1,743 research outputs found
MRI noise estimation and denoising using non-local PCA
NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image AnalysisChanges resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Image Analysis, [Volume 22, Issue 1, May 2015, Pages 35–47] DOI 10.1016/j.media.2015.01.004This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.We are grateful to Dr. Matteo Mangioni and Dr. Alessandro Foi for their help on running their BM4D method in our comparisons. We want also to thank Dr. Luis Marti-Bonmati and Dr. Angel Alberich-Bayarri from Quiron Hospital of Valencia for providing the real clinical data used in this paper. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).Manjón Herrera, JV.; Coupé, P.; Buades, A. (2015). MRI noise estimation and denoising using non-local PCA. Medical Image Analysis. 22(1):35-47. doi:10.1016/j.media.2015.01.004S354722
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter
The Magnetic Resonance Imaging (MRI) technologyused in clinical diagnosis demands high Peak Signal-to-Noise ratio(PSNR) and improved resolution for accurate analysis and treatmentmonitoring. However, MRI data is often corrupted by random noisewhich degrades the quality of Magnetic Resonance (MR) images.Denoising is a paramount challenge as removing noise causesreduction in the fine details of MRI images. We have developed anovel algorithm which employs Principal Component Analysis(PCA) decomposition and Wiener filtering. We have proposed a twostage approach. In first stage, non-local PCA thresholding is appliedon noisy image and second stage uses Wiener filter over this filteredimage. Our algorithm is implemented using MATLAB andperformance is measured via PSNR. The proposed approach hasalso been compared with related state-of-art methods. Moreover, wepresent both qualitative and quantitative results which prove thatproposed algorithm gives superior denoising performance
MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"
MP-PCA denoising has become the method of choice for denoising in MRI since
it provides an objective threshold to separate the desired signal from unwanted
thermal noise components. In rodents, thermal noise in the coils is an
important source of noise that can reduce the accuracy of activation mapping in
fMRI. Further confounding this problem, vendor data often contains zero-filling
and other effects that may violate MP-PCA assumptions. Here, we develop an
approach to denoise vendor data and assess activation "spreading" caused by
MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3
mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms
temporal resolution, respectively), during visual stimulation. MP-PCA denoising
produced SNR gains of 64% and 39% and Fourier spectral amplitude (FSA)
increases in BOLD maps of 9% and 7% for multislice and ultrafast data,
respectively, when using a small [2 2] denoising window. Larger windows
provided higher SNR and FSA gains with increased spatial extent of activation
that may or may not represent real activation. Simulations showed that MP-PCA
denoising causes activation "spreading" with an increase in false positive rate
and smoother functional maps due to local "bleeding" of principal components,
and that the optimal denoising window for improved specificity of functional
mapping, based on Dice score calculations, depends on the data's tSNR and
functional CNR. This "spreading" effect applies also to another recently
proposed low-rank denoising method (NORDIC). Our results bode well for
dramatically enhancing spatial and/or temporal resolution in future fMRI work,
while taking into account the sensitivity/specificity trade-offs of low-rank
denoising methods
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Adaptive Image Denoising by Targeted Databases
We propose a data-dependent denoising procedure to restore noisy images.
Different from existing denoising algorithms which search for patches from
either the noisy image or a generic database, the new algorithm finds patches
from a database that contains only relevant patches. We formulate the denoising
problem as an optimal filter design problem and make two contributions. First,
we determine the basis function of the denoising filter by solving a group
sparsity minimization problem. The optimization formulation generalizes
existing denoising algorithms and offers systematic analysis of the
performance. Improvement methods are proposed to enhance the patch search
process. Second, we determine the spectral coefficients of the denoising filter
by considering a localized Bayesian prior. The localized prior leverages the
similarity of the targeted database, alleviates the intensive Bayesian
computation, and links the new method to the classical linear minimum mean
squared error estimation. We demonstrate applications of the proposed method in
a variety of scenarios, including text images, multiview images and face
images. Experimental results show the superiority of the new algorithm over
existing methods.Comment: 15 pages, 13 figures, 2 tables, journa
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