In this work, we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions
A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robust...
Groupwise registration has recently been proposed for simultaneous and consistent registration of al...
We propose a multimodal free-form registration algorithm that matches voxel class labels rather than...
Medical imaging is nowadays a vital component of a large number of clinical applications. For compar...
We present a novel image registration method based on B-spline free-form deformation that simultaneo...
The automated analysis of medical images plays an increasingly significant part in many clinical app...
In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the ...
The automated analysis of medical images plays an increasingly significant part in many clini-cal ap...
Groupwise registration has been recently introduced to simultaneously register a group of images by ...
The purpose of deformable image registration is to recover acceptable spatial transformations that a...
Effective and efficient spatial normalization of a large population of brain images is critical for ...
All fields of neuroscience that employ brain imaging need to communicate their results with referenc...
Normalizing all images in a large data set into a common space is a key step in many clinical and re...
Non-rigid image registration is fundamentally important in analyzing large-scale population of medic...
In this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribu...
A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robust...
Groupwise registration has recently been proposed for simultaneous and consistent registration of al...
We propose a multimodal free-form registration algorithm that matches voxel class labels rather than...
Medical imaging is nowadays a vital component of a large number of clinical applications. For compar...
We present a novel image registration method based on B-spline free-form deformation that simultaneo...
The automated analysis of medical images plays an increasingly significant part in many clinical app...
In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the ...
The automated analysis of medical images plays an increasingly significant part in many clini-cal ap...
Groupwise registration has been recently introduced to simultaneously register a group of images by ...
The purpose of deformable image registration is to recover acceptable spatial transformations that a...
Effective and efficient spatial normalization of a large population of brain images is critical for ...
All fields of neuroscience that employ brain imaging need to communicate their results with referenc...
Normalizing all images in a large data set into a common space is a key step in many clinical and re...
Non-rigid image registration is fundamentally important in analyzing large-scale population of medic...
In this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribu...
A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robust...
Groupwise registration has recently been proposed for simultaneous and consistent registration of al...
We propose a multimodal free-form registration algorithm that matches voxel class labels rather than...