Improved quantification in end-to-end deep learning FastPET reconstruction using multi-view histo-images of attenuation correction factors.

End-to-end deep learning PET reconstruction significantly surpasses traditional iterative methods in speed and shows promise for surpassing them in specific scenarios, such as low-dose imaging. In 2019, a significant advancement was made by using histo-images instead of TOF sinograms as the network's input. Histo-images, by leveraging the image's geometry, are more compatible with convolutional neural networks than TOF sinograms. Typically, the network's input comprises a PET data histo-image patch and an attenuation map patch. However, this method has shown inconsistent bias in the reconstructed images. This work demonstrates that bias present in the prior method can be mitigated with alternative representations of attenuation information. Instead of using the attenuation map directly, we propose using a multi-view histo-image of the attenuation correction factors, inspired by the iterative DIRECT framework and standard statistical modeling practices. We tested using them as separate channels, as well as using them to pre-correct the data, or both. This histo-image encompasses the attenuation properties of each voxel from all directions within the entire lines of response. Our approaches significantly enhances image quantification, reducing the relative difference from MLEM to an average of 2.0 % to 3.0 % across 16 regions of interest, compared to 9.1 % with the previous method. Our statistical hypothesis test showed that the proposed methods significantly reduced absolute bias compared to the previous method, with p-values ranging from 0.002 to 0.007.