Few-view tomography using roughness-penalized nonparametric regression and periodic spline interpolation

The ability to reconstruct high-quality tomographic images from a smaller number of projections than is usually used could reduce imaging time for many nuclear-medicine studies. This would particularly benefit studies such as cardiac SPECT where patient motion during long acquisitions can lead to mo...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 1999-08, Vol.46 (4), p.1121-1128
Main Authors: La Riviere, P.J., Pan, X.
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Curve fitting
Image enhancement
Image quality
Image reconstruction
Interpolation
Mathematical models
Medical imaging
Nuclear power generation
Projection
Regression
Regression analysis
Signal filtering and prediction
Smoothing methods
Spline
Splines
Statistics
Testing
Tomography
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Summary:The ability to reconstruct high-quality tomographic images from a smaller number of projections than is usually used could reduce imaging time for many nuclear-medicine studies. This would particularly benefit studies such as cardiac SPECT where patient motion during long acquisitions can lead to motion artifacts in the reconstructed images. To this end, the authors have investigated sinogram pre-processing techniques designed to enable filtered backprojection (FBP) to produce high-quality reconstructions from a small number of views. Each projection is first smoothed by performing roughness-penalized nonparametric regression using a generalized linear model that explicitly accounts for the Poisson statistics of the data. The resulting fit curves are natural cubic splines. After smoothing, additional angular views are generated using periodic spline interpolation, and images are reconstructed using FBP. The algorithm was tested on data from SPECT studies of a cardiac phantom placed at various radial offsets to enable examination of the algorithm's dependence on the radial extent of the object being imaged.
ISSN:0018-9499
1558-1578
DOI:10.1109/23.790845