Objectives TrueFidelity (TF), a deep learning image reconstruction algorithm that was originally available only in standard kernel, has recently become available in lung kernel. This is the first study to assess TF in lung kernel (TF Lung) on image quality and nodule sharpness at ultra-low-dose CT (ULD CT). Materials and methods This study included patients who underwent both non-contrast ULD CT and contrast-enhanced CT (CE CT) of the chest. ULD CT scans were reconstructed using 6 algorithms, including Adaptive statistical iterative reconstruction-V in standard kernel (AR50 STD), AR50 in lung kernel (AR50 Lung), TF in standard kernel, and TF Lung at low, medium, and high strengths. CE CT scans were reconstructed using AR50 STD and AR50 Lung. In total, 8 sets of reconstruction images were obtained and reviewed for each patient. Objective image quality, such as image noise, signal-to-noise ratio, and contrast-to-noise ratio, was compared. Nodule sharpness was evaluated by calculating the full-width half-maximum value. Malignancy-related imaging features in nodules that had been pathologically confirmed as malignant were evaluated. CE CT reconstructed in the lung kernel (CE AR50 Lung) served as the reference standard. Results A total of 68 patients underwent analysis. ULD CT reconstructed with TF Lung at all strengths significantly decreased image noise compared to AR50 Lung (all p < 0.001). TF Lung in low and medium strengths showed similar nodule sharpness compared to the reference standard. Conclusion TF Lung has the potential to enhance both image quality and nodule evaluation in ULD CT scans. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> How does TrueFidelity in the lung kernel (TF Lung) perform in terms of image quality and lung nodule assessment in ultra-low-dose (ULD) CT scans? <Emphasis Type="BoldItalic">Findings</Emphasis> TF Lung significantly decreased image noise, and TF Lung in low and medium strengths showed similar nodule sharpness compared to the reference standard. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> TF Lung has the potential to reduce image noise without compromising the sharpness of nodules in ULD CT scans. TF Lung may support radiologists’ diagnostic decision-making for malignancy. Graphical Abstract

Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol

期刊论文
发表日期：

2026年3月12日

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Jaewon Kim,
Shinhyung Kang,
Min-Hee Hwang,
Ji Won Lee,
Yeon Joo Jeong,
Youseon Song,
Kyung Jin Nam,
Geewon Lee

摘要

Objectives

TrueFidelity (TF), a deep learning image reconstruction algorithm that was originally available only in standard kernel, has recently become available in lung kernel. This is the first study to assess TF in lung kernel (TF Lung) on image quality and nodule sharpness at ultra-low-dose CT (ULD CT).

Materials and methods

This study included patients who underwent both non-contrast ULD CT and contrast-enhanced CT (CE CT) of the chest. ULD CT scans were reconstructed using 6 algorithms, including Adaptive statistical iterative reconstruction-V in standard kernel (AR50 STD), AR50 in lung kernel (AR50 Lung), TF in standard kernel, and TF Lung at low, medium, and high strengths. CE CT scans were reconstructed using AR50 STD and AR50 Lung. In total, 8 sets of reconstruction images were obtained and reviewed for each patient. Objective image quality, such as image noise, signal-to-noise ratio, and contrast-to-noise ratio, was compared. Nodule sharpness was evaluated by calculating the full-width half-maximum value. Malignancy-related imaging features in nodules that had been pathologically confirmed as malignant were evaluated. CE CT reconstructed in the lung kernel (CE AR50 Lung) served as the reference standard.

Results

A total of 68 patients underwent analysis. ULD CT reconstructed with TF Lung at all strengths significantly decreased image noise compared to AR50 Lung (all p < 0.001). TF Lung in low and medium strengths showed similar nodule sharpness compared to the reference standard.

Conclusion

TF Lung has the potential to enhance both image quality and nodule evaluation in ULD CT scans.

Key Points

Question How does TrueFidelity in the lung kernel (TF Lung) perform in terms of image quality and lung nodule assessment in ultra-low-dose (ULD) CT scans?

Findings TF Lung significantly decreased image noise, and TF Lung in low and medium strengths showed similar nodule sharpness compared to the reference standard.

Clinical relevance TF Lung has the potential to reduce image noise without compromising the sharpness of nodules in ULD CT scans. TF Lung may support radiologists’ diagnostic decision-making for malignancy.

Graphical Abstract