Tatekawa Hiroyuki, Ueda Daiju, Takita Hirotaka, Matsumoto Toshimasa, Walston Shannon L, Mitsuyama Yasuhito, Horiuchi Daisuke, Matsushita Shu, Oura Tatsushi, Tomita Yuichiro, Tsukamoto Taro, Shimono Taro, Miki Yukio
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan.
Sci Rep. 2024 Feb 5;14(1):2911. doi: 10.1038/s41598-024-53278-8.
This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.
本研究创建了一种图像到图像的翻译模型，该模型可从传统扩散加权图像合成扩散张量图像（DTI），并验证了原始DTI与合成DTI之间的相似性。前瞻性招募了32名健康志愿者。分别在六个和三个运动探测梯度（MPG）方向上获取DTI和扩散加权成像（DWI）。为从DWI合成MPG一个方向的图像到图像翻译模型配对相同的成像平面。在各个MPG方向上重复此过程六次。在豆状核、丘脑、内囊后肢、丘脑后辐射和胼胝体压部创建感兴趣区域（ROI），并将其应用于从原始DTI和合成DTI得出的图谱。比较每个ROI的原始图谱和合成图谱的平均值及信噪比（SNR）。评估原始数据与合成数据之间的布兰德-奥特曼图。尽管测试数据集显示所有值的标准差更大，且合成数据中的SNR低于原始数据，但布兰德-奥特曼图显示每个图都定位在相似的分布中。利用图像到图像的翻译模型可以从传统DWI生成合成DTI。
