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<p>Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Reconstruction (DLIR-H) shows potential for maintaining image quality in low-dose protocols. In this prospective study, 180 patients undergoing dual-energy CTA were divided into three groups: a control group (ASIR-V 50%, NI = 4, contrast = 0.5&#xa0;mL/kg), a low-dose group (DLIR-H, NI = 11, contrast = 0.5&#xa0;mL/kg), and an ultra-low-dose group (DLIR-H, NI = 13, contrast = 0.4&#xa0;mL/kg). Objective (CTV[CT values], noise, SNR, CNR) and subjective (5-point Likert scale) image quality were evaluated. The ultra-low-dose group achieved a 20.3% reduction in contrast volume and a 53.3% reduction in effective dose compared to the control group (<i>P</i> &lt; 0.001). Both experimental groups showed lower noise and higher CNR/SNR (except at aortic arch) than controls. However, the ultra-low-dose group had significantly lower CNR/SNR than the low-dose group (<i>P</i> &lt; 0.05). Subjective image quality was superior in both experimental groups (<i>P</i> &lt; 0.001), with high inter-rater agreement. DLIR-H outperformed ASIR-V in low and ultra-low-dose protocols but could not fully compensate for image quality degradation when radiation and contrast were further reduced.</p>

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Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying Dose Reductions in Dual-Energy Carotid CT Angiography

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Chenzi Wang,
  • Juan Long,
  • Dapeng Zhang,
  • Lulu Fan,
  • Zhen Wang,
  • Xiaohan Liu,
  • He Zhang,
  • Chong Wang,
  • Yang Wu,
  • Aiyun Sun,
  • Kai Xu,
  • Yankai Meng

摘要

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Reconstruction (DLIR-H) shows potential for maintaining image quality in low-dose protocols. In this prospective study, 180 patients undergoing dual-energy CTA were divided into three groups: a control group (ASIR-V 50%, NI = 4, contrast = 0.5 mL/kg), a low-dose group (DLIR-H, NI = 11, contrast = 0.5 mL/kg), and an ultra-low-dose group (DLIR-H, NI = 13, contrast = 0.4 mL/kg). Objective (CTV[CT values], noise, SNR, CNR) and subjective (5-point Likert scale) image quality were evaluated. The ultra-low-dose group achieved a 20.3% reduction in contrast volume and a 53.3% reduction in effective dose compared to the control group (P < 0.001). Both experimental groups showed lower noise and higher CNR/SNR (except at aortic arch) than controls. However, the ultra-low-dose group had significantly lower CNR/SNR than the low-dose group (P < 0.05). Subjective image quality was superior in both experimental groups (P < 0.001), with high inter-rater agreement. DLIR-H outperformed ASIR-V in low and ultra-low-dose protocols but could not fully compensate for image quality degradation when radiation and contrast were further reduced.