Dynamic Computed Tomography Angiography for capturing vessel wall motion: A phantom study for optimal image reconstruction.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 22 12 2022
accepted: 11 10 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 22 12 2023
Statut: epublish

Résumé

Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis, and treatment decision making in vascular pathologies. The aim of this study is to determine the most suitable image reconstruction method for 4D CTA to accurately assess harmonic diameter changes of vessels. An elastic tube (inner diameter 6 mm, wall thickness 2 mm) was exposed to sinusoidal pressure waves with a frequency of 70 beats-per-minute. Five flow amplitudes were set, resulting in increasing sinusoidal diameter changes of the elastic tube, measured during three simulated pulsation cycles, using ECG-gated 4D CTA on a 320-detector row CT system. Tomographic images were reconstructed using one of the following three reconstruction methods: hybrid iterative (Hybrid-IR), model-based iterative (MBIR) and deep-learning based (DLR) reconstruction. The three reconstruction methods where based on 180 degrees (half reconstruction mode) and 360 degrees (full reconstruction mode) raw data. The diameter change, captured by 4D CTA, was computed based on image registration. As a reference metric for diameter change measurement, a 9 MHz linear ultrasound transducer was used. The sum of relative absolute differences (SRAD) between the ultrasound and 4D CTA measurements was calculated for each reconstruction method. The standard deviation was computed across the three pulsation cycles. MBIR and DLR resulted in a decreased SRAD and standard deviation compared to Hybrid-IR. Full reconstruction mode resulted in a decreased SRAD and standard deviations, compared to half reconstruction mode. 4D CTA can capture a diameter change pattern comparable to the pattern captured by US. DLR and MBIR algorithms show more accurate results than Hybrid-IR. Reconstruction with DLR is >3 times faster, compared to reconstruction with MBIR. Full reconstruction mode is more accurate than half reconstruction mode.

Sections du résumé

BACKGROUND BACKGROUND
Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis, and treatment decision making in vascular pathologies.
PURPOSE OBJECTIVE
The aim of this study is to determine the most suitable image reconstruction method for 4D CTA to accurately assess harmonic diameter changes of vessels.
METHODS METHODS
An elastic tube (inner diameter 6 mm, wall thickness 2 mm) was exposed to sinusoidal pressure waves with a frequency of 70 beats-per-minute. Five flow amplitudes were set, resulting in increasing sinusoidal diameter changes of the elastic tube, measured during three simulated pulsation cycles, using ECG-gated 4D CTA on a 320-detector row CT system. Tomographic images were reconstructed using one of the following three reconstruction methods: hybrid iterative (Hybrid-IR), model-based iterative (MBIR) and deep-learning based (DLR) reconstruction. The three reconstruction methods where based on 180 degrees (half reconstruction mode) and 360 degrees (full reconstruction mode) raw data. The diameter change, captured by 4D CTA, was computed based on image registration. As a reference metric for diameter change measurement, a 9 MHz linear ultrasound transducer was used. The sum of relative absolute differences (SRAD) between the ultrasound and 4D CTA measurements was calculated for each reconstruction method. The standard deviation was computed across the three pulsation cycles.
RESULTS RESULTS
MBIR and DLR resulted in a decreased SRAD and standard deviation compared to Hybrid-IR. Full reconstruction mode resulted in a decreased SRAD and standard deviations, compared to half reconstruction mode.
CONCLUSIONS CONCLUSIONS
4D CTA can capture a diameter change pattern comparable to the pattern captured by US. DLR and MBIR algorithms show more accurate results than Hybrid-IR. Reconstruction with DLR is >3 times faster, compared to reconstruction with MBIR. Full reconstruction mode is more accurate than half reconstruction mode.

Identifiants

pubmed: 38134125
doi: 10.1371/journal.pone.0293353
pii: PONE-D-22-35069
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0293353

Informations de copyright

Copyright: © 2023 Stam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Lotte B Stam (LB)

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.

Sabine M L Linden (SML)

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.
Technical Medical Center, University of Twente, Enschede, The Netherlands.

Luuk J Oostveen (LJ)

Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.

Hendrik H G Hansen (HHG)

Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.

René Aquarius (R)

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.

Cornelis H Slump (CH)

Technical Medical Center, University of Twente, Enschede, The Netherlands.

Chris L de Korte (CL)

Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.

Ronald H M A Bartels (RHMA)

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.

Mathias Prokop (M)

Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.

Hieronymus D Boogaarts (HD)

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.

Frederick J A Meijer (FJA)

Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.

Classifications MeSH