Parallel Imaging – making SENSE of GRAPPA

1. Parallel Imaging – making SENSE of GRAPPA Jolinda Smith Lewis Center for Neuroimaging January 12, 2005

2. Parallel Imaging • Data is collected from multiple coils simultaneously • k-space is subsampled (fewer phase encodes) by an “acceleration factor” • Data from individual coils is combined to produce an image with the full FOV • Acceleration factor must be less than or equal to the number of coils

3. 2D Sequence ky kx Scan time = NyTR

4. Parallel imaging • k-space is filled in one line at a time. • It would really speed things up if we could skip some of those lines.

6. Subsampling of k-space causes aliasing

7. Parallel imaging • Huchinson & Raff (1988) and Kwiat & Einav (1991) suggested using arrays of coils in place of all phase encoding steps. • Requires one coil for each line of k-space. • Signals seen by adjacent coils must be sufficiently distinct.

8. Parallel imaging • Kelton et al: “An algorithm for rapid image acquisition using multiple receiver coils,” 8th SMRM proc., 1172 (1989). • Ra & Rim: “Fast imaging using subencoding data sets from multiple detectors,” 10th SMRM proc., 1240 (1991); Mag. Res. Med. 30, 143 (1993). • A set of aliased images are produced by an array of receiver coils using subencoded data. • These images are resolved to an aliasing-free image by using the distance-dependant sensitivity information for each coil.

9. “Scissors” method Madore & Pelc, Mag. Res. Med. 45, 1105 (2001).

10. SMASH • Sodickson & Manning, Mag. Res. Med. 38, 591 (1997) • SiMultaneous Acquisition of Spatial Harmonics • Linear combinations of signals from each coil may be used to fill in missing k-space lines. • Operates on k-space data. • Reconstruction is very fast.

11. SMASH

12. SMASH

13. SMASH

14. odd k-space lines even k-space lines

15. SENSE • Pruessmann et al, 6th ISMRM proc., 579 (1998); Mag. Res. Med. 42, 952 (1999) • SENSitivity Encoding • Operates on real-space data. • Information about individual coil sensitivities is used to weight each pixel during image reconstruction.

16. slide courtesy of Douglas Noll

17. slide courtesy of Douglas Noll

18. slide courtesy of Douglas Noll

19. SENSE A) Raw array coil image B) Body coil image C) Raw coil sensitivity map obtained by dividing A by B. D) Intensity threshold E) Thresholded raw intensity map F) Final coil sensitivity map after polynomial fitting and extrapolation. Images courtesy of Marc Griswold

20. SNR vs acceleration Pruessmann et al, MRM 42, 952 (1999).

21. Autocalibration • Because coil sensitivities vary slowly, only the central lines of k-space are necessary to represent them • A few extra lines near the center of k-space may be used to compute coil sensitivities • Eliminates reference scans • Applicable to both SENSE and SMASH

22. Autocalibration • mSENSE – modified SENSE • AUTO-SMASH – AUTOcalibrating SMASH • VD-AUTO-SMASH – Variable Density AUTO-SMASH • GRAPPA – GeneRalized Autocalibrating Partially Parallel Acquisitions

23. GRAPPA • Griswold et al, MRM 47, 1202 (2002). • GeneRalized Autocalibrating Partially Parallel Acquisitions • SMASH-type technique • Less dependant on coil geometry than SMASH • Better SNR than SMASH

24. iPAT • integrated Parallel Acquisition Techniques • mSENSE • GRAPPA

25. iPAT

26. iPAT

27. iPAT 3D MPRAGE – left, 6:42 min without iPAT; right, 3:51 min with iPAT

28. iPAT ep2d_diff (b = 1000, trace) left: without iPAT, matrix 128 x 128 right: with iPAT, matrix 192 x 192

29. GRAPPA vs. mSENSE SENSE GRAPPA

30. GRAPPA vs. mSENSE SENSE GRAPPA

31. Conclusions • Parallel imaging techniques can dramatically reduce imaging times • Single-shot techniques may be performed with shorter echo trains, producing sharper images with less distortion • However, SNR is reduced by at least the square root of the acceleration factor