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Iterative reconstruction for metal artifact reduction in CT

Iterative reconstruction for metal artifact reduction in CT. the problem projection completion polychromatic ML model for CT local models, bowtie,… examples. Katrien Van Slambrouck, Johan Nuyts Nuclear Medicine, KU Leuven. the problem. CT. iron. y. ln(b/y). the problem.

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Iterative reconstruction for metal artifact reduction in CT

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  1. Iterative reconstruction for metal artifact reduction in CT • the problem • projection completion • polychromatic ML model for CT • local models, bowtie,… • examples Katrien Van Slambrouck, Johan Nuyts Nuclear Medicine, KU Leuven

  2. the problem CT iron y ln(b/y)

  3. the problem Dental fillings • Cause of metal artifacts: • Beam hardening • Nonlinear partial volume effects • Noise • Scatter • resolution (crosstalk, afterglow) • (Motion) Double knee prosthesis Double hip prosthesis Mouse bone and titanium screw (microCT)

  4. Artifacts in CT 10 cm water 10 cm water Normalized intensity (%) Normalized intensity (%) Normalized intensity (%) Energy (keV) Energy (keV) Energy (keV) Typical artifact appearance: dark streaks in between metals, dark shades around metals (and cupping) • Beam hardeningPolychromatic spectrum, beam hardens when going through the object Low energy photons are more likely absorbed Iron in water Amalgam in PMMA

  5. Artifacts in CT I0 µ2 µ1 I Typical artifact appearance: dark and white streaks connecting edges • (Non)-linear partial volume effects • Linear: voxels only partly filled with particular substance • Non-linear: averaging over beam width, focal spot, … Iron in water Amalgam in PMMA

  6. Artifacts in CT Typical artifact appearance: dark streaks in between metals, dark shades around metals (and cupping) I0 Iron in water Amalgam in PMMA III. Scatter • Compton scatter: deviation form original trajectory • Scatter grids?

  7. Artifacts in CT Typical artifact appearance: streaks around and in between metals Iron in water Amalgam in PMMA IV. Noise • Quantum nature: ± Poisson distribution

  8. projection completion • Initial FBP reconstruction • Segment the metals and project • Remove metal projections for sinogram • Interpolate (e.g. linear, polynomial, …) • Reconstruct (FBP) and paste metal parts • Kalender W. et aI. "Reduction of CT artifacts caused by metallic impants." Radiology, 1987 • Glover G. and Pelc N. "An algorithm for the reduction of metal clip artifacts in CT reconstructions." Med. Phys., 1981 • Mahnken A. et al, "A new algoritbm for metal artifact reduction in computed tomogrpaby, In vitro and in vivo evaluation after total hip replacement." Investigative Radiology, 2003

  9. projection completion window 600 HU PMMA H2O Fe

  10. projection completion window 600 HU true object FBP projection completion

  11. projection completion 1 2 zeroed metal trace linear interpolation • Muller I., Buzug T.M., "Spurious structures created by interpolation-based Ct metal artifact reduction." Proc. of SPIE, 2009 • Meyer E. et al, "Normalized metal artifact reduction (NMAR) in computed tomography." Med. Phys., 2010

  12. NMAR window 600 HU sinogram interpolated sinogram of segmentation normalized sinogram • Muller I., Buzug T.M., "Spurious structures created by interpolation-based Ct metal artifact reduction." Proc. of SPIE, 2009 • Meyer E. et al, "Normalized metal artifact reduction (NMAR) in computed tomography." Med. Phys., 2010

  13. NMAR 1 2 sinogram, metals erased sinogram of the segmented reconstruction

  14. NMAR 1 2 normalized sinogram, metals erased interpolated sinogram

  15. NMAR unnormalized interpolated sinogram

  16. proj.completion and NMAR window 300 HU projection completion true object FBP NMAR

  17. Maximum Likelihood for CT CT

  18. Maximum Likelihood for CT CT data recon

  19. Maximum Likelihood for CT one wishes to find recon that maximizes p(recon | data) data recon computing p(recon | data) difficult inverse problem computing p(data | recon) “easy” forward problem MAP Bayes: p(data | recon) p(recon) p(recon | data) = ~ ML p(data)

  20. Maximum Likelihood for CT p(recon | data) ~ p(data | recon) data recon projection Poisson mj p(data | recon) j = 1..J i = 1..I ln(p(data | recon)) = L(data | recon) = ~

  21. Maximum Likelihood for CT L(data | recon) iterative maximisation of L:

  22. MLTR convex algorithm [1] patchwork: local update [2,3] [1] Lange, Fessler, “Globally convergent algorithms for maximum a posteriori transmission tomography”, IEEE Trans Image Proc, 1995 [2] JA Fessler et al, "Grouped-coordinate ascent algorithm for penalized likelihood transmission image reconstruction." IEEE Trans Med Imaging 1997. [3] Fessler, Donghwan, "Axial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction.“ Fully 3D 2011

  23. MLTR MEASUREMENT COMPARE UPDATE RECON REPROJECTION

  24. MLTR validation Siemens Sensation 16 Siemens MLTR

  25. models for iterative reconstruction Poisson Likelihood: bi measured data data computed from current reconstruction image Projection model: • monochromatic:

  26. models for iterative reconstruction Poisson Likelihood: intensity bik measured data data computed from current reconstruction image Projection model: energy k • monochromatic: • 1 material polychromatic: MLTR_C “water correction” energy

  27. models for iterative reconstruction Poisson Likelihood: intensity bik Projection model: energy k • Full Polychromatic Model – IMPACT

  28. models for iterative reconstruction • Full Polychromatic Model – IMPACT al mjk = photo-electric + Compton at energy k water mjk = fj ∙ photok + qj ∙ Comptonk attenuation Comptonk= Klein-Nishina (energy) Photok ≈ 1 / energy3 Compton photo-electric

  29. models for iterative reconstruction • Full Polychromatic Model – IMPACT F and q (1/cm) f mjk = fj ∙ photok + qj ∙ Comptonk q mjk = f(mj)∙ photok + q(mj) ∙ Comptonk mmono (1/cm)

  30. models for iterative reconstruction F and q (1/cm) f f q q mmono (1/cm)

  31. patches, local models MLTR convex algorithm [1] patchwork: local update [2,3] [1] Lange, Fessler, “Globally convergent algorithms for maximum a posteriori transmission tomography”, IEEE Trans Image Proc, 1995 [2] JA Fessler et al, "Grouped-coordinate ascent algorithm for penalized likelihood transmission image reconstruction." IEEE Trans Med Imaging 1997. [3] Fessler, Donghwan, "Axial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction.“ Fully 3D 2011

  32. bowtie, BHC intensity bik e- energy k • raw CT data not corrected for beam hardening • send spectrum through filter and bowtie bik = spectrum(k) x bowtie(i)

  33. patches, local models IMPACT is complex and slow, MLTR and MLTR_C are simpler and faster PATCH 3 Find the metals Define patches IMPACT in metals MLTR_C elsewhere PATCH 2 PATCH 4 PATCH 1

  34. clinical CT (Siemens Sensation 16)Body shaped phantom

  35. sequential CT (Siemens Sensation 16)Body shaped phantom Regular PC PC NMAR FBP IMPACT IMPACT PATCH MLTR_C + IMPACT 20 iter x 116 subsets

  36. sequential CT (Siemens Sensation 16)Body shaped phantom Ti Al V CoCr.. water aluminum PMMA water Black = FBP Blue = PC-NMAR Red = IMPACT PATCH

  37. helical CT sequential 2 x 1mm helical 16 x 0.75mm

  38. helical CT MIP metal patches, uniform init. FBP no patches, NMAR init. IMPACT NMAR metal patches, NMAR init. 5 iter x 116 subsets

  39. helical CT MIP metal patches, uniform init. FBP no patches, NMAR init. IMPACT NMAR metal patches, NMAR init.

  40. helical CT 10 it FBP NMAR IMPACT 5 it

  41. helical CT We give patches same x-y sampling but increased z-sampling: impact, regular z z-sampling x 3

  42. to do • after 5..10 x 100 iterations with patches still incomplete convergence • persistent artifacts near flat edges of metal implants • we currently think it is not • scatter • non-linear partial volume effect • crosstalk, afterglow • detector dead space

  43. better physical model better reconstruction Katrien Van Slambrouck Bruno De Man Karl Stierstorfer, David Faul, Siemens thanks

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