.png){kind=icon}
返回
Iterative difference deblurring algorithm for linear computed laminography
.png){kind=icon}
OA
AI
DOI：10.1364/OE.435460.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Linear Computed Laminography (LCL) is used to yield slice images of plate-like objects (PLO) for the advantage of short exposure time, high control precision and low cost. Shift and Add (SAA) is a widely used reconstruction algorithm for LCL. One limitation of SAA is that the reconstructed image of the in-focus layer (IFL) contains information from off-focus layers (OFL), resulting in inter-slice aliasing and blurring. In this paper, an Iterative Difference Deblurring (IDD) algorithm based on LCL is proposed to reduce the blur in reconstructed images. The core idea of the IDD algorithm is: contributions from OFL are subtracted from the projection data to remove the blur from the IFL. The corrected projections are then reconstructed using the SAA to remove the superimposed contributions of OFL from the IFL. An iterative approach is utilized to adjust a weighting factor applied during the subtraction stage. The results demonstrate that IDD algorithm can achieve PLO reconstruction in the LCL system under extremely sparse sampling conditions, and can effectively reduce the inter-slice aliasing and blurring. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
DIGITAL FLASHING TOMOSYNTHESIS
RECONSTRUCTION
REMOVAL
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
分享收藏分享收藏分享收藏Evaluation of linear and nonlinear tomosynthetic reconstruction methods in digital mammography.png)
ACADEMIC RADIOLOGY
ACADEMIC RADIOLOGYIF3.9
PREAI
分享收藏分享收藏
查看更多内容