Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
High-resolution peripheral quantitative computed tomography (HR-pQCT) can provide important information about age-related changes in bone microstructure and strength. However, in elderly patients, uncontrollable tremors often induce motion artefacts that affect the accuracy of HR-pQCT measurements. Repeat acquisition protocols are commonly used to address this issue; however, they are ineffective in these patients, resulting in motion-blurred and streaked images. Deblurring these scans computationally is a difficult inverse problem that is severely ill-posed. Therefore, we present a deep learning approach to suppress motion-induced artefacts in HR-pQCT scans.
Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
Sudeep Sarkar, Kim L. Boyer
Computer Vision and Image Understanding
Michelle X. Zhou, Fei Wang, et al.
ICMEW 2013
James E. Gentile, Nalini Ratha, et al.
BTAS 2009