Hybrid attention structure preserving network for reconstruction of under-sampled OCT images

Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology that provides cross-sectional images of tissues. Dense acquisition of A-scans along the fast axis is required to obtain high digital resolution images. However, the dense acquisition will increase the acquisition time, causing the discomfort of patients. In addition, the longer acquisition time may lead to motion artifacts, thereby reducing imaging quality. In this work, we proposed a hybrid attention structure preserving network (HASPN) to achieve super-resolution of under-sampled OCT images to speed up the acquisition. It utilized adaptive dilated convolution-based channel attention (ADCCA) and enhanced spatial attention (ESA) to better capture the channel and spatial information of the feature. Moreover, convolutional neural networks (CNNs) exhibit a higher sensitivity of low-frequency than high-frequency information, which may lead to a limited performance on reconstructing fine structures. To address this problem, we introduced an additional branch, i.e., textures & details branch, using high-frequency decomposition images to better super-resolve retinal structures. The superiority of our method was demonstrated by qualitative and quantitative comparisons with mainstream methods. HASPN was applied to the diabetic macular edema retinal dataset, validating its good generalization ability.

A Fourier-enhanced multi-modal 3D small object optical mark recognition and positioning method for percutaneous abdominal puncture surgical navigation

Navigation for thoracoabdominal puncture surgery is used to locate the needle entry point on the patient's body surface. The traditional reflective ball navigation method is difficult to position the needle entry point on the soft, irregular, smooth chest and abdomen. Due to the lack of clear characteristic points on the body surface using structured light technology, it is difficult to identify and locate arbitrary needle insertion points. Based on the high stability and high accuracy requirements of surgical navigation, this paper proposed a novel method, a muti-modal 3D small object medical marker detection method, which identifies the center of a small single ring as the needle insertion point. Moreover, this novel method leverages Fourier transform enhancement technology to augment the dataset, enrich image details, and enhance the network's capability. The method extracts the Region of Interest (ROI) of the feature image from both enhanced and original images, followed by generating a mask map. Subsequently, the point cloud of the ROI from the depth map is obtained through the registration of ROI point cloud contour fitting. In addition, this method employs Tukey loss for optimal precision. The experimental results show this novel method proposed in this paper not only achieves high-precision and high-stability positioning, but also enables the positioning of any needle insertion point.