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1.S.A. Nehmeh, Y. E. Erdi, T. Pan, A. Pevsner, K. E. Rosenzweig, E. Yorke, G. S. Mageras, H. Schoder, P. Vernon, O. Squire, H. Mostafavi, S. M. Larson, and J. L. Humm, “Four-dimensional (4D) PET/CT imaging of the thorax,” Med. Phys. 31, 31793186 (2004).
2.S. A. Nehmeh, Y. E. Erdi, C. C. Ling, K. E. Rosenzweig, H. Schoder, S. M. Larson, H. A. Macapinlac, O. D. Squire, and J. L. Humm, “Effect of respiratory gating on quantifying PET images of lung cancer,” J. Nucl. Med. 43, 876881 (2002).
3.I. Polycarpou, C. Tsoumpas, A. P. King, and P. K. Marsden, “Impact of respiratory motion correction and spatial resolution on lesion detection in PET: A simulation study based on real MR dynamic data,” Phys. Med. Biol. 59, 697713 (2014).
4.D. Lardinois, W. Weder, T. F. Hany, E. M. Kamel, S. Korom, B. Seifert, G. K. von Schulthess, and H. C. Steinert, “Staging of non–small-cell lung cancer with integrated positron-emission tomography and computed tomography,” N. Engl. J. Med. 348, 25002507 (2003).
5.J. Vansteenkiste, B. M. Fischer, C. Dooms, and J. Mortensen, “Positron-emission tomography in prognostic and therapeutic assessment of lung cancer: Systematic review,” Lancet Oncol. 5, 531540 (2004).
6.Y. Otani, I. Fukuda, N. Tsukamoto, Y. Kumazaki, H. Sekine, E. Imabayashi, O. Kawaguchi, T. Nose, T. Teshima, and T. Dokiya, “A comparison of the respiratory signals acquired by different respiratory monitoring systems used in respiratory gated radiotherapy,” Med. Phys. 37, 61786186 (2010).
7.M. Dawood, F. Büther, N. Lang, O. Schober, and K. P. Schäfers, “Respiratory gating in positron emission tomography: A quantitative comparison of different gating schemes,” Med. Phys. 34, 30673076 (2007).
8.S. A. Nehmeh et al., “Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer,” Med. Phys. 29, 366371 (2002).
9.R. Fulton, I. Nickel, L. Tellmann, S. Meikle, U. Pietrzyk, and H. Herzog, “Event-by-event motion compensation in 3D PET,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2003), pp. 32863289.
10.K. Thielemans, S. Mustafovic, and L. Schnorr, “Image reconstruction of motion corrected sinograms,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2003), pp. 24012406.
11.L. Livieratos, L. Stegger, P. M. Bloomfield, K. Schafers, D. L. Bailey, and P. G. Camici, “Rigid-body transformation of list-mode projection data for respiratory motion correction in cardiac PET,” Phys. Med. Biol. 50, 33133322 (2005).
12.F. Lamare, T. Cresson, J. Savean, C. C. Le Rest, A. J. Reader, and D. Visvikis, “Respiratory motion correction for PET oncology applications using affine transformation of list mode data,” Phys. Med. Biol. 52, 121140 (2007).
13.G. J. Klein, B. W. Reutter, and R. H. Huesman, “Non-rigid summing of gated PET via optical flow,” IEEE Trans. Nucl. Sci. 44, 15091512 (1997).
14.M. Dawood, F. Buther, X. Jiang, and K. P. Schafers, “Respiratory motion correction in 3-d PET data with advanced optical flow algorithms,” IEEE Trans. Med. Imaging 27, 11641175 (2008).
15.W. Bai and M. Brady, “Regularized B-spline deformable registration for respiratory motion correction in PET images,” Phys. Med. Biol. 54, 27194619 (2009).
16.J. Qi and R. H. Huesman, “List mode reconstruction for pet with motion compensation: A simulation study,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2002), pp. 413416.
17.A. Rahmim, P. Bloomfield, S. Houle, M. Lenox, C. Michel, K. R. Buckley, T. J. Ruth, and V. Sossi, “Motion compensation in histogram-mode and list-mode em reconstructions: Beyond the event-driven approach,” IEEE Trans. Nucl. Sci. 51, 25882596 (2004).
18.F. Qiao, T. Pan, J. W. Clark, Jr., and O. R. Mawlawi, “A motion-incorporated reconstruction method for gated PET studies,” Phys. Med. Biol. 51, 37693783 (2006).
19.T. Li, B. Thorndyke, E. Schreibmann, Y. Yang, and L. Xing, “Model-based image reconstruction for four-dimensional PET,” Med. Phys. 33, 12881298 (2006).
20.F. Lamare, M. J. Ledesma Carbayo, T. Cresson, G. Kontaxakis, A. Santos, C. C. Le Rest, A. J. Reader, and D. Visvikis, “List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations,” Phys. Med. Biol. 52, 51875204 (2007).
21.D. R. Gilland, B. A. Mair, J. E. Bowsher, and R. J. Jaszczak, “Simultaneous reconstruction and motion estimation for gated cardiac ECT,” IEEE Trans. Nucl. Sci. 49, 23442349 (2002).
22.M. W. Jacobson and J. A. Fessier, “Joint estimation of respiratory motion and activity in 4D PET using CT side information,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2006), pp. 275278.
23.F. Qiao, T. Pan, J. W. Clark, Jr., and O. Mawlawi, “Joint model of motion and anatomy for PET image reconstruction,” Med. Phys. 34, 46264639 (2007).
24.J. A. Fessler, “Optimization transfer approach to joint registration/reconstruction for motion-compensated image reconstruction,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2010), pp. 596599.
25.R. Manjeshwar, X. Tao, E. Asma, and K. Thielemans, “Motion compensated image reconstruction of respiratory gated PET/CT,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2006), pp. 674677.
26.K. Thielemans, R. M. Manjeshwar, X. Tao, and E. Asma, “Lesion detectability in motion compensated image reconstruction of respiratory gated PET/CT,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2006), pp. 32783282.
27.E. Asma, R. Manjeshwar, and K. Thielemans, “Theoretical comparison of motion correction techniques for PET image reconstruction,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2006), pp. 17621767.
28.I. Polycarpou, C. Tsoumpas, and P. K. Marsden, “Analysis and comparison of two methods for motion correction in PET imaging,” Med. Phys. 39, 64746483 (2012).
29.C. Tsoumpas, S. Agarwal, P. K. Marsden, and A. P. King, “Evaluation of two PET motion correction techniques for simultaneous real-time PET-MR acquisitions using an MR-derived motion model,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2012), pp. 25192522.
30.H. J. Fayad, F. Lamare, C. C. Le Rest, V. Bettinardi, and D. Visvikis, “Generation of 4-dimensional CT images based on 4-dimensional PET–derived motion fields,” J. Nucl. Med. 54, 631638 (2013).
31.Y. E. Erdi et al., “The CT motion quantitation of lung lesions and its impact on PET-measured SUVs,” J. Nucl. Med. 45, 12871292 (2004).
32.T. Pan, O. Mawlawi, S. A. Nehmeh, Y. E. Erdi, D. Luo, H. H. Liu, R. Castillo, R. Mohan, Z. Liao, and H. A. Macapinlac, “Attenuation correction of PET images with respiration-averaged CT images in PET/CT,” J. Nucl. Med. 46, 14811487 (2005).
33.J. Dutta, G. El Fakhri, X. Shao, A. Lorsakul, N. Guo, and Q. Li, “Feasibility of respiratory motion compensated reconstruction using 4D PET-derived deformation fields,” J. Nucl. Med. 55, 2106 (2014).
34.B. A. Mair, D. R. Gilland, and J. Sun, “Estimation of images and nonrigid deformations in gated emission CT,” IEEE Trans. Med. Imaging 25, 11301144 (2006).
35.J. M. Blackall, S. Ahmad, M. E. Miquel, J. R. McClelland, D. B. Landau, and D. J. Hawkes, “Mri-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning,” Phys. Med. Biol. 51, 41474169 (2006).
36.C. Liu, L. A. Pierce II, A. M. Alessio, and P. E. Kinahan, “The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging,” Phys. Med. Biol. 54, 73457362 (2009).
37.J. R. McClelland, S. Hughes, M. Modat, A. Qureshi, S. Ahmad, D. B. Landau, S. Ourselin, and D. J. Hawkes, “Inter-fraction variations in respiratory motion models,” Phys. Med. Biol. 56, 251272 (2011).
38.C. Tsoumpas, J. E. Mackewn, P. Halsted, A. P. King, C. Buerger, J. J. Totman, T. Schaeffter, and P. K. Marsden, “Simultaneous PET–MR acquisition and MR-derived motion fields for correction of non-rigid motion in PET,” Ann. Nucl. Med. 24, 745750 (2010).
39.B. Guérin, S. Cho, S. Y. Chun, X. Zhu, N. M. Alpert, G. El Fakhri, T. G. Reese, and C. Catana, “Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging,” Med. Phys. 38, 30253038 (2011).
40.S. Y. Chun, T. G. Reese, J. Ouyang, B. Guérin, X. Zhu, C. Catana, N. M. Alpert, and G. El Fakhri, “MRI-based nonrigid motion correction in simultaneous PET/MRI,” J. Nucl. Med. 53, 12841291 (2012).
41.Y. Petibon, J. Ouyang, X. Zhu, C. Huang, T. G. Reese, S. Y. Chun, Q. Li, and G. El Fakhri, “Cardiac motion compensation and resolution modeling in simultaneous PET-MR: A cardiac lesion detection study,” Phys. Med. Biol. 58, 20852102 (2013).
42.C. Huang, J. L. Ackerman, Y. Petibon, M. D. Normandin, T. J. Brady, G. El Fakhri, and J. Ouyang, “Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET–MR: Phantom and non-human primate studies,” NeuroImage 91, 129137 (2014).
43.M. Von Siebenthal, G. Székely, U. Gamper, P. Boesiger, A. Lomax, and P. Cattin, “4D MR imaging of respiratory organ motion and its variability,” Phys. Med. Biol. 52, 15471564 (2007).
44.N. Dikaios, D. Izquierdo-Garcia, M. J. Graves, V. Mani, Z. A. Fayad, and T. D. Fryer, “MRI-based motion correction of thoracic PET: Initial comparison of acquisition protocols and correction strategies suitable for simultaneous PET/MRI systems,” Eur. Radiol. 22, 439446 (2012).
45.C. Kolbitsch, C. Prieto, C. Tsoumpas, and T. Schaeffter, “A 3D MR-acquisition scheme for nonrigid bulk motion correction in simultaneous PET-MR,” Med. Phys. 41, 082304 (14pp.) (2014).
46.T. Feng, M. Ahlman, B. M. W. Tsui, L. Guo, M. Guttman, E. McVeigh, and D. Bluemke, “Hybrid MR-guided and PET-guided motion correction of PET images in simultaneous PET/MR,” J. Nucl. Med. 55, 646 (2014).
47.J. Dutta, G. El Fakhri, C. Huang, Y. Petibon, T. G. Reese, and Q. Li, “Respiratory motion compensation in simultaneous PET/MR using a maximum a posteriori approach,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2013), pp. 800803.
48.C. Huang, J. Dutta, Y. Petibon, T. G. Reese, Q. Li, and G. El Fakhri, “A novel golden-angle radial FLASH motion-estimation sequence for simultaneous thoracic PET-MR,” in Proceedings of the International Society for Magnetic Resonance in Medicine (2012), Vol. 21, p. 2462.
49.C. Würslin, H. Schmidt, P. Martirosian, C. Brendle, A. Boss, N. F. Schwenzer, and L. Stegger, “Respiratory motion correction in oncologic PET using T1-weighted MR imaging on a simultaneous whole-body PET/MR system,” J. Nucl. Med. 54, 464471 (2013).
50.R. Grimm, S. Fürst, I. Dregely, C. Forman, J. M. Hutter, S. I. Ziegler, S. Nekolla, B. Kiefer, M. Schwaiger, and J. Hornegger, “Self-gated radial MRI for respiratory motion compensation on hybrid PET/MR systems,” inMedical Image Computing and Computer-Assisted Intervention–MICCAI, Lecture Notes in Computer Science Vol. 8151 (Springer, 2013), pp. 1724.
51.C. Tsoumpas, I. Polycarpou, K. Thielemans, C. Buerger, A. P. King, T. Schaeffter, and P. K. Marsden, “The effect of regularization in motion compensated PET image reconstruction: A realistic numerical 4D simulation study,” Phys. Med. Biol. 58, 17591773 (2013).
52.K. Lange, “Convergence of EM image reconstruction algorithms with Gibbs smoothing,” IEEE Trans. Med. Imaging 9, 439446 (1990).
53.R. M. Leahy and J. Qi, “Statistical approaches in quantitative positron emission tomography,” Stat. Comput. 10, 147165 (2000).
54.J. Qi and R. M. Leahy, “A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data,” IEEE Trans. Med. Imaging 18, 293305 (1999).
55.H. Kauczor, MRI of the Lung (Springer-Verlag, Berlin, Heidelberg, 2009).
56.R. L. Ehman and J. P. Felmlee, “Adaptive technique for high-definition MR imaging of moving structures,” Radiology 173, 255263 (1989).
57.L. Feng, R. Grimm, K. T. Block, H. Chandarana, S. Kim, J. Xu, L. Axel, D. K. Sodickson, and R. Otazo, “Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI,” Magn. Reson. Med. 72, 707717 (2014).
58.S. Winkelmann, T. Schaeffter, T. Koehler, H. Eggers, and O. Doessel, “An optimal radial profile order based on the golden ratio for time-resolved MRI,” IEEE Trans. Med. Imaging 26, 6876 (2007).
59.M. S. Hansen, T. S. Sørensen, A. E. Arai, and P. Kellman, “Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction,” Magn. Reson. Med. 68, 741750 (2012).
60.M. Usman, D. Atkinson, F. Odille, C. Kolbitsch, G. Vaillant, T. Schaeffter, P. G. Batchelor, and C. Prieto, “Motion corrected compressed sensing for free-breathing dynamic cardiac MRI,” Magn. Reson. Med. 70, 504516 (2013).
61.H. Jung, J. C. Ye, and E. Y. Kim, “Improved k-t BLAST and k-t SENSE using FOCUSS,” Phys. Med. Biol. 52, 32013226 (2007).
62.H. Jung, K. Sung, K. S. Nayak, E. Y. Kim, and J. C. Ye, “k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI,” Magn. Reson. Med. 61, 103116 (2009).
63.N. M. Wink, C. Panknin, and T. D. Solberg, “Phase versus amplitude sorting of 4D-CT data,” J. Appl. Clin. Med. Phys. 7, 7785 (2006).
64.D. J. Kroon and C. H. Slump, “MRI modalitiy transformation in demons registration,” inProceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2009), pp. 963966.
65.J. Ouyang, S. Y. Chun, Y. Petibon, A. A. Bonab, N. Alpert, and G. El Fakhri, “Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR,” IEEE Trans. Nucl. Sci. 60, 33733382 (2013).
66.C. Buerger, A. Aitken, C. Tsoumpas, A. P. King, V. Schulz, P. Marsden, and T. Schaeffter, “Investigation of 4D PET attenuation correction using ultra-short echo time MR,” in IEEE Nuclear Science Symposium Conference Record (IEEE, 2011), pp. 35583561.
67.M. M. Osman, C. Cohade, Y. Nakamoto, L. T. Marshall, J. P. Leal, and R. L. Wahl, “Clinically significant inaccurate localization of lesions with PET/CT: Frequency in 300 patients,” J. Nucl. Med. 44, 240243 (2003).
68.A. M. Alessio, S. Kohlmyer, K. Branch, G. Chen, J. Caldwell, and P. Kinahan, “Cine CT for attenuation correction in cardiac PET/CT,” J. Nucl. Med. 48, 794801 (2007).
69.W. P. Segars, D. S. Lalush, and B. M. W. Tsui, “Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms,” IEEE Trans. Nucl. Sci. 48, 8997 (2001).
70.Q. Li, J. Ouyang, Y. Petibon, X. Zhu, B. Bai, R. M. Leahy, and G. El Fakhri, “Maximum a posteriori reconstruction of biograph mMR scanner using point spread function,” J. Nucl. Med. 53, 2339 (2012).
71.J. Qi and R. H. Huesman, “Theoretical study of penalized-likelihood image reconstruction for region of interest quantification,” IEEE Trans. Med. Imaging 25, 640648 (2006).
72.W. D. Foley, J. B. Kneeland, J. D. Cates, G. M. Kellman, T. L. Lawson, W. D. Middleton, and R. E. Hendrick, “Contrast optimization for the detection of focal hepatic lesions by MR imaging at 1.5 T,” AJR, Am. J. Roentgenol. 149, 11551160 (1987).
73.C. Lartizien, P. E. Kinahan, and C. Comtat, “A lesion detection observer study comparing 2-dimensional versus fully 3-dimensional whole-body PET imaging protocols,” J. Nucl. Med. 45, 714723 (2004).
74.V. Mani, K. C. Briley-Saebo, V. V. Itskovich, D. D. Samber, and Z. A. Fayad, “Gradient echo acquisition for superparamagnetic particles with positive contrast (GRASP): Sequence characterization in membrane and glass superparamagnetic iron oxide phantoms at 1.5T and 3T,” Magn. Reson. Med. 55, 126135 (2006).
75.J. Qi and R. M. Leahy, “Resolution and noise properties of MAP reconstruction for fully 3-D PET,” IEEE Trans. Med. Imaging 19, 493506 (2000).
76.S. Y. Chun and J. A. Fessler, “Spatial resolution properties of motion-compensated tomographic image reconstruction methods,” IEEE Trans. Med. Imaging 31, 14131425 (2012).
77.S. Y. Chun and J. A. Fessler, “Noise properties of motion-compensated tomographic image reconstruction methods,” IEEE Trans. Med. Imaging 32, 141152 (2013).
78.J. Dutta, S. Ahn, and Q. Li, “Quantitative statistical methods for image quality assessment,” Theranostics 3, 741756 (2013).
79.C. Liu, A. Alessio, L. Pierce, K. Thielemans, S. Wollenweber, A. Ganin, and P. Kinahan, “Quiescent period respiratory gating for PET/CT,” Med. Phys. 37, 50375043 (2010).
80.D. H. Paulus, H. Braun, B. Aklan, and H. H. Quick, “Simultaneous PET/MR imaging: MR-based attenuation correction of local radiofrequency surface coils,” Med. Phys. 39, 43064315 (2012).
81.A. P. King, C. Buerger, C. Tsoumpas, P. K. Marsden, and T. Schaeffter, “Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator,” Med. Image Anal. 16, 252264 (2012).

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Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies.

The authors have developed an MCIR framework based on maximum or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing - FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner.

The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%–107% for 14 mm diameter lung lesions and 39%–120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors’ results show that the MC image yields 19%–190% increase in the CNR of high-intensity features of interest affected by respiratory motion relative to UG and a 6%–51% increase relative to OG.

Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.


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