"Pulmonary vessel tree matching for quantifying changes in vascular morphology"

Zhiwei Zhai, Marius Staring, Hideki Ota and Berend C. Stoel


Invasive right-sided heart catheterization (RHC) is currently the gold standard for assessing treatment effects in pulmonary vascular diseases, such as chronic thromboembolic pulmonary hypertension (CTEPH). Quantifying morphological changes by matching vascular trees (pre- and post-treatment) may provide a non-invasive alternative for assessing hemodynamic changes. In this work, we propose a method for quantifying morphological changes, consisting of three steps: constructing vascular trees from the detected pulmonary vessels, matching vascular trees with preserving local tree topology, and quantifying local morphological changes based on Poiseuille's law (changes in radius-4, Δr-4). Subsequently, median and interquartile range (IQR) of all local Δr-4 were calculated as global measurements for assessing morphological changes. The vascular tree matching method was validated with 10 synthetic trees and the relation between clinical RHC parameters and quantifications of morphological changes was investigated in 14 CTEPH patients, pre- and post-treatment. In the evaluation with synthetic trees, the proposed method achieved an average residual distance of 3:09 ± 1:28 mm, which is a substantial improvement over a coherent point drift method (4:32 ± 1:89 mm) and a method with global-local topology preservation (3:92 ± 1:59 mm). In the clinical evaluation, the morphological changes (IQR of Δr-4) was significantly correlated with the changes in RHC examinations, ΔsPAP (R=-0.62, p-value=0.019) and ΔmPAP (R=-0.56, p-value=0.038). Quantifying morphological changes may provide a noninvasive assessment of treatment effects in CTEPH patients, consistent with hemodynamic changes from invasive RHC.



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BibTeX entry

author = "{Zhiwei Zhai and Marius Staring and Hideki Ota and Berend C. Stoel}",
title = "{Pulmonary vessel tree matching for quantifying changes in vascular morphology}",
booktitle = "{Medical Image Computing and Computer-Assisted Intervention}",
address = "{Granada,Spain}",
series = "{Lecture Notes in Computer Science}",
month = "{September}",
year = "{2018}",

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