ArticleJournal of applied clinical medical physics2023
Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- Deep learning-based metal artifact reduction in CT for total knee arthroplasty.Scientific reports · 2025Article
- Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT.Infection and drug resistance · 2025Article
- Experimental and clinical validation of an artificial intelligence metal artifact correction algorithm for low-dose following up CT of percutaneous vertebroplasty.Quantitative imaging in medicine and surgery · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.Journal of applied clinical medical physics · 2023Article
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10 authors.
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Abstract
purposeTo validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique. MATERIALS AND
methodsAn experimental phantom was designed by consecutively inserting two sets of pedicle screws (size Φ 6.5 × 30-mm and Φ 7.5 × 40-mm) into a vertebral specimen to simulate the clinical scenario of metal implantation. The resulting MAC, VMI, and AI-MAC images were compared with respect to the metal-free reference image by subjective scoring, as well as by CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and correction accuracy via adaptive segmentation of the paraspinal muscle and vertebral body.
resultsThe AI-MAC and VMI images showed significantly higher subjective scores than the MAC image (all p < 0.05). The SNRs and CNRs on the AI-MAC image were comparable to the reference (all p > 0.05), whereas those on the VMI were significantly lower (all p < 0.05). The paraspinal muscle segmented on the AI-MAC image was 4.6% and 5.1% more complete to the VMI and MAC images for the Φ 6.5 × 30-mm screws, and 5.0% and 5.1% for the Φ 7.5 × 40-mm screws, respectively. The vertebral body segmented on the VMI was closest to the reference, with only 3.2% and 7.4% overestimation for Φ 6.5 × 30-mm and Φ 7.5 × 40-mm screws, respectively.
conclusionsUsing metal-free reference as the ground truth for comparison, the AI-MAC outperforms VMI in characterizing soft tissue, while VMI is useful in skeletal depiction.
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