Inverting deformation fields using a fixed point iteration scheme

Luethi, Marcel

doi:10.54294/93ee74

The Insight Journal

2010

DOI: 10.54294/93ee74

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Inverting deformation fields using a fixed point iteration scheme

Marcel Luethi¹

Abstract: Being able to quickly compute the inverse of a deformation ﬁeld is often useful in the context of medical image analysis. While ITK supports this functionality, the current algorithms are slow and do not always yield accurate results. In this paper we describe an ITK implementation of a ﬁxed point algorithm for the approximate inversion of deformation ﬁelds that was recently proposed by M. Chen and co-workers. The algorithm has been shown to be both faster and more accurate than those currently implemented in … Show more

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Cited by 2 publications

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Registration of Images With Varying Topology Using Embedded Maps

Long

Laurienti

et al. 2012

IEEE Trans. Med. Imaging

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In medical images, intensity changes caused by certain pathology can change the topology of image level-sets and are thus commonly referred to as topological changes. Topological changes cause false deformation in existing deformable registration algorithms, which in turn leads to unreliable observations in the clinical study that relies on the deformation fields, such as deformation based morphometry (DBM). In this work, we develop a new deformable registration algorithm for images with topological changes. In our proposed algorithm, 3D images are embedded as 4D surfaces in a Riemannian space. The registration is therefore conducted as a surface evolution, which is modeled by a diffusion process. Our algorithm differs from existing methods in the sense that it takes an a-priori estimation of areas with topological change as an additional input and generates dense deformation vector fields which are free of false deformation. In particular, the output of our algorithm is composed of a diffeomorphic deformation field and an intensity displacement which corrects intensity difference caused by topological changes. By conducting multiple sets of experiments, we demonstrate that our proposed algorithm is capable of accurately registering images with considerable topological changes. More importantly, the resulting deformation field is not impacted by topological changes, i.e., there is no false deformation.

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Registration of Images With Varying Topology Using Embedded Maps

Long

Laurienti

et al. 2012

IEEE Trans. Med. Imaging

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Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

2017

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This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation.

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Inverting deformation fields using a fixed point iteration scheme

Cited by 2 publications

References 5 publications

Registration of Images With Varying Topology Using Embedded Maps

Registration of Images With Varying Topology Using Embedded Maps

Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

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