Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis.

Dynamic assessment of cerebral blood flow (CBF) is crucial for guiding personalized management and treatment strategies, and improving the prognosis of stroke. However, a safe, reliable, and effective method for dynamic CBF evaluation is currently lacking in clinical practice. In this study, we developed a CBF monitoring system utilizing electromagnetic coupling sensing (ECS). This system detects variations in brain conductivity and dielectric constant by identifying the resonant frequency (RF) in an equivalent circuit containing both magnetic induction and electrical coupling. We evaluated the performance of the system using a self-made physical model of blood vessel pulsation to test pulsatile CBF. Additionally, we recruited 29 healthy volunteers to monitor cerebral oxygen (CO), cerebral blood flow velocity (CBFV) data and RF data before and after caffeine consumption. We analyzed RF and CBFV trends during immediate responses to abnormal intracranial blood supply, induced by changes in vascular stiffness, and compared them with CO data. Furthermore, we explored a method of dynamically assessing the overall level of CBF by leveraging image feature analysis. Experimental testing substantiates that this system provides a detection range and depth enhanced by three to four times compared to conventional electromagnetic detection techniques, thereby comprehensively covering the principal intracranial blood supply areas. And the system effectively captures CBF responses under different intravascular pressure stimulations. In healthy volunteers, as cerebral vascular stiffness increases and CO decreases due to caffeine intake, the RF pulsation amplitude diminishes progressively. Upon extraction and selection of image features, widely used machine learning algorithms exhibit commendable performance in classifying overall CBF levels. These results highlight that our proposed methodology, predicated on ECS and image feature analysis, enables the capture of immediate responses of abnormal intracranial blood supply triggered by alterations in vascular stiffness. Moreover, it provides an accurate diagnosis of the overall CBF level under varying physiological conditions.

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation.

The segmentation of blurred cell boundaries in cornea endothelium microscope images is challenging, which affects the clinical parameter estimation accuracy. Existing deep learning methods only consider pixel-wise classification accuracy and lack of utilization of cell structure knowledge. Therefore, the segmentation of the blurred cell boundary is discontinuous. This paper proposes a structural prior guided network (SPG-Net) for corneal endothelium cell segmentation. We first employ a hybrid transformer convolution backbone to capture more global context. Then, we use Feature Enhancement (FE) module to improve the representation ability of features and Local Affinity-based Feature Fusion (LAFF) module to propagate structural information among hierarchical features. Finally, we introduce the joint loss based on cross entropy and structure similarity index measure (SSIM) to supervise the training process under pixel and structure levels. We compare the SPG-Net with various state-of-the-art methods on four corneal endothelial datasets. The experiment results suggest that the SPG-Net can alleviate the problem of discontinuous cell boundary segmentation and balance the pixel-wise accuracy and structure preservation. We also evaluate the agreement of parameter estimation between ground truth and the prediction of SPG-Net. The statistical analysis results show a good agreement and correlation.

Point-of-care testing for cerebral edema types based on symmetric cancellation near-field coupling phase shift and support vector machine.

BACKGROUND: Cerebral edema is an extremely common secondary disease in post-stroke. Point-of-care testing for cerebral edema types has important clinical significance for the precise management to prevent poor prognosis. Nevertheless, there has not been a fully accepted bedside testing method for that. METHODS: A symmetric cancellation near-field coupling phase shift (NFCPS) monitoring system is established based on the symmetry of the left and right hemispheres and the fact that unilateral lesions do not affect healthy hemispheres. For exploring the feasibility of this system to reflect the occurrence and development of cerebral edema, 13 rabbits divided into experimental group (n = 8) and control group (n = 5) were performed 24-h NFCPS continuous monitoring experiments. After time difference offset and feature band averaging processing, the changing trend of NFCPS at the stages dominated by cytotoxic edema (CE) and vasogenic edema (VE), respectively, was analyzed. Furthermore, the features under the different time windows were extracted. Then, a discriminative model of cerebral edema types based on support vector machines (SVM) was established and performance of multiple feature combinations was compared. RESULTS: The NFCPS monitoring outcomes of experimental group endured focal ischemia modeling by thrombin injection show a trend of first decreasing and then increasing, reaching the lowest value of - 35.05° at the 6th hour. Those of control group do not display obvious upward or downward trend and only fluctuate around the initial value with an average change of - 0.12°. Furthermore, four features under the 1-h and 2-h time windows were extracted. Based on the discriminative model of cerebral edema types, the classification accuracy of 1-h window is higher than 90% and the specificity is close to 1, which is almost the same as the performance of the 2-h window. CONCLUSION: This study proves the feasibility of NFCPS technology combined with SVM to distinguish cerebral edema types in a short time, which is promised to become a new solution for immediate and precise management of dehydration therapy after ischemic stroke.

A noninvasive and comprehensive method for continuous assessment of cerebral blood flow pulsation based on magnetic induction phase shift.

Cerebral blood flow (CBF) monitoring is of great significance for treating and preventing strokes. However, there has not been a fully accepted method targeting continuous assessment in clinical practice. In this work, we built a noninvasive continuous assessment system for cerebral blood flow pulsation (CBFP) that is based on magnetic induction phase shift (MIPS) technology and designed a physical model of the middle cerebral artery (MCA). Physical experiments were carried out through different simulations of CBF states. Four healthy volunteers were enrolled to perform the MIPS and ECG synchronously monitoring trials. Then, the components of MIPS related to the blood supply level and CBFP were investigated by signal analysis in time and frequency domain, wavelet decomposition and band-pass filtering. The results show that the time-domain baseline of MIPS increases with blood supply level. A pulse signal was identified in the spectrum (0.2-2 Hz in 200-2,000 ml/h groups, respectively) of MIPS when the simulated blood flow rate was not zero. The pulsation frequency with different simulated blood flow rates is the same as the squeezing frequency of the feeding pump. Similar to pulse waves, the MIPS signals on four healthy volunteers all had periodic change trends with obvious peaks and valleys. Its frequency is close to that of the ECG signal and there is a certain time delay between them. These results indicate that the CBFP component can effectively be extracted from MIPS, through which different blood supply levels can be distinguished. This method has the potential to become a new solution for non-invasive and comprehensive monitoring of CBFP.

Early assessment of acute ischemic stroke in rabbits based on multi-parameter near-field coupling sensing.

BACKGROUND: Early diagnosis and continuous monitoring are the key to emergency treatment and intensive care of patients with acute ischemic stroke (AIS). Nevertheless, there has not been a fully accepted method targeting continuous assessment of AIS in clinical. METHODS: Near-field coupling (NFC) sensing can obtain the conductivity related to the volume of intracranial components with advantages of non-invasiveness, strong penetrability and real-time monitoring. In this work, we built a multi-parameter monitoring system that is able to measure changes of phase and amplitude in the process of electromagnetic wave (EW) reflection and transmission. For investigating its feasibility in AIS detection, 16 rabbits were chosen to establish AIS models by bilateral common carotid artery ligation and then were enrolled for monitoring experiments. RESULTS: During the 6 h after AIS, the reflection amplitude (RA) shows a decline trend with a range of 0.69 dB and reflection phase (RP) has an increased variation of 6.48° . Meanwhile, transmission amplitude (TA) and transmission phase (TP) decrease 2.14 dB and 24.29° , respectively. The statistical analysis illustrates that before ligation, 3 h after ligation and 6 h after ligation can be effectively distinguished by the four parameters individually. When all those parameters are regarded as recognition features in back propagation (BP) network, the classification accuracy of the three different periods reaches almost 100%. CONCLUSION: These results prove the feasibility of multi-parameter NFC sensing to assess AIS, which is promised to become an outstanding point-of-care testing method in the future.