Estimation of 2D magnetic-source parameters using analytic signals of the logarithm of different order analytic signals

2021 ◽  
pp. 1-15
Author(s):  
Yanguo Wang ◽  
Yazhou Ding ◽  
Hanbing Ai
2018 ◽  
Vol 15 (2) ◽  
pp. 353-360
Author(s):  
Guo-Qing Ma ◽  
Yan-Bo Ming ◽  
Jiang-Tao Han ◽  
Li-Li Li ◽  
Qing-Fa Meng

2007 ◽  
Vol 107 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Jeffrey I. Berman ◽  
Mitchel S. Berger ◽  
Sungwon Chung ◽  
Srikantan S. Nagarajan ◽  
Roland G. Henry

Object Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)–imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning. Methods Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode. Results A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 ±3.1 mm (±standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation. Conclusions Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.


2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.


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