背景:从扩散加权成像(DWI)得出的7特斯拉(7T)表观扩散系数(ADC)图显示出优于3特斯拉(3T)ADC图的图像质量和空间分辨率。然而,7T磁共振成像(MRI)目前患有有限的临床不可用,更高的成本,对伪影的敏感性增加。
目标:为了解决这些问题,我们提出了一种混合CNN-变压器模型来从多模态3TMRI合成高分辨率7TADC图。
方法:视觉CNN变换器(VCT),由视觉转换(ViT)块和卷积层组成,建议从3TADC图和3TT1加权(T1w)MRI生成高分辨率合成7TADC图。ViT块启用了全局图像上下文,而卷积层有效地捕获了精细细节。VCT模型在公开可用的HumanConnectomeProject年轻成人数据集上进行了验证,包括3TT1w,3TDWI,和7TDWI脑部扫描。Python库中的扩散成像用于从DWI扫描计算ADC图。将171例患者随机分为130例培训病例,20个验证案例,和21个测试用例。通过将其与地面真值体积的相似性与以下度量进行比较来评估合成ADC图:峰值信噪比(PSNR),结构相似性指数度量(SSIM),和均方误差(MSE)。此外,结果:PSNR:27.0±0.9dB,SSIM:0.945±0.010,MSE:2.0E-3±0.4E-3。定性和定量结果均表明,VCT优于其他最新方法。我们引入了各种效率改进措施,包括在176×208分辨率图像上实施闪光注意和训练。与ResViT相比,这些增强导致每个时期的参数和训练时间减少了50%。具体来说,每个时期的训练时间从7.67分钟缩短到3.86分钟。
结论:我们提出了一种新颖的方法来从低分辨率3TADC图和T1wMRI预测高分辨率7TADC图。与3TMRI和ResViT和pix2pix的预测结果相比,我们的预测图像显示出更好的空间分辨率和对比度。这些高质量的合成7TMR图像可能有助于疾病诊断和干预。产生更高的分辨率和共形轮廓,作为生成用于放射治疗的合成CT的中间步骤,特别是当7TMRI扫描仪不可用时。
BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts.
OBJECTIVE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI.
METHODS: The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E-3 ± 0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min.
CONCLUSIONS: We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.