Evaluation of patient‐specific MR distortion correction schemes for improved target localization accuracy in SRS

2020 ◽  
Author(s):  
Dimitrios Dellios ◽  
Eleftherios P. Pappas ◽  
Ioannis Seimenis ◽  
Chryssa Paraskevopoulou ◽  
Kostas I. Lampropoulos ◽  
...  
Keyword(s):  
Distortion Correction ◽  
Target Localization ◽  
Patient Specific ◽  
Localization Accuracy

Mr geometric distortion correction for improved frame-based stereotaxic target localization accuracy

1995 ◽  
Vol 34 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Thilaka Sumanaweera ◽  
Gary H. Glover ◽  
Paul F. Hemler ◽  
Petra A. Den Van Elsen ◽  
David Martin ◽  
...  
Keyword(s):  
Distortion Correction ◽  
Target Localization ◽  
Geometric Distortion ◽  
Localization Accuracy

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

scholarly journals Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

2020 ◽  
Author(s):  
Casey L. Trevino ◽  
Jack J. Lin ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour
Keyword(s):  
High Frequency ◽  
Classification Accuracy ◽  
Patient Specific ◽  
Seizure Onset ◽  
Data Set ◽  
Localization Accuracy ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Wide Range ◽  
The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

Impact analysis of attitude determination accuracy to target localization accuracy on Multi-BD receivers

2016 ◽  
Vol 31 (9) ◽  
pp. 902-912
Author(s):  
蔡明兵 CAI Ming-bing ◽  
王超 WANG Chao ◽  
刘晶红 LIU Jing-hong ◽  
周前飞 ZHOU Qian-fei ◽  
宋悦铭 SONG Yue-ming
Keyword(s):  
Impact Analysis ◽  
Attitude Determination ◽  
Target Localization ◽  
Localization Accuracy

Spatial Cueing and the Relation between the Accuracy of “Where” and “What” Decisions in Visual Search

1989 ◽  
Vol 41 (4) ◽  
pp. 747-773 ◽  
Author(s):  
Hermann J. Müller ◽  
Patrick M. A. Rabbitt
Keyword(s):  
Visual Search ◽  
Shape Analysis ◽  
Target Localization ◽  
Target Orientation ◽  
Spatial Cueing ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Orientation Experiment ◽  
Candidate Location ◽  
Accurate Matching

To study the processes underlying selective attention in visual search, the relation between the accuracy of “where” (location) and “what” (same/different orientation matching) decisions was analysed under various display conditions. Target-non-target discriminability was varied by contrasting single and multiple element displays; further, attention was directly manipulated by spatial cueing. In Experiment 1, analyses for both single and multiple displays showed that localization accuracy remained above chance when same/different matching failed; the inverse also obtained. It seems that accurate matching is not a prerequisite for target localization, nor is accurate localization a prerequisite for same/different matching. However, localization is a prerequisite for the accurate recognition of target orientation (Experiment 2). In this case, it seems that features critical for localization “call” attention to a particular candidate location. This facilitates further (shape) analysis of the stimulus that is found there. This orienting process is by-passed if attention is cued to the location in advance.

SU-E-J-164: Evaluation of Target Localization Accuracy of ExacTrac System for Spine SBRT

2011 ◽  
Vol 38 (6Part9) ◽  
pp. 3481-3481
Author(s):  
S Venkataraman ◽  
D Sasaki ◽  
J Butler ◽  
G Schroeder ◽  
M West ◽  
...  
Keyword(s):  
Target Localization ◽  
Localization Accuracy

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

SU-E-J-30: The Effect of Planning CT Slice Thickness On Evaluation of SRS Target Localization Accuracy

2015 ◽  
Vol 42 (6) ◽  
pp. 3270-3270
Author(s):  
J Briceno ◽  
H Li ◽  
Y Huang ◽  
N Wen ◽  
I Chetty
Keyword(s):  
Target Localization ◽  
Slice Thickness ◽  
Localization Accuracy ◽  
Planning Ct
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