Total variation-based neutron computed tomography.
Richard C BarnardHassina Z BilheuxTodd ToopsEric NafzigerCharles FinneyDerek A SplitterRick ArchibaldPublished in: The Review of scientific instruments (2018)
We perform the neutron computed tomography reconstruction problem via an inverse problem formulation with a total variation penalty. In the case of highly under-resolved angular measurements, the total variation penalty suppresses high-frequency artifacts which appear in filtered back projections. In order to efficiently compute solutions for this problem, we implement a variation of the split Bregman algorithm; due to the error-forgetting nature of the algorithm, the computational cost of updating can be significantly reduced via very inexact approximate linear solvers. We present the effectiveness of the algorithm in the significantly low-angular sampling case using synthetic test problems as well as data obtained from a high flux neutron source. The algorithm removes artifacts and can even roughly capture small features when an extremely low number of angles are used.
Keyphrases
- computed tomography
- high frequency
- machine learning
- deep learning
- image quality
- transcranial magnetic stimulation
- positron emission tomography
- neural network
- magnetic resonance imaging
- randomized controlled trial
- mental health
- big data
- systematic review
- drug delivery
- working memory
- signaling pathway
- artificial intelligence
- contrast enhanced
- electronic health record
- dual energy