ObjectiveTo study whether the pattern visual evoked potential (P-VEP) under different spatial frequency in patients with multiple sclerosis (MS) is different from normal people. MethodsP-VEP examination under high (15') and low (60') spatial frequency was performed on 18 MS patients (36 eyes) treated in our department from September 2011 to April 2012 and 20 normal volunteers (40 eyes). Then, we analyzed the difference between the two groups under the above-mentioned two kinds of spatial frequency. ResultsThe latency of P100 of P-VEP under high spatial frequency in MS patients was (120.50±13.04) ms which was significantly different from (109.21±5.38) ms of normal volunteers (P < 0.05). The latency of P100 of P-VEP under low spatial frequency in MS patients was (109.57±12.87) ms, which was also significantly different from (103.31±5.45) ms of normal volunteers (P < 0.05). The amplitude of P100 of P-VEP under high spatial frequency in MS patients was (9.17±5.69)μV and it was significantly lower than that[(15.69±8.45)μv] of normal volunteers (P < 0.05). The amplitude of P100 of P-VEP under low spatial frequency in MS patients was (11.93±16.75)μV and it was not significantly different from normal volunteers[(13.47±9.24μV)]. Based on different corrected vision, the MS patients were divided into two groups (vision≥1.0 and vision < 1.0). For patients with vision≥1.0, the latency of P100 and the amplitude of P100 of P-VEP under high spatial frequency was (113.43±8.28) ms and (12.94±5.46)μV; the latency of P100 and the amplitude of P100 of P-VEP under low spatial frequency was (111.13±11.50) ms and (11.57±5.60)μV. For patients with vision < 1.0, the latency of P100 and the amplitude of P100 of P-VEP under high spatial frequency was (126.69±13.49) ms and (5.87±3.43)μV; the latency of P100 and the amplitude of P100 of P-VEP under low spatial frequency was (108.26±14.11) ms and (12.24±5.82)μV. There was no significant difference in the latency and amplitude of P100 under low spatial frequency between the two groups with different corrected vision (P > 0.05), but the latency and amplitude of P100 under high spatial frequency were both significantly different between those two groups (P < 0.05). ConclusionsCompared with normal people, MS patients feature latency delay and amplitude reduction of the P-VEP, which was more severe under high spatial frequency. P-VEP under high spatial frequency may become an important evidence to evaluate visual function of MS patients.
There is a shared problem in current optical imaging technologies of how to obtain the optical parameters of biological tissues with complex profiles. In this work, an imaging system for obtaining the optical parameters of biological tissues with complex profile was presented. Firstly, Fourier transformation profilometry was used for obtaining the profile information of biological tissues, and then the difference of incident light intensity at different positions on biological tissue surface was corrected with the laws of illumination, and lastly the optical parameters of biological tissues were achieved with the spatial frequency domain imaging technique. Experimental results indicated the proposed imaging system could obtain the profile information and the optical parameters of biological tissues accurately and quickly. For the slab phantoms with height variation less than 30 mm and angle variation less than 40o, the maximum relative errors of the profile uncorrected optical parameters were 46.27% and 72.18%, while the maximum relative errors of the profile corrected optical parameters were 6.89% and 10.26%. Imaging experiments of a face-like phantom and a human’s prefrontal lobe were performed respectively, which demonstrated the proposed imaging system possesses clinical application value for the achievement of the optical parameters of biological tissues with complex profiles. Besides, the proposed profile corrected method can be used to combine with the current optical imaging technologies to reduce the influence of the profile information of biological tissues on imaging quality.
Coronary artery segmentation is a critical step in the clinical diagnosis of coronary heart disease. To tackle segmentation breaks and false segmentation caused by the thin and complex coronary vessels as well as the severe foreground-background imbalance in computed tomography angiography images, this paper proposes MSCNet, a coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement. The network constructed a multi-scale cascaded encoder using Swin Transformer and large-kernel convolutions. It sequentially modeled and fused multi-scale features by capturing long-range dependencies and local details, and reparameterized large-kernel convolutions via a spatial frequency matrix to strengthen fine detail capture. Meanwhile, a spatial transformer module was designed to dynamically guide multi-head attention learning and optimize decoding performance. On the ImageCAS dataset, MSCNet achieved an average Dice coefficient of 81.24%, which was 3.57%, 3.78%, and 3.85% higher than 3D UX-Net, SwinUNETR, and SegMamba, respectively. MSCNet effectively improves the accuracy of coronary artery segmentation and provides support for clinical evaluation.