Quadratic Regularization Design for Iterative Reconstruction in 3D multi-slice Axial CT

Shi, Hugo R.; Fessler, Jeffrey A.

doi:10.1109/nssmic.2006.356467

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Several extensions of this method have been reported. These techniques achieve isotropic resolution in 2D fan-beam CT 37 , 3D multi-slice axial CT 58 , 3D cylindrical PET 38 , and motion-compensated PET 45 .…”

Section: Applicationsmentioning

confidence: 99%

Quantitative Statistical Methods for Image Quality Assessment

Dutta¹,

Ahn²,

Li³

2013

Theranostics

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Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit).

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