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CT Lilun yu yingyong yanjiu
By Linsha CAI, Xiaojuan LIN, Borong TANG and 3 others.
To investigate the effectiveness and associated effects of combining deep learning image reconstruction (DLIR) with metal artifact reduction (MAR) to reduce metal artifacts from knee implants in spectral computed tomography (CT) images.
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This retrospective study included energy spectrum CT data from 30 patients who underwent knee arthroplasty. Image reconstruction was performed using adaptive statistical iterative reconstruction (50% ASiR-V) and three different levels of deep learning image reconstruction (DLIR-L/M/H). Subsequently, metal artifact reduction (MAR) was applied to each reconstruction to generate the second set of images. Images were reconstructed at energy levels ranging from 40 keV to 140 keV in 10 keV increments (11 single-energy levels in total), resulting in 44 image datasets across four groups (MAR combined with the four reconstruction algorithms). Image quality was assessed by measuring the CT number and standard deviation (SD) in the region of interest adjacent to the implant, as well as calculating the artifact index (AI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) as objective evaluation criteria. Concurrently, we performed a subjective scoring of image quality to evaluate both image quality and diagnostic confidence.
The objective evaluation results revealed that using the same algorithm, AI decreased as keV increased. The AI in the MAR-ASiR group was lower than that in the MAR-DLIR groups, with the MAR-ASiR group demonstrating the best metal artifact removal performance at 130 keV and 140 keV. Across multiple energy levels, the MAR-DLIR-H group exhibited superior CNR and SNR values compared to the MAR-ASiR group, with an optimal balance between soft tissue signal-to-noise ratio and tissue contrast observed at 110 keV. The results of the subjective evaluation showed that the image quality and diagnostic confidence scores were higher in the MAR-DLIR-H group than in the MAR-ASiR group.
VMI reconstructed using the DLIR algorithm combined with MAR exhibit superior quality compared to those reconstructed using the ASiR-V algorithm combined with MAR; the DLIR-H reconstruction algorithm yields the best image quality at 110 keV.
Published in CT Lilun yu yingyong yanjiu. The abstract is shown as published and remains the work of its authors; read the full article at the source.
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