Abstract

We propose a new approach to optical diffusion tomography that incorporates two orthogonal projections. All the data obtained in a double projection measurement are treated simultaneously. The second projection improves image quality due to the fact that the depth and transverse directions are interchanged. An image reconstruction algorithm is derived and illustrated with simulations. It is shown that the spatial resolution of images improves by a factor of 4–5 due to the second projection.

© 2004 Optical Society of America

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References

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2003 (1)

2002 (2)

V. A. Markel and J. C. Schotland, J. Opt. Soc. Am. A 19, 558 (2002).
[CrossRef]

V. A. Markel and J. C. Schotland, Appl. Phys. Lett. 81, 1180 (2002).
[CrossRef]

2001 (1)

2000 (1)

D. J. Hawrysz and E. M. Sevick-Muraca, Neoplasia 2, 388 (2000).
[CrossRef]

1999 (3)

1998 (1)

1997 (1)

Arridge, S. R.

Colak, S. B.

Culver, J. P.

Hawrysz, D. J.

D. J. Hawrysz and E. M. Sevick-Muraca, Neoplasia 2, 388 (2000).
[CrossRef]

Holboke, M. J.

Hooft, G. W.

Lionhart, W. R. B.

Liu, H. L.

Markel, V. A.

Matson, C. L.

McBride, T. O.

Melissen, J. B. M.

Ntziachristos, V.

Ostererg, U. L.

Paasschens, J. C. J.

Papaioannou, D. G.

Paulsen, K. D.

Pogue, B. W.

Schomberg, H.

Schotland, J. C.

Sevick-Muraca, E. M.

D. J. Hawrysz and E. M. Sevick-Muraca, Neoplasia 2, 388 (2000).
[CrossRef]

van Asten, N. A. A. J.

van der Mark, M. B.

Yodh, A. G.

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Figures (2)

Fig. 1
Fig. 1

Illustration of the reference frame used for the derivation of inversion formulas.

Fig. 2
Fig. 2

PSFs in reconstructed images of a point absorber for single (left column) and double (right column) projection imaging and different levels of random Gaussian noise n. Image quality parameter ηx for the x PSFs is shown in each graph.

Equations (8)

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ϕrs,rd=ΓArs,rd;rδαrd3r,
G0x,y,z;x,y,z=-dζ2π expiζz-z×gζ;x-x,y-y.
ϕ˜xs,ys,qs;xd,yd,qd=ms=-md=-ϕxs,ys,zs;×xd,yd,zdexpiqszsms+qdzdmd
ψk,ns,ndq=ϕ˜-L/2,ysns,q/2;L/2,ydnd,q/2,if k=1ϕ˜xsns,L/2,q/2;xdnd,-L/2,q/2,if k=2.
ψk,ns,ndq=-L/2L/2dx-L/2L/2dyκk,ns,ndq;x,yδα˜x,y;q,
κk,ns,ndq;x,y=l=-gQ2-2πlh;-L2-x,ysns-ygQ2+2πlh;x-L2,y-ydnd,k=1gQ2-2πlh;xsns-x,L2-ygQ2+2πlh;x-xdnd,y+L2,k=2
δα˜x,y;q=-δαx,y,zexpiqzdz.
δαx,y,z=-π/hπ/hδα˜x,y;qexp-iqzdq2π,

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