Evidence map›Paper›PMID 40235803›Full record

ArticleQuantitative imaging in medicine and surgery2025

Feasibility of magnetization-transfer-contrast relaxation-enhanced angiography without contrast and triggering (REACT) imaging at 1.5 T combined with deep learning-based reconstruction for cardiovascular visualization.

Sukran Erdem, Alexandra Jack, Pezad Doctor, Gerald Greil, Tarique Hussain, Qing Zou

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sukran ErdemDivision of Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Alexandra JackDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Pezad DoctorDivision of Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Gerald GreilDivision of Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Tarique HussainDivision of Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Qing ZouDivision of Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Relaxation-enhanced angiography without contrast and triggering (REACT) is a flow-independent three-dimensional (3D) magnetic resonance angiography sequence designed to minimize artifacts using the Dixon method and an inversion recovery pre-pulse. A magnetization transfer contrast (MTC) pre-pulse (MTC-REACT) may further enhance image quality. This study evaluates the feasibility of MTC-REACT and examines deep learning-based reconstruction to improve image quality for pulmonary veins. Methods: This study was approved by the institutional review board. Twenty participants were prospectively recruited. Ten-fold undersampling was applied for REACT and MTC-REACT data acquisition. The inline compressed sense (CS) reconstruction algorithm, and an off-line Adaptive-CS-Net algorithm, were used for MTC-REACT and REACT image reconstruction. Commercially available analysis software was used for multi-planar reformatting, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) measurements, and image quality analysis. Categorical data were compared with a Wilcoxon signed ranks test and normally distributed variables with a Results: The Adaptive CS-Net reconstructed MTC-REACT images provided significantly higher SNR and CNR for right pulmonary veins (SNR 33.9±4.1, 30.3±3.4; CNR 33.3±3.4, 29.6±4.3), and left pulmonary veins (SNR 35.0±3.6, 31.2±4.3; CNR 34.6±4.2, 30.6±4.4) with all P values <0.05 compared to the CS-reconstructed MTC-REACT sequence. When Adaptive CS-Net reconstruction is used for both techniques, MTC-REACT images displayed significantly higher SNR and CNR compared to the REACT sequence for only left pulmonary veins (SNR 35±3.3, 32.4±4.6; CNR 34.6±3.4, 31.8±4.6). The Adaptive-CS-Net reconstructed MTC-REACT sequence consistently outperforms the CS-reconstructed MTC-REACT for pulmonary artery and vein imaging. Conclusions: The Adaptive-CS-Net reconstructed MTC-REACT images consistently deliver superior CNR, and SNR compared to the CS reconstructed images for pulmonary veins. Notably, the MTC-REACT technique enhances image quality for pulmonary veins compared to the REACT sequence, irrespective of the deep-learning reconstruction method's impact.

Indexed as

Magnetization transfer contrast (MTC)pulmonary veinsrelaxation-enhanced angiography without contrast and triggering (REACT)whole-heart magnetic resonance imaging (WH MRI)

Identifiers

PMID40235803
PMCPMC11994563

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.