Reproducible Research: MRI Data

Miki Lustig just sent the following:

Dear Igor,

As part of our mission for reproducible research we recently launched a new website, 

http://mridata.org 

The web site aims to provide MRI data sets, both undersampled and fully sampled, so that developers can test algorithms and contribute to a community of reproducible research. Our hope is that these datasets will help the MRI or any other research community to accelerate their improvements in medical image processing. The website is still a work in progress, but at this point we think it would be quite useful to many.

It is a joint effort between researchers at the EECS department at UC Berkeley, Lucile Packard Children's hospital, and EE department at Stanford University.

We would appreciate if you could post that information.

Thanks!

------------------------------

KK6MRI

Michael Lustig, Ph.D.

Assistant Professor

Department of Electrical Engineering and Computer Sciences,

506 Cory Hall, University of California, Berkeley CA, 94720

http://www.mlustig.com

Thanks Miki !

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January-July 2014: Seven Months of Reproducible Research

Summer generally allows for some downtime at which point I can update other pages such as the Reproducible Research ( implementations ) page, the Big Picture in Compressive Sensing or the Advanced Matrix Factorization Jungle page. Here are the implementations I will add shortly to these pages. They include all implementations listed here on the Nuit Blanche in Review starting January 2014 till yesterday's entry:

Compressive Sensing

• Adaptive-Rate Compressive Sensing Using Side Information - implementation -
• NLCS : Non-Local Compressive Sampling Recovery - implementation -
• Multidimensional Compressed Sensing and their Applications - implementation -
• The SwAMP Thing! Sparse Estimation with the Swept Approximated Message-Passing Algorithm -implementation -
• On the Convergence of Approximate Message Passing with Arbitrary Matrices
• NLR-CS : Compressive Sensing via Nonlocal Low-rank Regularization - implementation -
• Matrix-free Interior Point Method for Compressed Sensing Problems / A Second order Method for Compressed Sensing with Coherent and Redundant Dictionaries - implementation -
• emd_flow : The Constrained Earth Mover Distance Model,with Applications to Compressive Sensing
• Tree_approx : A Fast Approximation Algorithm for Tree-Sparse Recovery
• CGIHT, ASD and ScaledASD: Compressed Sensing and Matrix Completion
• GAGA: GPU Accelerate Greedy Algorithms for Compressed Sensing - new version -
• RCoS : Image Compressive Sensing Recovery via Collaborative Sparsity
• GrAMPA: Generalized Approximate Message Passing for Analysis Compressive Sensing
• SparseFHT: A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain - implementation -
• EMaC : Robust Spectral Compressed Sensing via Structured Matrix Completion
• Summary of Contribution: Compressed sensing with linear correlation between signal and measurement noise
• STSBL-EM : Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
• Sparse Spikes Deconvolution with Continuous Basis-Pursuit
• Bayesian Pursuit Algorithms
• Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization

Regression

• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression

Optimization solvers

• UnLocBox / PyUnLocBox (Matlab and Python convex optimization toolbox)
• PNOPT: Proximal Newton-type methods for minimizing composite functions

Image restoration

• Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

Compressive Detection

• FCT : Fast Compressive Tracking
• Fast Tracking via Spatio-Temporal Context Learning

Sparse Approximation/Reconstruction/Denoising

• SASIR: LOFAR Sparse Image Reconstruction - implementation -
• GHP : Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising
• Group-based Sparse Representation for Image Restoration
• WESNR : Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization

Blind Deconvolution

• Blind Image Deblurring by Spectral Properties of Convolution Operators
• Blind Image Deblurring with Unknown Boundaries Using the Alternating Method of Multipliers
• Blind Identification via Lifting

Matrix Factorization (other than NMF)

• GreBsmo: Greedy Bilateral Sketch, Completion & Smoothing - implementation -
• Tight convex relaxations for sparse matrix factorization - implementation -
• Fast matrix completion without the condition number - implementation -
• Dynamic MR image reconstruction–separation from undersampled (k,t)-space via low-rank plus sparse prior - implementation -
• TGA: Grassmann Averages for Scalable Robust PCA
• Spectral redemption: clustering sparse networks
• Sparse Randomized Kaczmarz for Multiple Measurement Vectors
• Fast and Robust Archetypal Analysis for Representation Learning
• Probabilistic Archetypal Analysis
• ROSL : Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices - implementation -
• CGIHT, ASD and ScaledASD: Compressed Sensing and Matrix Completion
• PSPG: Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems
• CPCP: Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
• ADMIP: An Alternating Direction Method with Increasing Penalty for Stable Principal Component Pursuit
• OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
• Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
• Introduction into cross approximation / Fast multidimensional convolution in low-rank formats via cross approximation - implementation -
• LRRSC : Subspace Clustering by Exploiting a Low-Rank Representation with a Symmetric Constraint - implementation -
• WNNM: Weighted Nuclear Norm Minimization with Application to Image Denoising
• Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
• Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS): Implementation and Examples for Reproducible Research
• IRNN: Generalized Nonconvex Nonsmooth Low-Rank Minimization
• HASI: Probabilistic Low Rank Matrix Completion with Adaptive Spectral Regularization Algorithms
• Interest Zone Matrix Approximation

NMF

• Random Projections for Non-negative Matrix Factorization
• Enhancing Pure-Pixel Identification Performance via Preconditioning - implementation -

Nonlinear Compressive Sensing

• Nonlinear Basis Pursuit
• Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization

One-Bit

• One-bit compressive sensing with norm estimation
• Binary Compressed Imaging - implementation -

Phase Retrieval

• Phase Retrieval via Wirtinger Flow: Theory and Algorithms - implementation -
• PR-GAMP : Compressive Phase Retrieval via Generalized Approximate Message Passing
• Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit - implementation -

Randomized Numerical Linear Algebra

• Randomized Numerical Linear Algebra for Large Scale Data Analysis ( libSkylark: Sketching based Matrix computations for Machine Learning )
• Randomized LU Decomposition

Tensor

• Stable, Robust and Super Fast Reconstruction of Tensors Using Multi-Way Projections - implementation -

Machine Learning

• Mondrian Forests: Efficient Online Random Forests - implementation -
• Understanding Random Forests: From Theory to Practice - implementation -
• SPAL: Sparse Projection-based Adaptive Learning 
• Active Subspace: Towards Scalable Low-Rank Learning
• A Riemannian approach to low-rank algebraic Riccati equations - implementation -
• KNIFE: Automatic Feature Selection via Weighted Kernels and Regularization
• Avoiding pathologies in very deep networks
• Kernel LMS algorithm with forward-backward splitting for dictionary learning
• MISO: Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning

Subspace Learning

• LSR: Robust and Efficient Subspace Segmentation via Least Squares Regression
• PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares from Partial Observations

Applications

• Randomized Comments: Code for GWAS and CS study, a new blog, Geometric theory of information
• PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging - implementation -
• REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time
• HODLR and george: Fast Direct Methods for Gaussian Processes and the Analysis of NASA Kepler Mission Data - implementation -
• CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks - implementation -
• Saturday Morning Video: SVO: Fast Semi-Direct Monocular Visual Odometry - implementation -
• SUGAR : Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection
• Selecting thresholding and shrinking parameters with generalized SURE for low rank matrix estimation
• The Flutter Shutter Code Calculator - implementation -

Credit photo: OSIRIS narrow angle camera view of 67P/C-G from a distance of 1950 km on 29 July 2014. Credits: ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

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Another Year in Reproducible Research in Compressive Sensing, Advanced Matrix Factorization and more

Since last Year's entry on Reproducible Research in Compressive Sensing, Advanced Matrix Factorization and moreI, we have had quite few new implementations made available by different authors.: 87 in fact. Last year's count was 74. This is a 17% increase, not bad.

In the spirit of reproducible research, the rule is now that all Nuit Blanche entries that feature the release of an algorithm implementation deserve a whole entry. For the ones not yet listed on the Big Picture in Compressive Sensing or the Advanced Matrix Factorization Jungle page, this omission will be repaired soon. All these implementations are also listed in the compounded list of the Reproducible Research page. Here is the long list for this past year.

• SLRA: Structured Low-Rank Approximation as Optimization on a Grassmann Manifold 
• ScatNet: An implementation of Scattering Networks transforms and classification algorithms +7
• Optimally weighted recovery of a low-rank signal matrix from a high-dimensional signal-plus-noise matrix 
• Embedding Multiclass Data Hiding in Compressed Sensing
• The Fukushima Inverse Problem +4
• CO4OI: Convex Optimisation for Optical Interferometry
• Quikr: a Method for Rapid Reconstruction of Bacterial Communities via Compressive Sensing. 
• Finite rate of innovation based modeling and compression of ECG signals 
• FrameSense: Near-Optimal Sensor Placement for Linear Inverse Problems / Acoustic echoes reveal room shape - implementation -
• PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities - implementation-
• Quantized Iterative Hard Thresholding: Bridging 1-bit and High-Resolution Quantized Compressed Sensing - implementation -
• Sparse Recovery of Streaming Signals Using l_1-Homotopy - implementation -
• Structure Discovery in Nonparametric Regression through Compositional Kernel Search 
• qGeomMC: A Quotient Geometric approach to low-rank Matrix Completion - implementation 
• Sparse FFT implementations
• Analysis Based Blind Compressive Sensing 
• Manopt: A Matlab tool­box for opti­mization on manifolds 
• qGeomMC: A Quotient Geometric approach to low-rank Matrix Completion 
• Phase retrieval for imaging problems 
• SL0-mod: Surpassing the Theoretical L_1 norm phase transition in Compressive Sennsing by Tuning the Smoother l_0 Algorithm: 
• PalBOMP/PolBOMP: Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation 
• K-SVD/IPR: Learning Incoherent Dictionaries for Sparse Approximation using Iterative Projections and Rotations 
• The Phase Transition of Matrix Recovery from Gaussian Measurements Matches the Minimax MSE of Matrix Denoising 
• Squeezambler: Distilled Single Cell Genome Sequencing and De Novo Assembly for Sparse Microbial Communities 
• Enhanced Compressed Sensing Recovery with Level Set Normals 
• Correcting Errors in Linear Measurements and Compressed Sensing of Multiple Sources 
• Greedy Approach for Subspace Clustering from Corrupted and Incomplete Data
• Greedy Approach for Low-Rank Matrix Recovery 
• Sparsity Averaging Reweighted Analysis (SARA) 
• Bayesian methods for gene expression factor analysis 
• MOUSSE: Multiscale Online Union of SubSpaces Estimation 
• GAGA: GPU Accelerated Greedy Algorithms - implementation -
• GESPAR: Efficient Phase Retrieval of Sparse Signals and QCS: Sparsity based sub-wavelength imaging) 
• A Q&A with Nikolaos Freris and Anastasios Zouzias: Randomized Extended Kaczmarz 
• Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing 
• NACHOS: Nearfield Acoustic Holography using sparsity and compressive sampling principles -
• GOAL: Analysis Operator Learning and Its Application to Image Reconstruction 
• SPASH: Sparse Shape Reconstruction 
• Compressive Sensing for Spread Spectrum Receivers, Spectral Compressive Sensing with Polar Interpolation - implementations 
• Improving Smoothed l0 Norm in Compressive Sensing Using Adaptive Parameter Selection 
• A Note on k-support Norm Regularized Risk Minimization -implementation -
• Inductive Sparse Subspace Clustering - implementation - and Greedy Feature Selection for Subspace Clustering
• Structure-Based Bayesian Sparse Reconstruction - implementation -
• Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse - implementation -
• A Randomized Parallel Algorithm with Run Time $O(n^2)$ for Solving an $n \times n$ System of Linear Equations - 
• Compressed Sensing with Correlation Between Measurements and Noise - implementation -
• Convergence Speed of a Dynamical System for Sparse Recovery - implementation -
• SVDFeature: A Toolkit for Feature-based Collaborative Filtering - implementation -
• A Probabilistic Approach to Robust Matrix Factorization - implementation -
• Heliometric Stereo: Shape from Sun Position - implementation 
• AMP: Assembly Matching Pursuit, Metagenomic units (MGUs) discovery through sequence-based dictionary learning - implementation -
• A hand-waving introduction to sparsity for compressed tomography reconstruction - python,
• Scattering Representations for Recognition
• 24AM: 24 efficient parallel SPCA implementations
• Estimating Natural Illumination from a Single Outdoor Image
• Robust image reconstruction from multi-view measurements
• #Python implementation of BSBL code family
• #Python NMF/NTF with beta divergence
• Robustness Analysis of HottTopixx, a Linear Programming Model for Factoring Nonnegative Matrices
• Hott Topixx: Factoring nonnegative matrices with linear programs
• Scaled gradients on grassmann manifolds for matrix Completion
• PDRank: Penalty Decomposition Methods for Rank Minimization (and more)
• NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings
• Blind Deconvolution using Convex Programming
• Random Projections for Support Vector Machines
• From Bits to Images: Inversion of Local Binary Descriptors [8 G+]
• Fast Marginalized Block SBL Algorithm
• COSMOS: sparse modeling software
• Belief Propagation Reconstruction for Discrete Tomography [4 G+]
• FLAGLET: Exact wavelets on the ball
• S2LET: A code to perform fast wavelet analysis on the sphere
• Bayesian Blind Deconvolution With General Sparse Image Priors
• ReProCS: Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise 
• Low Rank Approximation and Regression in Input Sparsity Time
• Fast Earth Mover's Distance (EMD)
• Compressive Phase Retrieval via Generalized Approximate Message Passing 
• CPRL – An Extension of Compressive Sensing to the Phase Retrieval Problem 
• Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model , 
• Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators 
• GONDOLA: Generative-Discriminative Basis Learning for Medical Imaging 
• Eulerian Video Magnification for Revealing Subtle Changes in the World 
• MASTeR: Motion-Adaptive Spatio-Temporal Regularization for Accelerated Dynamic MRI 
• Complex Matrix Factorization Toolbox 
• Lipid Suppression in CSI with Spatial Priors and Highly Undersampled Peripheral K-Space 
• nGpFBMP: A Fast Non-Gaussian Bayesian Matching Pursuit Method for Sparse Reconstruction 
• Accelerated DSI with Compressed Sensing using Adaptive Dictionaries 
• SCoBeP: Dense Image Registration using Sparse Coding and Belief Propagation

Credit: NASA/ESA

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19 months of Reproducible Research

When Rich reminds us that "We generated more data than there are stars in the Universe", he also points to the obvious need to make sense of them. One strategy is to come up with new ideas for better sensors (These Technologies Do Not Exist ) or better estimation and detection schemes. 

Another surer strategy is to go through the Reproducible Research route as a means of making sure that your ideas do no die. I have added an implementation tag to blog entries that featured an algorithm implementation for the past 19 months and it looks like we have about 146 of them. That figure amounts to about 7.6 implementations released per month or about close to two releases per week

Here is a list of all these implementations that I will update every month after every Nuit Blanche Monthly Reviews:

• Reproducible Research ( implementations featured on Nuit Blanche)

Related entries and pages:

• Nobody Cares About You and Your Algorithm
• These Technologies Do Not Exist
• The Big Picture in Compressive Sensing 
• The Advanced Matrix Factorization Jungle
• Nuit Blanche blog entries featuring papers with attendant code implementations.

Image Credit: NASA/JPL/Space Science Institute

N00205148.jpg was taken on April 02, 2013 and received on Earth April 03, 2013. The camera was pointing toward SATURN, and the image was taken using the CL1 and CL2 filters. T



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Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.