期刊
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
卷 25, 期 7, 页码 1772-1782出版社
OPTICAL SOC AMER
DOI: 10.1364/JOSAA.25.001772
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- NCI NIH HHS [R01 CA120540-01A2, CA120540, R01 CA120540] Funding Source: Medline
- NIBIB NIH HHS [R01 EB00225, K01 EB003913, K01 EB003913-04, R01 EB000225, R01 EB000225-06A1] Funding Source: Medline
We present a method for obtaining accurate image reconstruction from highly sparse data in diffraction tomography (DT). A practical need exists for reconstruction from few-view and limited-angle data, as this can greatly reduce required scan times in DT. Our method does this by minimizing the total variation (TV) of the estimated image, subject to the constraint that the Fourier transform of the estimated image matches the measured Fourier data samples. Using simulation studies, we show that the TV-minimization algorithm allows accurate reconstruction in a variety of few-view and limited-angle situations in DT Accurate image reconstruction is obtained from far fewer data samples than are required by common algorithms such as the filtered-backpropagation algorithm. Overall our results indicate that the TV-minimization algorithm can be successfully applied to DT image reconstruction under a variety of scan configurations and data conditions of practical significance. (C) 2008 Optical Society of America.
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