Corina Kräuter1,2, Ursula Reiter1, Gabor Kovacs3,4, Clemens Reiter1, Marc Masana5, Horst Olschewski3,4, Michael Fuchsjäger1, Rudolf Stollberger2, and Gert Reiter1,6
1Department of Radiology, Medical University of Graz, Graz, Austria, 2Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 3Department of Internal Medicine, Medical University of Graz, Graz, Austria, 4Ludwig Boltzmann Institute for Lung Vascular Research Graz, Graz, Austria, 5Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 6Research and Development, Siemens Healthcare Diagnostics GmbH, Graz, Austria
Synopsis
Pulmonary hypertension is characterized by elevated
mean pulmonary arterial pressure. Visual vortex analysis revealed a strong
relation between elevated mean pulmonary arterial pressure and the duration of
vortical blood flow along the pulmonary artery, however, automated pulmonary
hypertension-related vortex detection methods are lacking. We propose a method
for automated detection and tracking of the pulmonary hypertension-related
vortex from 4D flow data and aim to compare it with visual analysis and to
validate automatically estimated mean pulmonary arterial pressure against
invasively measured results. The automated method agreed very well with visual vortex
detection and accurately estimated elevated mean pulmonary arterial pressure.
Introduction
Pulmonary
hypertension (PH) is a life-threatening condition that is diagnosed invasively
by measurement of mean pulmonary arterial pressure (mPAP) using right heart
catheterization (RHC). There is a piecewise linear model relating mPAP and the
duration of vortical blood flow along the main pulmonary artery1 as
visualized by time-resolved three-directional cardiovascular magnetic resonance
phase-contrast imaging (4D flow). While this model allows for accurate and precise
estimation of mPAP and therefore non-invasive diagnosis of PH1, studies
applying it performed PH-related vortex detection only visually.2,3
We propose an automated method for detection and tracking of the PH-related
vortex from 4D flow data and aim to compare it with visual vortex detection and
to validate automatically estimated mPAP against RHC-derived results.Methods
32
subjects with known or suspected PH and regular heart rhythm (male/female 7/25;
age 62±15 years) underwent RHC and 4D flow imaging at 3T (Magnetom Trio or
Magnetom Skyra, Siemens Healthcare, Erlangen, Germany). A stack of slices
covering the main pulmonary artery and the proximal parts of the side branches
was acquired using a two-dimensional retrospectively-ECG-gated phase-contrast
sequence with three-directional velocity encoding (temporal resolution 42-47 ms
interpolated to 25-30 cardiac frames, repetition time 5.2-5.9 ms, echo time 3.1
ms, flip angle 12-15°, voxel size 1.8x2.5x4.0 mm3, velocity encoding
90 cm/s, two-fold averaging). Prototype software (4DFlow, Siemens Healthcare,
Erlangen, Germany) was used for pre-processing of velocity fields (phase offset
error correction, pulmonary artery segmentation) and visual PH-related vortex
detection (Figure 1). Automated PH-related vortex detection and tracking was
performed using in-house software implemented in Matlab (MathWorks Inc.,
Natick, MA) in three steps (Figure 2). First, streamlines were seeded on the
divergence-free vector field4 in each voxel of the pulmonary artery
in forward and backward directions and selected if they exhibited vortex-like
characteristics. Second, streamline groups were detected in systolic frames to
identify the PH-related vortex when it is fully developed (start vortex) using streamline position and angulation criteria. Third,
the PH-related vortex was tracked forward and backward in the cine series by
applying a streamline angulation-based iterative procedure in each frame,
targeting continuous vortex volume evolution over time. PH-related vortex duration (tvortex) was calculated for
visual and automated analysis as the number of frames with a vortex relative to
all frames of the cardiac interval (in %). Vortex onset and dissolution times
were determined from the first and last frame with a PH-related vortex.
Elevated mPAP was estimated from tvortex
according to the piecewise linear model by Reiter et al.1 Visually
and automatically detected PH-related vortices in each frame were compared by
kappa statistics; tvortex
and onset and dissolution times were compared by the paired t-test in case of
normality of differences, otherwise by the Wilcoxon test. The
relationships between tvortex
determined by visual and automated analysis as well as between automatically estimated
mPAP and mPAP measured by RHC were assessed using Pearson correlation and Bland-Altman analysis.
P<0.05 was considered as
significant.Results
PH was diagnosed by RHC in 19 subjects. Visual analysis identified PH-related vortices in all PH subjects and none of the non-PH subjects; the proposed automated method detected PH-related vortices in all PH subjects and one non-PH subject. Figure 3 shows an example of PH-related vortex evolution from one PH subject. Agreement between visually and automatically detected PH-related vortices in each frame was very good (kappa = 0.87 [0.83, 0.90], Table 1). PH-related vortex duration determined by visual and automated analysis did not differ significantly (tvortex,vis = 25.7±27.1 %, tvortex,auto = 25.7±25.8 %, P=0.85) and correlated strongly (r = 0.98). Moreover, there was no significant bias in the respective vortex onset or dissolution times (biasonset = -1.9±4.9 %, P=0.11; biasdissol = -1.4±6.3 %, P=0.17). For all PH subjects, automatically estimated mPAP and mPAP measured by RHC did not differ significantly (mPAPauto = 43.0±12.3 mmHg, mPAPRHC = 42.7±14.6 mmHg, P=0.79; Figure 4) and correlated strongly (r = 0.94).Discussion
The proposed automated method successfully detected and tracked the PH-related vortex from 4D flow data. Swirling streamlines are a suitable flow topology-based criterion for PH-related vortex detection because they resemble the original definition of the PH-related vortex as groups of velocity vectors forming closed concentric tangent curves.5 Calculating streamlines in spatial forward and backward directions and only keeping the ones returning closer to the respective seeding points increases robustness of the method against noise in the measured velocity field. The streamline position and angulation as well as vortex volume criteria in PH-related vortex detection and tracking ensure that no other vortices in the pulmonary artery, e.g. purely valvular vortices, are mistaken for a PH-related vortex. While automated PH-related vortex detection agreed very well with visual analysis, the automated method has the advantages that it objectifies the PH-related vortex phenomenon and does not require knowledge and experience in the interpretation of the complex flow patterns in the pulmonary artery. Furthermore, the automated method allowed bias-free estimation of elevated mPAP with a standard deviation lower than the ones reported by other non-invasive mPAP estimation studies.6,7Conclusion
Automated PH-related vortex detection and tracking
from 4D flow data is feasible and demonstrated very good agreement with visual
analysis and accurate estimation of elevated mPAP.Acknowledgements
We thankfully
acknowledge the support of the OeNB Anniversary Fund (Grant No. 17934) and the “University
SAL Labs” initiative of Silicon Austria Labs (SAL).References
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