Zhu Hong, Kong Deyan, Qian Jiale, Shi Xiaomeng, Fan Jing
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, China.
CT Imaging Research Center, GE Healthcare China, Shanghai 201203, China.
Eur J Radiol Open. 2024 Aug 31;13:100599. doi: 10.1016/j.ejro.2024.100599. eCollection 2024 Dec.
To compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU).
A retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility.
DH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05).
The DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.
比较深度学习图像重建（DLIR）与自适应统计迭代重建-Veo（ASIR-V）重建的虚拟非增强（VNC）图像及真实非增强（TNC）图像在光谱CT尿路造影（CTU）中对肾结石的图像质量和检测准确性。
对70例接受腹部盆腔CTU检查的患者图像进行回顾性分析，其中TNC期采用非增强扫描，对比增强的皮质髓质期（CP）和排泄期（EP）采用光谱扫描。TNC扫描采用ASIR-V 70%进行重建（TNC-AR70），对比增强扫描采用AR70、DLIR中级（DM）和高级（DH）进行重建，分别获得CP-VNC-AR70/DM/DH和EP-VNC-AR70/DM/DH图像组。测量并比较各组的CT值、图像质量和肾结石定量准确性。由两名资深放射科医生使用5分李克特量表对图像质量和病变可见性进行独立主观评估。
在客观图像质量评估中，DH图像优于AR70和DM图像。VNC和TNC图像在肝脏和脾脏方面无统计学差异（均P>0.05），在肾脏和脂肪的CT值方面相差不超过6HU。EP-VNC-DH的图像噪声最低，SNR和CNR最高，VNC-AR70图像的噪声和SNR性能优于TNC-AR70图像（均p<0.05）。EP-VNC-DH的主观图像质量最高，CP-VNC-DH在病变可见性方面表现最佳。在结石CT值和体积测量方面，VNC和TNC之间无统计学差异（P>0.05）。
CTU中DLIR重建的VNC图像比ASIR-V重建的TNC图像具有更好的图像质量，在肾结石定量准确性方面相似，有可能节省辐射剂量。该研究强调，光谱CT尿路造影（CTU）中深度学习图像重建（DLIR）重建的虚拟非增强（VNC）图像与传统真实非增强（TNC）图像相比，图像质量有所提高，同时在肾结石检测中保持相似的准确性，表明在临床实践中有可能节省辐射剂量。
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