DMP-Net: Deep semantic prior compressed spectral reconstruction method towards intraoperative imaging of brain tissue

In the diagnosis and surgical resection of brain tumors, hyperspectral imaging, as a non-invasive detection technology, can effectively characterize the morphological structure and the physicochemical differences in cellular metabolism of different tissues. However, live tissues typically exhibit certain motion characteristics, and traditional hyperspectral imaging systems struggle to meet the demands for real-time and rapid imaging. The snapshot compressive spectral imaging (CSI) system can quickly acquire spatial spectral information of the lesion area in a single exposure and, combined with reconstruction algorithms, effectively restore the high-dimensional spectral information of brain tissue. High-quality reconstruction results are crucial for ensuring the reliability of spectral analysis of brain tissue. To improve the reconstruction performance of the CSI system, this paper proposes a compressive spectral reconstruction method based on deep semantic prior regularization. The predicted results of the deep convolutional prior model are used as the initial spectral estimate to establish a regularization term for the reconstruction process. This is combined with the Alternating Direction Method of Multipliers (ADMM) to optimize the solution for high-dimensional spectral images of brain tissue. The results show that using the CSI system for intraoperative brain tissue imaging can rapidly acquire spatial spectral information of the lesion area. By optimizing the reconstruction process with the deep convolutional prior model, this method not only better preserves the structural consistency of spectral images from different patients but also fully considers the spectral differences of different types of brain tumors, achieving higher reconstruction quality. This provides strong support for the precise localization and resection of brain tumors. The source code and related data of the proposed method can be downloaded at https://github.com/ccp1025/DMP-Net.