CT artifact correction for sparse and truncated projection data using generative adversarial networks. Issue 2 (30th December 2020)

• Record Type: Journal Article
• Authors: Podgorsak, Alexander R; Shiraz Bhurwani, Mohammad Mahdi; Ionita, Ciprian N
• Is Part Of: Medical physics. Volume 48:Issue 2(2021)
• Page Start: 615
• Page End: 626
• Publication Date: 2020-12-30
• Subjects: CT reconstruction -- deep convolutional generative adversarial network -- machine learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14504
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Computed tomography image reconstruction using truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts has been widely investigated. To continue these efforts, we investigated the use of machine learning‐based reconstruction techniques using deep convolutional generative adversarial networks (DCGANs) and evaluated its effect using standard imaging metrics. Methods: Ten thousand head computed tomography (CT) scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one‐third truncated and one‐third sparse manner. DCGANs were tasked with correcting the incomplete projection data, either in the sinogram domain where the full sinogram was recovered by the DCGAN and then reconstructed, or the reconstruction domain where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the DCGAN. Seventy‐five hundred images were used for network training and 2500 were withheld for network assessment using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal‐to‐noise ratio (PSNR) between results of different correction techniques. Image data from a quality‐assurance phantom were also resampled in the two manners and corrected and reconstructed for network performance assessment using line profiles across high‐contrast features, the modulation transfer function (MTF), noise power spectrum (NPS), and Hounsfield Unit (HU) linearity analysis. Results: Better agreement with the fully sampled reconstructions were achieved from sparse acquisition corrected in the sinogram domain and the truncated acquisition corrected in the reconstruction domain. MAE, SSIM, and PSNR showed quantitative improvement from the DCGAN correction techniques. HU linearity of the reconstructions was maintained by the correction techniques for the sparse and truncated acquisitions. MTF curves reached the 10% modulation cutoff frequency at 5.86 lp/cm for the truncated corrected reconstruction compared with 2.98 lp/cm for the truncated uncorrected reconstruction, and 5.36 lp/cm for the sparse corrected reconstruction compared with around 2.91 lp/cm for the sparse uncorrected reconstruction. NPS analyses yielded better agreement across a range of frequencies between the resampled corrected phantom and truth reconstructions. Conclusions: We demonstrated the use of DCGANs for CT‐image correction from sparse and truncated simulated projection data, while preserving imaging quality of the fully sampled projection data.