Affiliations: Department of Nuclear and Quantum Engineering, Korea
Advanced Institute of Science and Technology, Daejeon, Korea
Note: [] Corresponding author: Seungryong Cho, Department of Nuclear and
Quantum Engineering, Korea Advanced Institute of Science and Technology,
Daejeon, Korea. E-mail: [email protected]
Abstract: Computed tomography (CT) is widely used in medicine for diagnostics
or for image-guided therapies, and is also popular in industrial applications
for nondestructive testing. CT conventionally requires a large number of
projections to produce volumetric images of a scanned object, because the
conventional image reconstruction algorithm is based on
filtered-backprojection. This requirement may result in relatively high
radiation dose to the patients in medical CT unless the radiation dose at each
view angle is reduced, and can cause expensive scanning time and efforts in
industrial CT applications. Sparse- view CT may provide a viable option to
address both issues including high radiation dose and expensive scanning
efforts. However, image reconstruction from sparsely sampled data in CT is in
general very challenging, and much efforts have been made to develop algorithms
for such an image reconstruction problem. Image total-variation minimization
algorithm inspired by compressive sensing theory has recently been developed,
which exploits the sparseness of the image derivative magnitude and can
reconstruct images from sparse-view data to a similar quality of the images
conventionally reconstructed from many views. In successive CT scans, prior CT
image of an object and its projection data may be readily available, and the
current CT image may have not much difference from the prior image. Considering
the sparseness of such a difference image between the successive scans, image
reconstruction of the difference image may be achieved from very sparsely
sampled data. In this work, we showed that one can further reduce the number of
projections, resulting in a super-sparse scan, for a good quality image
reconstruction with the aid of a prior data. Both numerical and experimental
results are provided.
