Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy

By He Wang, Lei Dong, J. O'Daniel, R. Mohan, A. Garden, K. Ang, D. Kuban, M. Bonnen, Joe Y. Chang, Rex Cheung. Physics in Medicine and Biology, 2005

675 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9913e447e6f0a837bd7a93208c6124da7bcb83a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Nicholas Ayache
2. Albert C. S. Chung
3. Xavier Pennec
4. Tom Kamiel Magda Vercauteren
5. Tony C. W. Mok

In Radiology, Nuclear Medicine and Imaging

1. Xavier Pennec
2. Nicholas Ayache
3. Albert C. S. Chung
4. Tom Kamiel Magda Vercauteren
5. Aymeric Perchant
6. Tony C. W. Mok
7. John Ashburner
8. Christof Schötz

Citations in the corpus

• Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (2011, neutral): “, [23]–[25]) is to apply a known artificial transformation to a single dataset and then attempt to register the original scan with”
• Diffeomorphic demons: Efficient non-parametric image registration (2009, methodology): “On the other hand, previous studies that have used (but not theoretically justified) a very similar symmetrization of the demons forces have reported the practical advantage of this variant (Rogelj and Kovačič, 2006; Wang et al., 2005) even with the additive adjustment rule.”
• Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks (2020, methodology): “Baseline Methods We compare our method with two conventional approaches (denoted as Demons [27] and SyN [2]) and two cutting edge DLIR methods (denoted as VM[4] and DIF-VM [5]).”
• A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets (2009, methodology): “Hence, correlation and gray-scale similarity measures (Dawood et al 2008, Wang et al 2005a, 2005b, Lu et al 2004, 2006) and / or visual checks (Sharpe and Brock 2008) are inadequate for evaluation of DIR results.”
• Smart Align—a new tool for robust non-rigid registration of scanning microscope data (2015, background): “Such non-rigid registration has many applications in imaging where either the object or recording process vary over time including remote sensing [17], medical imaging [18, 19] and astronomy and photography [20].”