摘要：

磁共振成像是临床诊断中应用最广泛的成像方式之一。然而, 受到扫描设备成本和扫描时间的限制, 要获取高分辨率的磁共振图像相当困难。近年来, 为了提高图像质量, 扩散模型(DM)被应用于超分辨率技术。但已有的研究工作中模型推理效率低下且未充分提取高频特征, 使得重建出的效果差强人意。针对此问题, 构建了一种高效的磁共振成像单图超分辨率扩散模型ResDM。在该模型中, 首先使用预训练好的超分辨率模型来提供给定低分辨率图像的条件图像; 然后将噪声引导到高分辨率图像与条件图像之间的残差空间, 为了加快模型推理速度, 使用去噪扩散隐式模型结合U-Net结构, 以获得生成速度快且效果良好的结果; 接着引入基于频域的损失函数和注意力机制以促进其恢复高频细节信息; 最后在HCP、BraTS2019和FastMRI 3个公共数据集上进行实验, 采用峰值信噪比(PSNR)和结构相似性指数度量(SSIM)这2种图像客观评价指标进行评估。结果表明, 所提方法与7种已有的图像超分辨率重建方法相比, 在上采样因子为4的情况下, 在3个数据集的PSNR和SSIM上取得平均2.24 dB和0.06的增长; 可视化结果显示, 其获得了分辨率更高且细节信息更丰富的磁共振图像。

关键词: 磁共振成像, 超分辨率, 扩散模型, 频域, 条件图像

Abstract:

Magnetic Resonance Imaging (MRI) is one of the most widely used methods of clinical diagnosis. However, obtaining high-resolution MRI images is challenging because of the high cost of the scanning equipment and time limitations. In recent years, Diffusion Models (DMs) have been applied to super-resolution techniques to improve image quality. Nevertheless, existing research has demonstrated inefficiencies in model inference and insufficient extraction of high-frequency features, resulting in suboptimal reconstruction outcomes. To address these issues, an efficient MRI single-image super-resolution diffusion model called Residual Diffusion Model (ResDM) is developed. This model leverages a pretrained super-resolution model to provide a conditional image for a given low-resolution input. Noise is then guided to the residual space between the high-resolution and conditional images. To accelerate the model inference, an implicit denoised diffusion model is employed in conjunction with a U-Net structure to achieve rapid generation and high-quality results. Furthermore, a loss function and attention mechanism based on the frequency domain are introduced to enhance the recovery of high-frequency detailed information. Experiments are conducted on three public datasets: HCP, BraTS2019, and FastMRI. The results are evaluated using two objective image evaluation metrics: the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings indicate that, compared to seven existing image super-resolution reconstruction methods, the proposed method achieves an average increase of 2.24 dB in PSNR and 0.06 in SSIM across the three datasets with an upsampling factor of 4. This approach yields MRI images with higher resolution and richer detailed information, as demonstrated by the corresponding visualization results.

Key words: Magnetic Resonance Imaging (MRI), super-resolution, Diffusion Model (DM), frequency domain, conditional image