OBJECTIVE: Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. METHODS: This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. RESULTS: The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8% in the body and up to 31.0% for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15% for lesions located in the body and 30.7% for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30% of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. CONCLUSION: Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.
OBJECTIVE: Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. METHODS: This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. RESULTS: The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8% in the body and up to 31.0% for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15% for lesions located in the body and 30.7% for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30% of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. CONCLUSION: Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.
Authors: Axel Wetter; Christine Lipponer; Felix Nensa; Philipp Heusch; Herbert Rübben; Jens-Christian Altenbernd; Thomas Schlosser; Andreas Bockisch; Thorsten Pöppel; Thomas Lauenstein; James Nagarajah Journal: Eur J Nucl Med Mol Imaging Date: 2013-10-02 Impact factor: 9.236
Authors: Pengjiang Qian; Yangyang Chen; Jung-Wen Kuo; Yu-Dong Zhang; Yizhang Jiang; Kaifa Zhao; Rose Al Helo; Harry Friel; Atallah Baydoun; Feifei Zhou; Jin Uk Heo; Norbert Avril; Karin Herrmann; Rodney Ellis; Bryan Traughber; Robert S Jones; Shitong Wang; Kuan-Hao Su; Raymond F Muzic Journal: IEEE Trans Med Imaging Date: 2019-08-16 Impact factor: 10.048
Authors: Kevin T Chen; David Izquierdo-Garcia; Clare B Poynton; Daniel B Chonde; Ciprian Catana Journal: Eur J Nucl Med Mol Imaging Date: 2016-08-29 Impact factor: 9.236