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Parallel Imaging

- SMASH and SENSE
- High field advantage
- Pros and cons…
- But first, review of last few homework assignments

Parallel Imaging

Bibliography: parallel imaging

- Original SMASH and SENSE papers
- Griswold MA, Jakob PM, Chen Q, Goldfarb JW, Manning WJ, Edelman RR, Sodickson DK (1999). Resolution enhancement in single-shot imaging using simultaneous acquisition of spatial harmonics (SMASH). Magn Reson Med. 41(6):1236-45.
- Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999). SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 42(5):952-62.

- Additional references
- Sodickson, DK (2000). Tailored SMASH image reconstructions for robust in vivo parallel MR imaging. Magn Reson Med 44:243-251.
- Weiger, M, Boesiger, P, Hilfiker, PR, Weishaupt, D, Pruessmann, KP (2005). Sensitivity encoding as a means of enhancing the SNR efficiency in steady-state MRI. Magn Reson Med 53:177-185.
- Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. (2002). Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 47(6):1202-1210.
- Sodickson, DK, McKenzie, CA (2001). A generalized approach to parallel magnetic resonance imaging. Med Phys 28(8):1629.

Parallel Imaging

Parallel Imaging – simplistic overview

Coil sensitivity maps

Partial k-space data from 2 coils

Reconstructed image

+

=

Parallel Imaging

Undersampled k-space = aliased image

Full k-space

Undersampled k-space

Reconstructed image

Parallel Imaging

Multiple coils = information to fix aliasing

Coils with complementary sensitivities

Full k-space

Undersampled k-space

Parallel Imaging

Parallel Imaging – SENSE

ACQUIRE

RECONSTRUCT

COMBINE

Coil sensitivity, partial k-space

Images from each coil

Images from all coils, using coil sensitivity maps

Parallel Imaging

Parallel Imaging – SMASH

ACQUIRE

RECONSTRUCT

COMBINE

Coil sensitivity, partial k-space

Missing lines of k-space, using coil sensitivity maps

Images from all coils

Parallel Imaging

SENSE recon: idealized example

L1(x,y)

Reconstructed

Ln(x,y) = Cn(x,y)*(x,y) + Cn(x,y+NPE/2)*(x,y+NPE/2)

Parallel Imaging

Pineapple: fake 2-channel coil, noise

(with imperfect knowledge of coil sensitivity)

Parallel Imaging

Pineapple: fake 2-channel coil, not ind.

Parallel Imaging

Parallel imaging terminology

- Methods
- iPAT – Siemens name for all of its parallel imaging implementations
- SENSE
- SMASH
- GRAPPA – auto-calibrating SMASH-like technique

- Parameters
- Reduction factor: integer describing k-space undersampling
- g: describes degradation of reconstructed image due to lack of independence between coil sensitivities (limits useful reduction factor)

- High field advantage
- … of course …

Parallel Imaging

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