In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration

Abstract

Purpose: Bioluminescence imaging is a research tool for studying gene expression levels in small animal models of human disease. Bioluminescence light, however, is strongly scattered in biological tissue and no direct image of the light-emitting reporter probe's location can be obtained. Therefore, the authors have developed a linear image reconstruction method for bioluminescence tomography (BLT) that recovers the three-dimensional spatial bioluminescent source distribution in small animals.

Methods: The proposed reconstruction method uses third-order simplified spherical harmonics (SP3) solutions to the equation of radiative transfer for modeling the bioluminescence light propagation in optically nonuniform tissue. The SP3 equations and boundary conditions are solved with a finite-difference (FD) technique on a regular grid. The curved geometry of the animal surface was taken into account with a blocking-off region method for regular grids. Coregistered computed tomography (CT) and magnetic resonance (MR) images provide information regarding the geometry of the skin surface and internal organs. The inverse source problem is defined as an algebraic system of linear equations for the unknown source distribution and is iteratively solved given multiview and multispectral boundary measurements. The average tissue absorption parameters, which are used for the image reconstruction process, were calculated with an evolution strategy (ES) from in vivo measurements using an implanted pointlike source of known location and spectrum. Moreover, anatomical information regarding the location of the internal organs and other tissue structures within the animal's body are provided by coregistered MR images.

Results: First, the authors recovered the wavelength-dependent absorption coefficients (average error of 14%) with the ES under ideal conditions by using a numerical mouse model. Next, they reconstructed the average absorption coefficient of a small animal by using an artificial implanted light source and the validated ES. Last, they conducted two in vivo animal experiments and recovered the spatial location of the implanted light source and the spatial distribution of a bioluminescent reporter system located in the kidneys. The source reconstruction results were coregistered to CT and MR images. They further found that accurate bioluminescence image reconstructions could be obtained when segmenting a voidlike cyst with low-scattering and absorption parameters, whereas inaccurate image reconstructions were obtained when assuming a uniform optical parameter distribution instead. The image reconstructions were completed within 23 min on a 3 GHz Intel processor.

Conclusions: The authors demonstrated on in vivo examples that the combination of anatomical coregistration, accurate optical tissue properties, multispectral acquisition, and a blocking-off FD-SP3 solution of the radiative transfer model significantly improves the accuracy of the BLT reconstructions.

Figure 1

(a) Structured grid with physical boundary, active region, and blocked-off region. The solution is only sought in the active region. (b) CT image representing the physical domain with boundary (left) and structured grid representing the computational domain. (c) 3D structured grid of small animal (dorsal side to the left, ventral side to the right).

Figure 2

CT images (grayscale) and superimposed bioluminescence image reconstruction (hot iron) of GTLS bead. GTLS bead has been implanted into animal’s rectum: Sagittal (top row), coronal (center row), and transaxial (bottom row) views. The spatial location of the GTLS bead was accurately reconstructed in all three views.

Figure 3

Ventral (a) and dorsal (b) images of measured bioluminescence light distribution on tissue surface. Light originates from luciferase-expressing cells located in both kidneys. Dorsal view of surface-rendered image (c) of mouse skin, skeleton, kidneys, and cyst. Data are provided by CT and MR images.

Figure 4

MR images in grayscale [(a) and (c)] and superimposed bioluminescence image reconstructions in hot iron [(b) and (d)] of luciferase-expressing kidneys. Cyst is to the right. Correct location of both kidneys could be identified in bioluminescence image reconstructions.

Figure 5

Photograph (A) of dissected transgenic mouse onto which the measured bioluminescence image has been superimposed. The abdomen of the animal has been opened surgically and some organs have been removed to provide a clear view of its click-beetle luciferase-expressing kidneys. The bioluminescence image has been taken immediately post-mortem and following a luciferin injection. Different views of bioluminescence image reconstructions (B)–(G) of click-beetle reporter probe in kidneys prior to dissection. Reconstructions B and E show correct location of both kidneys when including the non-uniform optical property maps of segmented cyst. Conversely, reconstructions C and F show false location of left kidney (arrow) when optical property map of cyst is not included in BLT reconstruction and uniform optical property map is assumed. D and G show reconstruction results when optical property map is based on absorption coefficients of the kidney. The location of the right kidney (arrow) could not correctly be identified.