Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. A. Drzezga, M. Souvatzoglou, M. Eiber, A. J. Beer, S. Fürst, A. Martinez-Möller, S. G. Nekolla, S. Ziegler, C. Ganter, E. J. Rummeny, and M. Schwaiger, “First clinical experience with integrated whole-body PET/MR: Comparison to PET/CT in patients with oncologic diagnoses,” J. Nucl. Med. 53, 845855 (2012).
2. C. Buerger, C. Tsoumpas, A. Aitken, A. P. King, P. Schleyer, V. Schulz, P. K. Marsden, and T. Schaeffter, “Investigation of MR-based attenuation correction and motion compensation for hybrid PET/MR,” IEEE Trans. Nucl. Sci. 59, 19671976 (2012).
3. B. Guérin, S. Cho, S. Y. Chun, X. Zhu, N. M. Alpert, G. El Fakhri, T. Reese, and C. Catana, “Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging,” Med. Phys. 38, 30253038 (2011).
4. V. Keereman, P. Mollet, Y. Berker, V. Schulz, and S. Vandenberghe, “Challenges and current methods for attenuation correction in PET/MR,” MAGMA (N.Y.) 26, 8198 (2013).
5. G. Wagenknecht, H.-J. Kaiser, F. M. Mottaghy, and H. Herzog, “MRI for attenuation correction in PET: Methods and challenges,” MAGMA ( N.Y.) 26, 99113 (2013).
6. A. Martinez-Möller, M. Souvatzoglou, G. Delso, R. A. Bundschuh, C. Chef D’Hotel, S. I. Ziegler, N. Navab, M. Schwaiger, and S. G. Nekolla, “Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: Evaluation with PET/CT data,” J. Nucl. Med. 50, 520526 (2009).
7. P. E. Kinahan, D. W. Townsend, T. Beyer, and D. Sashin, “Attenuation correction for a combined 3D PET/CT scanner,” Med. Phys. 25, 20462053 (1998).
8. M. Hofmann, F. Steinke, V. Scheel, G. Charpiat, J. Farquhar, P. Aschoff, M. Brady, B. Scholkopf, and B. J. Pichler, “MRI-based attenuation correction for PET/MRI: A novel approach combining pattern recognition and atlas registration,” J. Nucl. Med. 49, 18751883 (2008).
9. E. Rota Kops and H. Herzog, “Alternative methods for attenuation correction for PET images in MR-PET scanners,” IEEE Nucl. Sci. Symp. Conf. Rec. 6, 43274330 (2007).
10. E. Rota Kops and H. Herzog, “Template based Attenuation Correction for PET in MR-PET Scanners,” IEEE Nucl. Sci. Symp. Conf. Rec. 1, 37863789 (2008).
11. I. B. Malone, R. E. Ansorge, G. B. Williams, P. J. Nestor, T. A. Carpenter, and T. D. Fryer, “Attenuation correction methods suitable for brain imaging with a PET/MRI scanner: A comparison of tissue atlas and template attenuation map approaches,” J. Nucl. Med. 52, 11421149 (2011).
12. E. Schreibmann, J. A. Nye, D. M. Schuster, D. R. Martin, J. Votaw, and T. Fox, “MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration,” Med. Phys. 37, 21012109 (2010).
13. Y. Berker, J. Franke, M. Palmowski, H. C. W. Donker, Y. Temur, F. M. Mottaghy, C. Kuhl, D. Izquierdo-Garcia, Z. A. Fayad, F. Kiessling, and V. Schulz, “MRI-based attenuation correction for hybrid PET/MRI systems: A 4-class tissue segmentation technique using a combined ultrashort-echo-time/dixon MRI sequence,” J. Nucl. Med. 53, 796804 (2012).
14. C. Catana, A. van der Kouwe, T. Benner, C. J. Michel, M. Hamm, M. Fenchel, B. Fischl, B. Rosen, M. Schmand, and A. G. Sorensen, “Toward Implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype,” J. Nucl. Med. 51, 14311438 (2010).
15. V. Keereman, Y. Fierens, T. Broux, Y. De Deene, M. Lonneux, and S. Vandenberghe, “MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences,” J. Nucl. Med. 51, 812818 (2010).
16. A. Santos Ribeiro, E. Rota Kops, H. Herzog, and P. Almeida, “Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR,” Nucl. Instrum. Methods Phys. Res. A 702, 114116 (2013).
17. G. Wagenknecht, E. R. Kops, L. Tellmann, and H. Herzog, “Knowledge-based segmentation of attenuation-relevant regions of the head in T1-weighted MR images for attenuation correction in MR/PET systems,” IEEE Nucl. Sci. Symp. Conf. Rec. 1, 33383343 (2009).
18. H. Zaidi, M. L. Montandon, and D. O. Slosman, “Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography,” Med. Phys. 30, 937948 (2003).
19. A. Johansson, M. Karlsson, and T. Nyholm, “CT substitute derived from MRI sequences with ultrashort echo time,” Med. Phys. 38, 27082714 (2011).
20. B. K. Navalpakkam, H. Braun, T. Kuwert, and H. H. Quick, “Magnetic resonance-based attenuation correction for PET/MR hybrid imaging using continuous valued attenuation maps,” Invest. Radiol. 48, 323332 (2013).
21. I. L. H. Reichert, M. D. Robson, P. D. Gatehouse, T. He, K. E. Chappell, J. Holmes, S. Girgis, and G. M. Bydder, “Magnetic resonance imaging of cortical bone with ultrashort TE pulse sequences,” Magn. Reson. Imaging 23, 611618 (2005).
22. D. C. Peters, J. A. Derbyshire, and E. R. McVeigh, “Centering the projection reconstruction trajectory: Reducing gradient delay errors,” Magn. Reson. Med. 50, 16 (2003).
23. I. C. Atkinson, A. Lu, and K. R. Thulborn, “Characterization and correction of system delays and eddy currents for MR imaging with ultrashort echo-time and time-varying gradients,” Magn. Reson. Med. 62, 532537 (2009).
24. V. Rasche, D. Holz, and R. Proksa, “MR fluoroscopy using projection reconstruction multi-gradient-echo (prMGE) MRI,” Magn. Reson. Med. 42, 324334 (1999).<324::AID-MRM15>3.0.CO;2-R
25. C. Barmet, N. De Zanche, and K. P. Pruessmann, “Spatiotemporal magnetic field monitoring for MR,” Magn. Reson. Med. 60, 187197 (2008).
26. G. F. Mason, T. Harshbarger, H. P. Hetherington, Y. Zhang, G. M. Pohost, and D. B. Twieg, “A Method to measure arbitrary k-space trajectories for rapid MR imaging,” Magn. Reson. Med. 38, 492496 (1997).
27. B. J. Wilm, C. Barmet, M. Pavan, and K. P. Pruessmann, “Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations,” Magn. Reson. Med. 65, 16901701 (2011).
28. D. Giese, M. Haeberlin, C. Barmet, K. P. Pruessmann, T. Schaeffter, and S. Kozerke, “Analysis and correction of background velocity offsets in phase-contrast flow measurements using magnetic field monitoring,” Magn. Reson. Med. 67, 12941302 (2012).
29. L. Greengard and J. Y. Lee, “Accelerating the nonuniform fast Fourier transform,” SIAM Rev. 46, 443454 (2004).
30. N. R. Zwart, K. O. Johnson, and J. G. Pipe, “Efficient sample density estimation by combining gridding and an optimized kernel,” Magn. Reson. Med. 67, 701710 (2012).
31. E. N. Yeh, M. Stuber, C. A. McKenzie, R. M. Botnar, T. Leiner, M. A. Ohliger, A. K. Grant, J. D. Willig-Onwuachi, and D. K. Sodickson, “Inherently self-calibrating non-Cartesian parallel imaging,” Magn. Reson. Med. 54, 18 (2005).
32. K. P. Pruessmann, M. Weiger, P. Börnert, and P. Boesiger, “Advances in sensitivity encoding with arbitrary k-space trajectories,” Magn. Reson. Med. 46, 638651 (2001).
33. C. Barmet, N. De Zanche, B. J. Wilm, and K. P. Pruessmann, “A transmit/receive system for magnetic field monitoring of in vivo MRI,” Magn. Reson. Med. 62, 269276 (2009).
34. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26, 297302 (1945).
35. C. Studholme, D. L. G. Hill, and D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recogn. 32, 7186 (1999).
36. B. Aubert-Broche, A. C. Evans, and L. Collins, “A new improved version of the realistic digital brain phantom,” NeuroImage 32, 138145 (2006).
37. C. Tsoumpas, C. Buerger, A. P. King, P. Mollet, V. Keereman, S. Vandenberghe, V. Schulz, P. Schleyer, T. Schaeffter, and P. K. Marsden, “Fast generation of 4D PET-MR data from real dynamic MR acquisitions,” Phys. Med. Biol. 56, 65976613 (2011).
38. C. Tsoumpas, P. Aguiar, K. S. Nikita, D. Ros, and K. Thielemans, “Evaluation of the single scatter simulation algorithm implemented in the STIR library,” IEEE Nucl. Sci. Symp. Conf. Rec. 6, 33613365 (2004).
39. K. Thielemans, C. Tsoumpas, S. Mustafovic, T. Beisel, P. Aguiar, N. Dikaios, and M. W. Jacobson, “STIR: Software for tomographic image reconstruction release 2,” Phys. Med. Biol. 57, 867883 (2012).
40. A. Johansson, M. Karlsson, J. Yu, T. Asklund, and T. Nyholm, “Voxel-wise uncertainty in CT substitute derived from MRI,” Med. Phys. 39, 32833290 (2012).
41. S. J. Doran, L. Charles-Edwards, S. A. Reinsberg, and M. O. Leach, “A complete distortion correction for MR images: I. Gradient warp correction,” Phys. Med. Biol. 50, 13431361 (2005).
42. C. P. Karger, A. Höss, R. Bendl, V. Canda, and L. Schad, “Accuracy of device-specific 2D and 3D image distortion correction algorithms for magnetic resonance imaging of the head provided by a manufacturer,” Phys. Med. Biol. 51, N253N261 (2006).
43.See supplementary material at for Figs. S1–S3. [Supplementary Material]

Data & Media loading...


Article metrics loading...



Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera.

The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction.

Images of the numerical phantom exhibited blurring and edge artifacts on the bone–tissue and air–tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV for simulated lesions in the range of 7.17%–12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and −0.21% to +1.81% (lesions).

Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd