X-ray ptychographic tomography is a nondestructive method for three dimensional(3D)imaging with nanometer-sized resolvable features.The size of the volume that can be imaged is almost arbitrary,limited only by the pen...X-ray ptychographic tomography is a nondestructive method for three dimensional(3D)imaging with nanometer-sized resolvable features.The size of the volume that can be imaged is almost arbitrary,limited only by the penetration depth and the available scanning time.Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors.The proposed 3D reconstruction method“RAPID”relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth.It is then trained to reproduce equal fidelity from much fewer angles.After training,it performs with similar fidelity on the hitherto unexamined portions of the object,previously not shown during training,with a limited set of acquisitions.In our experimental demonstration,the nominal number of angles was 349 and the reduced number of angles was 21,resulting in a×140 aggregate speedup over a volume of 4.48×93.18×3.92μm^(3) and with(14 nm)^(3) feature size,i.e.-10^(8) voxels.RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way.We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.展开更多
Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In ...Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is proposed.To extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling stage.And in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low levels.These two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational cost.By the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is indicated.It is expected to provide robust support for clinical diagnosis and treatment.展开更多
基金funded by the Intelligence Advanced Research Projects Activity,Office of the Director of National Intelligence(IARPA-ODNI)under contract FA8650-17-C-9113.
文摘X-ray ptychographic tomography is a nondestructive method for three dimensional(3D)imaging with nanometer-sized resolvable features.The size of the volume that can be imaged is almost arbitrary,limited only by the penetration depth and the available scanning time.Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors.The proposed 3D reconstruction method“RAPID”relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth.It is then trained to reproduce equal fidelity from much fewer angles.After training,it performs with similar fidelity on the hitherto unexamined portions of the object,previously not shown during training,with a limited set of acquisitions.In our experimental demonstration,the nominal number of angles was 349 and the reduced number of angles was 21,resulting in a×140 aggregate speedup over a volume of 4.48×93.18×3.92μm^(3) and with(14 nm)^(3) feature size,i.e.-10^(8) voxels.RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way.We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.
文摘Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is proposed.To extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling stage.And in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low levels.These two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational cost.By the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is indicated.It is expected to provide robust support for clinical diagnosis and treatment.
