Relative role of motion and PSF compensation in whole-body oncologic PET-MR imaging.
OBJECTIVE
Respiratory motion and partial-volume effects are the two main sources of image degradation in whole-body PET imaging. Simultaneous PET-MR allows measurement of respiratory motion using MRI while collecting PET events. Improved PET images may be obtained by modeling respiratory motion and point spread function (PSF) within the PET iterative reconstruction process. In this study, the authors assessed the relative impact of PSF modeling and MR-based respiratory motion correction in phantoms and patient studies using a whole-body PET-MR scanner.


METHODS
An asymmetric exponential PSF model accounting for radially varying and axial detector blurring effects was obtained from point source acquisitions performed in the PET-MR scanner. A dedicated MRI acquisition protocol using single-slice steady state free-precession MR acquisitions interleaved with pencil-beam navigator echoes was developed to track respiratory motion during PET-MR studies. An iterative ordinary Poisson fully 3D OSEM PET reconstruction algorithm modeling all the physical effects of the acquisition (attenuation, scatters, random events, detectors efficiencies, PSF), as well as MR-based nonrigid respiratory deformations of tissues (in both emission and attenuation maps) was developed. Phantom and(18)F-FDG PET-MR patient studies were performed to evaluate the proposed quantitative PET-MR methods.


RESULTS
The phantom experiment results showed that PSF modeling significantly improved contrast recovery while limiting noise propagation in the reconstruction process. In patients with soft-tissue static lesions, PSF modeling improved lesion contrast by 19.7%-109%, enhancing the detectability and assessment of small tumor foci. In a patient study with small moving hepatic lesions, the proposed reconstruction technique improved lesion contrast by 54.4%-98.1% and reduced apparent lesion size by 21.8%-34.2%. Improvements were particularly important for the smallest lesion undergoing large motion at the lung-liver interface. Heterogeneous tumor structures delineation was substantially improved. Enhancements offered by PSF modeling were more important when correcting for motion at the same time.


CONCLUSIONS
The results suggest that the proposed quantitative PET-MR methods can significantly enhance the performance of tumor diagnosis and staging as compared to conventional methods. This approach may enable utilization of the full potential of the scanner in oncologic studies of both the lower abdomen, with moving lesions, as well as other parts of the body unaffected by motion.
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114.
Yoann
Y
Petibon
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
Chuan
C
Huang
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
Jinsong
J
Ouyang
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114; Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115; and Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts 02129.
Timothy G
TG
Reese
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
Quanzheng
Q
Li
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114.
Aleksandra
A
Syrkina
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114.
Yen-Lin
YL
Chen
Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
Georges
G
El Fakhri
D000368:Aged; D000465:Algorithms; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D021621:Imaging, Three-Dimensional; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009068:Movement; D064847:Multimodal Imaging; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D012119:Respiration; D012509:Sarcoma / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D051598:Whole Body Imaging / Q000379:methods*
D019788:Fluorodeoxyglucose F18