A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete...A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image.To tackle this challenge,we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine,which integrates a convolutional neural network(CNN)with perceptional knowledge as a smart regularizer into an iterative solving engine.We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains,thereby achieving a physically coherent and visually enhanced result.We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques.All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail.Notably,it also improves the reconstruction in case of sparse projections,despite the network not being specifically trained for that.This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.展开更多
A binaural-loudness-model-based method for evaluating the spatial discrimination threshold of magnitudes of head-related transfer function（HRTF） is proposed.As the input of the binaural loudness model,the HRTF magni...A binaural-loudness-model-based method for evaluating the spatial discrimination threshold of magnitudes of head-related transfer function（HRTF） is proposed.As the input of the binaural loudness model,the HRTF magnitude variations caused by spatial position variations were firstly calculated from a high-resolution HRTF dataset.Then,three perceptualrelevant parameters,namely interaural loudness level difference,binaural loudness level spectra,and total binaural loudness level,were derived from the binaural loudness model.Finally,the spatial discrimination thresholds of HRTF magnitude were evaluated according to just-noticedifference of the above-mentioned perceptual-relevant parameters.A series of psychoacoustic experiments was also conducted to obtain the spatial discrimination threshold of HRTF magnitudes.Results indicate that the threshold derived from the proposed binaural-loudness-modelbased method is consistent with that obtained from the traditional psychoacoustic experiment,validating the effectiveness of the proposed method.展开更多
基金supported partly by BNL LDRD funding (24-067). We thank the user support provided by the CFN staff Kim Kisslinger and Fernando Camino for FIB-SEM training and help with sample preparation. We acknowledge the sample preparation of porous Cu and LiMn2O4 battery electrodes by Qingkun Meng and Cheng-Hung Lin from the Chen-Wiegart group at Stony Brook University and Brookhaven National Laboratory. We also thank the HXN beamline staff, Ajith Pattammattel and Xiaojing Huang, for their support of the XRF tomography measurement setup and data analysis.
文摘A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image.To tackle this challenge,we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine,which integrates a convolutional neural network(CNN)with perceptional knowledge as a smart regularizer into an iterative solving engine.We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains,thereby achieving a physically coherent and visually enhanced result.We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques.All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail.Notably,it also improves the reconstruction in case of sparse projections,despite the network not being specifically trained for that.This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.
基金Supported by the National Natural Science Foundation of China(11174087)
文摘A binaural-loudness-model-based method for evaluating the spatial discrimination threshold of magnitudes of head-related transfer function（HRTF） is proposed.As the input of the binaural loudness model,the HRTF magnitude variations caused by spatial position variations were firstly calculated from a high-resolution HRTF dataset.Then,three perceptualrelevant parameters,namely interaural loudness level difference,binaural loudness level spectra,and total binaural loudness level,were derived from the binaural loudness model.Finally,the spatial discrimination thresholds of HRTF magnitude were evaluated according to just-noticedifference of the above-mentioned perceptual-relevant parameters.A series of psychoacoustic experiments was also conducted to obtain the spatial discrimination threshold of HRTF magnitudes.Results indicate that the threshold derived from the proposed binaural-loudness-modelbased method is consistent with that obtained from the traditional psychoacoustic experiment,validating the effectiveness of the proposed method.
