2 research outputs found
A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning
In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch
between CT and PET due to patient body motion results in AC artifacts. In
addition, artifact caused by metal, beam-hardening and count-starving in CT
itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction
of activity and attenuation (MLAA) was proposed to solve those issues by
simultaneously reconstructing tracer activity (-MLAA) and attenuation
map (-MLAA) based on the PET raw data only. However, -MLAA suffers
from high noise and -MLAA suffers from large bias as compared to the
reconstruction using the CT-based attenuation map (-CT). Recently, a
convolutional neural network (CNN) was applied to predict the CT attenuation
map (-CNN) from -MLAA and -MLAA, in which an image-domain
loss (IM-loss) function between the -CNN and the ground truth -CT was
used. However, IM-loss does not directly measure the AC errors according to the
PET attenuation physics, where the line-integral projection of the attenuation
map () along the path of the two annihilation events, instead of the
itself, is used for AC. Therefore, a network trained with the IM-loss may yield
suboptimal performance in the generation. Here, we propose a novel
line-integral projection loss (LIP-loss) function that incorporates the PET
attenuation physics for generation. Eighty training and twenty testing
datasets of whole-body 18F-FDG PET and paired ground truth -CT were used.
Quantitative evaluations showed that the model trained with the additional
LIP-loss was able to significantly outperform the model trained solely based on
the IM-loss function.Comment: Accepted at MICCAI 201