compressed sensing for motion artifact reduction # 4593 joëlle k. barral & dwight g. nishimura...
TRANSCRIPT
Compressed Sensing for Motion Artifact
Reduction
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Joëlle K. Barral & Dwight G. Nishimura
Presentation: Wednesday @ 3pm
Electrical Engineering Stanford University
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In a Nutshell
a Compressed Sensing approach can be used to reduce motion artifacts in high-resolution MRI.
Navigators are useful to correct motion of small amplitude.
They can also be used to detect data that needs to be discarded.
Discarding data provides an undersampling dataset:
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Motion Artifacts
Blurring
78x156x500 μm3
11 min 02 s
CALF SKINGhosting
391x521x1000 μm3
2 min 04 s
LARYNX
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Ehman MRI 1989:173:255-263 -- Wang MRM 1996:36:117-123 -- Song MRM 1999:41:947-953
Zero-Zero-padding padding
FFTFFT
Cross-Cross-correlationcorrelation
ShiftShiftss
Fast Large Angle Spin Echo
3D FLASE
TR = 80 ms
kx ky kz
Phase-Phase-modulationmodulation
ProjectioProjectionsns
Projection along x
TR number
64512 64 kx
Navigators interleaved
Classical Navigators
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Rejecting Outliers
2% outliers256x12 encodes
LARYNX -- Volunteer scan
Shift of small amplitude, well approximated by a translation
Shift of large amplitude, that cannot be corrected= outlier
kz
ky
Resulting undersampled trajectory after outliers rejection:
SI: Superior/InferiorAP: Anterior/PosteriorLR: Left/Right
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Randomizing the Acquisition
Korin JMRI 1992:2:687-693 -- Wilman MRM 1997:38:793-802 -- Bernstein MRM 2003:50:802-812
256x16 encodes, 11% outliers
Sequential acquisitionPseudo-random acquisition
Phantom scans -- FLASE sequence
"A man has made all his decisions at random. He did not do worse than others who consider carefully their choices" Paul Valéry
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256x32 encodes, 30% outliers
Simulation with in-vivo data
3DFT
CSCS
Sequential acquisitionPseudo-random acquisition
Compressed Sensing (1/2)
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POCS
Compressed Sensing (2/2)
Haacke JMR 1991:92:126-145 -- Lustig MRM 2007:58:1182-1195
256x32 encodes, 30% outliers
3DFT CS
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Discussion
A pseudo-random acquisition often avoids getting corrupted samples that are contiguous in k-space.
If the undersampled trajectory (after outlier rejection) is incoherent, Compressed Sensing allows an accurate reconstruction.
However, how can the acquisition be robust against the worst case scenario (since motion is truly random) where the undersampled trajectory that we first get is not incoherent?
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Diminishing Variance Algorithm
Acquire encodes and navigators
Compute shifts
Determine prioritized list of encodes to
reacquire
Outliers
Sachs MRM 1995:34:412-422
Priority = distance from histogram mode
(weighted by distance from k-space origin)
Number of pixels
mm
mode
Scan time:
6 min 12 s
(38 s overhead to
reacquire the outliers)
Scan time:
5 min 34 s
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Coherency
Determine prioritized Determine prioritized list of encodes to list of encodes to
reacquirereacquire
Acquire encodes and navigators
Compute shifts
Determine prioritized list of encodes to
reacquire
pseudo-
randomly
Priority = incoherency of the underlying undersampled
trajectory
Future work: Diminishing Variance Algorithm
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Conclusion
Undersampled trajectory
Diminishing Coherency AlgorithmIncoherent undersampled
trajectory
Pseudo-random acquisition
Outliers rejection
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Thank you!
Contact:
Acknowledgments:
Michael Lustig, Bob Schaffer, Uygar Sümbül, Juan Santos