Purpose To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared with routine CTPA using hybrid iterative reconstruction (HIR). Materials and methods This prospective two-center study included 130 patients who underwent CTPA for suspected pulmonary embolism. Patients were randomly divided into two groups: the routine CTPA group, reconstructed using HIR; and the dual-low dose CTPA group, reconstructed using HIR and DLR, additionally combined with the CE-boost to generate HIR-boost and DLR-boost images. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary arteries were quantitatively assessed. Two experienced radiologists independently ordered CT images (5, best; 1, worst) based on overall image noise and vascular contrast. Diagnostic performance for PE detection was calculated for each dataset. Results Patient demographics were similar between groups. Compared to HIR images of the routine group, DLR-boost images of the dual-low dose group were significantly better at qualitative scores (p < 0.001). The CT values of pulmonary arteries between the DLR-boost and the HIR images were comparable (p > 0.05), whereas the SNRs and CNRs of pulmonary arteries in the DLR-boost images were the highest among all five datasets (p < 0.001). The AUCs of DLR, HIR-boost, and DLR-boost were 0.933, 0.924, and 0.986, respectively (all p > 0.05). Conclusion DLR combined with CE-boost technique can significantly improve the image quality of CTPA with reduced radiation and contrast doses, facilitating a more accurate diagnosis of pulmonary embolism. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> The dual-low dose protocol is essential for detecting pulmonary emboli (PE) in follow-up CT pulmonary angiography (PA), yet effective solutions are still lacking. <Emphasis Type="BoldItalic">Findings</Emphasis> Deep learning reconstruction (DLR)-boost with reduced radiation and contrast doses demonstrated higher quantitative and qualitative image quality than hybrid-iterative reconstruction in the routine CTPA. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> DLR-boost based low-radiation and low-contrast-dose CTPA protocol offers a novel strategy to further enhance the image quality and diagnosis accuracy for pulmonary embolism patients. Graphical Abstract

Deep learning reconstruction combined with contrast-enhancement boost in dual-low dose CT pulmonary angiography: a two-center prospective trial

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发表日期：

2025年5月24日

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Leilei Shen,
Jinjuan Lu,
Chun Zhou,
Zhenghong Bi,
Xiaodan Ye,
Zicheng Zhao,
Min Xu,
Mengsu Zeng,
Mingliang Wang

摘要

Purpose

To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared with routine CTPA using hybrid iterative reconstruction (HIR).

Materials and methods

This prospective two-center study included 130 patients who underwent CTPA for suspected pulmonary embolism. Patients were randomly divided into two groups: the routine CTPA group, reconstructed using HIR; and the dual-low dose CTPA group, reconstructed using HIR and DLR, additionally combined with the CE-boost to generate HIR-boost and DLR-boost images. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary arteries were quantitatively assessed. Two experienced radiologists independently ordered CT images (5, best; 1, worst) based on overall image noise and vascular contrast. Diagnostic performance for PE detection was calculated for each dataset.

Results

Patient demographics were similar between groups. Compared to HIR images of the routine group, DLR-boost images of the dual-low dose group were significantly better at qualitative scores (p < 0.001). The CT values of pulmonary arteries between the DLR-boost and the HIR images were comparable (p > 0.05), whereas the SNRs and CNRs of pulmonary arteries in the DLR-boost images were the highest among all five datasets (p < 0.001). The AUCs of DLR, HIR-boost, and DLR-boost were 0.933, 0.924, and 0.986, respectively (all p > 0.05).

Conclusion

DLR combined with CE-boost technique can significantly improve the image quality of CTPA with reduced radiation and contrast doses, facilitating a more accurate diagnosis of pulmonary embolism.

Key Points

Question The dual-low dose protocol is essential for detecting pulmonary emboli (PE) in follow-up CT pulmonary angiography (PA), yet effective solutions are still lacking.

Findings Deep learning reconstruction (DLR)-boost with reduced radiation and contrast doses demonstrated higher quantitative and qualitative image quality than hybrid-iterative reconstruction in the routine CTPA.

Clinical relevance DLR-boost based low-radiation and low-contrast-dose CTPA protocol offers a novel strategy to further enhance the image quality and diagnosis accuracy for pulmonary embolism patients.

Graphical Abstract