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21 results on '"Xu, Qianyi"'

3. Feasibility of patient‐specific quality assurance (PSQA) for real‐time robotic stereotactic body radiotherapy (SBRT) based on tumor motion traces.

Author

Xu, Qianyi, Fan, Jiajin, Vinogradskiy, Yevgeniy, Chawla, Ashish K., Kubicek, Gregory, Yang, Haihua, Huynh, Kiet, LaCouture, Tamara, Grimm, Jimm, and Nie, Wei

Subjects
STEREOTACTIC radiotherapy, MEDICAL dosimetry, SYNCHRONIC order, QUALITY assurance, ANGLES
Abstract

Purpose: To design a patient specific quality assurance (PSQA) process for the CyberKnife Synchrony system and quantify its dosimetric accuracy using a motion platform driven by patient tumor traces with rotation. Methods: The CyberKnife Synchrony system was evaluated using a motion platform (MODUSQA) and a SRS MapCHECK phantom. The platform was programed to move in the superior‐inferior (SI) direction based on tumor traces. The detector array housed by the StereoPhan was placed on the platform. Extra rotational angles in pitch (head down, 4.0° ± 0.15° or 1.2° ± 0.1°) were added to the moving phantom to examine robot capability of angle correction during delivery. A total of 15 Synchrony patients were performed SBRT PSQA on the moving phantom. All the results were benchmarked by the PSQA results based on static phantom. Results: For smaller pitch angles, the mean gamma passing rates were 99.75% ± 0.87%, 98.63% ± 2.05%, and 93.11% ± 5.52%, for 3%/1 mm, 2%/1 mm, and 1%/1 mm, respectively. Large discrepancy in the passing rates was observed for different pitch angles due to limited angle correction by the robot. For larger pitch angles, the corresponding mean passing rates were dropped to 93.00% ± 10.91%, 88.05% ± 14.93%, and 80.38% ± 17.40%. When comparing with the static phantom, no significant statistic difference was observed for smaller pitch angles (p = 0.1 for 3%/1 mm), whereas a larger statistic difference was observed for larger pitch angles (p < 0.02 for all criteria). All the gamma passing rates were improved, if applying shift and rotation correction. Conclusions: The significance of this work is that it is the first study to benchmark PSQA for the CyberKnife Synchrony system using realistically moving phantoms with rotation. With reasonable delivery time, we found it may be feasible to perform PSQA for Synchrony patients with a realistic breathing pattern. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Assessing base-resolution DNA mechanics on the genome scale

Author

Jiang, Wen-Jie, primary, Hu, Congcong, additional, Lai, Futing, additional, Pang, Weixiong, additional, Yi, Xinyao, additional, Xu, Qianyi, additional, Wang, Haojie, additional, Zhou, Jialu, additional, Zhu, Hanwen, additional, Zhong, Chunge, additional, Kuang, Zeyu, additional, Fan, Ruiqi, additional, Shen, Jing, additional, Zhou, Xiaorui, additional, Wang, Yu-Juan, additional, Wong, Catherine C L, additional, Zheng, Xiaoqi, additional, and Wu, Hua-Jun, additional

Published
2023
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6. Serum Spermidine in Relation to Risk of Stroke: A Multilevel Study

Author

Zheng, Liqiang, primary, Xie, Yanxia, additional, Sun, Zhaoqing, additional, Zhang, Rui, additional, Ma, Yanan, additional, Xu, Jiahui, additional, Zheng, Jia, additional, Xu, Qianyi, additional, Li, Zhao, additional, Guo, Xiaofan, additional, Sun, Guozhe, additional, Xing, Fuguo, additional, Sun, Yingxian, additional, and Wen, Deliang, additional

Published
2022
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7. Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

Author

Zhonghan Zhang, Jie Yan, Xu Qianyi, Jiang Huiming, Minghui Sun, and Guihe Qin

Subjects
Artificial neural network, business.industry, Computer science, Applied Mathematics, Dynamic data, Frame (networking), Feature extraction, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Gesture recognition, Softmax function, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Key frame, 020201 artificial intelligence & image processing, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

Published
2020

12. Mixpred: Video Prediction Beyond Optical Flow

Author

Liang Yanhua, Guihe Qin, Xu Qianyi, Jie Yan, and Rui Zhao

Subjects
General Computer Science, Computer science, business.industry, video prediction, spatial subnet, General Engineering, Optical flow, 02 engineering and technology, Video prediction, 010501 environmental sciences, 01 natural sciences, parallel interaction, 0202 electrical engineering, electronic engineering, information engineering, End-to-end training, 020201 artificial intelligence & image processing, General Materials Science, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, lcsh:TK1-9971, 0105 earth and related environmental sciences, temporal subnet
Abstract

Video prediction is a meaningful task for it has a wide range of application scenarios. And it is also a challenging task since it needs to learn the internal representation of a given video for both appearance and motion dynamics. The existing methods regard this problem as a spatiotemporal sequence forecasting problem and try to resolve it in a one-shot fashion, which causes the prediction result being blurry or inaccurate. So, a more intuitional thought is to split this problem into two parts: model the dynamic pattern of the given video and learn the appearance representation of given video frames. In this paper, we develop a novel network structure named MixPred based on this idea to address this issue. We divide the prediction problem into two parts as mentioned above and build two subnets to solve these two parts separately. Instead of fusing the results of subnets at the final layer, we put forward a parallel interaction style through the whole process to merge the dynamic information and content information in a more natural way. Besides, we propose three different connection methods for exploring the most effective connection structure. We trained the model on UCF-101 and KITTI, and testing our model on UCF-101, KITTI, and Caltech. The results demonstrate that our method achieves state-of-the-art both quantitatively and qualitatively.

Published
2019

21. Preoperative Stereotactic Body Radiation Therapy for Soft-Tissue Sarcoma: Results of Phase 2 Study.

Author

Kubicek GJ, Kim TW, Gutowski CJ, Kaden M, Eastwick G, Khrizman P, Xu Q, and Lackman R

Abstract

Purpose: Preoperative radiation followed by surgical resection is a standard treatment for soft-tissue sarcomas (STS). We report on 2 consecutive, phase 2, single-arm studies evaluating 5 fraction stereotactic body radiation therapy (SBRT) treatments followed by surgical resection for STS (clinical trails.gov NCT02706171)., Methods and Materials: A total of 16 patients were treated with preoperative SBRT. Tumor size in the greatest dimension was a median 6.7 cm (maximum: 14 cm) and the majority of STS were in the extremities. SBRT consisted of 35 to 40 Gy in 5 fractions every other day., Results: Median follow-up time was 1719 days (4.7 years). Grade ≥3 acute toxicity occurred in 1 patient (grade 3 skin changes). Fifteen patients proceeded with surgical resection. Three patients had a wound complication after surgery, 1 patient had grade ≥3 late toxicity (grade 4 requiring surgical intervention). There was 1 local recurrence and 5 distant recurrences., Conclusions: Long-term follow-up on SBRT for STS found acceptable control and toxicity rates, and warrants further evaluation., (© 2021 The Authors.)

Published
2021
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