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Accelerated gradient based diffuse optical tomographic image reconstruction
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10.1118/1.3531572
/content/aapm/journal/medphys/38/1/10.1118/1.3531572
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/1/10.1118/1.3531572
View: Figures

Figures

Image of FIG. 1.
FIG. 1.

Flowchart for image reconstruction by Newton’s method based MOBIIR algorithm.

Image of FIG. 2.
FIG. 2.

Flowchart for image reconstruction by Broyden-based MOBIIR [Eq. (15)] and adjoint Broyden-based MOBIIR [Eq. (19)] algorithm.

Image of FIG. 3.
FIG. 3.

(a), (c), (e), (g), and (i) are Jacobian through the inhomogeneity at 1st, 3rd, 6th, 12th, and 18th iterations, respectively. (b), (d), (f), (h), and (j) are corresponding Jacobian away from the inhomogeneity using Newton’s method. (k) and (l) are cross-sectional line through the Jacobian.

Image of FIG. 4.
FIG. 4.

[(a),(c)(e),(g),(i)] are Jacobian through the inhomogeneity at 1st, 3rd, 6th, 12th, and 18th iterations, respectively. (b), (d), (f), (h), and (j) are corresponding Jacobian away from the inhomogeneity using Broyden’s method. (k) and (l) are cross-sectional line through the Jacobian.

Image of FIG. 5.
FIG. 5.

(a) Phantom with one inhomogeneity. Reconstructed images using (b) Newton-based MOBIIR, (c) Broyden-based MOBIIR, and (d) adjoint Broyden-based MOBIIR

Image of FIG. 6.
FIG. 6.

(a) Newton, Broyden, and adjoint Broyden methods converge at 18th, 23rd, and 21st iterations, respectively. (b) Reconstructed for Newton, Broyden, and adjoint Broyden methods. (c) Percentage of contrast recovery (PCR). (d) Standard deviation of background . (e) Contrast to noise ratio (CNR). (f) Execution time for various algorithms.

Image of FIG. 7.
FIG. 7.

Reconstruction results with initial guess values of (first column), (second column), (third column), (fourth column), and (fifth column). Reconstruction results by Newton’s method [(a)–(e)], Broyden method [(f)–(j)], and adjoint Broyden method [(k)–(o)].

Image of FIG. 8.
FIG. 8.

The external percentage of noise (N added to the simulated data is varied from 2% to 10% in interval of 2% along the rows. The percentage of noise along column is constant. The first row [(a)–(e)] is reconstructed by Newton’s method, second row [(f)–(j)] by Broyden’s method, and third row [(k)–(o)] by adjoint Broyden method.

Image of FIG. 9.
FIG. 9.

CNR and Noise variation for various algorithms

Image of FIG. 10.
FIG. 10.

(a) Original simulated phantom with two inhomogeneities. Reconstructed results using (b) Newton, (c) Broyden, and (d) adjoint Broyden method.

Image of FIG. 11.
FIG. 11.

(a) MSE of reconstructed images with two inhomogeneities. (b) Newton, Broyden, and adjoint Broyden methods converge at 20th, 40th, and 25th iterations, respectively. (c) Recovered absorption coefficient using Newton’s and proposed algorithms. (d) Execution time at convergence point for Newton’s and proposed methods.

Image of FIG. 12.
FIG. 12.

Model system and data collection geometry for diffuse optical tomography.

Image of FIG. 13.
FIG. 13.

Experimental setup. The diameter of tissue pork sample is and the inclusion (fat) is diameter.

Image of FIG. 14.
FIG. 14.

(a) Pork phantom. Reconstructed images using (b) Newton, (c) Broyden, and (d) adjoint Broyden methods.

Image of FIG. 15.
FIG. 15.

(a) MSE vs. iteration. (b) Cross- sectional line plot through inhomogeneity. (c) Execution time at convergence points.

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/content/aapm/journal/medphys/38/1/10.1118/1.3531572
2010-12-30
2014-04-23
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Accelerated gradient based diffuse optical tomographic image reconstruction
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/1/10.1118/1.3531572
10.1118/1.3531572
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