Receive Coil Arrays and Parallel Imaging for fMRI of the Human Brain Jacco de Zwart No longer accelerated. Accelerated to indicate that data were undersampled, but not all examples here are faster imaging Advanced MRI section, LFMI, NINDS National Institutes of Health Bethesda, MD, USA

Outline Part I - Receive Coil Arrays Part II - Parallel Imaging Part III - Parallel Imaging & fMRI Nicely introduced by Kamil Ugurbil yesterday

Part I Receive Coil Arrays

Multi-Coil Imaging ≠ Parallel Imaging?!? "Parallel Imaging" = multi-coil imaging where data are undersampled during acquisition Will be discussed in Part II of this talk Parallel Imaging (PI) requires a receive coil array Typically results in a loss of SNR compared to the equivalent fully sampled case when using the same hardware Receive coil arrays used without PI Covered here in Part I of this talk Generally yields an SNR increase compared to a conventional volume coil

Why Use a Receive Coil Array? Smaller coils 'see' less noise  increased SNR close to the coil BUT: small coils have a limited field-of-view SOLUTION: cover the object with small coils SNR in center same as with equally-sized volume coil (e.g. birdcage) SNR everywhere else , highest gain close to coils Signal originates from single voxel Noise originates from all tissue "observed" by coil

What is the Limit? Sample noise should be the dominant noise source Other noise sources: coil, preamplifier Sample noise decreases with coil size (to the 3rd power!) Sample noise increases with field strength (~linearly) Optimal number of elements (our guesstimates for the human head): ~20 coil elements @ 1.5 T ~32 coils @ 3.0 T ~64 coils @ 7.0 T

Number of Coil Elements & Image SNR average over entire brain center of the brain acquired 16-channel data 1, 2, 4 & 8 channel data derived from 16-channel data

16-Element Array vs. Head Coil @ 3 T image intensity scaling factor SNR SNR GE head coil 128×96 192×144 rate-2 SENSE 128×96 16-element array Performance gain: 2-fold in center, up to 6-fold in peripheral cortex!

Conclusion for Part I Receive coil arrays outperform similarly-sized volume coils Equal performance in center of object Performance gain everywhere else, greatest in periphery

Part II Parallel Imaging

Undersampled MR Imaging R-fold undersampling of MR-data Yields R-fold reduction of acquisition time BUT: Aliasing in the image Information is lost due to this folding artifact Signals from different object regions are superimposed and cannot be distinguished Unless… R=2

Undersampled MR Imaging Unless… a receive coil array was used: Sensitivity profile for each coil element different  Relative contribution of superimposed signals different for each coil Allows unaliasing images in post-processing in image domain: "SENSE" [Pruesmann, Magn Reson Med 1999, 42:952] in k-space: "SMASH" [Sodickson, Magn Reson Med 1997, 38:591] Undersampled acquisition with receive coil array + Unaliasing during image reconstruction = Parallel Imaging

Parallel Imaging Penalty With n coil elements, up to n-fold acceleration BUT: SNR reduced due to reduced sampling AND: additional noise introduced by reconstruction generally referred to as g-factor (= spatially varying) depends on coil configuration acceleration rate Parallel Imaging (PI) penalty increases with: higher acceleration factors lower number of coil elements

Example – human brain imaging w. PI + Image obtained using SENSE reconstruction 1.5 T GE Signa LX EPI w. 50% ramp sampling 64×48 / 64×24 matrix 220×165 / 220×83 mm2 FOV 2000 ms TR 40 ms TE 4 mm slice thickness 24.1 / 12.3 ms echo train Full-FOV images for each individual coil  coil sensitivity information (acquired only once!) Undersampled images 4-element dome coil courtesy of Patrick Ledden, Nova Medical Inc, Wakefield, MA, USA

Conclusion for Part II Parallel Imaging increases imaging speed at the cost of image SNR

Part III Parallel Imaging & fMRI

Does PI Make SENSE For fMRI? Disadvantage: PI  reduced image SNR ~ g√R [Pruessman et al., MRM 1999, 42:952] DUE TO: g-factor + R-fold reduction in sampling time But: temporal stability determines fMRI sensitivity temporal stability determined by sum of: image SNR scanner stability physiologic noise Therefore: PI penalty for fMRI typically less than reduction in image SNR SNR loss is associated with most PI applications, which I call accelerated PI here, like acquisition train shortening or resolution increase not affected by PI

PI-fMRI Sensitivity Penalty gR = full PI loss PI noise increase 1 = no loss! Illustrated by this figure / all voxels in brain of a normal volunteer / solid line is model / basically demonstrates that resolution can be optimized for given hardware+experiment such that experiment is not dominated by image SNR intrinsic noise contribution stability-limited SNR-limited All superior brain voxels; normal volunteer; 1.5 T; 4-element coil; 3.8×3.8×4.0 mm3 voxels; rate-2 SENSE; gradient-echo EPI [de Zwart et al., MRM 2002, 48:1011]

Does PI Make SENSE For fMRI? But there are several advantages of PI use: artifacts  geometrical distortions  signal loss in inhomogeneous areas  temporal resolution  gradient acoustic noise  spatial resolution  important for single-shot imaging at high field

Is PI Essential For fMRI At High Field? When B0  (A) NMR signal   CNR  (B) More large vessel suppression  specificity  BUT: (C) T2  & T2*  (D) T1  (A)&(B)  allows higher spatial resolution BUT: (C)  blurring  Parallel Imaging can help:  higher spatial resolution for given sampling window

Example: 3 T Results, R=2 @ 192×144 Mention first EPI time series image Single-shot gradient echo EPI @ 3.0 T, rate-2 SENSE - 16-channel coil [de Zwart et al., Magn Reson Med 2004, 51:22] - 16-channel receiver [Bodurka et al., Magn Reson Med 2004, 51:165] - 1.1×1.1×1.5 mm3 resolution (192×144 matrix) - 2000 ms TR, 48 ms TE, 14 slices, 73.1 ms readout train - 5-min scan; visual paradigm stimulates alternately peripheral (red/yellow) and foveal (blue) vision

7 T Results: Single-Shot R=3 @ 192×120 single-shot EPI, rate-3 SENSE, 39.9 ms readout, 5 min scan time 192×120 = 1.25×1.25×1.0 mm3 background = first EPI volume finger tapping paradigm zoom on next slide

7 T Results: R=3 @ 192×120 EPI image from 1st time point same slice with functional overlay single-shot EPI, rate-3 SENSE, 39.9 ms readout, 5 min scan time 192×120 = 1.25×1.25×1.0 mm3 finger tapping paradigm

Conclusion for Part III PI: fMRI penalty < image SNR penalty PI-fMRI benefits: Reduce geometrical distortions Reduce signal loss due to inhomogeneity Increase spatial resolution Increase temporal resolution Reduce gradient acoustic noise PI-fMRI is important (essential?) for BOLD-fMRI at high field

Further Information Fifteen minutes is too short to cover it all, so if you have questions… ask me! E-mail = Jacco.deZwart@nih.gov And/or: The journal NMR in Biomedicine recently dedicated a special issue to Parallel Imaging (May 2006)

Acknowledgements National Institutes of Health, Bethesda, MD, USA Jerzy Bodurka Jeff Duyn Peter van Gelderen Martijn Jansma Peter Kellman Nova Medical, Wilmington, MA, USA Patrick Ledden

Thanks a lot for your attention! Advanced MRI section LFMI/NINDS/NIH http://www.amri.ninds.nih.gov/