Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2024 Feb 28;24(5):1567. doi: 10.3390/s24051567.
(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
(1) 背景：高光谱成像是一种很有前途的保乳手术切缘评估技术。然而，要将其应用于术中，它应该既快速又能够生成高质量的图像，以便在整个手术过程中提供准确的指导和决策。由于图像质量和数据采集时间之间存在权衡，较高的分辨率图像需要更长的采集时间，反之亦然。
(2) 方法：因此，在这项研究中，我们引入了一个深度学习的空间-光谱重建框架，该框架可以将低分辨率高光谱图像与高分辨率 RGB 图像相结合，从低分辨率高光谱图像中获得高分辨率的高光谱图像。
(3) 结果：使用该框架，我们展示了在手术过程中进行快速数据采集的能力，同时保持了较高的图像质量，即使在复杂的情况下，如由于运动伪影导致的模糊、相机传感器上的死像素、传感器在光谱极端处灵敏度降低引起的噪声、以及组织平滑区域的镜面反射等，也能如此。
(4) 结论：这为通过术中高光谱成像进行准确的切缘评估提供了机会。
