661 research outputs found
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion
artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of
the whole uterus. Contrary to current slice-to-volume registration (SVR)
methods, requiring an inflexible anatomical enclosure of a single investigated
organ, the proposed patch-to-volume reconstruction (PVR) approach is able to
reconstruct a large field of view of non-rigidly deforming structures. It
relaxes rigid motion assumptions by introducing a specific amount of redundant
information that is exploited with parallelized patch-wise optimization,
super-resolution, and automatic outlier rejection. We further describe and
provide an efficient parallel implementation of PVR allowing its execution
within reasonable time on commercially available graphics processing units
(GPU), enabling its use in the clinical practice. We evaluate PVR's
computational overhead compared to standard methods and observe improved
reconstruction accuracy in presence of affine motion artifacts of approximately
30% compared to conventional SVR in synthetic experiments. Furthermore, we have
evaluated our method qualitatively and quantitatively on real fetal MRI data
subject to maternal breathing and sudden fetal movements. We evaluate
peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and
cross correlation (CC) with respect to the originally acquired data and provide
a method for visual inspection of reconstruction uncertainty. With these
experiments we demonstrate successful application of PVR motion compensation to
the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical
Imaging. v2: wadded funders acknowledgements to preprin
Highly efficient MRI through multi-shot echo planar imaging
Multi-shot echo planar imaging (msEPI) is a promising approach to achieve
high in-plane resolution with high sampling efficiency and low T2* blurring.
However, due to the geometric distortion, shot-to-shot phase variations and
potential subject motion, msEPI continues to be a challenge in MRI. In this
work, we introduce acquisition and reconstruction strategies for robust,
high-quality msEPI without phase navigators. We propose Blip Up-Down
Acquisition (BUDA) using interleaved blip-up and -down phase encoding, and
incorporate B0 forward-modeling into Hankel structured low-rank model to enable
distortion- and navigator-free msEPI. We improve the acquisition efficiency and
reconstruction quality by incorporating simultaneous multi-slice acquisition
and virtual-coil reconstruction into the BUDA technique. We further combine
BUDA with the novel RF-encoded gSlider acquisition, dubbed BUDA-gSlider, to
achieve rapid high isotropic-resolution MRI. Deploying BUDA-gSlider with
model-based reconstruction allows for distortion-free whole-brain 1mm isotropic
T2 mapping in about 1 minute. It also provides whole-brain 1mm isotropic
diffusion imaging with high geometric fidelity and SNR efficiency. We finally
incorporate sinusoidal wave gradients during the EPI readout to better use coil
sensitivity encoding with controlled aliasing.Comment: 13 pages, 10 figure
Comparison of Super Resolution Reconstruction Acquisition Geometries for Use in Mouse Phenotyping
3D isotropic imaging at high spatial resolution (30–100 microns) is important for comparing mouse phenotypes. 3D imaging at high spatial resolutions is limited by long acquisition times and is not possible in many in vivo settings. Super resolution reconstruction (SRR) is a postprocessing technique that has been proposed to improve spatial resolution in the slice-select direction using multiple 2D multislice acquisitions. Any 2D multislice acquisition can be used for SRR. In this study, the effects of using three different low-resolution acquisition geometries (orthogonal, rotational, and shifted) on SRR images were evaluated and compared to a known standard. Iterative back projection was used for the reconstruction of all three acquisition geometries. The results of the study indicate that super resolution reconstructed images based on orthogonally acquired low-resolution images resulted in reconstructed images with higher SNR and CNR in less acquisition time than those based on rotational and shifted acquisition geometries. However, interpolation artifacts were observed in SRR images based on orthogonal acquisition geometry, particularly when the slice thickness was greater than six times the inplane voxel size. Reconstructions based on rotational geometry appeared smoother than those based on orthogonal geometry, but they required two times longer to acquire than the orthogonal LR images
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine
clinical magnetic resonance images (MRI), of arbitrary orientation and
contrast. The model recasts the recovery of high resolution images as an
inverse problem, in which a forward model simulates the slice-select profile of
the MR scanner. The paper introduces a prior based on multi-channel total
variation for MRI super-resolution. Bias-variance trade-off is handled by
estimating hyper-parameters from the low resolution input scans. The model was
validated on a large database of brain images. The validation showed that the
model can improve brain segmentation, that it can recover anatomical
information between images of different MR contrasts, and that it generalises
well to the large variability present in MR images of different subjects. The
implementation is freely available at https://github.com/brudfors/spm_superre
Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding
Purpose: Asymmetric gradient waveforms are attractive for diffusion encoding
due to their superior efficiency, however, the asymmetry may cause a residual
gradient moment at the end of the encoding. Depending on the experiment setup,
this residual moment may cause significant signal bias and image artifacts. The
purpose of this study was to develop an asymmetric gradient waveform design for
tensor-valued diffusion encoding that is not affected by concomitant gradient.
Methods: The Maxwell index was proposed as a scalar invariant that captures the
effect of concomitant gradients and was constrained in the numerical
optimization to 100 (mT/m)ms to yield Maxwell-compensated waveforms. The
efficacy of this design was tested in an oil phantom, and in a healthy human
brain. For reference, waveforms from literature were included in the analysis.
Simulations were performed to investigate if the design was valid for a wide
range of experiments and if it could predict the signal bias. Results:
Maxwell-compensated waveforms showed no signal bias in oil or in the brain. By
contrast, several waveforms from literature showed gross signal bias. In the
brain, the bias was large enough to markedly affect both signal and parameter
maps, and the bias could be accurately predicted by theory. Conclusion:
Constraining the Maxwell index in the optimization of asymmetric gradient
waveforms yields efficient tensor-valued encoding with concomitant gradients
that have a negligible effect on the signal. This waveform design is especially
relevant in combination with strong gradients, long encoding times, thick
slices, simultaneous multi-slice acquisition and large/oblique FOVs
Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study.
We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2). Twenty-five normal fetal brain MRI scans (18-34 weeks of gestation) including orthogonal 3-mm-thick T2WSs were analyzed retrospectively. One 3D SR volume was reconstructed per subject based on multiple series of T2WSs. The two observers performed 11 2D biometric measurements (specifying their level of confidence) on T2WS and SR volumes. Measurements were compared using the paired Wilcoxon rank sum test between observers for each dataset (T2WS and SR) and between T2WS and SR for each observer. Bland-Altman plots were used to assess the agreement between each pair of measurements. Measurements were made with low confidence in three subjects by obs1 and in 11 subjects by obs2 (mostly concerning the length of the corpus callosum on T2WS). Inter-rater intra-dataset comparisons showed no significant difference (p > 0.05), except for brain axial biparietal diameter (BIP) on T2WS and for brain and skull coronal BIP and coronal transverse cerebellar diameter (DTC) on SR. None of them remained significant after correction for multiple comparisons. Inter-dataset intra-rater comparisons showed statistical differences in brain axial and coronal BIP for both observers, skull coronal BIP for obs1, and axial and coronal DTC for obs2. After correction for multiple comparisons, only axial brain BIP remained significantly different, but differences were small (2.95 ± 1.73 mm). SR allows similar fetal brain biometry as compared to using the conventional T2WS while improving the level of confidence in the measurements and using a single reconstructed volume
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