Nagaraj Usha D, Meineke Jakob, Sriwastwa Aakanksha, Tkach Jean A, Leach James L, Doneva Mariya
Cincinnati Children's Hospital Medical Center, University of Cincinnati Department of Radiology, 3333 Burnet Avenue, Cincinnati, OH 45229-3026, United States; University of Cincinnati Department of Radiology, 222 Piedmont Ave Ste 1200, Cincinnati, OH 45219, United States.
Philips Innovative Technologies, Röntgenstr. 24-26, 22335 Hamburg, Germany.
Magn Reson Imaging. 2025 Sep;121:110427. doi: 10.1016/j.mri.2025.110427. Epub 2025 May 17.
Synthetic MRI (SyMRI) is a technique used to estimate tissue properties and generate multiple MR sequence contrasts from a single acquisition. However, image quality can be suboptimal.
To evaluate a neural network approach using artificial intelligence-based direct contrast synthesis (AI-DCS) of the multi-contrast weighted images to improve image quality.
This prospective, IRB approved study enrolled 50 pediatric patients undergoing clinical brain MRI. In addition to the standard of care (SOC) clinical protocol, 2D multi-delay multi-echo (MDME) sequence was obtained. SOC 3D T1-weighted (T1W), 2D T2-weighted (T2W) and 2D T2W fluid-attenuated inversion recovery (FLAIR) images from 35 patients were used to train a neural network generating synthetic T1W, T2W, and FLAIR images. Quantitative analysis of grey matter (GM) and white matter (WM) apparent signal to noise (aSNR) and grey-white matter (GWM) apparent contrast to noise (aCNR) ratios was performed.
8 patients were evaluated. When compared to SyMRI, T1W AI-DCS had better overall image quality, reduced noise/artifacts, and better subjective SNR in 100 % (16/16) of evaluations. When compared to SyMRI, T2W AI-DCS overall image quality and diagnostic confidence was better in 93.8 % (15/16) and 87.5 % (14/16) of evaluations, respectively. When compared to SyMRI, FLAIR AI-DCS was better in 93.8 % (15/16) of evaluations in overall image quality and in 100 % (16/16) of evaluations for noise/artifacts and subjective SNR. Quantitative analysis revealed higher WM aSNR compared with SyMRI (p < 0.05) for T1W, T2W and FLAIR.
AI-DCS demonstrates better overall image quality than SyMRI on T1W, T2W and FLAIR.
合成磁共振成像（SyMRI）是一种用于估计组织特性并从单次采集生成多个磁共振序列对比度的技术。然而，图像质量可能并不理想。
评估一种使用基于人工智能的多对比度加权图像直接对比度合成（AI-DCS）的神经网络方法，以提高图像质量。
这项经机构审查委员会（IRB）批准的前瞻性研究纳入了50名接受临床脑部磁共振成像的儿科患者。除了标准护理（SOC）临床方案外，还获取了二维多延迟多回波（MDME）序列。来自35名患者的SOC三维T1加权（T1W）、二维T2加权（T2W）和二维T2W液体衰减反转恢复（FLAIR）图像用于训练生成合成T1W、T2W和FLAIR图像的神经网络。对灰质（GM）和白质（WM）的表观信噪比（aSNR）以及灰白质（GWM）的表观对比噪声比（aCNR）进行了定量分析。
对8名患者进行了评估。与SyMRI相比，T1W AI-DCS在100%（16/16）的评估中具有更好的整体图像质量、更低的噪声/伪影以及更好的主观信噪比。与SyMRI相比，T2W AI-DCS在93.8%（15/16）的评估中整体图像质量更好，在87.5%（14/16）的评估中诊断置信度更高。与SyMRI相比，FLAIR AI-DCS在93.8%（15/16）的评估中整体图像质量更好，在100%（16/16）的评估中噪声/伪影和主观信噪比方面表现更好。定量分析显示，T1W、T2W和FLAIR的WM aSNR与SyMRI相比更高（p < 0.05）。
在T1W、T2W和FLAIR上，AI-DCS显示出比SyMRI更好的整体图像质量。
