17,212 research outputs found
Joint Image Reconstruction and Nonrigid Motion Estimation with a Simple Penalty That Encourages Local Invertibility
Motion artifacts are a significant issue in medical image reconstruction. There are many methods for incorporating motion
information into image reconstruction. However, there are fewer studies that focus on deformation regularization in motioncompensated
image reconstruction. The usual choice for deformation regularization has been penalty functions based on
the assumption that tissues are elastic. In the image registration field, there have been some methods proposed that impose
deformation invertibility using constraints or regularization, assuming that organ motions are invertible transformations.
However, most of these methods require very high memory or computation complexity, making them poorly suited for
dealing with multiple images simultaneously in motion-compensated image reconstruction. Recently we proposed an
image registration method that uses a simple penalty function based on a sufficient condition for the local invertibility of
deformations.1 That approach encourages local invertibility in a fast and memory-efficient way. This paper investigates
the use of that regularization method for the more challenging problem of joint image reconstruction and nonrigid motion
estimation. A 2D PET simulation (based on realistic motion from real patient CT data) demonstrates the benefits of such
motion regularization for joint image reconstruction/registration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85929/1/Fessler237.pd
A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images
Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated
Motion compensated iterative reconstruction for cardiac X-ray tomography
Within this Ph.D. project, three-dimensional reconstruction methods for moving objects (with a focus on the human heart) from cone-beam X-ray projections using iterative reconstruction algorithms were developed and evaluated. This project was carried in collaboration with the Digital Imaging Group of Philips Research Europe – Hamburg.
In cardiac cone-beam computed tomography (CT) a large effort is continuously dedicated to increase scanning speed in order to minimize patient or organ motion during acquisition. In particular, motion causes severe artifacts such as blurring and streaks in tomographic images. While for a large class of applications the current scanning speed is sufficient, in cardiac CT image reconstruction improvements are still required. Whereas it is currently feasible to achieve stable image quality in the resting phases of the cardiac cycle, in the phase of fast motion data acquisition is too slow. A variety of algorithms to reduce or compensate for motion artifacts have been proposed in literature. Most of the correction methods address the calculation of consistent projection data belonging to the same motion state (gated CT reconstruction). Even if gated CT leads to better results, not only with respect to the processing time but also regarding the image quality, it is also limited in its temporal and spatial resolution due to the mechanical movement of the gantry. This can lead to motion blurring, especially in the phases of fast cardiac motion during the RR interval. A motion-compensated reconstruction method for CT can be used to improve the resolution of the reconstructed image and to suppress motion blurring. Iterative techniques are a promising approach to solve this problem, since no direct inversion methods are known for arbitrarily moving objects.
In this work, we therefore introduced motion compensation into image reconstruction. In order to determine the unknown cardiac motion, 3 different cardiac-motion estimation methodologies were implemented. Visual and quantitative assessment of the method in a number of applications, including: phantoms; cardiac CT reconstructions; Region of Interest (ROI) CT reconstructions of left and right coronaries of several clinical patients, confirmed its potential
Surrogate-Driven Motion Model for Motion Compensated Cone-beam CT Reconstruction using Unsorted Projection Data
Cone-beam CT (CBCT) is widely used in image guided radiotherapy, but motion due to breathing can blur the image.
Similar to 4DCT, 4D CBCT can reduce motion blur but 4D
CBCT acquisitions take 2˜4 times longer than 3D CBCT
and often suffer from phase sorting artefact. This study aims
to obtain motion models and motion-free images simultaneously from unsorted 3D CBCT projection data, using a
general motion modelling framework previously proposed by
our group, which was for the first time successfully applied to
real CBCT data equivalent to a one-minute acquisition. The
performance of our method was comprehensively evaluated
through digital phantom simulation and also validated on real
patient data. This study demonstrated the feasibility of our
proposed framework for simultaneous motion model fitting
and motion compensated reconstruction using unsorted 3D
CBCT projection data
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A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images.
Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated
Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes
This work presents a framework to exploit the synergy between Digital Volume Correlation ( DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-mu CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360 degrees rotation
Aggregated motion estimation for real-time MRI reconstruction
Real-time magnetic resonance imaging (MRI) methods generally shorten the
measuring time by acquiring less data than needed according to the sampling
theorem. In order to obtain a proper image from such undersampled data, the
reconstruction is commonly defined as the solution of an inverse problem, which
is regularized by a priori assumptions about the object. While practical
realizations have hitherto been surprisingly successful, strong assumptions
about the continuity of image features may affect the temporal fidelity of the
estimated images. Here we propose a novel approach for the reconstruction of
serial real-time MRI data which integrates the deformations between nearby
frames into the data consistency term. The method is not required to be affine
or rigid and does not need additional measurements. Moreover, it handles
multi-channel MRI data by simultaneously determining the image and its coil
sensitivity profiles in a nonlinear formulation which also adapts to
non-Cartesian (e.g., radial) sampling schemes. Experimental results of a motion
phantom with controlled speed and in vivo measurements of rapid tongue
movements demonstrate image improvements in preserving temporal fidelity and
removing residual artifacts.Comment: This is a preliminary technical report. A polished version is
published by Magnetic Resonance in Medicine. Magnetic Resonance in Medicine
201
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