Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a 1H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists’ evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment, extending its potential clinical applications.

中文翻译：

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一种基于流的截断降噪扩散模型，用于超分辨磁共振光谱成像


磁共振波谱成像 （MRSI） 是一种用于研究新陈代谢的非侵入性成像技术，已成为了解神经系统疾病、癌症和糖尿病的重要工具。需要高空间分辨率的 MRSI 来表征病变，但在实践中，由于低代谢物浓度引起的时间和敏感性限制，MRSI 是在低分辨率下获得的。因此，迫切需要一种后处理方法，从可以快速、高灵敏度采集的低分辨率数据中生成高分辨率 MRSI。基于深度学习的超分辨率方法为提高 MRSI 的空间分辨率提供了有希望的结果，但它们生成准确和高质量图像的能力仍然有限。最近，扩散模型在各种任务中表现出优于其他生成模型的学习能力，但从扩散模型中采样需要迭代大量的扩散步骤，这很耗时。这项工作介绍了一种用于超分辨率 MRSI 的基于流的截断降噪扩散模型 （FTDDM），该模型通过截断扩散链来缩短扩散过程，并使用基于流的归一化网络估计截断步骤。该网络以放大因子为条件，以实现多尺度超分辨率。为了训练和评估深度学习模型，我们开发了一个从 25 名高级别胶质瘤患者那里获得的 1H-MRSI 数据集。我们证明 FTDDM 优于现有的生成模型，同时与基线扩散模型相比，采样过程加快了 9 倍以上。 神经放射学家的评估证实了我们方法的临床优势，该方法还支持不确定性估计和清晰度调整，扩展了其潜在的临床应用。