3,861 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
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution.Comment: Final publication available at ACS Nano Letter
Enhanced Singular Value Decomposition based Fusion for Super Resolution Image Reconstruction
The singular value decomposition (SVD) plays a very important role in the field of image processing for applications such as feature extraction, image compression, etc. The main objective is to enhance the resolution of the image based on Singular Value Decomposition. The original image and the subsequent sub-pixel shifted image, subjected to image registration is transferred to SVD domain. An enhanced method of choosing the singular values from the SVD domain images to reconstruct a high resolution image using fusion techniques is proposesed. This technique is called as enhanced SVD based fusion. Significant improvement in the performance is observed by applying enhanced SVD method preceding the various interpolation methods which are incorporated. The technique has high advantage and computationally fast which is most needed for satellite imaging, high definition television broadcasting, medical imaging diagnosis, military surveillance, remote sensing etc
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