Marker-based C-arm self-calibration with unknown calibration pattern.
CT
C‐arm
self‐calibration
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
30 Apr 2024
30 Apr 2024
Historique:
revised:
04
04
2024
received:
11
06
2023
accepted:
05
04
2024
medline:
30
4
2024
pubmed:
30
4
2024
entrez:
30
4
2024
Statut:
aheadofprint
Résumé
Accurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C-arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition-specific calibration procedures. Most of geometric self-calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self-calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinical applications. We present a three-step marker based self-calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markers positions. The first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection-wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle-adjustment algorithm. The calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self-calibration method and exhibited sub-millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibility improvement. The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations in clinics.
Sections du résumé
BACKGROUND
BACKGROUND
Accurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C-arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition-specific calibration procedures. Most of geometric self-calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self-calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinical applications.
PURPOSE
OBJECTIVE
We present a three-step marker based self-calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markers positions.
METHODS
METHODS
The first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection-wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle-adjustment algorithm.
RESULTS
RESULTS
The calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self-calibration method and exhibited sub-millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibility improvement.
CONCLUSIONS
CONCLUSIONS
The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations in clinics.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : French National Research Agency
ID : ANR-15-IDEX-02
Organisme : French National Research Agency
ID : ANR-11-LABX-0004
Organisme : French National Research Agency
ID : ANR-19-P3IA-0003
Organisme : French National Research Agency
ID : ANR-19-CE19-0021-01
Organisme : European Union FEDER in Auvergne Rhône Alpes
ID : 3D4Carm project
Organisme : Fonds Unique Interministeriel
ID : 3D4Carm project
Informations de copyright
© 2024 American Association of Physicists in Medicine.
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