成像体模
高光谱成像
迭代重建
计算机科学
软件
计算机视觉
重建算法
人工智能
窗口(计算)
算法
三维重建
图像质量
数据质量
事件重构
质量(理念)
信号重构
医学影像学
遥感
图像分辨率
作者
Dr Jakob Jorgensen,Mrs Evelina Ametova,Dr Edoardo Pasca,Dr Gemma Fardell,Mr Alex Liptak,Dr Daniil Kazantsev,Professor William Lionheart,Dr Martin Turner,Dr Genoveva Burca,Dr VAGGELIS PAPOUTSELLIS
出处
期刊:Science and Technology Facilities Council
日期:2019-01-01
被引量:2
标识
DOI:10.5286/isis.e.rb1820541
摘要
New energy-sensitive imaging techniques promise a 3D spectroscopic window deep into material microstructure, which is essential to understand in order to design new materials for applications in medicine, energy, etc. However, massive data sets, poor signal levels as well as lack of dedicated multi-channel reconstruction software pose critical barriers to the efficient exploitation of these technologies. Our research is developing a new software toolkit of novel multi-channel reconstruction methods for hyperspectral data with 100s or 1000s of channels, including neutron time-of-flight and energy-resolved X-ray imaging data.The aim of this proposal is to produce high- and low-quality test data for a carefully designed multi-material contrast phantom to allow characterisation and comparison of reconstruction quality for newly developed algorithms under varying imaging conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI