Authors
Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen
Publication date
2015
Conference
Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data: Third International Workshop, STIA 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 18, 2014, Revised Selected Papers 3
Pages
67-76
Publisher
Springer International Publishing
Description
Neonatal images have low spatial resolution and insufficient tissue contrast. Generally, interpolation methods are used to upsample neonatal images to a higher resolution for more effective image analysis. However, the resulting images are often blurry and are susceptible to partial volume effect. In this paper, we propose an algorithm that utilizes longitudinal prior information for effective super-resolution reconstruction of neonatal images. We use a non-local approach to learn the spatial relationships of brain structures in high-resolution longitudinal images and apply this information to the super-resolution reconstruction of the neonatal image. In other words, the recurring patterns throughout the longitudinal scans are leveraged for reconstructing the neonatal image with high resolution. To solve this otherwise ill-posed inverse problem, low-rank and total-variation regularizations are enforced. Experiments …
Total citations
2016201720182019202020212022202313111
Scholar articles
F Shi, J Cheng, L Wang, PT Yap, D Shen - Spatio-temporal Image Analysis for Longitudinal and …, 2015