Computed Tomography Artefact Detection Using Deep Learning—Towards Automated Quality Assurance
Inkinen, S. I.; Kotiaho, A. O.; Hanni, M.; Nieminen, M. T.; Brix, M. A.K. (2024-05-05)
Springer
05.05.2024
Inkinen, S.I., Kotiaho, A.O., Hanni, M., Nieminen, M.T., Brix, M.A.K. (2024). Computed Tomography Artefact Detection Using Deep Learning—Towards Automated Quality Assurance. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_2
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405304092
https://urn.fi/URN:NBN:fi:oulu-202405304092
Tiivistelmä
Abstract
Image artefacts in computed tomography (CT) limit the diagnostic quality of the images. The objective of this proof-of-concept study was to apply deep learning (DL) for automated CT artefact classification. Openly available Head CT data from Johns Hopkins University was used. Three common artefacts (patient movement, beam hardening, and ring artefacts (RAs)) and artefact free images were simulated using 2D axial slices. Simulated data were split into a training set (Ntrain = 1040 × 4(4160)), two validation sets (Nval1 = 130 × 4(520) and Nval2 = 130 × 4(520)), and a separate test set (Ntest = 201 × 4(804); two individual subjects). VGG-16 model architecture was used as a DL classifier, and the Grad-CAM approach was used to produce attention maps. Model performance was evaluated using accuracy, average precision, area under the receiver operating characteristics (ROC) curve, precision, recall, and F1-score. Sensitivity analysis was performed for two test set slice images in which different RA radiuses (4 pixels to 245) and movement artefacts, i.e., head tilt with rotation angles (0.2° to 3°), were generated. Artefact classification performance was excellent on the test set, as accuracy, average precision, and ROC area under curve over all classes were 0.91, 0.86, and 0.99, respectively. The precision, recall, and F1-scores were over 0.84, 0.71, and 0.85 for all class-wise cases. Sensitivity analysis revealed that the model detected movement at all rotation angles, yet it failed to detect the smallest RAs (4-pixel radius). DL can be used for effective detection of CT artefacts. In future, DL could be applied for automated quality assurance of clinical CT.
Image artefacts in computed tomography (CT) limit the diagnostic quality of the images. The objective of this proof-of-concept study was to apply deep learning (DL) for automated CT artefact classification. Openly available Head CT data from Johns Hopkins University was used. Three common artefacts (patient movement, beam hardening, and ring artefacts (RAs)) and artefact free images were simulated using 2D axial slices. Simulated data were split into a training set (Ntrain = 1040 × 4(4160)), two validation sets (Nval1 = 130 × 4(520) and Nval2 = 130 × 4(520)), and a separate test set (Ntest = 201 × 4(804); two individual subjects). VGG-16 model architecture was used as a DL classifier, and the Grad-CAM approach was used to produce attention maps. Model performance was evaluated using accuracy, average precision, area under the receiver operating characteristics (ROC) curve, precision, recall, and F1-score. Sensitivity analysis was performed for two test set slice images in which different RA radiuses (4 pixels to 245) and movement artefacts, i.e., head tilt with rotation angles (0.2° to 3°), were generated. Artefact classification performance was excellent on the test set, as accuracy, average precision, and ROC area under curve over all classes were 0.91, 0.86, and 0.99, respectively. The precision, recall, and F1-scores were over 0.84, 0.71, and 0.85 for all class-wise cases. Sensitivity analysis revealed that the model detected movement at all rotation angles, yet it failed to detect the smallest RAs (4-pixel radius). DL can be used for effective detection of CT artefacts. In future, DL could be applied for automated quality assurance of clinical CT.
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