
3D reconstruction using single X-ray image for Pediatric Orthopedic applications - coolwulf
https://www.reddit.com/r/artificial/comments/e9q6j2/project_i_created_3d_reconstruction_using_single/
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coolwulf
Author here.

The motivation behind this project is the need for 3D representation by
doctors in Orthopedic assist diagnosis. However the limit on the amount of
dose could be used for pediatric patients inhibit the amount of CT scans could
be done. There are other systems on the market such EOS Imaging Flex Dose
which is based on an atlas based registration method to reconstruct two 2D
x-ray projections into 3D spinal representation. However such system is not
only very expensive (selling in USA at $1M for one system) but also produce
certain image artifacts due to the atlas is based on normal people. This work
is to show the feasibility of deep learning could surpass performance of these
other systems and achieve high resolution spinal 3D representations using 2D
images.

Because X-ray is attenuation based. Or we can say image intensity of each
pixel is I = I_0*exp(-ut). I_0 is the incoming X-ray fluence, u is the
attenuation coefficient, t is the path length for the x-ray. So different
contrast and also the contour of the features on the resulting image I bears
information of features behind the front vertebrate. These info can be
extracted using feature extraction of the deep learning network. With enough
data training, these could be learned and this is the basis of this work.

In orthopedic, the spinal angle etc. info is more important than the detailed
bone artifacts such as small fractures. By combining both AP and lateral
x-rays, together with this generated 3D info, it should be sufficient for this
kind of application.

