6 research outputs found
Alternating Deep Low Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Exponential function is a fundamental signal form in general signal
processing and biomedical applications, such as magnetic resonance spectroscopy
and imaging. How to reduce the sampling time of these signals is an important
problem. Sub-Nyquist sampling can accelerate signal acquisition but bring in
artifacts. Recently, the low rankness of these exponentials has been applied to
implicitly constrain the deep learning network through the unrolling of low
rank Hankel factorization algorithm. However, only depending on the implicit
low rank constraint cannot provide the robust reconstruction, such as sampling
rate mismatches. In this work, by introducing the explicit low rank prior to
constrain the deep learning, we propose an Alternating Deep Low Rank approach
(ADLR) that utilizes deep learning and optimization solvers alternately. The
former solver accelerates the reconstruction while the latter one corrects the
reconstruction error from the mismatch. The experiments on both general
exponential functions and realistic biomedical magnetic resonance data show
that, compared with the state-of-the-art methods, ADLR can achieve much lower
reconstruction error and effectively alleviates the decrease of reconstruction
quality with sampling rate mismatches.Comment: 14 page
CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis
Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful
analytical tool for studying molecular structure and dynamics in chemistry and
biology. However, the processing of raw data acquired from NMR spectrometers
and subsequent quantitative analysis involves various specialized tools, which
necessitates comprehensive knowledge in programming and NMR. Particularly, the
emerging deep learning tools is hard to be widely used in NMR due to the
sophisticated setup of computation. Thus, NMR processing is not an easy task
for chemist and biologists. In this work, we present CloudBrain-NMR, an
intelligent online cloud computing platform designed for NMR data reading,
processing, reconstruction, and quantitative analysis. The platform is
conveniently accessed through a web browser, eliminating the need for any
program installation on the user side. CloudBrain-NMR uses parallel computing
with graphics processing units and central processing units, resulting in
significantly shortened computation time. Furthermore, it incorporates
state-of-the-art deep learning-based algorithms offering comprehensive
functionalities that allow users to complete the entire processing procedure
without relying on additional software. This platform has empowered NMR
applications with advanced artificial intelligence processing. CloudBrain-NMR
is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure
XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures
Background: Nuclear Magnetic Resonance (NMR) spectroscopy is an important
bio-engineering tool to determine the metabolic concentrations, molecule
structures and so on. The data acquisition time, however, is very long in
multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling
is an effective way but may encounter severe spectral distortions and
unfaithful quantitative measures when the acceleration factor is high.
Objective: To reconstruct high fidelity spectra from highly accelerated NMR and
achieve much better quantitative measures. Methods: A virtual peak (VIP)
approach is proposed to self-learn the prior spectral information, such as the
central frequency and peak lineshape, and then feed these information into the
reconstruction. The proposed method is further implemented with cloud computing
to facilitate online, open, and easy access. Results: Results on synthetic and
experimental data demonstrate that, compared with the state-of-the-art method,
the new approach provides much better reconstruction of low-intensity peaks and
significantly improves the quantitative measures, including the regression of
peak intensity, the distances between nuclear pairs, and concentrations of
metabolics in mixtures. Conclusion: Self-learning prior peak information can
improve the reconstruction and quantitative measures of spectra. Significance:
This approach enables highly accelerated NMR and may promote time-consuming
applications such as quantitative and time-resolved NMR experiments
CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method
for diagnosis of diseases. MRS spectrum is used to observe the signal intensity
of metabolites or further infer their concentrations. Although the magnetic
resonance vendors commonly provide basic functions of spectra plots and
metabolite quantification, the widespread clinical research of MRS is still
limited due to the lack of easy-to-use processing software or platform. To
address this issue, we have developed CloudBrain-MRS, a cloud-based online
platform that provides powerful hardware and advanced algorithms. The platform
can be accessed simply through a web browser, without the need of any program
installation on the user side. CloudBrain-MRS also integrates the classic
LCModel and advanced artificial intelligence algorithms and supports batch
preprocessing, quantification, and analysis of MRS data from different vendors.
Additionally, the platform offers useful functions: 1) Automatically
statistical analysis to find biomarkers for diseases; 2) Consistency
verification between the classic and artificial intelligence quantification
algorithms; 3) Colorful three-dimensional visualization for easy observation of
individual metabolite spectrum. Last, both healthy and mild cognitive
impairment patient data are used to demonstrate the functions of the platform.
To the best of our knowledge, this is the first cloud computing platform for in
vivo MRS with artificial intelligence processing. We have shared our cloud
platform at MRSHub, providing free access and service for two years. Please
visit https://mrshub.org/software_all/#CloudBrain-MRS or
https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure
Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria