Download presentation

Presentation is loading. Please wait.

Published byBryce Oscar Modified over 2 years ago

1
Toward 0-norm Reconstruction, and A Nullspace Technique for Compressive Sampling Christine Law Gary Glover Dept. of EE, Dept. of Radiology Stanford University

2
Outline 0-norm Magnetic Resonance Imaging (MRI) reconstruction. 0-norm Magnetic Resonance Imaging (MRI) reconstruction. Homotopic Homotopic Convex iteration Convex iteration Signal separation example Signal separation example 1-norm deconvolution in fMRI. 1-norm deconvolution in fMRI. Improvement in cardinality constraint problem with nullspace technique. Improvement in cardinality constraint problem with nullspace technique.

3
Shannon vs. Sparse Sampling Nyquist/Shannon : sampling rate ≥ 2*max freq Nyquist/Shannon : sampling rate ≥ 2*max freq Sparse Sampling Theorem: (Candes/Donoho 2004) Sparse Sampling Theorem: (Candes/Donoho 2004) Suppose x in R n is k -sparse and we are given m Fourier coefficients with frequencies selected uniformly at random. If Suppose x in R n is k -sparse and we are given m Fourier coefficients with frequencies selected uniformly at random. If m ≥ k log 2 (1 + n / k) m ≥ k log 2 (1 + n / k) then then reconstructs x exactly with overwhelming probability. n k = 2

4
Rat dies 1 week after drinking poisoned wine Example by Anna Gilbert

5
x1 x2 x3 x4 x5 x6 x7 y1 y2 y3

6
x1 x2 x3 x4 x5 x6 x7 y1 y2 y3 n=7 m=3 k=1

7
Reconstruction by Optimization Compressed Sensing theory (2004 Donoho, Candes): under certain conditions, Candes et al. IEEE Trans. Information Theory 2006 52(2):489 Donoho. IEEE Trans. Information Theory 2006 52(4):1289 y are measurements (rats) x are sensors (wine)

8
0-norm reconstruction Try to solve 0-norm directly. Try to solve 0-norm directly. For p-norm, where 0 < p < 1 For p-norm, where 0 < p < 1 Chartrand (2006) demonstrated fewer samples y required than 1-norm formulation. Chartrand. IEEE Signal Processing Letters. 2007: 14(10) 707-710. Chartrand. IEEE Signal Processing Letters. 2007: 14(10) 707-710.

9
Trzasko (2007): Rewrite the problem Trzasko et al. IEEE SP 14th workshop on statistical signal processing. 2007. 176-180. Trzasko et al. IEEE SP 14th workshop on statistical signal processing. 2007. 176-180. where is tanh, laplace, log, etc. such that

10
Homotopic function in 1D Start as 1-norm problem, then reduce slowly and approach 0-norm function.

11
Homotopic method

13
Demonstration when is big (1 st iteration), solving 1-norm problem. when is big (1 st iteration), solving 1-norm problem. reduce to approach 0-norm solution. reduce to approach 0-norm solution. original x∆x∆

14
Example 1 original subsampled

15
Zero-filled Reconstruction Fourier sample mask Homotopic Homotopic result: use 4% Fourier data error: -66.2 dB 85 seconds 1-norm result: use 4% Fourier dataerror: -11.4 dB542 seconds 1-norm recon homotopic recon

16
Example 2 Angiography Angiography 360x360, 27.5% radial samples 360x360, 27.5% radial samples original

17
reconstruction 1-norm method: error: -24.7 dB, 1151 seconds 360x360 reconstruction homotopic method: error: -26.5 dB, 101 seconds original 27.5% samples

18
Chretien, An Alternating l1 approach to the compressed sensing problem, arXiv.org. Dattorro, Convex Optimization & Euclidean Distance Geometry, Meboo. Convex Iteration

21
Convex Iteration demo zero-filled reconstruction Fourier samp l e mask use 4% Fourier dataerror: -104 dB96 seconds reconstruction

22
Signal Separation by Convex Iteration

23
1-norm formulation as convex iteration

24
Signal Construction = + Cardinality(steps) = 7 Cardinality(cosine) = 4 u u∆u∆ uDuD

25
Donoho, Tanner, 2005. Baron, Wakin et al., 2005 Minimum Sampling Rate k/n m/k m measurements k cardinality n record length

26
m = 28 measurements m / n = 0.1 subsample m / k = 2.5 sample rate k / n = 0.04 sparsity Cardinality(steps) = 7 Cardinality(cosine) = 4 Signal Separation by Convex Iteration

27
1-norm Convex iteration TV only DCT only -241dB reconstruction error u∆u∆ uDuD

28
functional Magnetic Resonance Imaging (fMRI) Haemodynamic Response Function (HRF)

29
How to conduct fMRI? Huettel, Song, Gregory. Functional Magnetic Resonance Imaging. Sinauer. Stimulus Timing

30
How to conduct fMRI? Stimulus Timing

31
What does fMRI measure? Neural activitySignallingVascular response Vascular tone (reactivity) Autoregulation Metabolic signalling BOLD signal glia arteriole venule B 0 field Synaptic signalling Blood flow, oxygenation and volume dendrite End bouton

32
fMRI signal origin rest Oxygenated Hb Deoxygenated Hb task Oxygenated Hb Deoxygenated Hb

33
Haemodynamic Response Function (HRF) Stimulus Timing Canonical HRF Predicted Data = = Time

34
Which part of the brain is activated? http://www.fmrib.ox.ac.uk/ Time Actual measurement Prediction Signal Intensity

35
= Stimulus TimingMeasurement Actual HRF HRF calibration Variability of HRF

36
Deconvolve HRF h 10 0 -10 1010 y Dh D: convolution matrix D(:, 1) W: Coiflet E: monotone cone (d) Wh Discrete wavelet transform

37
Deconvolve HRF h 10 0 -10 1010 y stimulus timing W: Coiflet wavelet E: monotone cone D: convolution matrix measurement smoothness Dh D( :, 1)

38
Deconvolution results

39
in vivo deconvolution results … HRF calibration … HRF deconvolution … Time (s)

42
Cardinality Constraint Problem 3 5 Ab 3 1 x 5 1 A = b = A(:,2) * rand(1) + A(:,5) * rand(1)

43
Find x with desired cardinality e.g. k = 2, want =

44
m=3 n=5 Ab 3 1 x 5 1 Check every pair for k = 2 Possible # solution: In general, From the Range perspective …

45
3 5 A 2 5 = 0 Perspective Nullspace Perspective Z Particular soln. General soln.

46
Z = 0.570.34 -0.03 -0.26 -0.160.02 -0.68 -0.63 0.220.47 xp = 0.34 1.11 -0.30 0

47
How to find intersection of lines? Sum of normalized wedges

48
x = 0.34 1.11 -0.30 0 x = 0 0.68 0 0.51 Z = -0.59-0.36 0.06-0.55 0.15 0.36 0.74-0.08 -0.27 0.66 Znew = -0.41 -0.18 -0.80 -0.30 0.480.19 -0.26 -0.07 1.000.37 Znew = Z * randn(2);

49
Summary Ways to find 0-norm solutions other than Ways to find 0-norm solutions other than 1-norm (homotopic, convex iteration) 1-norm (homotopic, convex iteration) fewer measurements fewer measurements faster faster In cardinality constraint problem, convex iteration and nullspace technique success more often than 1-norm. In cardinality constraint problem, convex iteration and nullspace technique success more often than 1-norm.

Similar presentations

Presentation is loading. Please wait....

OK

The basics for simulations

The basics for simulations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on classical economics assumptions Download ppt on mineral and power resources Ppt on power grid failure prep Mis ppt on nokia cell Download ppt on life process for class 10 cbse Ppt on airbags in cars Ppt on carburetor system Ppt on laws of liquid pressure Ppt on supply chain mechanism Ppt on means of communication in olden days