Objective: Given the high-risk nature of arteriovenous malformation (AVM) resections, accurate pre- and intraoperative imaging of the vascular morphology is a crucial component that may contribute to successful surgical results. Surprisingly, current gold standard imaging techniques for surgical guidance of AVM resections are mostly preoperative, lacking the necessary flexibility to cater to intraoperative changes. Micro-Doppler imaging is a unique high-resolution technique relying on high frame rate ultrasound and subsequent Doppler processing of microvascular hemodynamics. In this paper the authors report the first application of intraoperative, coregistered magnetic resonance/computed tomograpy, micro-Doppler imaging during the neurosurgical resection of an AVM in the parietal lobe.
Observations: The authors applied intraoperative two-dimensional and three-dimensional (3D) micro-Doppler imaging during resection and were able to identify key anatomical features including draining veins, supplying arteries and microvasculature in the nidus itself. Compared to the corresponding preoperative 3D-digital subtraction angiography (DSA) image, the micro-Doppler images could delineate vascular structures and visualize hemodynamics with higher, submillimeter scale detail, even at significant depths (>5 cm). Additionally, micro-Doppler imaging revealed unique microvascular morphology of surrounding healthy vasculature.
Lessons: The authors conclude that micro-Doppler imaging in its current form has clear potential as an intraoperative counterpart to preoperative contrast-dependent DSA, and the microvascular details it provides could build new ground to further study cerebrovascular pathophysiology.
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http://dx.doi.org/10.3171/CASE22177 | DOI Listing |
Sensors (Basel)
January 2025
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China.
Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks.
View Article and Find Full Text PDFActa Neurochir (Wien)
October 2024
Hospital Management Department, National Centre for Neurosurgery, Astana, Kazakhstan.
Purpose: This technical note introduces the novel faucet technique, which enables neurosurgeons to evaluate the patency of a bypass during superficial temporal artery-middle cerebral artery bypass surgery. The technique is particularly useful when there is a lack of equipment such as micro-Doppler or indocyanine green in the operating rooms. This is often the case in Central Asian countries.
View Article and Find Full Text PDFSensors (Basel)
September 2024
Department of Computer Engineering, Korea Aerospace University, Goyang-si 10504, Republic of Korea.
This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN.
View Article and Find Full Text PDFSensors (Basel)
June 2024
Institute of Navigation, Polish Air Force University, 08-521 Dęblin, Poland.
The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time-frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety.
View Article and Find Full Text PDFSensors (Basel)
May 2024
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2050, Australia.
With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination.
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