The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform

Stephen Rudin - One of the best experts on this subject based on the ideXlab platform.

  • SU‐E‐I‐98: Neurovascular Device Localization Accuracy Dependence on Exposure, Temporal Filtering, and Motion Blur for Real Time Imaging with the Microangiographic Fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • su e i 98 neurovascular device localization accuracy dependence on exposure temporal Filtering and motion blur for real time imaging with the microangiographic fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Daniel R. Bednarek, Anil K Jain, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • SU‐E‐I‐194: Motion Detection Based Adaptive Temporal Filtering for Image Guided Procedures Using the High Resolution Microangiographic Fluoroscope (MAF)
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Ciprian N. Ionita, Stephen Rudin
    Abstract:

    Purpose: To implement real‐time adaptive temporal Filtering based on amount of motion detected for an object of interest during fluoroscopy. For a stationary object, higher‐Weight temporal Filtering is used and during movement the Weight is reduced in real‐time. Methods: A phantom consisting of two stents (stainless steel and nitinol) mounted on a linear stage, and a stationary third stent (nitinol stent with platinum markers) was assembled. The stent strut sizes ranged between 80 to 100 micron. Fluoroscopy was done using the custom high resolution Micro Angiographic Fluoroscope (MAF) with an x‐ray spectrum hardened by a head equivalent phantom. The stainless steel stent was selected as the object of interest and was localized using a pattern matching algorithm available as a part of LabVIEW IMAQ Vision software. A real time adaptive temporal Filter was developed. When the stent was found to be stationary, the temporal Filtering Weight was increased. Depending on the amount of motion the Weight was reduced; more the motion, lesser the Weight. Results: Without temporal Filtering, the nitinol stent could not be seen. When the stents were stationary, and the temporal Filter Weight was increased, the nitinol stent could be clearly visualized due to the quantum noise reduction. When the same temporal Filter was used while the stents were in motion, then motion blur reduced the visibility of the nitinol stent and the struts of stainless steel stent also could not be distinguished from each other. In this case when the temporal Filtering Weight was reduced the stainless steel stent struts were visualized better. Conclusions: Motion based adaptive temporal Filtering can be implemented to aid during interventional procedures by tracking the motion of the high contrast interventional device to improve the visualization of lower contrast neurovascular objects. Support: NIH Grants R01‐EB008425, R01‐EB002873

A Panse - One of the best experts on this subject based on the ideXlab platform.

  • SU‐E‐I‐98: Neurovascular Device Localization Accuracy Dependence on Exposure, Temporal Filtering, and Motion Blur for Real Time Imaging with the Microangiographic Fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • su e i 98 neurovascular device localization accuracy dependence on exposure temporal Filtering and motion blur for real time imaging with the microangiographic fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Daniel R. Bednarek, Anil K Jain, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • SU‐E‐I‐194: Motion Detection Based Adaptive Temporal Filtering for Image Guided Procedures Using the High Resolution Microangiographic Fluoroscope (MAF)
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Ciprian N. Ionita, Stephen Rudin
    Abstract:

    Purpose: To implement real‐time adaptive temporal Filtering based on amount of motion detected for an object of interest during fluoroscopy. For a stationary object, higher‐Weight temporal Filtering is used and during movement the Weight is reduced in real‐time. Methods: A phantom consisting of two stents (stainless steel and nitinol) mounted on a linear stage, and a stationary third stent (nitinol stent with platinum markers) was assembled. The stent strut sizes ranged between 80 to 100 micron. Fluoroscopy was done using the custom high resolution Micro Angiographic Fluoroscope (MAF) with an x‐ray spectrum hardened by a head equivalent phantom. The stainless steel stent was selected as the object of interest and was localized using a pattern matching algorithm available as a part of LabVIEW IMAQ Vision software. A real time adaptive temporal Filter was developed. When the stent was found to be stationary, the temporal Filtering Weight was increased. Depending on the amount of motion the Weight was reduced; more the motion, lesser the Weight. Results: Without temporal Filtering, the nitinol stent could not be seen. When the stents were stationary, and the temporal Filter Weight was increased, the nitinol stent could be clearly visualized due to the quantum noise reduction. When the same temporal Filter was used while the stents were in motion, then motion blur reduced the visibility of the nitinol stent and the struts of stainless steel stent also could not be distinguished from each other. In this case when the temporal Filtering Weight was reduced the stainless steel stent struts were visualized better. Conclusions: Motion based adaptive temporal Filtering can be implemented to aid during interventional procedures by tracking the motion of the high contrast interventional device to improve the visualization of lower contrast neurovascular objects. Support: NIH Grants R01‐EB008425, R01‐EB002873

Suiyang Khoo - One of the best experts on this subject based on the ideXlab platform.

  • a variable step size transform domain lms algorithm based on minimum mean square deviation for autoregressive process
    Conference on Industrial Electronics and Applications, 2013
    Co-Authors: Shengkui Zhao, Zhihong Man, Douglas L Jones, Suiyang Khoo
    Abstract:

    In this paper, we investigate the optimal variable step-size approach for the transform-domain least-mean-square (TDLMS) algorithm to achieve fast convergence speed and low steady-state misadjustment. By minimizing the mean-square deviation (MSD) between the Filter Weight vector and the true vector, we derive and approximate the optimal variable step-size for the TDLMS algorithm given autoregressive (AR) process as input signals. The resulted variable step-size has simple formulation and easily-setting parameters. Computer simulation is demonstrated in the framework of adaptive system modeling with a fourth-order AR input process. The overall performance are observed superior to the existing popular variable step-size approaches of the TDLMS algorithm.

Haiquan Zhao - One of the best experts on this subject based on the ideXlab platform.

  • variable step size affine projection maximum correntropy criterion adaptive Filter with correntropy induced metric for sparse system identification
    IEEE Transactions on Circuits and Systems Ii-express Briefs, 2020
    Co-Authors: Haiquan Zhao, Bing Liu, Pucha Song
    Abstract:

    In this brief, an affine projection maximum correntropy criterion with correntropy induced metric (APMCCCIM) algorithm is proposed for robust sparse adaptive Filtering, and it is derived by using the cost function based on affine projection maximum correntropy criterion and correntropy induced metric to eliminate the adverse effects of impulsive noise on Filter Weight update in sparse systems. In order to further improve the convergence speed and steady-state misalignment of the proposed APMCCCIM algorithm, the variable step-size method is incorporated into the APMCCCIM algorithm. Hence, the variable step-size APMCCCIM (VSS-APMCCCIM) algorithm is presented. Besides, the computational complexity and the range of step-size of the proposed APMCCCIM algorithm are analyzed. Simulation results show that the proposed APMCCCIM and VSS-APMCCCIM algorithms have faster convergence speed and lower steady-state misalignment for sparse system identification and echo cancellation scenarios in the impulsive noise environments.

  • affine projection m estimate subband adaptive Filters for robust adaptive Filtering in impulsive noise
    Signal Processing, 2016
    Co-Authors: Zongsheng Zheng, Haiquan Zhao
    Abstract:

    We propose an affine projection M-estimate subband adaptive Filter (APM-SAF), which is characterized by its robustness against impulsive noise. Instead of the conventional mean square error (MSE) function, the proposed APM-SAF employs a robust M-estimator-based cost function which is able to suppress the effect of impulsive noise on the Filter Weight update. Furthermore, to improve the performance of the APM-SAF for sparse impulse response, an improved proportionate APM-SAF (IP-APM-SAF) is proposed by exploiting the sparsity of the impulse response. Simulations in an acoustic echo cancellation (AEC) context show that the APM-SAF performs better than the conventional NSAFs and SSAFs. The improvement in convergence rate is also demonstrated for the IP-APM-SAF. An APM-SAF is proposed for robust adaptive Filtering in impulsive noise.A stability analysis of the APM-SAF is carried out by the energy conservation technique.An improved proportionate APM-SAF is developed to further improve the performance of the APM-SAF.The computational complexity of the proposed algorithms is briefly discussed.

Daniel R. Bednarek - One of the best experts on this subject based on the ideXlab platform.

  • SU‐E‐I‐98: Neurovascular Device Localization Accuracy Dependence on Exposure, Temporal Filtering, and Motion Blur for Real Time Imaging with the Microangiographic Fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • su e i 98 neurovascular device localization accuracy dependence on exposure temporal Filtering and motion blur for real time imaging with the microangiographic fluoroscope
    Medical Physics, 2011
    Co-Authors: A Panse, Daniel R. Bednarek, Anil K Jain, Stephen Rudin
    Abstract:

    Purpose: To study the accuracy of localization during fluoroscopy for static and moving objects and its dependence on quantum noise determined by exposure and temporal Filtering and on motion blur determined by speed of object movement. Methods: A stainless steel stent (90 micron struts) used as object of interest was mounted on a stepper motor controlled linear stage with effective step size of 1.7 micron. The new high resolution 35‐ micron‐ pixel Microangiographic Fluoroscope (MAF) was used for imaging the stent as it was moved forward 7500 steps and brought back to the original position for different speeds. Fluoroscopy was performed at 4 different exposures. The object detection algorithm available in LabVIEW IMAQ Vision software was used to localize the stent using different temporal Filtering Weights. Results: (a) For fixed exposure, stationary object localization accuracy is improved by a factor of 5 for higher temporal Filtering compared to no temporal Filtering; however, for moving objects the error in localization increased 50 percent due to motion blurring when the Filter Weight was increased from 4 to 8. (b) For fixed temporal Filtering, localization accuracy improved with higher exposure. Doubling the exposure improved accuracy 3 times for stationary and 1.5 times for the moving object. (c) Localization accuracy for stationary objects at a given dose and temporal Filtering is similar to that at twice the dose and half the temporal Filtering Weight, whereas that for moving objects was found to be slightly better for the latter case. (d) Localization accuracy was observed to decrease proportionally with increased object speed due to increased motion blur. Conclusions: Localization accuracy in high quantum noise situations can be improved by increasing the temporal Filtering. There is a need to implement variable Weight temporal Filtering depending on the amount of motion detected. Support: NIH Grants R01‐EB008425, R01‐EB002873

  • SU‐E‐I‐194: Motion Detection Based Adaptive Temporal Filtering for Image Guided Procedures Using the High Resolution Microangiographic Fluoroscope (MAF)
    Medical Physics, 2011
    Co-Authors: A Panse, Amit Jain, Daniel R. Bednarek, Ciprian N. Ionita, Stephen Rudin
    Abstract:

    Purpose: To implement real‐time adaptive temporal Filtering based on amount of motion detected for an object of interest during fluoroscopy. For a stationary object, higher‐Weight temporal Filtering is used and during movement the Weight is reduced in real‐time. Methods: A phantom consisting of two stents (stainless steel and nitinol) mounted on a linear stage, and a stationary third stent (nitinol stent with platinum markers) was assembled. The stent strut sizes ranged between 80 to 100 micron. Fluoroscopy was done using the custom high resolution Micro Angiographic Fluoroscope (MAF) with an x‐ray spectrum hardened by a head equivalent phantom. The stainless steel stent was selected as the object of interest and was localized using a pattern matching algorithm available as a part of LabVIEW IMAQ Vision software. A real time adaptive temporal Filter was developed. When the stent was found to be stationary, the temporal Filtering Weight was increased. Depending on the amount of motion the Weight was reduced; more the motion, lesser the Weight. Results: Without temporal Filtering, the nitinol stent could not be seen. When the stents were stationary, and the temporal Filter Weight was increased, the nitinol stent could be clearly visualized due to the quantum noise reduction. When the same temporal Filter was used while the stents were in motion, then motion blur reduced the visibility of the nitinol stent and the struts of stainless steel stent also could not be distinguished from each other. In this case when the temporal Filtering Weight was reduced the stainless steel stent struts were visualized better. Conclusions: Motion based adaptive temporal Filtering can be implemented to aid during interventional procedures by tracking the motion of the high contrast interventional device to improve the visualization of lower contrast neurovascular objects. Support: NIH Grants R01‐EB008425, R01‐EB002873