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Anatomical accuracy of abdominal lesion localization Retrospective automatic rigid image registration between FDG-PET and MRI

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NUKLEARMEDIZIN-NUCLEAR MEDICINE
卷 50, 期 4, 页码 147-154

出版社

GEORG THIEME VERLAG KG
DOI: 10.3413/nukmed-0364

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PET; FDG; MRI; image registration; image fusion

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Software-based image registration can improve the diagnostic value of imaging procedures and is an alternative to hybrid scanners. The aim of this study was to evaluate the anatomical accuracy of automatic rigid image registration of independently acquired datasets of positron emission tomography with F-18-deoxyglucose and abdominal magnetic resonance imaging. Patients, methods: Analyses were performed on 28 abdominal lesions from 20 patients. The PET data were obtained using a stand-alone PET camera in 14 cases and a hybrid PET/CT scanner in 9 cases. The abdominal T1- and T2-weighted MR1 scans were acquired on 1.5 T MRI scanners. The mean time interval between MRI and PET was 7.3 days (0-28 days). Automatic rigid registration was carried out using a self-developed registration tool integrated into commercial available software (In Space for Siemens Syngo). Distances between the centres of gravity of 28 manually delineated neoplastic lesions represented in PET and MRI were measured in X-, Y-, and Z-direction. The intra- (intraclass correlation 0.94) and inter- (intraclass correlation 0.86) observer repeatability were high. Results: The average distance in all MRI sequences was 5.2 +/- 7.6 mm in X-direction, 4.0 +/- 3.7 mm in Y-direction and 6.1 +/- 5.1 mm in Z-direction. There was a significantly higher misalignment in Z-direction (p < 0.05). The misalignment was not significantly different for the registration of T1- and T2- weighted sequences (p = 0.7). Conclusion: The misalignment between FDG-PET and abdominal MRI registered using an automated rigid registration tool was comparable to data reported for software-based fusion between PET and CT. Although this imprecision may not affect diagnostic accuracy, it is not sufficient to allow for pixel-wise integration of MRI and PET information.

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