Abstract
BackgroundDue to the harmful radiation dose effects for patients, minimizing the x-ray exposure risk has been an area of active research in medical computed tomography (CT) imaging. In CT, reducing the number of projection views is an effective means for reducing dose. The use of fewer projection views can also lead to a reduced imaging time and minimizing potential motion artifacts. However, conventional CT image reconstruction methods will appears prominent streak artifacts for few-view data. Inspired by the compressive sampling (CS) theory, iterative CT reconstruction algorithms have been developed and generated impressive results.MethodIn this paper, we propose a few-view adaptive prior image total variation (API-TV) algorithm for CT image reconstruction. The prior image reconstructed by a conventional analytic algorithm such as filtered backprojection (FBP) algorithm from densely angular-sampled projections.ResultsTo validate and evaluate the performance of the proposed algorithm, we carried out quantitative evaluation studies in computer simulation and physical experiment.ConclusionThe results show that the API-TV algorithm can yield images with quality comparable to that obtained with existing algorithms.
Highlights
Due to the harmful radiation dose effects for patients, minimizing the x-ray exposure risk has been an area of active research in medical computed tomography (CT) imaging
The results show that the adaptive prior image total variation (API-total variation (TV)) algorithm can yield images with quality comparable to that obtained with existing algorithms
We have evaluated and demonstrated the performance of our algorithm in a number of few-view reconstruction problems
Summary
Due to the harmful radiation dose effects for patients, minimizing the x-ray exposure risk has been an area of active research in medical computed tomography (CT) imaging. The use of fewer projection views can lead to a reduced imaging time and minimizing potential motion artifacts. Conventional CT image reconstruction methods will appears prominent streak artifacts for few-view data. Inspired by the compressive sampling (CS) theory, iterative CT reconstruction algorithms have been developed and generated impressive results. Few-view CT image reconstruction is of great importance in clinical imaging for its potential to reduce the x-ray radiation dose to the human subject and the scan time. In few-view CT, less number of projection data than is required to satisfy the Nyquist sampling theorem is used. Conventional filtered backprojection (FBP) based image reconstruction algorithms will occurs severe streak artifacts. It is important to develop new algorithms in order to obtain more accurate images from few-view projections
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