154 research outputs found
Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T1 and T2 quantification
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156191/2/nbm4323_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156191/1/nbm4323.pd
Optimal Transport-based Graph Matching for 3D retinal OCT image registration
Registration of longitudinal optical coherence tomography (OCT) images
assists disease monitoring and is essential in image fusion applications. Mouse
retinal OCT images are often collected for longitudinal study of eye disease
models such as uveitis, but their quality is often poor compared with human
imaging. This paper presents a novel but efficient framework involving an
optimal transport based graph matching (OT-GM) method for 3D mouse OCT image
registration. We first perform registration of fundus-like images obtained by
projecting all b-scans of a volume on a plane orthogonal to them, hereafter
referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph
Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence
between nodes of graphs extracted from segmented vessels within the OCT
projection images. The AWVGD comprises scaling, translation and rotation, which
are computationally efficient, whereas CD exploits 3D spatial and frequency
domain information. The OT-GM method subsequently performs the correct
alignment in the x-y plane. Finally, registration along the direction
orthogonal to the x-y plane (the z-direction) is guided by the segmentation of
two important anatomical features peculiar to mouse b-scans, the Internal
Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and
objective evaluation results demonstrate that our framework outperforms other
well-established methods on mouse OCT images within a reasonable execution
time
Efficient dense non-rigid registration using the free-form deformation framework
Medical image registration consists of finding spatial correspondences between two images or more. It
is a powerful tool which is commonly used in various medical image processing tasks. Even though
medical image registration has been an active topic of research for the last two decades, significant
challenges in the field remain to be solved. This thesis addresses some of these challenges through
extensions to the Free-Form Deformation (FFD) registration framework, which is one of the most widely
used and well-established non-rigid registration algorithm.
Medical image registration is a computationally expensive task because of the high degrees of freedom
of the non-rigid transformations. In this work, the FFD algorithm has been re-factored to enable
fast processing, while maintaining the accuracy of the results. In addition, parallel computing paradigms
have been employed to provide near real-time image registration capabilities. Further modifications have
been performed to improve the registration robustness to artifacts such as tissues non-uniformity. The
plausibility of the generated deformation field has been improved through the use of bio-mechanical
models based regularization. Additionally, diffeomorphic extensions to the algorithm were also developed.
The work presented in this thesis has been extensively validated using brain magnetic resonance
imaging of patients diagnosed with dementia or patients undergoing brain resection. It has also been
applied to lung X-ray computed tomography and imaging of small animals.
Alongside with this thesis an open-source package, NiftyReg, has been developed to release the
presented work to the medical imaging community
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the
statistical dependency and finding the anatomical correspondences among an
arbitrary number of medical images. The method builds on a novel formulation of
the -dimensional joint intensity distribution by representing the common
anatomy as latent variables and estimating the appearance model with
nonparametric estimators. Through connection to maximum likelihood and the
expectation-maximization algorithm, an information\hyp{}theoretic metric called
-metric and a co-registration algorithm named -CoReg
are induced, allowing groupwise registration of the observed images with
computational complexity of . Moreover, the method naturally
extends for a weakly-supervised scenario where anatomical labels of certain
images are provided. This leads to a combined\hyp{}computing framework
implemented with deep learning, which performs registration and segmentation
simultaneously and collaboratively in an end-to-end fashion. Extensive
experiments were conducted to demonstrate the versatility and applicability of
our model, including multimodal groupwise registration, motion correction for
dynamic contrast enhanced magnetic resonance images, and deep combined
computing for multimodal medical images. Results show the superiority of our
method in various applications in terms of both accuracy and efficiency,
highlighting the advantage of the proposed representation of the imaging
process
HNSF Log-Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features
© 2021 The Authors. Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nystr ö
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m approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.National Natural Science Foundation of China. Grant Numbers: 61762058, 61861024, 61871259;
Natural Science Foundation of Gansu Province of China. Grant Number: 20JR5RA404;
Natural Science Basic Research Program of Shaanxi. Grant Number: 2021JC-47
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
MMORF—FSL’s MultiMOdal Registration Framework
We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL
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