1. HighlY Constrained Back PRojection (HYPR) Thank you to Oliver Wieben!!

2. K-space time 13579 ‘Composite’ HighlY Constrained Back PRojection (HYPR)

3. An approximate acquisition and reconstruction method Images should be sparse (few pixels w/signal) No movement allowed! Fairly high spatio-temporal correlations Radial under-sampling at each time frame Decrease total scan time Improve temporal resolution Composite image Combines data from many time frames Allows higher SNR for each time frame Constrains the backprojection reconstruction for each time frame which reduces streak artifacts Mistretta, et al., MRM 55:30-40;2006

4. Interleaving – Dynamic Imaging 1. Acquire data in interleaves Highly undersample each time frame 123456789…. K-space CE-MRA time artery vein signal

5. All Inclusive Composite 2. Calculate composite images Sum of ALL projections through time 123456789…. K-space All inclusive composite

6. Sliding Composite 2. Calculate composite images Sum of “some” projections through time 123456789…. K-space

7. 2. Calculate composite images Sum of “some” projections through time 123456789…. K-space Sliding Composite

8. 2. Calculate composite images Sum of “some” projections through time 123456789…. K-space Sliding Composite

9. All-inclusive versus Sliding Composite All-inclusive Composite + High SNR + Few streak artifacts Good for nearly homogeneous temporal behaviour –In CE-MRA: contains early and late filling vessels The SNR of the composite dictates the SNR in the time-resolved images Sliding Composite –Lower SNR –More streak artifacts + Better separates early and late filling vessels

10. k-space projections Image-space projections N2 1 1D FT N2 1 Time frames with interleaved angular projections time Filtered backproject. Composite image or sum, regrid, and FT Multiply HYPR time frame N Radon + Unfiltered backprojection P/Pc P Pc Sum over all projections H  C. p p c i  Unfiltered backproject.

11. Backprojection

12. k-space projections Image-space projections N2 1 1D FT N2 1 Time frames with interleaved angular projections time Filtered backproject. Composite image or sum, regrid, and FT Multiply HYPR time frame N Radon + Unfiltered backprojection P/Pc P Pc Sum over all projections Unfiltered backproject. H  C. p p c i 

13. Input (Truth) CompositeWeightingHYPR ×= Schematic Two Vessels – Horizontal – All-inclusive F. Korosec & Y. Wu

14. Input (Truth) CompositeWeightingHYPR ×= Schematic Wrong! Two Vessels – Vertical – All-inclusive F. Korosec & Y. Wu

15. More Projections per HYPR Time Frame HYPR Weighting 1 Projection 4 Projections 2 Projections8 Projections F. Korosec & Y. Wu

16. Input Curves and Vessel Locations Input CurvesVessel Locations F. Korosec & Y. Wu

17. Composite Image #5 Composite 100 projections 5 frames x 20proj/frame Time Frame #5 Input Curves Time frame #5 F. Korosec & Y. Wu

18. Composite Image #5 Composite 100 projections 5 frames x 20proj/frame Time Frame #11 Input Curves Time frame #11 F. Korosec & Y. Wu

19. Time Curves – Input and HYPR Input HYPR F. Korosec & Y. Wu

20. HYPR Simulation Parameters –Gd-doped water injected in tube –2D Fourier acquisition @ 1 frame / s –Generate k-space projections from images (Matlab) –Simulate HYPR acquisition –32 projections per interleave –8 unique sets of interleaves -> 256 angles sampled –Composite image: moving window [-4.. +3]

21. Simulation – Time Frame 5 OriginalUndersampled CompositeHYPR

22. Simulation – Time Frame 9 OriginalUndersampled CompositeHYPR

23. Comparison Videos: Foot Undersampled 16 projections per time frame  T = 2.0 s HYPR  T = 2.0 s

24. Comparison Videos: Calfs Undersampled 16 projections per time frame  T = 2.1 s HYPR  T = 2.1 s FOV = 48 cm, BW = 62.5 kHz, flip = 25 deg. TR/TE = 5.2 / 1.1 ms HYPR frame rate = 2.1 s Composite: sliding window (duration: 16*2 = 32s)

25. Applications HYPR Applications –Dynamic Contrast-enhanced MR Angiography –Quantitative Flow Imaging –Diffusion Tensor Imaging –MR and CT Perfusion Imaging –Cardiac Function

26. HYPR Summary –Improves temporal resolution (also reduces total imaging time) –Improves SNR by incorporation of a time averaged composite image –Small number of projections are used to produce weighting images that are multiplied by high SNR composite image –Composite image constrains backprojection to reduce streak artifacts –Degree of achievable undersampling depends on sparsity spatio-temporal correlation acceptable error