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Synthesis of patient-specific transmission image for PET attenuation correction for PET/MR imaging of the brain using a convolutional neural network'.
Journal of Nuclear Medicine 2018 August 31
Attenuation correction is a notable challenge associated with simultaneous PET/MRI, particularly in neuroimaging, where sharp boundaries between air and bone volumes exist. This leads to concerns regarding the visual and, more specifically, quantitative accuracy of PET reconstructions for data obtained with PET/MRI. Recently developed techniques can synthesize attenuation maps using only MRI data and are likely adequate for clinical use, however, little work has been conducted to assess their suitability for the dynamic PET studies frequently employed in research to derive physiological information such as binding potential of neuroreceptors in a region. At the same time, existing PET/MRI attenuation correction methods are predicated upon synthesizing CT data, which is not ideal, as CT data are acquired with much lower energy photons than PET data and thus do not optimally reflect the PET attenuation map. Methods: We trained a convolutional neural network to generate patient-specific transmission data from T1-weighted MRI. Using the trained network, we generated transmission data for a testing set comprising 11 subjects scanned with 11 C-WAY-100635 and 10 subjects scanned with 11 C-DASB. We assessed both static and dynamic reconstructions. For dynamic PET data, we report differences in BPND and BPF for 11 C-WAY-100635 and VT for 11 C-DASB. Results: The mean bias for generated transmission data was -1.06±0.81%. Global biases in static PET uptake were -0.49±1.7%, and -1.52±0.73% for 11 C-WAY-100635 and 11 C-DASB, respectively. Conclusion: Our neural network approach is capable of synthesizing patient-specific transmission data with sufficient accuracy for both static and dynamic PET studies.
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