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Objectives <p>This study aimed to assess and compare the efficacy of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V) in improving the image quality of 40&#xa0;keV virtual monochromatic images (VMIs) in dual-energy CT carotid angiography (DE-CTA) under a dual-low scan protocol.</p> Methods <p>A prospective study was conducted between December 2024 and January 2025, involving a total of 100 patients who underwent DE-CTA at our institution. Patients were assigned into two distinct cohorts: Control Group (<i>n</i> = 50), scanned with a noise index (NI) of 4 and reconstructed using ASIR-V 50%; and Experimental Group (<i>n</i> = 50), scanned with a noise index (NI) of 11. For the Experimental Group, images were reconstructed using three different methods: ASIR-V 50%, DLIR at a medium-strength setting (DLIR-M), and DLIR at a high-strength setting (DLIR-H). Accordingly, the 50 patients in the Experimental Group were further categorized into Experimental Group 1 (EG 1) for ASIR-V 50%, Experimental Group 2 (EG 2) for DLIR-M, and Experimental Group 3 (EG 3) for DLIR-H. Thus, the same 50 patients contributed to all three experimental subgroups, while the Control Group remained independent with its 50 patients. Objective image quality was assessed at four anatomical levels (aortic arch, subclavian artery, common carotid artery, and internal carotid artery), with measurements of CT values (HU), image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was independently evaluated by two experienced radiologists using a five-point Likert scale, focusing on noise, resolution, and overall image quality.</p> Results <p>Among all reconstruction methods, DLIR-H yielded the lowest image noise and the highest SNR and CNR at all anatomical levels (<i>P</i> &lt; 0.05). Subjective evaluation scores were significantly higher for DLIR-H, indicating superior image clarity, noise reduction, and overall diagnostic confidence (<i>P</i> &lt; 0.05). No statistically significant differences were observed in vascular CT values among the groups.</p> Conclusion <p>DLIR reconstruction improves the image quality of 40&#xa0;keV VMIs in DE-CTA under dual-low scanning conditions, with a reduction in image noise and enhanced SNR and CNR, providing optimal image quality for diagnostic purposes at reduced radiation doses.</p>

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Comparison of deep learning reconstruction algorithms to improve image quality of dual-energy carotid CT angiography under dual-low scan

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Shi Qiu,
  • Kai Xu,
  • Yankai Meng,
  • Yang Wu,
  • Chenzi Wang,
  • Juan Long,
  • Wenbei Xu,
  • He Zhang,
  • Meng Yu,
  • Zhongxiao Liu,
  • Aijun Sun

摘要

Objectives

This study aimed to assess and compare the efficacy of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V) in improving the image quality of 40 keV virtual monochromatic images (VMIs) in dual-energy CT carotid angiography (DE-CTA) under a dual-low scan protocol.

Methods

A prospective study was conducted between December 2024 and January 2025, involving a total of 100 patients who underwent DE-CTA at our institution. Patients were assigned into two distinct cohorts: Control Group (n = 50), scanned with a noise index (NI) of 4 and reconstructed using ASIR-V 50%; and Experimental Group (n = 50), scanned with a noise index (NI) of 11. For the Experimental Group, images were reconstructed using three different methods: ASIR-V 50%, DLIR at a medium-strength setting (DLIR-M), and DLIR at a high-strength setting (DLIR-H). Accordingly, the 50 patients in the Experimental Group were further categorized into Experimental Group 1 (EG 1) for ASIR-V 50%, Experimental Group 2 (EG 2) for DLIR-M, and Experimental Group 3 (EG 3) for DLIR-H. Thus, the same 50 patients contributed to all three experimental subgroups, while the Control Group remained independent with its 50 patients. Objective image quality was assessed at four anatomical levels (aortic arch, subclavian artery, common carotid artery, and internal carotid artery), with measurements of CT values (HU), image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was independently evaluated by two experienced radiologists using a five-point Likert scale, focusing on noise, resolution, and overall image quality.

Results

Among all reconstruction methods, DLIR-H yielded the lowest image noise and the highest SNR and CNR at all anatomical levels (P < 0.05). Subjective evaluation scores were significantly higher for DLIR-H, indicating superior image clarity, noise reduction, and overall diagnostic confidence (P < 0.05). No statistically significant differences were observed in vascular CT values among the groups.

Conclusion

DLIR reconstruction improves the image quality of 40 keV VMIs in DE-CTA under dual-low scanning conditions, with a reduction in image noise and enhanced SNR and CNR, providing optimal image quality for diagnostic purposes at reduced radiation doses.