Nagaraj Usha D, Dillman Jonathan R, Tkach Jean A, Greer Joshua S, Leach James L
Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA.
Department of Radiology, University of Cincinnati, Cincinnati, OH, USA.
Pediatr Radiol. 2024 Jul;54(8):1337-1343. doi: 10.1007/s00247-024-05968-8. Epub 2024 Jun 18.
Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice.
To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging.
This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios.
AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively.
We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.
人工智能（AI）重建技术有提高图像质量和缩短成像时间的潜力。然而，必须评估这些技术在临床实践中的安全有效使用情况。
评估人工智能重建在儿童脑部液体衰减反转恢复（FLAIR）成像中的图像质量和诊断置信度。
这项经机构审查委员会（IRB）批准的前瞻性研究纳入了50例接受临床脑部MRI检查的儿科患者（中位年龄=12岁，第一四分位数=10岁，第三四分位数=14岁）。T2加权（T2W）FLAIR图像通过标准临床和人工智能重建算法（强去噪）进行重建。图像由两名神经放射科医生在专用的研究图像存档与通信系统（PACS）上独立评分，以表明与标准重建相比，人工智能对图像质量是提高、降低还是没有影响。还进行了信号强度的定量分析，以计算表观信噪比（aSNR）和表观对比噪声比（aCNR）。
在总体图像质量方面，99%（读者1，49/50；读者2，50/50）的人工智能重建优于标准重建；在主观信噪比方面，99%（读者1，49/50；读者2，50/50）；在诊断偏好方面，98%（读者1，49/50；读者2，49/50）。定量分析显示，与标准重建（分别为18±2.7、14.2±2.8、4.4±0.8，p<0.001）相比，人工智能重建图像中的灰质aSNR（30.6±6.5）、白质aSNR（21.4±5.6）和灰白质aCNR（7.1±1.6）显著更高。
我们得出结论，与儿科患者的标准重建相比，人工智能重建在大多数患者中改善了T2W FLAIR图像质量。
