39 research outputs found
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
High-resolution three-dimensional (3D) cardiovascular magnetic resonance
(CMR) is a valuable medical imaging technique, but its widespread application
in clinical practice is hampered by long acquisition times. Here we present a
novel compressed sensing (CS) reconstruction approach using shearlets as a
sparsifying transform allowing for fast 3D CMR (3DShearCS). Shearlets are
mathematically optimal for a simplified model of natural images and have been
proven to be more efficient than classical systems such as wavelets. Data is
acquired with a 3D Radial Phase Encoding (RPE) trajectory and an iterative
reweighting scheme is used during image reconstruction to ensure fast
convergence and high image quality. In our in-vivo cardiac MRI experiments we
show that the proposed method 3DShearCS has lower relative errors and higher
structural similarity compared to the other reconstruction techniques
especially for high undersampling factors, i.e. short scan times. In this
paper, we further show that 3DShearCS provides improved depiction of cardiac
anatomy (measured by assessing the sharpness of coronary arteries) and two
clinical experts qualitatively analyzed the image quality
Algorithms for Least-Squares Noncartesian MR Image Reconstruction
Iterative least-squares MR reconstructions typically use the Conjugate
Gradient algorithm, despite known numerical issues. This paper demonstrates
that the more recent LSMR algorithm has favourable numerical properties, and is
to be preferred in situations where Toeplitz embedding cannot be used to
accelerate the Conjugate Gradient method.Comment: 11 pages, 5 figure
Motion estimation and correction for simultaneous PET/MR using SIRF and CIL
SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'
A Pseudo Non-Cartesian Pulse Sequence For Hyperpolarized Xenon-129 Gas MRI of Rodent Lungs At Low Magnetic Field Strength
Background: Early diagnosis of radiation-induced lung injury (RILI) following radiation therapy is critical for prevention of permanent lung damage. Pulmonary imaging using magnetic resonance imaging (MRI) of the apparent diffusion coefficient (ADC) of hyperpolarized xenon (129Xe) gas shows promise for early measurement of RILI.
Methods: An ultra-short echo time imaging sequence based on a pseudo-Cartesian k-space trajectory, known as Sectoral, is implemented at low magnetic field (0.07 T) for efficient use of the non-renewable magnetization of hyperpolarized 129Xe gas. A pilot study was performed to demonstrate the feasibility of ADC mapping using the Sectoral sequence on healthy and 2-weeks post irradiated rats.
Results: A significant (p \u3c 0.05) correlation between mean ADC values from Sectoral ADC maps and the mean linear intercept (Lm), as a measure of interalveolar wall distance, from histological sections of the lungs was observed (p = 0.0061) and a significant (p \u3c 0.05) separation between healthy and irradiated lungs was observed with full width at half maximum ADC (p = 0.0317).
Conclusion: Sectoral MRI with 129Xe is feasible in rats. Decreases in ADC were measured following lung irradiations which correlate with Lm
Motion estimation and correction for simultaneous PET/MR using SIRF and CIL
SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'
Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI
Random matrix theory (RMT) combined with principal component analysis has
resulted in a widely used MPPCA noise mapping and denoising algorithm, that
utilizes the redundancy in multiple acquisitions and in local image patches.
RMT-based denoising relies on the uncorrelated identically distributed noise.
This assumption breaks down after regridding of non-Cartesian sampling. Here we
propose a Universal Sampling Denoising (USD) pipeline to homogenize the noise
level and decorrelate the noise in non-Cartesian sampled k-space data after
resampling to a Cartesian grid. In this way, the RMT approaches become
applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the
denoising pipeline on MRI data acquired using radial trajectories, including
diffusion MRI of a numerical phantom and ex vivo mouse brains, as well as in
vivo MRI of a healthy subject. The proposed pipeline robustly estimates
noise level, performs noise removal, and corrects bias in parametric maps, such
as diffusivity and kurtosis metrics, and relaxation time. USD stabilizes
the variance, decorrelates the noise, and thereby enables the application of
RMT-based denoising approaches to MR images reconstructed from any
non-Cartesian data. In addition to MRI, USD may also apply to other medical
imaging techniques involving non-Cartesian acquisition, such as PET, CT, and
SPECT