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Assessing potential errors of MRI-based measurements of pulmonary blood flow using a detailed network flow model.

Abstract:
MRI images of pulmonary blood flow using arterial spin labeling (ASL) measure the delivery of magnetically tagged blood to an image plane during one systolic ejection period. However, the method potentially suffers from two problems, each of which may depend on the imaging plane location: 1) the inversion plane is thicker than the imaging plane, resulting in a gap that blood must cross to be detected in the image; and 2) ASL includes signal contributions from tagged blood in conduit vessels (arterial and venous). By using an in silico model of the pulmonary circulation we found the gap reduced the ASL signal to 64-74% of that in the absence of a gap in the sagittal plane and 53-84% in the coronal. The contribution of the conduit vessels varied markedly as a function of image plane ranging from ∼90% of the overall signal in image planes that encompass the central hilar vessels to <20% in peripheral image planes. A threshold cutoff removing voxels with intensities >35% of maximum reduced the conduit vessel contribution to the total ASL signal to ∼20% on average; however, planes with large contributions from conduit vessels underestimate acinar flow due to a high proportion of in-plane flow, making ASL measurements of perfusion impractical. In other image planes, perfusion dominated the resulting ASL images with good agreement between ASL and acinar flow. Similarly, heterogeneity of the ASL signal as measured by relative dispersion is a reliable measure of heterogeneity of the acinar flow distribution in the same image planes.
Publication status:
Published

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Publisher copy:
10.1152/japplphysiol.00894.2011

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Journal:
Journal of applied physiology (Bethesda, Md. : 1985) More from this journal
Volume:
113
Issue:
1
Pages:
130-141
Publication date:
2012-07-01
DOI:
EISSN:
1522-1601
ISSN:
8750-7587


Language:
English
Keywords:
Pubs id:
pubs:325593
UUID:
uuid:0c625807-dba8-40ca-88fe-149b54113c3f
Local pid:
pubs:325593
Source identifiers:
325593
Deposit date:
2013-11-17
ARK identifier:

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