Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information

By Alexis Roche, Xavier Pennec, Grégoire Malandain, Nicholas Ayache. IEEE Transactions on Medical Imaging, 2001

342 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/33698d7b12b12208affa21d00580f983f3c8e9e2

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deformable Medical Image Registration: A Survey (2013, background): “[363] tackled ultrasound (US) to MR rigid registration by simulating an US image from the MR one.”
Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements (2006, background): “In medical image analysis, selected applications cover the validation of the rigid registration accuracy [4, 66, 67], shape statistics [7] and more recently tensor computing, either for processing and analyzing diffusion tensor images [8–11], or to model the brain variability [12].”
Medical image registration: a review (2014, methodology): “The affine transformations, both rigid and non-rigid, have been used in the registration of ultrasound images (Meyer et al. 1999; Roche et al. 2001; Shekhar and Zagrodsky 2002; Shekhar et al. 2004; King et al. 2010), since the low resolution and low signal-to-noise ratio of the ultrasound images mak”
Mutual-information-based registration of medical images: a survey (2003, background): “[156] study the possibilities of using mutual”
Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention (2008, background): “Guetter et al. (2005), Kullback–Leibler divergence), as well as bootstrapping parameters for a polynomial intensity mapping in the actual registration process itself ( Roche et al., 2001 ).”
A review of multimodal image matching: Methods and applications (2021, background): “[361] transformed MR images into the domain of US images on the basis of their intensities and gradient information.”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “assign a rigid transformation to the registration framework [87], [88], [90]–[93], [96], [100]–[103], [106], [108], [109], [111], [112], [114], [117]–[119], [123], [124], [126], [129], [131]–[133], [135]–[148].”
An approach to multimodal biomedical image registration utilizing particle swarm optimization (2004, methodology): “The sum of squared intensity difference [7], generalized correlation coefficient [8], ratio image uniformity [7], and information theoretic measures [2], [3], [9]–[11] are commonly used similarity measures.”
Human motion tracking for rehabilitation - A survey (2008, methodology): “These systems are used in medical applications [48,120,133], but have not been used in motion tracking.”