Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

by   Rushil Anirudh, et al.

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180^∘ view of the object. This is impractical in a limited angle scenario, when the viewing angle is less than 180^∘, which can occur due to different factors including restrictions on scanning time, limited flexibility of scanner rotation, etc. The sinograms obtained as a result, cause existing techniques to produce highly artifact-laden reconstructions. In this paper, we propose to address this problem through implicit sinogram completion, on a challenging real world dataset containing scans of common checked-in luggage. We propose a system, consisting of 1D and 2D convolutional neural networks, that operates on a limited angle sinogram to directly produce the best estimate of a reconstruction. Next, we use the x-ray transform on this reconstruction to obtain a "completed" sinogram, as if it came from a full 180^∘ measurement. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by our network. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction preserves the 3D structure of objects effectively.



There are no comments yet.


page 6

page 7

page 8

page 12

page 13

page 14

page 15

page 16


Generalizable Limited-Angle CT Reconstruction via Sinogram Extrapolation

Computed tomography (CT) reconstruction from X-ray projections acquired ...

Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Limited angle CT reconstruction is an under-determined linear inverse pr...

An iterative algorithm for computed tomography image reconstruction from limited-angle projections

In application of tomography imaging, limited-angle problem is a quite p...

A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning

Unlike previous works, this open data collection consists of X-ray cone-...

Deep Microlocal Reconstruction for Limited-Angle Tomography

We present a deep learning-based algorithm to jointly solve a reconstruc...

Assessing Robustness to Noise: Low-Cost Head CT Triage

Automated medical image classification with convolutional neural network...

CNN-based regularisation for CT image reconstructions

X-ray computed tomographic infrastructures are medical imaging modalitie...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.