Improving Undersampled MRI Reconstruction Using Non-local Means

Adluru, Ganesh; Taşdizen, Tolga; Whitaker, Ross T.; DiBella, Edward

doi:10.1109/icpr.2010.973

Cited by 6 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A kz-first segmented acquisition was implemented on a Siemens 3T scanner. This scheme results in having consistent in-plane data with inconsistencies along kz leading to improved image quality as shown with simulation studies in [ 5 ]. As well, each plane is rotated by an angle based on golden ratio - Figure 1 .…”

Section: Methodsmentioning

confidence: 67%

Late Gadolinium Enhancement imaging using stack of stars and compressed sensing

Adluru

Chen

Kim

et al. 2011

J Cardiovasc Magn Reson

Self Cite

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 67%

Late Gadolinium Enhancement imaging using stack of stars and compressed sensing

Adluru

Chen

Kim

et al. 2011

J Cardiovasc Magn Reson

Self Cite

View full text Add to dashboard Cite

“…5). We compare the proposed algorithm with 2D non-local schemes and the method of Adluru et al [8], which uses a combination of 2D spatial and 1-D temporal non-local penalties. We observe that the proposed scheme to improve the SNR by approximately 3dB.…”

Section: Resultsmentioning

confidence: 99%

“…Another option is to majorize both the data and penalty terms. The resulting algorithm involves one steepest descend update, followed by one fixed point step as in [8]. However, both of these schemes will involve at least one evaluation of weights per steepest-descend step.…”

Section: Robust Non-local Regularization Of Dynamic Imaging Problemsmentioning

confidence: 99%

Robust non-local regularization framework for motion compensated dynamic imaging without explicit motion estimation

Zhang

Jacob

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

We introduce an regularized reconstruction scheme to recover dynamic imaging datasets with significant inter frame motion from undersampled Fourier data. The proposed non-local regularization penalty is an unweighted sum of distances between image patch pairs in the 3-D dataset. We use robust distance metrics to compute the distance between image patches; these metrics encourage the smoothing between similar patches, while discouraging the averaging of dissimilar patches. Hence, this algorithm is capable of exploiting the similarities between patch pairs in adjacent frames even when they are well separated due to motion, eventhough it does not perform explicit motion estimation. Unlike current non-local regularization schemes, the proposed penalty does not need good initial guesses to estimate the weights. Hence, this approach is readily applicable to accelerated dynamic imaging problems, where good initial guesses are challenging to obtain. The validation of the proposed scheme on numerical phantoms and dynamic MRI datasets demonstrate the superior performance of the proposed scheme over current dynamic imaging schemes.

show abstract

“…An estimator using a priori information for devising a single dimensional noise cancellation for the variance of the thermal noise in magnetic resonance imaging (MRI) systems called ML estimator has been proposed in [11]. Non-Local Means (NLM) filtering method for reducing artifacts caused in MRI due to under sampling of k-space (to reduce scan time) is proposed in [12]. A maximum a posteriori estimation technique that operates directly on the diffusion weighted images and accounts for the biases introduced by Rician noise is introduced in [13] for filtering diffusion tensor magnetic resonance images.…”

Section: Related Workmentioning

confidence: 99%

Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique

Devasena¹,

Hemalatha²

2011

IJCA

View full text Add to dashboard Cite

In Medical Diagnostic, Magnetic Resonance Images play a major role. Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. Because of this reason noise removal methods have been customarily applied to improve MR image quality. This work proposed a new scheme based on applying a series of filters, each used to modify the estimate into greater agreement, so that the output converges to a stable estimate providing noise free image. In this work, we have introduced a novel hybrid filter to reduce random noise in MR images by the combination of Kernel, Sobel and Low-pass (KSL) filtering techniques. The proposed method has been implemented using Matlab and compared with related state of art methods over synthetic and real clinical MR images showing a superior performance in all cases analyzed. General Terms: Noise Removal -Magnetic ResonanceImages.

show abstract

Improving Undersampled MRI Reconstruction Using Non-local Means

Abstract: Obtaining high quality images in MR is

Cited by 6 publications

References 21 publications

Late Gadolinium Enhancement imaging using stack of stars and compressed sensing

Late Gadolinium Enhancement imaging using stack of stars and compressed sensing

Robust non-local regularization framework for motion compensated dynamic imaging without explicit motion estimation

Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique

Contact Info

Product

Resources

About