An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Jason P. Stockmann and R. Todd Constable #549Session – Parallel Imaging: Stretching.

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Presentation transcript:

An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Jason P. Stockmann and R. Todd Constable #549Session – Parallel Imaging: Stretching the Limit

Declaration of Conflict of Interest or Relationship Jason P. Stockmann: I have no conflicts of interest to disclose with regard to the subject matter of this presentation. #549 Introduction Theory/Methods Results Conclusion An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

Motivation Parallel imaging developments have focused on coil arrays with ever-larger numbers of RX coils –Problem of diminishing returns New Approach : O-Space Imaging Examine parallel imaging with attention to: –Encoding provided by receiver coils –Encoding provided by spatial magnetic field gradients #549 Introduction Theory/Methods Results Conclusion O-Space imaging An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

Instead of tailoring coil profiles to conventional gradients…. …design gradients to provide complementary information. #549 Introduction Theory/Methods Results Conclusion Tailor gradient shapes to coil profiles An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

Instead of encoding frequency in columns 1.Frequency is encoded radially (2D coverage) 2. Eliminate phase encoding 3. Use multiple Center Placements (CPs) and projection reconstruction Projection Imaging along Rings #549 Introduction Theory/Methods Results Conclusion Tailor gradient shapes to coil profiles An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

Choose gradient shape to optimally complement coil profiles Select a subset of spherical harmonics in the axial plane: –Dynamic Shim Updating XYZ2 X2-Y2XYZ2X Z2YX3Y3 De Graaf RA, MRM, 2003;49: Hennig J et al, MAGMA, 2008;21:5-14. ISMRM 2009 #564, #762, #4557, #3052, #3061, #3073 PatLoc #549 Introduction Theory/Methods Results Conclusion Parallel imaging with spherical harmonics An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

#549 Introduction Theory/Methods Results Conclusion Acceleration performance An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Stockmann JP et al., MRM, 2010, in press.

#549 Introduction Theory/Methods Results Conclusion Noise performance An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Stockmann JP et al., MRM, 2010, in press.

#549 Introduction Theory/Methods Results Conclusion Effects of ring density An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Stockmann JP et al., MRM, 2010, in press.

See E-poster #4556 Multiple center placements take the place of phase encodes ++ + B0 offset = Projection Echo FFT r2r2 #549 Introduction Theory/Methods Results Conclusion Choosing an encoding scheme An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

See poster #2857 Express integral equation as a matrix problem and solve using Kaczmarz iterative projection algorithm Flexible approach that works for all gradient functions, coil profiles, and gradient pulse shapes Herman GT et al. Comput. Biol. Med. 1976;6: Kaczmarz S. Angenäherte Auflösung von Systemen linearer Gleichungen. Bull. Internat. Acad. Polon.Sci. Lettres A, pages , Coil Non-linear gradient Echo #549 Introduction Theory/Methods Results Conclusion Image reconstruction An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

Reference Model Incorporate Tikhonov regularization (truncated SVD) into Kaczmarz reconstruction #549 Introduction Theory/Methods Results Conclusion Sensitivity to B0 inhomogeneity An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

0 Hz 50 Hz 150 Hz 250 Hz Unreg. r = 0.6 Unreg. r = 0.6 Unreg. r = 0.6 R = 16 R = 8 R = 4  B peak (Break up into smaller figures for improved visibility) #549 Introduction Theory/Methods Results Conclusion Sensitivity to B0 inhomogeneity An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

… RF … GZGZ … GYGY … GXGX … G Z2 … DAQ TE 90  180  90  3T Tim Trio System In-house 8-channel microstrip transceive array #549 Introduction Theory/Methods Results Conclusion Experimental Data An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities Resonance Research Z2 gradient insert 18,000 Hz/cm  s rise time

Low Z2 gradient strength (0.07 g/cm 2 ) = long readout window (50 ms) –B0 Inhomogeneity –B0 Offset Eddy currents Solution: Build dedicated high-strength, shielded gradient coil for O-Space imaging. #549 Introduction Theory/Methods Results Conclusion Obstacles An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

O-space shows promise for highly accelerated imaging Design spatial gradients to be more complementary to coil spatial encoding for highly-efficient parallel imaging [ Leo Tam, ISMRM 2009, Abstract #3072 ] PatLoc: [ Fa-Hsuan Lin, ISMSM 2009, Abstract #4557 ] Currently seeking optimal gradient shapes and trajectories for non-linear projection imaging #549 Introduction Theory/Methods Results Conclusion Outlook An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities

#549 Introduction Theory/Methods Results Conclusion Thank you An Assessment of O-Space Imaging Robustness to Local Field Inhomogeneities This work would not have been possible without the following people: Christoph Juchem Robin de Graaf Hemant Tagare Douglas Rothman Laura Sacolick Terry Nixon Peter Brown NIH Neuroengineering Training Grant 5T90DK O-space abstracts at ISMRM: #4556 Pelin Aksit Ciris, "O-Space Imaging: Tailoring Encoding Gradients to Coil Profiles for Highly Accelerated Imaging“ #2857 Jason Stockmann, “Kaczmarz Iterative Reconstruction for Arbitrary Hybrid Encoding Functions” #3072 Leo Tam, “Non-linear encoding gradient optimization for O-Space imaging with a microstrip coil array” #3078 Jason Stockmann, “Refocusing Method for Mapping Imaging Gradients with High SNR” #564, #762 Gerrit Schultz, #3052, #3061, #3073 Anna Welz #2859, #2676 Maxim Zaitsev #4557 Fa-Hsuan Lin Slides available at: mri.med.yale.edu/individual/stockmann PatLoc-related Abstracts: