Compressive sensing MR imaging based on adaptive tight frame and reference image

It is challenging to save acquisition time and reconstruct a medical magnetic resonance (MR) image with important details and features from its compressive measurements. In this paper, a novel method is proposed for longitudinal compressive sensing (LCS) MR imaging (MRI), where the similarity between reference and acquired image is combined with joint sparsifying transform. Furthermore, the joint sparsifying transform with the wavelet and the Contourlet can efficiently represent both isotropic and anisotropic features and the objective function is solved by extended smooth-based monotone version of the fast iterative shrinkage thresholding algorithm (SFISTA). The experiment results demonstrate that the existing regularization model obtains better performance with less acquisition time and recovers both edges and fine details of MR images, much better than the existing regularization model based on the similarity and the wavelet transform for LCS-MRI.

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Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2013.2245661 ◽

2013 ◽

Vol 61 (8) ◽

pp. 2016-2029 ◽

Cited By ~ 69

Author(s):

Wei Chen ◽

Miguel R. D. Rodrigues ◽

Ian J. Wassell

Keyword(s):

Compressive Sensing ◽

Tight Frame ◽

Projection Design

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Accelerated MR imaging using compressive sensing with no free parameters

Magnetic Resonance in Medicine ◽

10.1002/mrm.24143 ◽

2012 ◽

Vol 68 (5) ◽

pp. 1450-1457 ◽

Cited By ~ 30

Author(s):

Kedar Khare ◽

Christopher J. Hardy ◽

Kevin F. King ◽

Patrick A. Turski ◽

Luca Marinelli

Keyword(s):

Mr Imaging ◽

Compressive Sensing ◽

Free Parameters

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Background subtraction in STEM energy-loss mapping

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100112774 ◽

1984 ◽

Vol 42 ◽

pp. 568-569

Author(s):

R.D. Leapman ◽

K.E. Gorlen ◽

C.R. Swyt

Keyword(s):

Energy Loss ◽

Signal To Noise Ratio ◽

Statistical Error ◽

Scanning Transmission Electron Microscope ◽

Reference Image ◽

Inverse Power Law ◽

Transmission Electron ◽

Least Squares Fit ◽

Scanning Transmission ◽

Single Number

The determination of elemental distributions by electron energy loss spectroscopy necessitates removal of the non-characteristic spectral background from a core-edge at each point in the image. In the scanning transmission electron microscope this is made possible by computer controlled data acquisition. Data may be processed by fitting the pre-edge counts, at two or more channels, to an inverse power law, AE-r, where A and r are parameters and E is energy loss. Processing may be performed in real-time so a single number is saved at each pixel. Detailed analysis, shows that the largest contribution to noise comes from statistical error in the least squares fit to the background. If the background shape remains constant over the entire image, the signal-to-noise ratio can be improved by fitting only one parameter. Such an assumption is generally implicit in subtraction of the “reference image” in energy selected micrographs recorded in the CTEM with a Castaing-Henry spectrometer.

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Interactive multiple area spectrum integration (MASI)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100172449 ◽

1994 ◽

Vol 52 ◽

pp. 942-943

Author(s):

John A. Hunt ◽

Richard D. Leapman ◽

David B. Williams

Keyword(s):

Image Processing ◽

Low Dose ◽

Routine Analysis ◽

Reference Image ◽

Area Spectrum ◽

List Type ◽

Type Number ◽

Image Processor ◽

X Ray ◽

Scanning Path

Interactive MASI involves controlling the raster of a STEM or SEM probe to areas predefined byan integration mask which is formed by image processing, drawing or selecting regions manually. EELS, x-ray, or other spectra are then acquired while the probe is scanning over the areas defined by the integration mask. The technique has several advantages: (1) Low-dose spectra can be acquired by averaging the dose over a great many similar features. (2) MASI can eliminate the risks of spatial under- or over-sampling of multiple, complicated, and irregularly shaped objects. (3) MASI is an extremely rapid and convenient way to record spectra for routine analysis. The technique is performed as follows:Acquire reference imageOptionally blank beam for beam-sensitive specimensUse image processor to select integration mask from reference imageCalculate scanning path for probeUnblank probe (if blanked)Correct for specimen drift since reference image acquisition

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Atomic resolution characterisation of interface structures by Electron Energy Loss Spectroscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014871x ◽

1993 ◽

Vol 51 ◽

pp. 576-577

Author(s):

N. D. Browning ◽

M. M. McGibbon ◽

M. F. Chisholm ◽

S. J. Pennycook

Keyword(s):

High Resolution ◽

Energy Loss ◽

Electron Energy ◽

Electron Energy Loss Spectroscopy ◽

Imaging Technique ◽

Atomic Scale ◽

Atomic Resolution ◽

Reference Image ◽

The Impact ◽

Electron Energy Loss

The recent development of the Z-contrast imaging technique for the VG HB501 UX dedicated STEM, has added a high-resolution imaging facility to a microscope used mainly for microanalysis. This imaging technique not only provides a high-resolution reference image, but as it can be performed simultaneously with electron energy loss spectroscopy (EELS), can be used to position the electron probe at the atomic scale. The spatial resolution of both the image and the energy loss spectrum can be identical, and in principle limited only by the 2.2 Å probe size of the microscope. There now exists, therefore, the possibility to perform chemical analysis of materials on the scale of single atomic columns or planes.In order to achieve atomic resolution energy loss spectroscopy, the range over which a fast electron can cause a particular excitation event, must be less than the interatomic spacing. This range is described classically by the impact parameter, b, which ranges from ~10 Å for the low loss region of the spectrum to <1Å for the core losses.

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Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181002 ◽

1990 ◽

Vol 48 (1) ◽

pp. 450-451

Author(s):

Michael schatz ◽

Joachim Jäger ◽

Marin van Heel

Keyword(s):

Image Interpretation ◽

Lumbricus Terrestris ◽

Reference Image ◽

Large Set ◽

Multivariate Statistical ◽

Data Set ◽

Low Contrast ◽

Negative Stain ◽

Reference Images ◽

Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

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