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Objective <p>The aim was to evaluate the performance of a single-breath-hold accelerated 3D-T1w-FFE 3-T MRI sequence for screening lung nodules, compared with photon-counting detector CT (PCD-CT), which served as the reference standard.</p> Materials and methods <p>In this single-center prospective study, 148 healthy adults underwent lung 3-T MRI between June 2024 and June 2025 using a single-breath-hold, AI-aided compressed sensing (AI-CS)- accelerated 3D-T1-FFE sequence (acceleration factor of 9) to evaluate for the presence of lung nodules. Patients scanned within 24 h using PCD-CT, for comparison, were enrolled. Lung-RADS scores were evaluated per patient, and all nodules were assessed for size and composition. The diagnostic performance of MRI was compared with PCD-CT, including interobserver and intermodality agreement, using Lin’s Concordance Correlation Coefficient (CCC).</p> Results <p>97 patients were enrolled (65 men, 67%; mean age 58 ± 12 years old) and 33 (34%) were active smokers. MRI and CT showed complete agreement in the Lung-RADS 4A and 4B categories. MRI demonstrated an overall sensitivity of 83.1% (95% CI: 73.2–89.9) for solid nodules, increasing to 98.1% (95% CI: 90.2–99.7) for nodules ≥ 4 mm. Lin’s CCC for nodule size was 0.985, indicating excellent agreement, with MRI showing a slight underestimation of 1.02 mm (95% CI: [−1.16, 3.20]) in Bland–Altman analysis. MRI failed to detect calcified nodules &lt; 4 mm, which accounted for 51.2% of undetected nodules.</p> Conclusions <p>A single breath-hold AI-CS accelerated 3D-T1-FFE MRI scan can be a valuable tool for solid lung nodule screening, showing very good agreement with CT.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> May lung MRI using an optimized AI-CS accelerated 3D-T1-FFE sequence acquired in a single breath hold be a radiation-free alternative to CT in lung cancer screening?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> Lung MRI using AI-CS 3D-T1-FFE sequence achieved an overall accuracy of 87.3% for detecting solid nodules overall, increasing to 97.7% for nodules ≥ 4 mm.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Lung MRI using an optimized AI-CS 3D-T1-FFE is a promising alternative to chest CT in lung nodules screening, offering high diagnostic accuracy, especially for clinically relevant nodules, excellent Lung-RADS classification agreement, and short acquisition time (17.2 s [IQR, 17.2–17.3])</i>.</p> Graphical Abstract <p></p>

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Diagnostic performance of a single breath-hold lung MRI scan with AI-powered compressed sensing for nodule detection in comparison to photon counting detector-CT

  • 期刊论文

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  • 发表日期：

    2026年7月10日

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  • Anna Palmisano,
  • Giulia Piccinni,
  • Davide Serra,
  • Elisa Bruno,
  • Giulio Ferrazzi,
  • Davide Vignale,
  • Carlo Tacchetti,
  • Antonio Esposito

摘要

Objective

The aim was to evaluate the performance of a single-breath-hold accelerated 3D-T1w-FFE 3-T MRI sequence for screening lung nodules, compared with photon-counting detector CT (PCD-CT), which served as the reference standard.

Materials and methods

In this single-center prospective study, 148 healthy adults underwent lung 3-T MRI between June 2024 and June 2025 using a single-breath-hold, AI-aided compressed sensing (AI-CS)- accelerated 3D-T1-FFE sequence (acceleration factor of 9) to evaluate for the presence of lung nodules. Patients scanned within 24 h using PCD-CT, for comparison, were enrolled. Lung-RADS scores were evaluated per patient, and all nodules were assessed for size and composition. The diagnostic performance of MRI was compared with PCD-CT, including interobserver and intermodality agreement, using Lin’s Concordance Correlation Coefficient (CCC).

Results

97 patients were enrolled (65 men, 67%; mean age 58 ± 12 years old) and 33 (34%) were active smokers. MRI and CT showed complete agreement in the Lung-RADS 4A and 4B categories. MRI demonstrated an overall sensitivity of 83.1% (95% CI: 73.2–89.9) for solid nodules, increasing to 98.1% (95% CI: 90.2–99.7) for nodules ≥ 4 mm. Lin’s CCC for nodule size was 0.985, indicating excellent agreement, with MRI showing a slight underestimation of 1.02 mm (95% CI: [−1.16, 3.20]) in Bland–Altman analysis. MRI failed to detect calcified nodules < 4 mm, which accounted for 51.2% of undetected nodules.

Conclusions

A single breath-hold AI-CS accelerated 3D-T1-FFE MRI scan can be a valuable tool for solid lung nodule screening, showing very good agreement with CT.

Key Points

Question May lung MRI using an optimized AI-CS accelerated 3D-T1-FFE sequence acquired in a single breath hold be a radiation-free alternative to CT in lung cancer screening?

Findings Lung MRI using AI-CS 3D-T1-FFE sequence achieved an overall accuracy of 87.3% for detecting solid nodules overall, increasing to 97.7% for nodules ≥ 4 mm.

Clinical relevance Lung MRI using an optimized AI-CS 3D-T1-FFE is a promising alternative to chest CT in lung nodules screening, offering high diagnostic accuracy, especially for clinically relevant nodules, excellent Lung-RADS classification agreement, and short acquisition time (17.2 s [IQR, 17.2–17.3]).

Graphical Abstract