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Verfasst von:Magonov, Jan [VerfasserIn]   i
 Maier, Joscha [VerfasserIn]   i
 Erath, Julien [VerfasserIn]   i
 Sunnegårdh, Johan [VerfasserIn]   i
 Fournié, Eric [VerfasserIn]   i
 Stierstorfer, Karl [VerfasserIn]   i
 Kachelrieß, Marc [VerfasserIn]   i
Titel:Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling
Titelzusatz:method and robustness evaluation
Verf.angabe:Jan Magonov, Joscha Maier, Julien Erath, Johan Sunnegårdh, Eric Fournié, Karl Stierstorfer, Marc Kachelrieß
Ausgabe:Online version of record before inclusion in an issue
E-Jahr:2024
Jahr:16 January 2024
Umfang:20 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 11.03.2024
Titel Quelle:Enthalten in: Medical physics
Ort Quelle:Hoboken, NJ : Wiley, 1974
Jahr Quelle:2024
Band/Heft Quelle:51(2024), 3, Seite 1597-1616
ISSN Quelle:2473-4209
 1522-8541
Abstract:Background Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contrast structures. Purpose The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternative solution. Methods We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitch value. Results In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifact reduction. Conclusions Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components for zFFS.
DOI:doi:10.1002/mp.16938
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1002/mp.16938
 kostenfrei: Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.16938
 DOI: https://doi.org/10.1002/mp.16938
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:clinical spiral CT
 computed tomography
 convolutional neural network
 deep learning
 image quality
 medical imaging
 projection rawdata upsampling
 windmill artifact reduction
 z-flying focal spot
K10plus-PPN:1883061423
Verknüpfungen:→ Zeitschrift

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