Cardiac C-arm computed tomography

Transcription

1 Cardiac C-arm computed tomography PhD defense Cyril Mory CRAI, yon, France Cyril Mory - PhD Defense - February

2 Introduction What is a C-arm? Cyril Mory - PhD Defense - February

3 Introduction What is tomography? Projections Volume Cyril Mory - PhD Defense - February

4 Contents Introduction Introduction wo different problems tate of the art in cardiac C-arm C he acquisition protocol Moving hepp & ogan phantom CG-gating Angular distribution of projections Artifacts / Blur tradeoff 3D compressed sensing A bit of math 4D compressed sensing Perspectives Conclusion Cyril Mory - PhD Defense - February

5 Introduction wo different problems oft tissue analysis 3D diastole reconstruction Beating motion = trouble Functional analysis 3D + time reconstruction Whole cardiac cycle Beating motion = information In both cases Respiratory motion = trouble => Apnea Cyril Mory - PhD Defense - February

6 Introduction CG-gating Cyril Mory - PhD Defense - February

7 Introduction Angular distribution of projections packets of consecutive projections arge gaps between packets #Packets = #Cardiac cycles in acq. Cyril Mory - PhD Defense - February

8 Introduction Moving hepp & ogan phantom um of ellipsoids xact line-integral calculation Modified to beat Cyril Mory - PhD Defense - February

9 Introduction Angular distribution of projections AR reconstructions from 60 projections, starting from zero 10 packets of 6 projections 20 packets of 3 projections 30 packets of 2 projections 60 packets of 1 projection More packets = ess artifacts #Packets = #Cardiac cycles in acquisition Fast beating heart ong acquisition Cyril Mory - PhD Defense - February

10 Introduction Angular distribution of projections AR reconstructions from 60 projections, starting from ungated 5 packets of 12 projections 10 packets of 6 projections 20 packets of 3 projections 30 packets of 2 projections More packets = ess motion blur Cyril Mory - PhD Defense - February

11 Introduction Artifacts / Blur tradeoff CG gated Ungated Undersampling artifacts - Amount of data used + Motion blurring radeoff? Cyril Mory - PhD Defense - February

12 Introduction tate of the art in cardiac C-arm C Multiple sweep acquisitions High image quality on a single phase Motion-compensated reconstruction techniques High dose ong apnea (at least 12s, in practice around 20s) High amount of contrast Mostly used on animals as of today auritsch, Jan Boese, ars Wigström, Herbert Kemeth, and Rebecca Fahrig. owards Cardiac C-Arm Computed omography. I ransactions on Medical Imaging 25, no. 7 (July 2006): Prümmer, M., Joachim Hornegger, Guenter auritsch, ars Wigström, rin Girard-Hughes, and Rebecca Fahrig. Cardiac C-Arm C: A Unified Framework for Motion stimation and Dynamic C. I ransactions on Medical Imaging 28, no. 11 (November 2009): doi: /mi Girard, rin, Amin Al-Ahmad, Jarrett Rosenberg, Richard uong, eri Moore, Günter auritsch, Jan Boese, and Rebecca Fahrig. Contrast-nhanced C-Arm C valuation of Radiofrequency Ablation esions in the eft Ventricle. JACC. Cardiovascular Imaging 4, no. 3 (March 2011): doi: /j.jcmg Cyril Mory - PhD Defense - February

13 Introduction tate of the art in cardiac C-arm C ingle sweep acquisitions Compressed sensing techniques (AD-PC, PICC) => Regularized images ong apnea (14s in a 2012 paper) and high heart rate (around 90 bpm) High amount of contrast (37 cc for a 10kg swine) auzier, Pascal hériault, Jie ang, and Guang-Hong Chen. ime-resolved Cardiac Interventional Cone- Beam C Reconstruction from Fully runcated Projections Using the Prior Image Constrained Compressed ensing (PICC) Algorithm. Physics in Medicine and Biology 57, no. 9 (May 7, 2012): doi: / /57/9/2461. Chen, G.-H., P. heriault-auzier, J. ang, B. Nett,. eng, J. Zambelli, Z. Qi, et al. ime-resolved Interventional Cardiac C-Arm Cone-Beam C: An Application of the PICC Algorithm. Medical Imaging, I ransactions on 31, no. 4 (April 2012): doi: /mi Cyril Mory - PhD Defense - February

14 Introduction he acquisition protocol ingle breath hold 10.3 seconds 308 projections 1024 * 792 pixels 38 cm * 29 cm 0.74 mm * 0.74 mm pixels 210 (short scan) About 60 cc of iodine CG-recording => Nothing in the literature with similar constraints Cyril Mory - PhD Defense - February

15 Contents 3D compressed sensing Introduction 3D compressed sensing Augmented agrangian + ADMM + total variation Augmented agrangian + ADMM + wavelets PICC Animated sequences A bit of math 4D compressed sensing Perspectives Conclusion Cyril Mory - PhD Defense - February

16 3D compressed sensing A + ADMM + V f = arg min f G Rf p αv f R f G is the forward projection operator (Radon or X-ray transform) is the volume we seek is the gating operator V V f = x f v 2 + y f v 2 + z f v 2 v=1 V favors piecewise constant images Real images are not piecewise constant Regularization must remain limited Cyril Mory - PhD Defense - February

17 3D compressed sensing A + ADMM + V Y D I A Cyril Mory - PhD Defense - February

18 3D compressed sensing A + ADMM + V Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

19 3D compressed sensing A + ADMM + V Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

20 3D compressed sensing A + ADMM + Wavelets f = arg min f G Rf p α Wf 1 Daubechies wavelets Ineffective on piecewise constant phantoms Well suited to real images Regularization can be strong Cyril Mory - PhD Defense - February

21 3D compressed sensing A + ADMM + Wavelets Y D I A Cyril Mory - PhD Defense - February

22 3D compressed sensing A + ADMM + Wavelets Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

23 3D compressed sensing A + ADMM + Wavelets Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

24 3D compressed sensing PICC f = arg min f μ G Rf p α V f + αv f f Prior Image Constrained Compressed ensing tate-of-the-art method AR to minimize data-attachment teepest descent for V minimization Prior = ungated FDK No texture-erasing effect Cyril Mory - PhD Defense - February

25 3D compressed sensing PICC Y D I A Cyril Mory - PhD Defense - February

26 3D compressed sensing PICC Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

27 3D compressed sensing PICC Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

28 3D compressed sensing Animated sequences ADMM 3D V ADMM 3D Wavelets PICC Cyril Mory - PhD Defense - February

29 Contents A bit of math Introduction 3D compressed sensing A bit of math n kernels More on kernels Initialization and regularization 4D compressed sensing Perspectives Conclusion Cyril Mory - PhD Defense - February

30 A bit of math n kernels f = arg min f Rf p 2 2 f = P Ker R f + P Ker R f = f Ker + f Rf = Rf Ker + Rf = Rf f Ker does not drive the search for f Cyril Mory - PhD Defense - February

31 A bit of math More on kernels f = arg min f Rf p 2 2 Rf p 2 2 = 2R Rf p Im R Ker R Gradient descent does not even modify ame for conjugate gradient f Ker Cyril Mory - PhD Defense - February

32 A bit of math More on kernels f = arg min f G Rf p 2 2 f = arg min f GRf Gp 2 2 Ker GR is huge Cyril Mory - PhD Defense - February

33 A bit of math Initialization and regularization 20% gating window 4 times more information in A good f Ker is crucial f Ker than in f Initialization f Ker remains in its initial state throughout iterations Regularization Updates f Ker from f More regularization = better reconstruction of motion Cyril Mory - PhD Defense - February

34 Contents 4D compressed sensing Introduction 3D compressed sensing A bit of math 4D compressed sensing Augmented agrangian + ADMM + V 4D RR Animated sequences Perspectives Conclusion Cyril Mory - PhD Defense - February

35 4D compressed sensing A + ADMM + V f = arg min f θ R θ θ f p θ αri_v f f : 4D sequence of volumes : projection operator, source at angle R θ : linear interpolation operator θ p θ : measured projection, source at angle θ θ xample, with a 4D sequence of 10 phases Projection p θ0 was acquired at 87% of the cardiac cycle θ0 will interpolate between phase 80% and phase 90% θ0 f = 0.3f f 9 Cyril Mory - PhD Defense - February

36 4D compressed sensing A + ADMM + V f = arg min f θ R θ θ f p θ αri_v f M RI_V f = x f m 2 + y f m 2 + z f m 2 + ω m t f m 2 m=1 : motion weighting, high outside RI, low inside ω m Cyril Mory - PhD Defense - February

37 4D compressed sensing A + ADMM + V Cyril Mory - PhD Defense - February

38 4D compressed sensing A + ADMM + V Y D I A Cyril Mory - PhD Defense - February

39 4D compressed sensing A + ADMM + V Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

40 4D compressed sensing A + ADMM + V Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

41 4D compressed sensing 4D RR 4D Recnstructin using patial and mporal Regularization For iter = 1 to max_iter Conjugate gradient on Positivity enforcement θ R θ θ f p θ 2 2 Averaging along time outside RI patial V minimization emporal V minimization Cyril Mory - PhD Defense - February

42 4D compressed sensing 4D RR Y D I A Cyril Mory - PhD Defense - February

43 4D compressed sensing 4D RR Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

44 4D compressed sensing 4D RR Y ong axis hort axis D I A Cyril Mory - PhD Defense - February

45 3D compressed sensing Animated sequences ADMM 4D V 4D RR Cyril Mory - PhD Defense - February

46 Contents Perspectives Introduction 3D compressed sensing A bit of math 4D compressed sensing Perspectives he 4D RR method Clinical use Conclusion Cyril Mory - PhD Defense - February

47 Perspectives he 4D RR method ther regularization methods patial V => Wavelets emporal Non-ocal Means Fully automatic heart segmentation Currently performed manually (semi-automatic tool) Improve performance Already implemented in CUDA Can probably be optimized Cyril Mory - PhD Defense - February

48 Perspectives Clinical use nline processing for injected data Requires a prototype ffline processing for late enhancement Disappointing Compressed sensing in cardiac MRI Replace projection by Fourier transform Free-breathing thorax imaging Replace CG-gating by respiratory gating Cyril Mory - PhD Defense - February

49 Contents Conclusion Introduction 3D compressed sensing A bit of math 4D compressed sensing Perspectives Conclusion Improvement over PICC ake-home messages Cyril Mory - PhD Defense - February

50 Conclusion Improvement over PICC PICC is the current state of the art in cardiac C-arm C Results published only on animals ur study demonstrates PICC on human cardiac C-arm C 4D RR outperforms PICC No motion outside the heart Consistent motion inside the heart harper edges ower noise Cyril Mory - PhD Defense - February

51 Conclusion ake-home messages imited data? ry to reduce the number of unknowns Use compressed sensing iterative methods Regularize as much as possible Initialize carefully est by starting from zero Cyril Mory - PhD Defense - February

52 Conclusion hank you for you attention Cyril Mory - PhD Defense - February