The recent developments of digitized and segmented mouse atlases (Dhenain et al 2001
, Dogdas et al 2007
, Segars et al 2004
) and software for generating forward solutions in heterogeneous tissue (Boas et al 2002
) have enabled high-resolution, realistic simulations of photon propagation in tissue. These resources are now freely available to the public and can be used to provide insight on disease-specific imaging questions. Others have used atlas-based simulations to test and optimize measurement geometries and reconstruction algorithms (Joshi et al 2008
, Bourayou et al 2008
, Dogdas et al 2007
). In this work, we used a digital mouse model of Alzheimer’s disease to establish optimal fluorophore parameters and imaging capabilities for detecting amyloid-β
plaques in transgenic mouse models.
The best NIR amyloid probe to date is AO1987, an oxazine-derived amyloid-binding fluorophore that exhibits absorption and emission peaks at 650 and 670 nm (Hintersteiner et al 2005
); in initial studies, AO1987 pharmacokinetics were used to distinguish wild-type from aged AD transgenic mice. Based on our simulations, a NIR amyloid-binding fluorophore emitting at 800 nm would improve current NIR amyloid imaging capabilities beyond AO1987. For the parameters tested, fluorophores with 800 nm emission resulted in greater signal (27 to 370-fold greater CCD counts) and were more sensitive to changes in amyloid burden than fluorophores emitting at 630 nm, which had much lower CCD counts and SNR (). Measurements in reflection geometry (similar to planar fluorescence imaging) resulted in much higher signal than transmission (at both 630 and 800 nm), because amyloid plaques are predominantly located in the cortex (a few millimeters below the dorsal surface of the head); the SNR for 630 emission in reflection was > 20 dB for almost all contrast ratios and amyloid burden tested.
It is important to note that CCD counts, SNR and CNR are affected by a number of imaging hardware considerations, including CCD noise and exposure time, laser power and excitation and emission filters. The CCD counts and SNR values in this work were calculated assuming standard measurement parameters (1 s exposure time; 50 mW laser power; CCD noise characteristics as described above) without tissue autofluorescence or excitation contamination of the emission signal. For some parameters (exposure time, laser power, quantum yield and molar extinction coefficient), the signal scales linearly and the SNR is trivially related via the scaling coefficient. For example, detector counts (yf
) acquired for a fluorophore with non-unity quantum yield, f
, are related to the original counts (y
) as yf
, which is simplified further for
. Thus, these simulations provide guidelines for detection feasibility but should be scaled appropriately when considering different measurement and fluorophore parameters.
To quantitatively assess the sensitivity of fluorophores to changes in amyloid burden, we estimated the contrast-to-noise ratio (CNR) for a 1% change in amyloid burden (from 4 to 5%). Amyloid burden in aged transgenic mouse models is between 1 and 10%, and changes of 1% are not uncommon for preclinical therapeutic trials (Sadowski et al 2006
). We anticipate that, given inter-animal variability and system noise characteristics, CNR = 2 is a generous criterion for sensitivity to 1% change in burden. To meet this criterion, future fluorophore design should aim for an amyloid-binding probe that emits at 800 nm and has a contrast ratio ≥ 5 for planar imaging and ≥ 20 for tomography (see ).
Meeting these contrast ratio targets will require an advance in current probe characteristics. Contrast ratios have not traditionally been reported, but it is likely that the plaque-to-background contrast for most amyloid imaging fluorophores is <
10. Methoxy-X04, a blue-green fluorophore with excellent amyloid specificity (Klunk et al 2002
), has plaque-to-background contrast of ~9 in an aged transgenic mouse, as measured by two-photon microscopy of post-mortem tissue. The plaque-to-background contrast of AO1987, although not measured by Hintersteiner et al
, was estimated in our laboratory at ~5, using confocal microscopy of post-mortem tissue taken from aged AD transgenic mice that received 1 mg kg−1
AO1987. For biomarkers besides amyloid, target to background ratios (TBR) have been reported in the literature from 1.5 to 10 (Frisoli et al 1993
, Figueiredo et al 2005
, Houston et al 2005
, Leevy et al 2008
). Of these, ‘smart’ or activatable probes, which are enzymatically or otherwise turned on at the target (Weissleder et al 1999
), generally have the best TBR. New smart amyloid probes could utilize the unique environment around amyloid plaques, such as oxidative stress (McLellan et al 2003
) or amyloid binding itself (Nesterov et al 2005
), in order to turn on fluorophore emission. Other smart approaches using lifetime and spectral contrast may offer additional improvements in TBR (Raymond et al 2008
). Future probe design will most likely require smart capabilities to improve the plaque-to-background contrast.
In addition to testing fluorophore parameters, we also compared the imaging capabilities of transmission and full-angle tomographic reconstructions for amyloid imaging. Transmission-based reconstructions showed an increased yield in a regional pattern similar to plaque deposition, with an average cortical fluorescence yield that was linearly correlated with average cortical yield in the input model. However, the reconstructed yield was dramatically lower than the input model, and suffered from poor sensitivity in the middle of the mouse, especially at the ventral region of the brain. Although this is not a critical region for AD, reduced sensitivity in the middle of the head could be problematic for imaging other disease targets in the mouse head.
Despite the more complete angular coverage, full-angle measurements did not dramatically improve the qualitative appearance of the reconstructions or semi-quantitative measures of accuracy (see and ). Full-angle reconstructions did not improve the recovery of average cortical yield and only slightly increased sensitivity at the center of the mouse. Both transmission and full-angle reconstructions were improved by the use of atlas-based and initial guess(ηo
) priors. Analysis using linear (R2
) and model-independent, entropy-based (ρ2
) correlation confirmed that transmission and full-angle reconstructions were roughly equivalent, at least for this specific problem of distributed fluorophores. These observations are consistent with other simulation studies which demonstrated improved sensitivity to the rodent brain with transmission versus full-angle SD schemes (Xu et al 2003
). Priors improved linear correlation and mutual information primarily for non-brain regions, with only a moderate improvement in brain voxel-to-voxel correlation (). In general, reconstructed yield was poorly correlated with the input model yield, and thus individual voxels in high-resolution reconstructions (0.5 mm) did not contribute additional information beyond volume-averaged metrics.
The relatively poor correlation at the voxel level was in contrast to the strong linear correlation for global or volume-averaged fluorescence yield in the plaque-prone regions (see e.g. ). Based on this observation, we hypothesized that a region-based, reduced-unknown inversion, utilizing atlas-generated tissue regions, would provide accurate yield estimates. We observed very good fluorescence yield estimates with region-based inversion of 144 SD pairs (12 sources and 12 detectors) over three regions (cortex, hippocampus and plaque-free brain) for a variety of amyloid burdens in the cortex and hippocampus. Thus, in the case of a relatively uniform target distribution across tissue regions (as simulated here), the non-uniform sensitivity of the tomography measurement could be circumvented by reducing the unknowns from tissue voxels to a few regions.
These results corroborate a recent study on combined FMT-CT imaging of amyloid plaques (Hyde et al 2009
) using AO1987 (Hintersteiner et al 2005
). Hyde et al
used a region-based approach to generate average yield estimates for major brain regions, which were then input as soft spatial priors in a full-angle reconstruction. They observed strong linear correlation between average reconstructed cortical yield and the post-mortem average cortical fluorescence. However, qualitative analysis of the reconstructions revealed a yield distribution within the cortex (favoring high yield near the dorsal surface) which was inconsistent with the post-mortem tissue fluorescence. Based on our findings, it is likely that the strong average correlation demonstrated in the study by Hyde et al
was primarily a result of the initial region-based inversion and not the sophisticated full-angle, high SD number measurement scheme.
We conclude that although reliable voxel-to-voxel correlation may not be achievable for tomography of distributed, sub-resolution targets in the brain, region-based average yield can be accurately estimated from a small number of measurements, given known anatomical regions. Mouse anatomical regions can be determined from a co-registered CT scans of the same animal (Hyde et al 2009
), or might be obtained from a generic mouse atlas that is transformed to map onto each FMT-imaged mouse. In preliminary experiments, we have been able to map the Digimouse head atlas onto 3D boundaries of a mouse imaged in our FMT system using an affine transformation (data not shown). Others have shown that reconstructions from ‘hard’ anatomical priors, as in the region-based averaging presented here, are more susceptible to errors in the anatomical priors than reconstructions utilizing ‘soft’ priors (Yalavarthy et al 2007
). Thus, future work is needed to quantify the accuracy of using a generic atlas for specifying tissue type.
In conclusion, we used a high-resolution model of a transgenic Alzheimer’s disease mouse to determine optimal NIR fluorophore parameters for imaging amyloid-β plaques. We found that fluorophores with 800 nm emission and higher plaque-to-background contrast would significantly improve amyloid detection beyond current amyloid imaging fluorophores. Simulated tomographic imaging of amyloid in transmission geometry demonstrated that large changes in amyloid could be detected tomographically, albeit with limited voxel-to-voxel correlation and artifact-degraded localization. Qualitative image reconstruction and voxel-to-voxel correlation was improved with the use of anatomical priors, but a full-angle measurement scheme did not perform significantly better than the canonical transmission scheme. Highly accurate region-based yield estimates were achievable by reducing the unknowns from all tissue voxels to a small number of anatomically distinct tissue regions.