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1.
Figure 6

Figure 6. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

Reconstruction of fluorescence absorption in the three phantom domains without DDZ. Scale and grayscale are as in Fig. .

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.
2.
Figure 5

Figure 5. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

Reconstruction of fluorescence absorption in the three phantom domains by using static, spatially invariant P as the damping matrix. Scale and grayscale are as in Fig. .

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.
3.
Figure 3

Figure 3. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

Reconstruction of fluorescence absorption in the three phantom domains by using the full (correlated) measurement error covariance R. Scale and grayscale are as in Fig. .

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.
4.
Figure 2

Figure 2. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

Measurement error variance for each source-detector pair used in the inversions, shown for each of the three test domains. Measurement error variance of the referenced logarithm of AC amplitude is shown in the left half of each panel, and measurement error variance of referenced phase is shown in the right half of each panel.

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.
5.
Figure 4

Figure 4. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

Reconstruction of fluorescence absorption in the three phantom domains by using the spatially invariant diagonal measurement error covariance R, using median error variances on the diagonal. Scale and grayscale are as in Fig. .

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.
6.
Figure 1

Figure 1. From: Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography.

(a) The initial homogeneous estimate discretized onto the 9 × 17 × 17 grid used for the initial inversion iteration, and shown with the true locations of the three heterogeneities and the 50 detectors (small dots). (b) Case 1: The reconstructed absorption including the middle fluorescing heterogeneity, interpolated onto the 17 × 33 × 33 grid used for prediction, and shown with the locations of the four sources used (open circles). (c) Case 2: The reconstructed absorption including the top and bottom fluorescing heterogeneities shown with the locations of the eight sources used (open circles). (d) Case 3: The reconstructed absorption of a homogeneous phantom shown with the locations of the four sources used (open circles). Although the phantoms and reconstructions were actually 8 cm in the vertical dimension, only the center 4 vertical cm are shown here.

Margaret J. Eppstein, et al. Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9619-9624.

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