Aortic length measurements for pulse wave velocity calculation: manual 2D vs automated 3D centreline extraction.

J Cardiovasc Magn Reson

Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas' Hospital, 4th floor Lambeth Wing, Westminster Bridge Road, London, SE17EH, UK.

Published: March 2017


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: Pulse wave velocity (PWV) is a biomarker for the intrinsic stiffness of the aortic wall, and has been shown to be predictive for cardiovascular events. It can be assessed using cardiovascular magnetic resonance (CMR) from the delay between phase-contrast flow waveforms at two or more locations in the aorta, and the distance on CMR images between those locations. This study aimed to investigate the impact of different distance measurement methods on PWV. We present and evaluate an algorithm for automated centreline tracking in 3D images, and compare PWV calculations using distances derived from 3D images to those obtained from a conventional 2D oblique-sagittal image of the aorta.

Methods: We included 35 patients from a twin cohort, and 20 post-coarctation repair patients. Phase-contrast flow was acquired in the ascending, descending and diaphragmatic aorta. A 3D centreline tracking algorithm is presented and evaluated on a subset of 30 subjects, on three CMR sequences: balanced steady-state free precession (SSFP), black-blood double inversion recovery turbo spin echo, and contrast-enhanced CMR angiography. Aortic lengths are subsequently compared between measurements from a 2D oblique-sagittal plane, and a 3D geometry.

Results: The error in length of automated 3D centreline tracking compared with manual annotations ranged from 2.4 [1.8-4.3] mm (mean [IQR], black-blood) to 6.4 [4.7-8.9] mm (SSFP). The impact on PWV was below 0.5m/s (<5%). Differences between 2D and 3D centreline length were significant for the majority of our experiments (p < 0.05). Individual differences in PWV were larger than 0.5m/s in 15% of all cases (thoracic aorta) and 37% when studying the aortic arch only. Finally, the difference between end-diastolic and end-systolic 2D centreline lengths was statistically significant (p < 0.01), but resulted in small differences in PWV (0.08 [0.04 - 0.10]m/s).

Conclusions: Automatic aortic centreline tracking in three commonly used CMR sequences is possible with good accuracy. The 3D length obtained from such sequences can differ considerably from lengths obtained from a 2D oblique-sagittal plane, depending on aortic curvature, adequate planning of the oblique-sagittal plane, and patient motion between acquisitions. For accurate PWV measurements we recommend using 3D centrelines.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341448PMC
http://dx.doi.org/10.1186/s12968-017-0341-yDOI Listing

Publication Analysis

Top Keywords

automated centreline
12
centreline tracking
12
pulse wave
8
wave velocity
8
phase-contrast flow
8
aortic length
4
length measurements
4
measurements pulse
4
velocity calculation
4
calculation manual
4

Similar Publications

DECODE: An open-source cloud-based platform for the noninvasive management of peripheral artery disease.

Comput Methods Programs Biomed

August 2025

Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Biomedical Research Institute, Foundation for Research and Technology-Hellas, University Campus of Ioannina, Ioannina, GR45110, Greece. Elect

Background And Objective: Peripheral artery disease (PAD) is a progressive vascular condition affecting >237 million individuals worldwide. Accurate diagnosis and patient-specific treatment planning are critical but are often hindered by limited access to advanced imaging tools and real-time analytical support. This study presents DECODE, an open-source, cloud-based platform that integrates artificial intelligence, interactive 3D visualization, and computational modeling to improve the noninvasive management of PAD.

View Article and Find Full Text PDF

The tilt angle of sunflower flower heads is an important phenotypic characteristic that influences their growth and development, as well as the efficiency of mechanised harvesting in precision agriculture. Addressing the issues of low accuracy, high cost, and the risk of plant damage associated with traditional manual measurement methods, this study proposes a non-contact measurement method combining deep learning and geometric analysis to achieve precise measurement of sunflower flower head tilt angles. The specific method involves optimising the lightweight YOLO11-seg model to enhance instance segmentation performance for sunflower flower heads and stems (compared to the initial YOLO11 model, recall rate improved by 3.

View Article and Find Full Text PDF

This paper presents an open-source pipeline for simulating flow and flow-related processes in (embedded) tubular structures. Addressing a gap in computational fluid dynamics (CFD) and simulation sciences, it facilitates the transition from raw three-dimensional imaging, graph networks or computer aided design (CAD) models of tubular objects to refined, simulation-ready meshes. This transition, traditionally labour-intensive, is streamlined through a series of innovative steps that include surface mesh processing, centre-line construction, anisotropic mesh generation and volumetric meshing, leading to finite element method (FEM) simulations.

View Article and Find Full Text PDF

The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation.

View Article and Find Full Text PDF

Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net.

J Cardiovasc Magn Reson

June 2025

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Mill

Background: Magnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation, a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for automatic segmentation of the aortic lumen and vessel wall in high-resolution electrocardiogram-triggered free-breathing respiratory motion-corrected three-dimensional (3D) bright- and black-blood MRA images.

View Article and Find Full Text PDF