Guan Sylvie, Poujol Julie, Gouhier Elodie, Touloupas Caroline, Delpla Alexandre, Boulay-Coletta Isabelle, Zins Marc
Department of Medical Imaging, Saint Joseph Hospital, Paris, France.
Department of Medical Imaging, Rothschild Foundation Hospital, Paris, France.
Insights Imaging. 2025 Sep 24;16(1):204. doi: 10.1186/s13244-025-02066-7.
To compare overall image quality, lesion conspicuity and detectability on 3D-T1w-GRE arterial phase high-resolution MR images with deep learning reconstruction (3D-DLR) against standard-of-care reconstruction (SOC-Recon) in patients with suspected pancreatic disease.
Patients who underwent a pancreatic MR exam with a high-resolution 3D-T1w-GRE arterial phase acquisition on a 3.0-T MR system between December 2021 and June 2022 in our center were retrospectively included. A new deep learning-based reconstruction algorithm (3D-DLR) was used to additionally reconstruct arterial phase images. Two radiologists blinded to the reconstruction type assessed images for image quality, artifacts and lesion conspicuity using a Likert scale and counted the lesions. Signal-to-noise ratio and lesion contrast-to-noise ratio were calculated for each reconstruction. Quantitative data were evaluated using paired t-tests. Ordinal data such as image quality, artifacts and lesions conspicuity were analyzed using paired-Wilcoxon tests. Interobserver agreement for image quality and artifact assessment was evaluated using Cohen's kappa.
Thirty-two patients (mean age 62 years ± 12, 16 female) were included. 3D-DLR significantly improved SNR for each pancreatic segment and lesion CNR compared to SOC-Recon (p < 0.01), and demonstrated significantly higher average image quality score (3.34 vs 2.68, p < 0.01). 3D DLR also significantly reduced artifacts compared to SOC-Recon (p < 0.01) for one radiologist. 3D-DLR exhibited significantly higher average lesion conspicuity (2.30 vs 1.85, p < 0.01). The sensitivity was increased with 3D-DLR compared to SOC-Recon for both reader 1 and reader 2 (1 vs 0.88 and 0.88 vs 0.83, p = 0.62 for both results).
3D-DLR images demonstrated higher overall image quality, leading to better lesion conspicuity.
3D deep learning reconstruction can be applied to gadolinium-enhanced pancreatic 3D-T1w arterial phase high-resolution images without additional acquisition time to further improve image quality and lesion conspicuity.
3D DLR has not yet been applied to pancreatic MRI high-resolution sequences. This method improves SNR, CNR, and overall 3D T1w arterial pancreatic image quality. Enhanced lesion conspicuity may improve pancreatic lesion detectability.
比较在怀疑患有胰腺疾病的患者中，3D-T1w-GRE动脉期高分辨率磁共振图像经深度学习重建（3D-DLR）与标准护理重建（SOC-Recon）后的整体图像质量、病变清晰度和可检测性。
回顾性纳入2021年12月至2022年6月期间在本中心接受3.0-T磁共振系统高分辨率3D-T1w-GRE动脉期胰腺磁共振检查的患者。使用一种新的基于深度学习的重建算法（3D-DLR）额外重建动脉期图像。两位对重建类型不知情的放射科医生使用李克特量表评估图像的质量、伪影和病变清晰度，并对病变进行计数。计算每种重建的信噪比和病变对比噪声比。定量数据采用配对t检验进行评估。图像质量、伪影和病变清晰度等有序数据采用配对威尔科克森检验进行分析。使用科恩kappa系数评估观察者间在图像质量和伪影评估方面的一致性。
纳入32例患者（平均年龄62岁±12岁，16例女性）。与SOC-Recon相比，3D-DLR显著提高了每个胰腺节段的信噪比和病变对比噪声比（p < 0.01），并显示出显著更高的平均图像质量评分（3.34对2.68，p < 0.01）。对于一位放射科医生来说，与SOC-Recon相比，3D DLR也显著减少了伪影（p < 0.01）。3D-DLR的平均病变清晰度显著更高（2.30对1.85，p < 0.01）。与SOC-Recon相比，3D-DLR使读者1和读者2的敏感性均有所提高（1对0.88以及0.88对0.83，两个结果的p均为0.62）。
3D-DLR图像显示出更高的整体图像质量，从而使病变清晰度更高。
3D深度学习重建可应用于钆增强胰腺3D-T1w动脉期高分辨率图像，无需额外采集时间，以进一步提高图像质量和病变清晰度。
3D DLR尚未应用于胰腺MRI高分辨率序列。该方法提高了信噪比、对比噪声比以及3D T1w动脉期胰腺图像的整体质量。增强的病变清晰度可能会提高胰腺病变的可检测性。
