Abstract
Purpose
MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets.
Methods
Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR–CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PETCT) and MR-based AC (PETMRI). Lesion-to-background (L/B) ratios were estimated on PETCT and PETMRI.
Results
MR–CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PETMRI and PETCT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PETMRI.
Conclusion
Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PETMRI.
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References
Czernin J, Allen-Auerbach M, Schelbert H. Improvements in cancer staging with PET/CT: literature-based evidence as of September 2006. J Nucl Med. 2007;48(Supplement):78S–88S.
Kinahan P, Hasegawa B, Beyer T. X-ray based attenuation correction for PET/CT scanners. Semin Nucl Med. 2003;XXXIII(3):166–79.
Schlemmer H, Pichler B, Wienhard K, Schmand M, Nahmias C, Townsend D, et al. Simultaneous MR/PET for brain imaging: first patient scans. J Nucl Med. 2007;48(6):45P.
Zaidi H, Montandon M-L, Slosman DO. Attenuation compensation in cerebral 3D PET: effect of the attenuation map on absolute and relative quantitation. Eur J Nucl Med Mol Imaging. 2004;31(1):52–63.
Hofmann M, Steinke F, Judenhofer M, Claussen C, Schoelkopf B, Pichler B. A machine learning approach for determining the PET attenuation map from magnetic resonance images. In: IEEE Nuclear Science Symposium and Medical Imaging Conference; 2006; San Diego, CA: IEEE; 2006.
Antoch G, Vogt FM, Freudenberg LS, Nazaradeh F, Goehde SC, Barkhausen J, et al. Whole-body dual-modality PET/CT and whole-body MRI for tumor staging in oncology. JAMA. 2003;290(24):3199–206.
Beyer T, Antoch G, Blodgett T, Freudenberg L, Akhurst T, Mueller S. Dual-modality PET/CT imaging: the effect of respiratory motion on combined image quality in clinical oncology. Eur J Nucl Med. 2003;30(4):588–96.
Weigert M, Beyer T, Quick HH, Pietrzyk U, Palm C, Müller SP. Generation of a MRI reference data set for the validation of automatic, non-rigid image co-registration algorithms. Nuklearmedizin. 2007;46(2):A116.
Slomka PJ. Software approach to merging molecular with anatomic information. J Nucl Med. 2004;45(90010):36S–45S.
Martínez-Möller A, Souvatzoglou M, Botnar R, Navab N, Ziegler S, Schwaiger M, et al. An approach for MR-based attenuation correction for combined MR/PET: effects of ignoring bones. J Nucl Med. 2007;48(suppl 2):156.
Acknowledgements
We thank our technologists from the Department of Radiology and Nuclear Medicine for their support in acquiring the clinical data.
The selection, validation and implementation of the co-registration algorithms were supported by the German Research Foundation (DFG): BE 3169/2-2.
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Beyer, T., Weigert, M., Quick, H.H. et al. MR-based attenuation correction for torso-PET/MR imaging: pitfalls in mapping MR to CT data. Eur J Nucl Med Mol Imaging 35, 1142–1146 (2008). https://doi.org/10.1007/s00259-008-0734-0
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DOI: https://doi.org/10.1007/s00259-008-0734-0