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Bioengineering
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Recent Advances in Cardiac MRI
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Recent Advances in Cardiac MRI

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 283

Special Issue Editors

Dr. Qing Zou

E-Mail Website
Guest Editor
Division of Cardiology, Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: advanced cardiac imaging for congenital heart diseases; coronary imaging; translational cardiology based on advanced cardiac imaging
Prof. Dr. Gerald Greil

E-Mail Website
Guest Editor
Division of Cardiology, Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: advanced cardiac imaging for congenital heart diseases; coronary imaging; translational cardiology based on advanced cardiac imaging

Special Issue Information

Dear Colleagues,

Advanced cardiac MRI is crucial for managing both congenital and acquired heart diseases, prized for its lack of radiation and superior soft-tissue imaging capabilities. However, it faces challenges such as prolonged scan durations and image artifacts in clinical settings. This Special Issue is dedicated to showcasing cutting-edge methods in cardiac MRI acquisition and processing, bridging the gap between fundamental research and clinical application. Topics to be explored include, but are not limited to, the following:

  1. Innovative cardiac MRI reconstruction techniques, focusing on the development and clinical integration of AI-driven algorithms.
  2. Innovative cardiac MRI post-processing applications, including denoising, segmentation, and super-resolution.
  3. Techniques and applications of novel fast imaging acquisition methods.
  4. Advanced techniques and applications for first-pass perfusion cardiac MRI.
  5. Techniques and applications in non-contrast parametric mapping.
  6. Development of new imaging biomarkers.
  7. Development and applications of novel cardiac MRI sequences, such as 4D flow sequences.
  8. Novel clinical practices of cardiac MRI.
  9. Development and applications of non-breath-hold and non-ECG-gated cardiac MR techniques.
  10. Novel contrast agents for cardiac MRI.

This Special Issue aims to highlight significant advancements and applications that improve diagnostic accuracy and patient outcomes in cardiac MRI.

Dr. Qing Zou
Prof. Dr. Gerald Greil
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiac MRI
  • post-processing
  • deep learning
  • fast imaging
  • novel sequences

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Published Papers (1 paper)

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Research

17 pages, 8549 KiB  
Article
A Fully Automated Analysis Pipeline for 4D Flow MRI in the Aorta
by Ethan M. I. Johnson, Haben Berhane, Elizabeth Weiss, Kelly Jarvis, Aparna Sodhi, Kai Yang, Joshua D. Robinson, Cynthia K. Rigsby, Bradley D. Allen and Michael Markl
Bioengineering 2025, 12(8), 807; https://doi.org/10.3390/bioengineering12080807 - 27 Jul 2025
Abstract
Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a [...] Read more.
Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a fully automated artificial intelligence (AI) 4D flow analysis pipeline was developed and evaluated in a cohort of over 350 subjects. The 4D flow MRI analysis pipeline integrated a series of previously developed and validated deep learning networks, which replaced traditionally manual processing tasks (background-phase correction, noise masking, velocity anti-aliasing, aorta 3D segmentation). Hemodynamic parameters (global aortic pulse wave velocity (PWV), peak velocity, flow energetics) were automatically quantified. The pipeline was evaluated in a heterogeneous single-center cohort of 379 subjects (age = 43.5 ± 18.6 years, 118 female) who underwent 4D flow MRI of the thoracic aorta (n = 147 healthy controls, n = 147 patients with a bicuspid aortic valve [BAV], n = 10 with mechanical valve prostheses, n = 75 pediatric patients with hereditary aortic disease). Pipeline performance with BAV and control data was evaluated by comparing to manual analysis performed by two human observers. A fully automated 4D flow pipeline analysis was successfully performed in 365 of 379 patients (96%). Pipeline-based quantification of aortic hemodynamics was closely correlated with manual analysis results (peak velocity: r = 1.00, p < 0.001; PWV: r = 0.99, p < 0.001; flow energetics: r = 0.99, p < 0.001; overall r ≥ 0.99, p < 0.001). Bland–Altman analysis showed close agreement for all hemodynamic parameters (bias 1–3%, limits of agreement 6–22%). Notably, limits of agreement between different human observers’ quantifications were moderate (4–20%). In addition, the pipeline 4D flow analysis closely reproduced hemodynamic differences between age-matched adult BAV patients and controls (median peak velocity: 1.74 m/s [automated] or 1.76 m/s [manual] BAV vs. 1.31 [auto.] vs. 1.29 [manu.] controls, p < 0.005; PWV: 6.4–6.6 m/s all groups, any processing [no significant differences]; kinetic energy: 4.9 μJ [auto.] or 5.0 μJ [manu.] BAV vs. 3.1 μJ [both] control, p < 0.005). This study presents a framework for the complete automation of quantitative 4D flow MRI data processing with a failure rate of less than 5%, offering improved measurement reliability in quantitative 4D flow MRI. Future studies are warranted to reduced failure rates and evaluate pipeline performance across multiple centers. Full article
(This article belongs to the Special Issue Recent Advances in Cardiac MRI)
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