A review of cardiac image registration methods

By Timo Mäkelä, Patrick Clarysse, Outi Sipilä, Nicoleta Pauna, Quoc Cuong Pham, T. Katila, Isabelle E. Magnin. IEEE Transactions on Medical Imaging, 2002

422 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4ebe1ff8523b82b5cae087754dd996a64b63cca9

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Nikos Paragios
2. Aristeidis Sotiras
3. Christos Davatzikos
4. Thomas Martin Deserno
5. Benjamin Berkels
6. András P. Keszei
7. Ben Glocker
8. João Manuel Ribeiro da Silva Tavares

In Radiology, Nuclear Medicine and Imaging

1. João Manuel Ribeiro da Silva Tavares
2. Francisco P. M. Oliveira
3. Nikos Paragios
4. Derek L. G. Hill
5. Thomas Martin Deserno
6. William R. Crum
7. Ben Glocker
8. Christos Davatzikos

Citations in the corpus

• Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “Key examples of geometric registration might be matching points sampled from two surfaces such as in imageguided brain surgery (37–39) or for marker-based registration for instance used in cardiac motion validation problems (40, 41).”
• Comparison of quantitative methods for cell‐shape analysis (2007, neutral): “,1996;Makela et al. , 2002; Sivaramakrishna, 2005).”
• Deformable Medical Image Registration: A Survey (2013, background): “cardiac and respiratory motion) the key element in diagnosis becomes their spatio-temporal behavior [1, 2].”
• Non-rigid image registration: theory and practice. (2004, methodology): “For more specific aspects of image registration, the reader is referred to other reviews; there is good technical coverage in Hill et al [1], Brown [15], Lester and Arridge [16], Maintz and Viergever [17] and Zitova and Flusser [18], reviews of cardiac applications in Makela et al [19], nuclear medi”
• Survey of Medical Image Registration (2013, neutral): “MRI and CT or PET) [2-10].”
• Medical image registration: a review (2014, background): “specific anatomical parts, such as: cardiac (Mäkelä et al., 2002), retina (Laliberté et al.”
• Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation (2005, methodology): “Registration methods have been used in cardiac imaging; they are usually applied to data acquired at the same time point in the cardiac cycle, with the aim of achieving either multimodal integration [14] or to compensate for small misalignments [15].”
• Survey of Non-Rigid Registration Tools in Medicine (2017, background): “Other authors have drawn attention to specific anatomic regions, for example, cardiac image registration [20], or on techniques used for specific purposes, such as brain functional image localization [21], optical breast imaging [22], minimally invasive surgical procedures [23], and coronary heart d”