What is pmri? Overview. The Need for Speed: A Technical and Clinical Primer for Parallel MR Imaging 8/1/2011

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1 The Need for Speed: A Technical and Clinical Primer for Parallel MR Imaging Nathan Yanasak, Ph.D. Chair, AAPM TG118 Assistant Professor Department of Radiology Director, Core Imaging Facility for Small Animals (CIFSA) Georgia Health Sciences University General Description of Parallel Imaging Applications of Parallel Imaging pmri Considerations Quality Control Overview What is pmri? Speed: less phase encodes = smaller FOV (with same resolution) Uses spatial information obtained from arrays of RF coils sampling data in parallel Information is used to perform some portion of spatial encoding usually done by gradient fields (typically phase-encoding gradient) Speeds up MRI acquisition times without needing faster-switching gradients without additional RF power deposited (key for higher field MR) Smaller FOV aliasing 4 1

2 Phased-Array of Coil Elements Uneven SNR throughout volume, but SNR: Surface Coil vs. Volume Coil Surface Coil (single element) Volume Coil Non-uniform SNR Uniform SNR Great SNR up close Average SNR Very high SNR at edge Lower SNR in middle SNR in middle is generally better than comparable volume coil. 5 SIGNAL-TO-NOISE RATIO (Arbitrary Units) a (a) 8 cm dia. Surface coil (b) 10 cm dia. Surface coil (c) 14 cm dia. Surface coil (d) Head coil b c d DEPTH (in cm) head body 6 12-channel head coil Multichannel Coils Multi-element full body coil Use of Phased Array Coil in Parallel Imaging Spatial sensitivity varies for each element can use this in conjunction with undersampling. 7 Conventional use of phased-array (unaliased) Parallel reconstruction of data (aliased) 2

3 Coil Sensitivity Profiles Different approaches to solving the inverse problem that recovers spatial information. The key information always required to solve this problem is information on the spatial distribution of the RF coils sensitivity. How you collect and use this information different methods. Sensitivity Map The spatial sensitivity of each coil element = sensitivity map. A calibration scan is usually required to calculate this. Total s 1 s 2 10 Using Coil Sensitivity to Un-alias an Image: An Example Coil Locations and Sensitivity Maps Object being imaged

4 Using Coil Sensitivity to Un-alias an Image Two Parallel Approaches Image based: Reconstruct images from each element, then untangle (SENSE, ASSET) (our demo) k-space based: Untangle data to create fully-filled k- space, then reconstruct image (SMASH, GRAPPA) The Encoding Matrix S j p B pj S p: signal received by the coil, p. j : proton density at the pixel index, j B pj : encoding function that connects the coil response to the proton signal at a location. In matrix notation: S = B or inverting: = B -1 S Thus if B -1 can be calculated, can be determined. j 4

5 A B A B A Simplistic SENSE Example S alias,1 =B 1,A I A + B 1,B I B I A s 1 I 1 I B I 2 s 2 S alias,2 =B 2,A I A + B 2,B I B k-space Based pmri Assumes spatial harmonics of phaseencoding gradients can be omitted and emulated by a linear combination of coil sensitivities Coil sensitivity still required (measured in some manner, and complex). 17 A Simplistic SMASH Example A Simplistic SMASH Example (from Sodickson, et al., MRM 41: 1009, 1999) Weighted linear combination of element responses are sensitive to different spatial scales (but no recon yet). 5

6 A Simplistic SMASH Example A Simplistic SMASH Example The black curves represent two of the effective sensitivities using elements in this example. The upper combination is sensitive Fundamental: C A (add signal for all three) to these spatial variations: First Harmonic: C B (subtract middle signal) while the lower combination is sensitive to these spatial variations: Resultant combinations (spatial harmonics) allow for filling of all lines in a composite k-space. 22 Auto-Calibration Methods Acquire reference lines (ACS lines) in k-space rather than whole coil sensitivity images (data from center of k-space acts like a sensitivity profile) Example: GRAPPA (k-space based) Missing k-space lines are synthesized by fitting between reference data and nearest neighbor lines of data Fitting determines the weighting factors for generating missing lines for each coil Parallel Imaging (Technique Pros/Cons) Image-based reconstruction: More artifacts, but easier to implement the sequence. K-space based reconstruction: Depends more strongly on coil design, less artifacts, but longer to reconstruct. Hybrids between both also exist

7 Parallel Imaging Flavors Name Acronym Method Manufacturer SENSitivity Encoding SENSE Image-based, Philips reference scan Array Spatial Sensitivity Encoding ASSET Image-based, General Electric Technique reference scan Auto-calibrating Reconstruction for ARC Hybrid (imageand General Electric Cartesian imaging k-space based) integrated Parallel Acquisition Techniques ipat Used by all pmri Siemens GeneRalized Auto-calibrating Partially Parallel Acquisition GRAPPA k-space based, auto-calibrated with reference scan option modified SENSitivity Encoding msense image based, auto-calibrated with reference scan option SPEEDER image-based, reference scan Siemens Siemens Toshiba 25 Advantages/Uses of pmri 26 When Should You Use Parallel MR Imaging? To reduce total scan time To speed up single-shot MRI methods To reduce TE on long echo-train methods To mitigate susceptibility, chemical shift and other artifacts (may cause others) To decrease RF heating (SAR) by minimizing number of RF pulses ( B 2 ) Use #1: Body Imaging A: 22 sec acquisition w/ 15 sec breathhold B: 11 sec acquisition w/ 11 sec breathhold + R=2 To reduce total scan time (or eliminate breath holds) To decrease RF heating (SAR) by minimizing number of RF pulses Margolis D et al. Top Magn Reson Imag 2004; 15:

8 Use #2: Spinal Imaging D: non-pmri Use #3: Reduce T2 Blurring (FSE) E: R=2 Image quality is of similar quality for ½ the scan time Problem #1: Greater ETL lower SNR Problem #2: T2 relaxation during acquisition of ETL results in T2 blurring. Noebauer-Huhmann et al. Eur Radiol 2007; 17: Reductions in TEeff would be useful. Use #3: Reduce T2 Blurring Use #4: Susceptibility Artifacts Air Sinuses Augustine Me et al. Top Magn Reson Imag 2004; 15:207 Glockner et all. RadioGraphics 2005; 25: Regions of air/bone/soft tissue causes local gradients due to differences in magnetic field susceptibility 8

9 Susceptibility Artifact Reduction with Parallel Imaging Shortening TE helps (must have less phase encodes to do this). EPI-based sequences gain more in general (e.g., DWI, perfusion) Top normal acquisition, Bottom R=2 acceleration Turn Key Parallel Imaging? R=1 R=2.0 R=2.8 R=3.2 R=4.0 Turn Key Parallel Imaging? R=1 R=2.0 R=2.8 R=3.2 R=4.0 Use #5: Contrast-enhanced MR (MRA) Left: R~ 1.5; Right: non-pmri with reduced FOV Improved spatial resolution for a given scan time. Wilson, et al. Top Magn Reson Imag. 2004; 15:

10 Use #6: Cardiac Imaging Balanced FFE MRI A&B: 11 sec breath holds C&D: 5 sec breath holds + R=2 Drawbacks/Consideration of pmri: SNR Properties & Artifacts Van den Brink, et al. Eur. J. Rad. 2003; 46: SNR Non-Uniformity of Noise SNR is a concern with pmri for three reasons: Non-uniformity of signal (array coils) Non-uniformity of noise (pmri) Lower signal from acceleration (pmri) Larkman DJ et al. Magn Reson Med 2006; 55:

11 Key SNR Parameters in Parallel Imaging Key SNR Parameters in Parallel Imaging SNR depends on number, size and orientation of the coil elements SNR depends on number, size and orientation of the coil elements SNR PI i, j, k SNR g i, j, k norm i, j, k R 1 SNR 2 g( r, R) R SNR PI norm ( r ) R: acceleration factor g: coil-dependent noise amplification factor (non-uniformity that we observed) R: acceleration factor g: coil-dependent noise amplification factor (non-uniformity that we observed) SNR vs. Acceleration g-factor Calculated Maps 32-channel coil, 1.5 T magnet R=2 R=3 R=2 R=3 R=4 R=5 R=4 R=5 Short-axis cardiac images 32-channel coil 1.5 T magnet Reeder SB et al. MRM 54:748, 2005 R=6 R=7 R-L Phase Encoding g-factor changes with R R=6 R=7 A-P Phase Encoding Reeder SB et al. MRM 54:748,

12 Artifacts Artifacts associated with pmri may or may not be subtle. Artifact #1: Tissue Outside of FOV (SENSE) Wrap-around artifact Unalias What Undersampling happens with pmri when the FOV is too small? Similarities to conventional MRI artifacts (aliasing, ghosting). Important to prescribe the acquisition properly, and to avoid movement. Center region in this example should be unaliased, for acceleration R=2. Normal FOV Smaller FOV Treated as non-aliased tissue during reconstruction. Examples: Phantom and Patient With SENSE-based technique, tissue outside of the FOV yields wrap-into artifact Goldfarb, JMagn Reson Imag Artifact #2: Motion After Calibration Scan (SENSE or GRAPPA) Calibration scan must accurately represent tissue position. Normal SENSE Small FOV Small displacement Medium displacement Large displacement 12

13 Artifact #2: Motion After Calibration Scan (SENSE or GRAPPA) Affected by FOV choice as well. Clinical Artifact Examples Small FOV Large FOV Not aliasing, folks! Pseudo- failure of fat sat: Patient moved between reference and 3D artifact: SENSE faint scans ghost near IAC the structure middle of ghosting FOV that resembles structures located at the edges of scanned volume (nose, ear). Clinical Artifact Examples pmri and Traditional Artifacts Appearance of traditional artifacts may be modified by pmri Susceptibility (artifact not perfectly represented on sensitivity map) Thin, bright structures in the periphery of sensitivity map mismatch between sensitivity and anatomy. simulation phantom 13

14 ACR QC vs. pmri QC SNR=Signal U=1- max ROI Large -min ROI /Noise / max ROI Small +min ROI ROI Quality Control ACR approach to SNR, Uniformity (using volume coil, signal and noise are fairly uniform in many cases) ACR QC vs. pmri QC SNR=mean(I NAAD=1- j,1 +I 1/N j,2 ) ROI (I /[sqrt(2 I j - <I>)/<I> j,1 -I j,2 ) ROI ] TG118 Preliminary Results: SNR SNR In pmri, signal is not uniform (array coils), and noise is not uniform (gfactor). With Update: SNR TG118 ROI in has center, will focused artifact on perturb differences in measurement? SNR, uniformity as acceleration changes. 14

15 TG118 Preliminary Results: Uniformity Uniformity Update: TG118 has Is focused more sensitivity on differences good? in SNR, uniformity as acceleration changes. Thoughts from TG118 on pmri QA One must always remember that metrics and system performance can be interconnected (e.g., SNR vs. uniformity vs. artifacts) Not clear that any uniformity measure can satisfy all three criteria below easy to measure Sensitive to aberrant non-uniformity Insensitive to coil-inherent non-uniformity 2D SENSE (with 3DFT MRI) Acceleration > 1D 2D SENSE reconstruction (2X in L-R and 2X in A-P) from an 8-channel head array coil conjugated gradient iterative solver after 10 iterations. 15

16 Undersampling in the Temporal Domain Parallel imaging in the temporal domain: Time Auto-Calibrated GRAPPA (TGRAPPA); TSENSE Self-Calibrating Non-Cartesian SENSE TSENSE 2D Cine TrueFISP R = 2 6 heartbeats R = 3 4 heartbeats R = 4 3 heartbeats 36 msec true temporal resolution, 144 x 256 matrix Peter Kellman, NHLBI, and Al Zhang, Siemens R&D, Chicago Future Directions pmri with massively parallel arrays Transmit parallel imaging Importance of pmri Increases MR imaging speed Is applicable to all MRI sequences Is complimentary to all existing MRI acceleration methods Can often reduce artifacts Alters SNR in MR images 16

17 Application of pmri pmri offers the promise of high resolution MR imaging at speeds as fast as MSCT Applications of parallel imaging include FSE, cardiac MR, diffusion and perfusion EPI brain imaging methods, 3D FT MRI (and MRA). Parallel imaging is tool for managing RF heating in the body at 3T and higher field strengths Parallel imaging and dedicated RF coil design are enabling technologies for high B o MRI Acknowledgments Current and Past Members of TG118 Jason Stafford, Lisa Lemen, Max Amurao, Geoff Clarke, Ron Price, Ishtiaq Bercha, Michael Steckner Frank Goerner (UTHSCSA) Ed Jackson (MDA), Lawrence Wald (MGH), Jerry Allison (MCG) The Future of the Future? 64 strip detectors used to image a test object with a single readout (MacDougal and Wright, 2005). 17