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Optical image reconstruction based on the third-order diffusion equations

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Abstract

This paper presents a third-order diffusion equations-based optical image reconstruction algorithm. The algorithm has been implemented using finite element discretizations coupled with a hybrid regularization that combines both Marquardt and Tikhonov schemes. Numerical examples are used to compare between the third- and first-order reconstructions. The results show that the third-order reconstruction codes are more stable than the first-order codes, and are capable of reconstructing void-like regions. From the examples given, it has also been shown that the first-order codes fail to both qualitatively and quantitatively reconstruct the void-like regions.

©1999 Optical Society of America

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Figures (2)

Fig. 1.
Fig. 1. (a) Geometry of the test case under study; (b) reconstructed D image for the first test case; (c) reconstructed μa image for the first test case.
Fig. 2. (a)
Fig. 2. (a) Recovered D image for the second case using the third-order codes; (b) recovered μa image for the second case using the third-order codes; (c) recovered D image for the second case using the first-order codes; (b) recovered μa image for the second case using the firs-order codes;

Equations (14)

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· D ( r ) Φ ( 1 ) ( r ) μ a ( r ) Φ ( 1 ) ( r ) · D ( r ) Φ ( 2 ) ( r ) + 6 · D ( r ) 1 Φ ( 3 ) ( r ) + 6 · D ( r ) 2 Φ ( 4 ) ( r ) = S ( r )
· D ( r ) Φ ( 1 ) ( r ) + 25 7 · D ( r ) Φ ( 2 ) ( r ) 5 μ t ( r ) Φ ( 2 ) ( r ) 60 7 · D ( r ) 1 Φ ( 3 ) ( r ) 60 7 · D ( r ) 2 Φ ( 4 ) ( r ) = 0
· D ( r ) 1 Φ ( 1 ) ( r ) 10 7 · D ( r ) 1 Φ ( 2 ) ( r ) + 90 7 · D ( r ) Φ ( 3 ) ( r ) 10 μ t ( r ) Φ ( 3 ) ( r ) = 0
1 2 · D ( r ) 2 Φ ( 1 ) ( r ) 5 7 · D ( r ) 2 Φ ( 2 ) ( r ) + 45 7 · D ( r ) Φ ( 4 ) ( r ) 5 μ t ( r ) Φ ( 4 ) ( r ) = 0
j = 1 N { Φ j ( 1 ) [ p = 1 P D p ϕ p ϕ j · ϕ i q = 1 Q μ p ϕ p ϕ j ϕ i ] + Φ j ( 2 ) p = 1 P D p ϕ p ϕ j · ϕ i 6 Φ j ( 3 ) p = 1 P D p ϕ p 1 ϕ j · ϕ i 6 Φ j ( 4 ) p = 1 P D p ϕ p 2 ϕ j · ϕ i }
= S ϕ i D n ̂ · Φ ( 1 ) ϕ i ds + D n ̂ · Φ ( 2 ) ϕ i ds 6 D n ̂ · 1 Φ ( 3 ) ϕ i ds 6 D n ̂ · 2 Φ ( 4 ) ϕ i ds
j = 1 N { Φ j ( 1 ) p = 1 P D p ϕ p ϕ j · ϕ i 25 7 Φ j ( 2 ) [ p = 1 P D p ϕ p ϕ j · ϕ i + 5 3 p = 1 Q D p 1 ϕ q ϕ j ϕ i ] + 60 7 Φ j ( 3 ) p = 1 P D p ϕ p 1 ϕ j · ϕ i 60 7 Φ j ( 4 ) p = 1 P D p ϕ p 2 ϕ j · ϕ i }
= D n ̂ · Φ ( 1 ) ϕ i ds + 25 7 D n ̂ · Φ ( 2 ) ϕ i ds 60 7 D n ̂ · 1 Φ ( 3 ) ϕ i ds 60 7 D n ̂ · 2 Φ ( 4 ) ϕ i ds
j = 1 N { Φ j ( 1 ) p = 1 P D p ϕ p 1 ϕ j · ϕ i + 10 7 Φ j ( 2 ) p = 1 P D p ϕ p 1 ϕ j · ϕ i 90 7 Φ j ( 3 ) [ p = 1 P D p ϕ p ϕ j · ϕ i + 10 3 p = 1 P D p 1 ϕ p ϕ j ϕ i ] }
= D n ̂ · 1 Φ ( 1 ) ϕ i ds + 10 7 D n ̂ · 1 Φ ( 2 ) ϕ i ds 90 7 D n ̂ · Φ ( 3 ) ϕ i ds
j = 1 N { Φ j ( 1 ) 1 2 p = 1 P D p ϕ p 2 ϕ j · ϕ i + 5 7 Φ j ( 2 ) p = 1 P D p ϕ p 2 ϕ j · ϕ i 45 7 Φ j ( 4 ) [ p = 1 P D p ϕ p ϕ j · ϕ i + 5 3 p = 1 P D p 1 ϕ p ϕ j ϕ i ] }
= 1 2 D n ̂ · 2 Φ ( 1 ) ϕ i ds + 5 7 D n ̂ · 2 Φ ( 2 ) ϕ i ds 45 7 D n ̂ · Φ ( 4 ) ϕ i ds
( T + λI ) Δχ = T ( Φ o Φ c )
= [ Φ 1 ( 1 ) D 1 Φ 1 ( 1 ) D 2 Φ 1 ( 1 ) D N Φ 1 ( 1 ) μ a , 1 Φ 1 ( 1 ) μ a , 2 Φ 1 ( 1 ) μ a , N Φ 2 ( 1 ) D 1 Φ 2 ( 1 ) D 2 Φ 2 ( 1 ) D N Φ 2 ( 1 ) μ a , 1 Φ 2 ( 1 ) μ a , 2 Φ 2 ( 1 ) μ a , N Φ M ( 1 ) D 1 Φ M ( 1 ) D 2 Φ M ( 1 ) D N Φ M ( 1 ) μ a , 1 Φ M ( 1 ) μ a , 2 Φ M ( 1 ) μ a , N ]
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