  In computed tomography (CT), automatic exposure control (AEC) is frequently
used to reduce radiation dose exposure to patients. For organ-specific AEC, a
preliminary CT reconstruction is necessary to estimate organ shapes for dose
optimization, where only a few projections are allowed for real-time
reconstruction. In this work, we investigate the performance of automated
transform by manifold approximation (AUTOMAP) in such applications. For proof
of concept, we investigate its performance on the MNIST dataset first, where
the dataset containing all the 10 digits are randomly split into a training set
and a test set. We train the AUTOMAP model for image reconstruction from 2
projections or 4 projections directly. The test results demonstrate that
AUTOMAP is able to reconstruct most digits well with a false rate of 1.6% and
6.8% respectively. In our subsequent experiment, the MNIST dataset is split in
a way that the training set contains 9 digits only while the test set contains
the excluded digit only, for instance "2". In the test results, the digit "2"s
are falsely predicted as "3" or "5" when using 2 projections for
reconstruction, reaching a false rate of 94.4%. For the application in medical
images, AUTOMAP is also trained on patients' CT images. The test images reach
an average root-mean-square error of 290 HU. Although the coarse body outlines
are well reconstructed, some organs are misshaped.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning