Coprime dual-velocity encoding for extended velocity dynamic range in 4D flow magnetic resonance imaging

In the field of cardiovascular imaging, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) provides non-invasive assessment of blood flow. Dual velocity encoding (dual-VENC) strategies have emerged to obtain quantitative information on both low and high blood flow velocities simultan...

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Bibliographic Details
Published inJournal of cardiovascular magnetic resonance Vol. 27; no. 1; p. 101871
Main Authors Bartoli, Marta Beghella, Boccalini, Sara, Chechin, David, Boussel, Loic, Douek, Philippe, Garcia, Damien, Sigovan, Monica
Format Journal Article
LanguageEnglish
Published England Elsevier Inc 2025
BioMed Central : Elsevier
Elsevier
Subjects
VNR
CFD
FH
sDV
MRI
PRF
LV
AAo
4D
VNR
MR
CMR
AP
TE
PC
BAV
RL
FA
MRA
TR
Online AccessGet full text
ISSN1097-6647
1532-429X
1532-429X
DOI10.1016/j.jocmr.2025.101871

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Summary:In the field of cardiovascular imaging, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) provides non-invasive assessment of blood flow. Dual velocity encoding (dual-VENC) strategies have emerged to obtain quantitative information on both low and high blood flow velocities simultaneously. However, these strategies often encounter difficulties in coping with large velocity ranges. This work presents a dual-VENC 4D flow CMR sequence that utilizes the coprime rule to define the VENC ratio. A dual-VENC 4D flow CMR sequence and reconstruction algorithm were developed and validated in vitro at two different field strengths, using a flow phantom generating realistic complex flow patterns. A digital twin of the phantom allowed comparison of the MRI measurements with computational fluid dynamics (CFD) simulations. Three patients with different cardiac pathologies were scanned in order to evaluate the in vivo feasibility of the proposed method. The results of the in vitro acquisitions demonstrated significant improvement in velocity-to-noise ratio (VNR) with respect to single-VENC acquisitions (110±3%) and conventional dual-VENC de-aliasing approach (75±3%). Furthermore, the effectiveness of aliasing correction was demonstrated even when both sets of images from the dual-VENC acquisition presented velocity aliasing artifacts. We observed a high degree of agreement between the measured and simulated velocity fields. The strength of this approach lies in the fact that, unlike the conventional de-aliasing method, no data is discarded. The final image is obtained by a weighted average of the VENClow and VENChigh datasets. Consequently, setting the value of the VENChigh to prevent aliasing is no longer necessary, and higher VNR gains are possible [Display omitted]
ISSN:1097-6647
1532-429X
1532-429X
DOI:10.1016/j.jocmr.2025.101871