UNLABELLED: Present attenuation-correction algorithms in whole-body PET/MRI do not consider variations in lung density, either within or between patients; this may adversely affect accurate quantification. In this work, a technique to incorporate patient-specific lung density information into MRI-based attenuation maps is developed and compared with an approach that assumes uniform lung density. METHODS: Five beagles were scanned with (18)F-FDG PET/CT and MRI. The relationship between MRI and CT signal in the lungs was established, allowing the prediction of attenuation coefficients from MRI. MR images were segmented into air, lung, and soft tissue and converted into attenuation maps, some with constant lung density and some with patient-specific lung densities. The resulting PET images were compared by both global metrics of quantitative fidelity (accuracy, precision, and root mean squared error) and locally with relative error in volumes of interest. RESULTS: A linear relationship was established between MRI and CT signal in the lungs. Constant lung density attenuation maps did not perform as well as patient-specific lung density attenuation maps, regardless of what constant density was chosen. In particular, when attenuation maps with patient-specific lung density were used, precision, accuracy, and root mean square error improved in lung tissue. In volumes of interest placed in the lungs, relative error was significantly reduced from a minimum of 12% to less than 5%. The benefit extended to tissues adjacent to the lungs but became less important as distance from the lungs increased. CONCLUSION: A means of using MRI to infer patient-specific attenuation coefficients in the lungs was developed and applied to augment whole-body MRI-based attenuation maps. This technique has been shown to improve the quantitative fidelity of PET images in the lungs and nearby tissues, compared with an approach that assumes uniform lung density.
UNLABELLED: Present attenuation-correction algorithms in whole-body PET/MRI do not consider variations in lung density, either within or between patients; this may adversely affect accurate quantification. In this work, a technique to incorporate patient-specific lung density information into MRI-based attenuation maps is developed and compared with an approach that assumes uniform lung density. METHODS: Five beagles were scanned with (18)F-FDG PET/CT and MRI. The relationship between MRI and CT signal in the lungs was established, allowing the prediction of attenuation coefficients from MRI. MR images were segmented into air, lung, and soft tissue and converted into attenuation maps, some with constant lung density and some with patient-specific lung densities. The resulting PET images were compared by both global metrics of quantitative fidelity (accuracy, precision, and root mean squared error) and locally with relative error in volumes of interest. RESULTS: A linear relationship was established between MRI and CT signal in the lungs. Constant lung density attenuation maps did not perform as well as patient-specific lung density attenuation maps, regardless of what constant density was chosen. In particular, when attenuation maps with patient-specific lung density were used, precision, accuracy, and root mean square error improved in lung tissue. In volumes of interest placed in the lungs, relative error was significantly reduced from a minimum of 12% to less than 5%. The benefit extended to tissues adjacent to the lungs but became less important as distance from the lungs increased. CONCLUSION: A means of using MRI to infer patient-specific attenuation coefficients in the lungs was developed and applied to augment whole-body MRI-based attenuation maps. This technique has been shown to improve the quantitative fidelity of PET images in the lungs and nearby tissues, compared with an approach that assumes uniform lung density.
Authors: Hsin-Jung Yang; Damini Dey; Jane Sykes; Michael Klein; John Butler; Michael S Kovacs; Olivia Sobczyk; Behzad Sharif; Xiaoming Bi; Avinash Kali; Ivan Cokic; Richard Tang; Roya Yumul; Antonio H Conte; Sotirios A Tsaftaris; Mourad Tighiouart; Debiao Li; Piotr J Slomka; Daniel S Berman; Frank S Prato; Joseph A Fisher; Rohan Dharmakumar Journal: J Nucl Med Date: 2017-03-02 Impact factor: 10.057
Authors: Jinsong Ouyang; Se Young Chun; Yoann Petibon; Ali A Bonab; Nathaniel Alpert; Georges El Fakhri Journal: IEEE Trans Nucl Sci Date: 2013-10-01 Impact factor: 1.679