The Experts below are selected from a list of 6714 Experts worldwide ranked by ideXlab platform
J Burmeister - One of the best experts on this subject based on the ideXlab platform.
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dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2010Co-Authors: Yimei Huang, M C Joiner, B Zhao, Yixiang Liao, J BurmeisterAbstract:Purpose: A model is introduced to integrate biological factors such as cell migration and bystander effects into physical dose distributions, and to incorporate spatial dose information in plan analysis and optimization. Methods: The model consists of a dose Convolution Filter (DCF) with single parameter {sigma}. Tissue response is calculated by an existing NTCP model with DCF-applied dose distribution as input. The authors determined {sigma} of rat spinal cord from published data. The authors also simulated the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying the DCF, the NTCP model successfully fits the rat spinal cord data with a predicted value of {sigma}=2.6{+-}0.5 mm, consistent with 2 mm migration distances of remyelinating cells. Moreover, it enables the appropriate prediction of a high relative seriality for spinal cord. The model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to {sigma}. Conclusions: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance inmore » small or highly nonuniform fields.« less
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su gg aud 02 dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2007Co-Authors: Yimei Huang, M C Joiner, Yixiang Liao, J BurmeisterAbstract:Purpose:Radiotherapytreatment planning commonly involves analysis of static dose distributions and corresponding Dose Volume Histograms (DVHs). However, such analysis does not account for biological effects of spatial variations in the physical dose distribution. We introduce the Dose Convolution Filter (DCF) model capable of incorporating spatialdoseinformation in plan analysis and optimization, and integrating biological factors such as cell migration and bystander effects into physical dose distributions. The DCF model should allow more accurate prediction of tissue response from complex radiotherapydose distributions, and can facilitate modeling of the effects of patient motion. Method and Materials: We use a Gaussian Convolution Filter with standard deviation, σ, determining the degree of dose washout. To test this model, Filtered dose distributions are applied to a NTCP model to calculate tissue response. As an illustration, we determine σ from existing rat spinal cord data, and compare model‐predicted NTCP with published data. We also simulate the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying DCF, an NTCP model can predict dependence of tissue response on variations in spatialdose distribution. The model successfully fits the rat spinal cord data with a predicted value of σ=2.6±0.5mm, consistent with 2mm migration distances of remyelinating cells. Moreover, it enables the correct prediction of a high relative seriality for spinal cord. Finally, this model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to σ. Conclusion: The DCF model incorporates spatialdoseinformation and offers an improved way to estimate tissue response from complex radiotherapydose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly non‐uniform fields. Partially supported by Varian Medical Systems.
Yimei Huang - One of the best experts on this subject based on the ideXlab platform.
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dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2010Co-Authors: Yimei Huang, M C Joiner, B Zhao, Yixiang Liao, J BurmeisterAbstract:Purpose: A model is introduced to integrate biological factors such as cell migration and bystander effects into physical dose distributions, and to incorporate spatial dose information in plan analysis and optimization. Methods: The model consists of a dose Convolution Filter (DCF) with single parameter {sigma}. Tissue response is calculated by an existing NTCP model with DCF-applied dose distribution as input. The authors determined {sigma} of rat spinal cord from published data. The authors also simulated the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying the DCF, the NTCP model successfully fits the rat spinal cord data with a predicted value of {sigma}=2.6{+-}0.5 mm, consistent with 2 mm migration distances of remyelinating cells. Moreover, it enables the appropriate prediction of a high relative seriality for spinal cord. The model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to {sigma}. Conclusions: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance inmore » small or highly nonuniform fields.« less
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su gg aud 02 dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2007Co-Authors: Yimei Huang, M C Joiner, Yixiang Liao, J BurmeisterAbstract:Purpose:Radiotherapytreatment planning commonly involves analysis of static dose distributions and corresponding Dose Volume Histograms (DVHs). However, such analysis does not account for biological effects of spatial variations in the physical dose distribution. We introduce the Dose Convolution Filter (DCF) model capable of incorporating spatialdoseinformation in plan analysis and optimization, and integrating biological factors such as cell migration and bystander effects into physical dose distributions. The DCF model should allow more accurate prediction of tissue response from complex radiotherapydose distributions, and can facilitate modeling of the effects of patient motion. Method and Materials: We use a Gaussian Convolution Filter with standard deviation, σ, determining the degree of dose washout. To test this model, Filtered dose distributions are applied to a NTCP model to calculate tissue response. As an illustration, we determine σ from existing rat spinal cord data, and compare model‐predicted NTCP with published data. We also simulate the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying DCF, an NTCP model can predict dependence of tissue response on variations in spatialdose distribution. The model successfully fits the rat spinal cord data with a predicted value of σ=2.6±0.5mm, consistent with 2mm migration distances of remyelinating cells. Moreover, it enables the correct prediction of a high relative seriality for spinal cord. Finally, this model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to σ. Conclusion: The DCF model incorporates spatialdoseinformation and offers an improved way to estimate tissue response from complex radiotherapydose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly non‐uniform fields. Partially supported by Varian Medical Systems.
B S Adiga - One of the best experts on this subject based on the ideXlab platform.
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a design and implementation of orthonormal symmetric wavelet transform using prcc Filter banks
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Abhijin Adiga, K R Ramakrishnan, B S AdigaAbstract:We propose a framework for orthonormal symmetric wavelet transform given a cyclic zero-phase half band Filter. The scheme consists of a design for cyclic wavelet transform with real symmetric Filters based on the perfect reconstruction circular Convolution Filter banks accompanied by an implementation in the discrete trigonometric transform domain. This is followed by a brief discussion on its performance in the context of applications such as image compression and implementation of symmetric wavelet transforms based on bandlimited wavelets.
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perfect reconstruction circular Convolution Filter banks and their application to the implementation of bandlimited discrete wavelet transforms
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: Ajit S Bopardikar, M R Raghurveer, B S AdigaAbstract:This paper introduces a new Filter bank structure called the perfect reconstruction circular Convolution (PRCC) Filter bank. These Filter banks satisfy the perfect reconstruction properties, namely, the paraunitary properties in the discrete frequency domain. We further show how the PRCC analysis and synthesis Filter banks are completely implemented in this domain and give a simple and a flexible method for the design of these Filters. Finally, we use this Filter bank structure for a frequency sampled implementation of the discrete wavelet transform based on orthogonal bandlimited scaling functions and wavelets.
Junjie Yan - One of the best experts on this subject based on the ideXlab platform.
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pod practical object detection with scale sensitive network
International Conference on Computer Vision, 2019Co-Authors: Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie YanAbstract:Scale-sensitive object detection remains a challenging task, where most of the existing methods not learn it explicitly and not robust to scale variance. In addition, the most existing methods are less efficient during training or slow during inference, which are not friendly to real-time application. In this paper, we propose a practical object detection with scale-sensitive network.Our method first predicts a global continuous scale ,which shared by all position, for each Convolution Filter of each network stage. To effectively learn the scale, we average the spatial features and distill the scale from channels. For fast-deployment, we propose a scale decomposition method that transfers the robust fractional scale into combinations of fixed integral scales for each Convolution Filter, which exploit the dilated Convolution. We demonstrate it on one-stage and two-stage algorithm under almost different configure. For practical application, training of our method is of efficiency and simplicity which gets rid of complex data sampling or optimize strategy. During testing, the proposed method requires no extra operation and is very friendly to hardware acceleration like TensorRT and TVM.On the COCO test-dev, our model could achieve a 41.5mAP on one-stage detector and 42.1 mAP on two-stage detectors based on ResNet-101, outperforming baselines by 2.4 and 2.1 respectively without extra FLOPS.
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pod practical object detection with scale sensitive network
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie YanAbstract:Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance. In addition, the most existing methods are less efficient during training or slow during inference, which are not friendly to real-time applications. In this paper, we propose a practical object detection method with scale-sensitive network.Our method first predicts a global continuous scale ,which is shared by all position, for each Convolution Filter of each network stage. To effectively learn the scale, we average the spatial features and distill the scale from channels. For fast-deployment, we propose a scale decomposition method that transfers the robust fractional scale into combination of fixed integral scales for each Convolution Filter, which exploits the dilated Convolution. We demonstrate it on one-stage and two-stage algorithms under different configurations. For practical applications, training of our method is of efficiency and simplicity which gets rid of complex data sampling or optimize strategy. During test-ing, the proposed method requires no extra operation and is very supportive of hardware acceleration like TensorRT and TVM. On the COCO test-dev, our model could achieve a 41.5 mAP on one-stage detector and 42.1 mAP on two-stage detectors based on ResNet-101, outperforming base-lines by 2.4 and 2.1 respectively without extra FLOPS.
M C Joiner - One of the best experts on this subject based on the ideXlab platform.
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dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2010Co-Authors: Yimei Huang, M C Joiner, B Zhao, Yixiang Liao, J BurmeisterAbstract:Purpose: A model is introduced to integrate biological factors such as cell migration and bystander effects into physical dose distributions, and to incorporate spatial dose information in plan analysis and optimization. Methods: The model consists of a dose Convolution Filter (DCF) with single parameter {sigma}. Tissue response is calculated by an existing NTCP model with DCF-applied dose distribution as input. The authors determined {sigma} of rat spinal cord from published data. The authors also simulated the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying the DCF, the NTCP model successfully fits the rat spinal cord data with a predicted value of {sigma}=2.6{+-}0.5 mm, consistent with 2 mm migration distances of remyelinating cells. Moreover, it enables the appropriate prediction of a high relative seriality for spinal cord. The model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to {sigma}. Conclusions: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance inmore » small or highly nonuniform fields.« less
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su gg aud 02 dose Convolution Filter incorporating spatial dose information into tissue response modeling
Medical Physics, 2007Co-Authors: Yimei Huang, M C Joiner, Yixiang Liao, J BurmeisterAbstract:Purpose:Radiotherapytreatment planning commonly involves analysis of static dose distributions and corresponding Dose Volume Histograms (DVHs). However, such analysis does not account for biological effects of spatial variations in the physical dose distribution. We introduce the Dose Convolution Filter (DCF) model capable of incorporating spatialdoseinformation in plan analysis and optimization, and integrating biological factors such as cell migration and bystander effects into physical dose distributions. The DCF model should allow more accurate prediction of tissue response from complex radiotherapydose distributions, and can facilitate modeling of the effects of patient motion. Method and Materials: We use a Gaussian Convolution Filter with standard deviation, σ, determining the degree of dose washout. To test this model, Filtered dose distributions are applied to a NTCP model to calculate tissue response. As an illustration, we determine σ from existing rat spinal cord data, and compare model‐predicted NTCP with published data. We also simulate the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying DCF, an NTCP model can predict dependence of tissue response on variations in spatialdose distribution. The model successfully fits the rat spinal cord data with a predicted value of σ=2.6±0.5mm, consistent with 2mm migration distances of remyelinating cells. Moreover, it enables the correct prediction of a high relative seriality for spinal cord. Finally, this model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to σ. Conclusion: The DCF model incorporates spatialdoseinformation and offers an improved way to estimate tissue response from complex radiotherapydose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly non‐uniform fields. Partially supported by Varian Medical Systems.