GPUs Open New Avenues in Medical MRI

Transcription

1 GPUs Open New Avenues in Medical MRI Chris A. Cocosco D. Gallichan, F. Testud, M. Zaitsev, and J. Hennig Dept. of Radiology, Medical Physics, UNIVERSITY MEDICAL CENTER FREIBURG 1

2 Our research group: Biomedical Magnetic Resonance Imaging University Medical Center Freiburg, Germany: > 50 scientists & PhD students 2

3 B0 gradients (SEMs) for spatial encoding SEMs: spatial encoding magnetic fields + 0 -G +G k-space : 3

4 B0 gradients (SEMs) for spatial encoding SEMs: spatial encoding magnetic fields Traditional (Linear) + 0 -G +G Quadratic (Non-linear) + 4

5 PatLoc: PatLoc = Parallel Acquisition Technique using Localized Gradients [ Hennig J. et al., MAGMA 21(1-2):5-14 (2008) ]. has the potential to allow: (1) higher gradient switching rates while not exceeding the Peripheral Nerve Stimulation (PNS) limits; (2) novel encoding strategies (e.g. better suited to the anatomy). 5

6 First ever human PatLoc images: [ Schultz G. et al., Reconstruction of MRI Data Encoded with Arbitrarily Shaped, Curvilinear, Non-bijective Magnetic Fields, MRM 64(5): (2010) ] 6

7 Why PatLoc: TSE 256x256, TR 5000 ms, slice thickness 2mm, acquisition time ~3min for 5 slices. 7

8 Imaging forward model: m = E * p p : image [NP] NP : number of image pixels m : measured data [NT,NC] NT : number of measured ( k-space ) samples NC : number of RF receive coils E [ NT*NC, NP ] Typical magnitudes: NT,NC = 256 x 256 NC = 8 8

9 Conjugate Gradient Algorithm: Conjugate Gradient Algorithm: numerically estimate an image consistent with the measured data [ Pruessman et al., MRM 2001;46: ]. But: no gridding, no FFT! Repeat times : q = E * (E * p) 1. E * p 2. E * Ep update p 9

10 Compute-on-demand Implementation: E is very large, but: E = E ( Traj, SEM, B1map, B0map ) Traj [NT, NS] SEM [NP, NS] B1map [NP, NC] B0map [NP] Foreach( NP ) Foreach( NT ) Foreach( NC ) where NS = number of SEMs (B0 gradients) CUDA implementation: blocks + threads accumulator in shared memory + block reduce 10

11 Matlab implementation: key to performance: vectorize your code! vector / matrix operations are automatically multi-threaded Parallel Computing Toolbox matlabpool + parfor : loop-level run CUDA ptx kernels both: spmd 11

12 PatLoc wardware setup: Siemens MAGNETOM Trio Tim. PatLoc gradient insert coil [ Cocosco C.A. et al., ISMRM 2010 #3946 ]. Additional set of 3 gradient amplifiers; can synchronously drive all the available gradients simultaneously and independently. 12

13 First PatLoc gradient human coil: 13

14 Application 1: Higher-dim gradient encoding 4DRIO [ Gallichan D. et al., Simultaneously driven linear and nonlinear spatial encoding fields in MRI, MRM 65(3), 2011 ] NS= 4 NP= 320^2 NT= 256^2 NC= 8 E ~ 450 GB 14

15 Throughput CPU vs GPU: quad-socket Intel Xeon Nehalem-EX X7560 with 1024G RAM : 16 threads : 615s to compute E, 29s / iter 32 threads : 565s to compute E, 27s / iter dual-socket Intel Xeon Westmere-EP X5690 : 12 threads : 252s / iter Nvidia Tesla C2075 GPUs 8.1s / iter 7s / iter with hardcoded NS 4x Nvidia Tesla C2075 GPUs 2.3s / iter (3.5x) ( Matlab R2012a ; CUDA 4.1 ) 15

16 Application 2: Ultra-fast imaging single-shot imaging Layton et al: Region-specific trajectory design for single-shot imaging using linear and nonlinear magnetic encoding fields, ISMRM NS= 16 gradients (harmonics) NP= 128^2 NT= 131^2 NC= 8 E ~ 18 GB 16

17 Application 2: Ultra-fast imaging Use a Field Camera : C. Barmet, K. Pruessmann, Inst. for Biomedical Eng., University and ETH Zuerich [ Wilm et al, MRM 2011 ] 17

18 Throughput CPU vs GPU: dual-socket Intel Xeon Westmere-EP X5690, 96GB RAM : 12 threads : 37s to compute E, 3.7s/iter Nvidia Tesla C2075 GPUs 0.56s / iter 4x Nvidia Tesla C2075 GPUs 0.26s / iter ( Matlab R2012a ; CUDA 4.1 ) 18

19 What if the subject is... moving? E = E ( Traj, SEM, B1map, B0map ) Apply a 3D rigid-body transformation to SEM, B1map, B0map for each segment of measured data (e.g. 256 segments for a 256^2 image) Size explosion for pre-computing E, but approachable with the compute-on-demand GPU solution. Work in Progress No FFT, like in [ Bammer et al. Augmented generalized SENSE MRM2007;57(1):90-102] 19

20 Conclusions and outlook GPUs open new avenues in medical MRI Faster imaging: shorter sessions, more information Address limitations imposed by: physics, MRI hardware technology, human subject Practical R&D process Feasible clinical implementation Wish list: more memory bandwidth, more registers & shared memory, or both ;-) 20

21 Special Thanks to: Research funding: German Federal Ministry of Education and Research, grant #13N9208; European Research Council Advanced Grant 'OVOC' grant agreement Travel funding: Wissenschaftliche Gesellschaft in Freiburg im Breisgau. C. Barmet, K. Pruessmann, (Institute for Biomedical Engineering, University and ETH Zuerich, Switzerland). K. Layton (The University of Melbourne, Australia). J. Maclaren, and our colleagues in Medical Physics, Dept. of Radiology, University Medical Center Freiburg. Bruker Biospin, Siemens Healthcare. 21

22 GPUs Open New Avenues in Medical MRI Chris A. Cocosco 22