cardiac CTA. aug: au: Ganesh, Shambavi Abozeed, Mostafa Aziz, Usman Tridandapani, Srini Bhatti, Pamela T. affil: https://ror.org/01zkghx44 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA sug: subj: Computed Tomography Angiography Methods Electrocardiography Prediction Models Blood Pressure Determination Diagnosis, Cardiovascular Human Conceptual Framework Neural Networks (Computer) Systolic Pressure Diastolic Pressure Ultrasonography Image Processing, Computer Assisted Cardiovascular Diseases Diagnosis Sensitivity and Specificity Regression Descriptive Statistics Funding Source ab: Cardiovascular disease (CVD) is the leading cause of death worldwide. Coronary artery disease (CAD), a prevalent form of CVD, is typically assessed using catheter coronary angiography (CCA), an invasive, costly procedure with associated risks. While cardiac computed tomography angiography (CTA) presents a less invasive alternative, it suffers from limited temporal resolution, often resulting in motion artifacts that degrade diagnostic quality. Traditional ECG-based gating methods for CTA inadequately capture cardiac mechanical motion. To address this, we propose a novel multimodal approach that enhances CTA imaging by predicting cardiac quiescent periods using seismocardiogram (SCG) and ECG data, integrated through a weighted fusion (WF) approach and artificial neural networks (ANNs). We developed a regression-based ANN framework (r-ANN WF) designed to improve prediction accuracy and reduce computational complexity, which was compared with a classification-based framework (c-ANN WF), ECG gating, and US data. Our results demonstrate that the r-ANN WF approach improved overall diastolic and systolic cardiac quiescence prediction accuracy by 52.6% compared to ECG-based predictions, using ultrasound (US) as the ground truth, with an average prediction time of 4.83 ms. Comparative evaluations based on reconstructed CTA images show that both r-ANN WF and c-ANN WF offer diagnostic quality comparable to US-based gating, underscoring their clinical potential. Additionally, the lower computational complexity of r-ANN WF makes it suitable for real-time applications. This approach could enhance CTA's diagnostic quality, offering a more accurate and efficient method for CVD diagnosis and management. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|