Comparing implementations of magnetic-resonance-guided fluorescence molecular tomography for diagnostic classification of brain tumors

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.

Examining the Feasibility of Quantifying Receptor Availability Using Cross-Modality Paired-Agent Imaging

Purpose

The ability to noninvasively quantify receptor availability (RA) in solid tumors is an aspirational goal of molecular imaging, often challenged by the influence of non-specific accumulation of the contrast agent. Paired-agent imaging (PAI) techniques aim to compensate for this effect by imaging the kinetics of a targeted agent and an untargeted isotype, often simultaneously, and comparing the kinetics of the two agents to estimate RA. This is usually accomplished using two spectrally distinct fluorescent agents, limiting the technique to superficial tissues and/or preclinical applications. Applying the approach in humans using conventional imaging modalities is generally infeasible since most modalities are unable to routinely image multiple agents simultaneously. We examine the ability of PAI to be implemented in a cross-modality paradigm, in which the targeted and untargeted agent kinetics are imaged with different modalities and used to recover receptor availability.

Procedures

Eighteen mice bearing orthotopic brain tumors were administered a solution containing three contrast agents: (1) a fluorescent agent targeted to epidermal growth factor receptor (EGFR), (2) an untargeted fluorescent isotype, and (3) a gadolinium-based contrast agent (GBCA) for MRI imaging. The kinetics of all three agents were imaged for 1 h after administration using an MRI-coupled fluorescence tomography system. Paired-agent receptor availability was computed using (1) the conventional all-optical approach using the targeted and untargeted optical agent images and (2) the cross-modality approach using the targeted optical and untargeted MRI-GBCA images. Receptor availability estimates between the two methods were compared.

Results

Receptor availability values using the cross-modality approach were highly correlated to the conventional, single-modality approach (r = 0.94; p < 0.00001).

Conclusion

These results suggest that cross-modality paired-agent imaging for quantifying receptor availability is feasible. Ultimately, cross-modality paired-agent imaging could facilitate rapid, noninvasive receptor availability quantification in humans using hybrid clinical imaging modalities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11307-021-01629-6.

A review of the application of machine learning in molecular imaging

Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

Monitoring cancer cell surface receptor expression during anti-angiogenesis therapy in vivo

Concurrent administration of cancer therapeutics with tumor vasculature targeting treatment has been shown to improve overall survival in multiple human cancer types, as such combinations aim to destroy different compartments of tumors. Anti-angiogenesis therapeutics designed to inhibit tumor induced vessel sprouting have also been shown to re-model the tumor vasculature through a transient vessel normalization effect, which leads to improved perfusion of oxygen and drug in tumor. However, the effects that this normalized vasculature has on the availability of cancer receptor, such as EGFR, is unknown. Herein, we examined the use of MRI-PAFT to estimate cancer surface receptor availability in response to anti-angiogenesis therapy, using MRI-coupled paired agent fluorescence tomography. Bevacizumab treated tumors showed increase in RA compared to control tumors, but this was not statistically significant.

Noninvasive quantification of target availability during therapy using paired-agent fluorescence tomography

Immuno-oncological treatment strategies that target abnormal receptor profiles of tumors are an increasingly important feature of cancer therapy. Yet, assessing receptor availability (RA) and drug-target engagement, important determinants of therapeutic efficacy, is challenging with current imaging strategies, largely due to the complex nonspecific uptake behavior of imaging agents in tumors. Herein, we evaluate whether a quantitative noninvasive imaging approach designed to compensate for nonspecific uptake, MRI-coupled paired-agent fluorescence tomography (MRI-PAFT), is capable of rapidly assessing the availability of epidermal growth factor receptor (EGFR) in response to one dose of anti-EGFR antibody therapy in orthotopic brain tumor models.

Methods: Mice bearing orthotopic brain tumor xenografts with relatively high EGFR expression (U251) (N=10) or undetectable human EGFR (9L) (N=9) were considered in this study. For each tumor type, mice were either treated with one dose of cetuximab, or remained untreated. All animals were scanned using MRI-PAFT, which commenced immediately after paired-agent administration, and values of RA were recovered using a model-based approach, which uses the entire dynamic sequence of agent uptake, as well as a simplified “snapshot” approach which requires uptake measurements at only two time points. Recovered values of RA were evaluated between groups and techniques. Hematoxylin & eosin (H&E) and immunohistochemical (IHC) staining was performed on tumor specimens from every animal to confirm tumor presence and EGFR status.

Results: In animals bearing EGFR(+) tumors, a significant difference in RA values between treated and untreated animals was observed (RA = 0.24 ± 0.15 and 0.61 ± 0.18, respectively, p=0.027), with an area under the curve - receiver operating characteristic (AUC-ROC) value of 0.92. We did not observe a statistically significant difference in RA values between treated and untreated animals bearing EGFR(-) tumors (RA = 0.18 ± 0.19 and 0.27 ± 0.21, respectively; p = 0.89; AUC-ROC = 0.55), nor did we observe a difference between treated EGFR(+) tumors compared to treated and untreated EGFR(-) tumors. Notably, the snapshot paired-agent strategy quantified drug-receptor engagement within just 30 minutes of agent administration. Examination of the targeted agent alone showed no capacity to distinguish tumors either by treatment or receptor status, even 24h after agent administration.

Conclusions: This study demonstrated that a noninvasive imaging strategy enables rapid quantification of receptor availability in response to therapy, a capability that could be leveraged in preclinical drug development, patient stratification, and treatment monitoring.

Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence

Optical molecular tomography (OMT) is an imaging modality which uses an optical signal, especially near-infrared light, to reconstruct the three-dimensional information of the light source in biological tissue. With the advantages of being low-cost, noninvasive and having high sensitivity, OMT has been applied in preclinical and clinical research. However, due to its serious ill-posedness and ill-condition, the solution of OMT requires heavy data analysis and the reconstruction quality is limited. Recently, the artificial intelligence (commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem. In this paper, we review the progress on OMT algorithms, from conventional methods to AI-based methods, and we also give a prospective towards future developments in this domain.

Estimating paired-agent uptake in altered tumor vasculature using MRI-coupled fluorescence tomography

Angiogenesis inhibiting cancer therapy has become a standard treatment for many cancer types. The ability to examine the effects of these drugs in tumors noninvasively could help assess efficacy early in the treatment course or identify optimal times to introduce other combinatorial treatments. Herein, we examine whether a paired agent MRI-coupled fluorescence tomography approach can be used to monitor the effects of anti-angiogenesis therapy. Using small animal models bearing orthotopic glioma xenografts, we demonstrate noninvasive quantification of paired-agent uptake in response to anti-angiogenesis therapy in vivo. The result provides insights on receptor targeted drug delivery in altered vasculature, a potential important development for treatment monitoring and combinatorial strategies.

Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography

Fluorescence molecular tomography (FMT), as a powerful imaging technique in preclinical research, can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labelled probe noninvasively. However, because of the light scattering effect and the ill-pose of inverse problem, it is challenging to develop an efficient reconstruction method, which can provide accurate location and morphology of the fluorescence distribution. In this research, we proposed a novel adaptive Gaussian weighted Laplace prior (AGWLP) regularization method, which assumed the variance of fluorescence intensity between any two voxels had a non-linear correlation with their Gaussian distance. It utilized an adaptive Gaussian kernel parameter strategy to achieve accurate morphological reconstructions in FMT. To evaluate the performance of the AGWLP method, we conducted numerical simulation and in vivo experiments. The results were compared with fast iterative shrinkage (FIS) thresholding method, split Bregman-resolved TV (SBRTV) regularization method, and Gaussian weighted Laplace prior (GWLP) regularization method. We validated in vivo imaging results against planar fluorescence images of frozen sections. The results demonstrated that the AGWLP method achieved superior performance in both location and shape recovery of fluorescence distribution. This enabled FMT more suitable and practical for in vivo visualization of biomarkers.

Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography

OBJECTIVE

Bioluminescence tomography (BLT) is a non-invasive technique designed to enable three-dimensional (3D) visualization and quantification of viable tumor cells in living organisms. However, despite the excellent sensitivity and specificity of bioluminescence imaging (BLI), BLT is limited by the photon scattering effect and ill-posed inverse problem. If the complete structural information of a light source is considered when solving the inverse problem, reconstruction accuracy will be improved.

METHODS

This article proposed a block sparse Bayesian learning method based on K-nearest neighbor strategy (KNN-BSBL), which incorporated several types of a priori information including sparsity, spatial correlations among neighboring points, and anatomical information to balance over-sparsity and morphology preservation in BLT. Furthermore, we considered the Gaussian weighted distance prior in a light source and proposed a KNN-GBSBL method to further improve the performance of KNN-BSBL.

RESULTS

The results of numerical simulations and in vivo glioma-bearing mouse experiments demonstrated that KNN-BSBL and KNN-GBSBL achieved superior accuracy for tumor spatial positioning and morphology reconstruction.

CONCLUSION

The proposed method KNN-BSBL incorporated several types of a priori information is an efficient and robust reconstruction method for BLT.

Fluorescence Molecular Tomography Based on Group Sparsity Priori for Morphological Reconstruction of Glioma

OBJECTIVE

Fluorescence molecular tomography (FMT) is an important tool for life science, which can noninvasive real-time three-dimensional (3-D) visualization for fluorescence source location. FMT is widely used in tumor research due to its high-sensitive and low cost. However, the reconstruction of FMT is difficult. Although the reconstruction methods of FMT have developed rapidly in recent years, the morphological reconstruction of FMT is still a challenge problem. Thus, the purpose of this study is to realize the morphological reconstruction performance of FMT in glioma research.

METHODS

In this study, group sparsity was used as a new priori information for FMT. Besides sparsity, group sparsity also takes the group structure of the fluorescent sources, which can maintain the morphological information of the sources. Fused LASSO method (FLM) was proved it can efficiently model the group sparsity prior. Thus, we utilize FLM to reconstruct the morphological information of glioma. Furthermore, to reduce the influence of the high scattering of skull, we modified the FLM for improving the accuracy of morphological reconstruction.

RESULTS

Glioma numerical simulation model and in vivo glioma model were established to evaluate the performance of morphological reconstruction of the proposed method. The results demonstrated that the proposed method was efficient to reconstruct the morphological information of glioma.

CONCLUSION

Group sparsity priori can effectively improve the morphological accuracy of FMT reconstruction.

SIGNIFICANCE

Group sparsity can maintain the morphological information of fluorescent sources effectively, which has great application potential in FMT. The group sparsity based methods can realize the morphological reconstruction, which is of great practical significance in tumor research.

Noninvasive imaging of dual-agent uptake in glioma and normal tissue using MRI-coupled fluorescence tomography

As the role of immuno-oncological therapeutics expands, the capacity to noninvasively quantify molecular targets and drug-target engagement is increasingly critical to drug development efforts and treatment monitoring. Previously, we showed that MRI-coupled dual-agent fluorescence tomography (FMT) is capable of estimating the concentration of epidermal growth factor receptor (EGFR) in orthotopic glioma models noninvasively. This approach uses the dynamic information of two fluorescent agents (a targeted agent and untargeted isotype) to estimate tumor receptor concentration in vivo. This approach generally relies on the two tracers having similar kinetics in normal tissues, which may not always be the case. Herein, we describe an additional channel added to the MRI-FMT system which measures the uptake of both agents in the normal muscle, data which can be used to compensate for differing kinetic behavior.

Bioluminescence Tomography Based on Gaussian Weighted Laplace Prior Regularization for In Vivo Morphological Imaging of Glioma

Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weighted Laplace prior (GWLP) regularization method. It considered the variance of the bioluminescence energy between any two voxels inside an organ had a non-linear inverse relationship with their Gaussian distance to solve the over-smoothed tumor morphology in BLT reconstruction. We compared the GWLP with conventional Tikhonov and Laplace regularization methods through various numerical simulations and in vivo orthotopic glioma mouse model experiments. The in vivo magnetic resonance imaging and ex vivo green fluorescent protein images and hematoxylin-eosin stained images of whole head cryoslicing specimens were utilized as gold standards. The results demonstrated that GWLP achieved the highest accuracy in tumor localization and tumor morphology preservation. To the best of our knowledge, this is the first study that achieved such accurate BLT morphological reconstruction of orthotopic glioma without using any segmented tumor structure from any other structural imaging modalities as the prior for reconstruction guidance. This enabled BLT more suitable and practical for in vivo imaging of orthotopic glioma mouse models.

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and ill-conditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. An approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach’s feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method.

Small-Animal Imaging Using Diffuse Fluorescence Tomography

Diffuse fluorescence tomography (DFT) has been developed to image the spatial distribution of fluorescence-tagged tracers in living tissue. This capability facilitates the recovery of any number of functional parameters, including enzymatic activity, receptor density, blood flow, and gene expression. However, deploying DFT effectively is complex and often requires years of know-how, especially for newer mutlimodal systems that combine DFT with conventional imaging systems. In this chapter, we step through the process of using MRI-DFT imaging of a receptor-targeted tracer in small animals.

Tomography of epidermal growth factor receptor binding to fluorescent Affibody in vivo studied with magnetic resonance guided fluorescence recovery in varying orthotopic glioma sizes

The ability to image targeted tracer binding to epidermal growth factor receptor (EGFR) was studied in vivo in orthotopically grown glioma tumors of different sizes. The binding potential was quantified using a dual-tracer approach, which employs a fluorescently labeled peptide targeted to EGFR and a reference tracer with similar pharmacokinetic properties but no specific binding, to estimate the relative bound fraction from kinetic compartment modeling. The recovered values of binding potential did not vary significantly as a function of tumor size (1 to 33 mm3), suggesting that binding potential may be consistent in the U251 tumors regardless of size or stage after implantation. However, the fluorescence yield of the targeted fluorescent tracers in the tumor was affected significantly by tumor size, suggesting that dual-tracer imaging helps account for variations in absolute uptake, which plague single-tracer imaging techniques. Ex vivo analysis showed relatively high spatial heterogeneity in each tumor that cannot be resolved by tomographic techniques. Nonetheless, the dual-tracer tomographic technique is a powerful tool for longitudinal bulk estimation of receptor binding.

In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles

Dual-modality imaging combines the complementary advantages of different modalities, and offers the prospect of improved preclinical research. The combination of fluorescence imaging and magnetic resonance imaging (MRI) provides cross-validated information and direct comparison between these modalities. Here, we report on the application of a novel tumor-targeted, dual-labeled nanoparticle (NP), utilizing iron oxide as the MRI contrast agent and near infrared (NIR) dye Cy5.5 as the fluorescent agent. Results of in vitro experiments verified the specificity of the NP to tumor cells. In vivo tumor targeting and uptake of the NPs in a mouse model were visualized by fluorescence and MR imaging collected at different time points. Quantitative analysis was carried out to evaluate the efficacy of MRI contrast enhancement. Furthermore, tomographic images were also acquired using both imaging modalities and cross-validated information of tumor location and size between these two modalities was revealed. The results demonstrate that the use of dual-labeled NPs can facilitate the dual-modal detection of tumors, information cross-validation, and direct comparison by combing fluorescence molecular tomography (FMT) and MRI.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.

Regularization functional semi-automated incorporation of anatomical prior information in image-guided fluorescence tomography

The use of anatomical priors in fluorescence tomography is known to improve image quality and accuracy significantly. However, the use of prior information is often implemented by incorporating user segmented structural images into the optical reconstruction algorithm, a process requiring significant time and expertise. We propose an automated implementation which encodes the gray-scale prior image directly into the regularization term, eliminating the need for direct prior image segmentation, which is extendable to any spatially defined prior data. The proposed method is supported by in vivo studies.

Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo

The up-regulation of cell surface receptors has become a central focus in personalized cancer treatment; however, because of the complex nature of contrast agent pharmacokinetics in tumor tissue, methods to quantify receptor binding in vivo remain elusive. Here, we present a dual-tracer optical technique for noninvasive estimation of specific receptor binding in cancer. A multispectral MRI-coupled fluorescence molecular tomography system was used to image the uptake kinetics of two fluorescent tracers injected simultaneously, one tracer targeted to the receptor of interest and the other tracer a nontargeted reference. These dynamic tracer data were then fit to a dual-tracer compartmental model to estimate the density of receptors available for binding in the tissue. Applying this approach to mice with deep-seated gliomas that overexpress the EGF receptor produced an estimate of available receptor density of 2.3 ± 0.5 nM (n = 5), consistent with values estimated in comparative invasive imaging and ex vivo studies.

Čerenkov excited fluorescence tomography using external beam radiation

Radiation treatment of cancer induces an optical Čerenkov emission throughout the treated volume, which could be used to excite molecular reporters in vivo, allowing molecular sensing of tissue response during fractionated therapy. In this Letter, the idea that spatial mapping of this signal can be achieved with tomographic recovery of the fluorophore distribution is tested for the first time using 6 MV photons from a linear accelerator in a heterogeneous tissue phantom. Čerenkov light excited fluorophores throughout the tissue phantom, and diffuse tomography was used to recover images. Measurements from 13 locations were used, with spectrometer detection and spectral fitting, to separate the fluorophore emission from the Čerenkov continuum. Fluorescent diffuse tomographic images showed a linear response between the concentration and the reconstructed values. The potential to apply this molecular imaging in treatment with molecular reporters appears promising.

White light-informed optical properties improve ultrasound-guided fluorescence tomography of photoactive protoporphyrin IX

Subsurface fluorescence imaging is desirable for medical applications, including protoporphyrin-IX (PpIX)-based skin tumor diagnosis, surgical guidance, and dosimetry in photodynamic therapy. While tissue optical properties and heterogeneities make true subsurface fluorescence mapping an ill-posed problem, ultrasound-guided fluorescence-tomography (USFT) provides regional fluorescence mapping. Here USFT is implemented with spectroscopic decoupling of fluorescence signals (auto-fluorescence, PpIX, photoproducts), and white light spectroscopy-determined bulk optical properties. Segmented US images provide a priori spatial information for fluorescence reconstruction using region-based, diffuse FT. The method was tested in simulations, tissue homogeneous and inclusion phantoms, and an injected-inclusion animal model. Reconstructed fluorescence yield was linear with PpIX concentration, including the lowest concentration used, 0.025 μg/ml. White light spectroscopy informed optical properties, which improved fluorescence reconstruction accuracy compared to the use of fixed, literature-based optical properties, reduced reconstruction error and reconstructed fluorescence standard deviation by factors of 8.9 and 2.0, respectively. Recovered contrast-to-background error was 25% and 74% for inclusion phantoms without and with a 2-mm skin-like layer, respectively. Preliminary mouse-model imaging demonstrated system feasibility for subsurface fluorescence measurement in vivo. These data suggest that this implementation of USFT is capable of regional PpIX mapping in human skin tumors during photodynamic therapy, to be used in dosimetric evaluations.

Construction of the Jacobian matrix for fluorescence diffuse optical tomography using a perturbation Monte Carlo method

Image formation in fluorescence diffuse optical tomography is critically dependent on construction of the Jacobian matrix. For clinical and preclinical applications, because of the highly heterogeneous characteristics of the medium, Monte Carlo methods are frequently adopted to construct the Jacobian. Conventional adjoint Monte Carlo method typically compute the Jacobian by multiplying the photon density fields radiated from the source at the excitation wavelength and from the detector at the emission wavelength. Nonetheless, this approach assumes that the source and the detector in Green's function are reciprocal, which is invalid in general. This assumption is particularly questionable in small animal imaging, where the mean free path length of photons is typically only one order of magnitude smaller than the representative dimension of the medium. We propose a new method that does not rely on the reciprocity of the source and the detector by tracing photon propagation entirely from the source to the detector. This method relies on the perturbation Monte Carlo theory to account for the differences in optical properties of the medium at the excitation and the emission wavelengths. Compared to the adjoint methods, the proposed method is more valid in reflecting the physical process of photon transport in diffusive media and is more efficient in constructing the Jacobian matrix for densely sampled configurations.

Polymer-capped fiber-optic Raman probe for non-invasive Raman spectroscopy

Advances in fiber optic probe design are moving Raman spectroscopy into the clinic, although there remain important practical problems. While much effort has been devoted to minimizing Raman and fluorescence background from fibers, less attention has been given to the need to generate reference Raman signals that can correct for variations in tissue albedo, which is important in quantifying changes in tissue composition. To address this shortcoming, we have developed a fiber optic probe that incorporates a fluorinated ethylene-propylene copolymer (FEP) cap at the end of each excitation fiber. Transmission of laser light through the transparent cap generates a 732 cm(-1) Raman band whose intensity scales linearly with the laser power delivered to the tissue of interest. In our first design, the FEP cap functions as a waveguide with only a small insertion loss (~5%). Laser transmission through 1 mm of the polymer is sufficient to generate a usable reference Raman signal. We show the application of the probe to quantitative non-invasive Raman spectroscopy of animal tissues using rat leg phantoms as models. Ex-vivo Raman spectroscopy of excised rat tibia supports the use of the probe for spectroscopy of various tissues. These results provide proof of principle that the Raman probe can be used in multiple spectroscopic applications.

Fluorescence molecular tomography: principles and potential for pharmaceutical research

Fluorescence microscopic imaging is widely used in biomedical research to study molecular and cellular processes in cell culture or tissue samples. This is motivated by the high inherent sensitivity of fluorescence techniques, the spatial resolution that compares favorably with cellular dimensions, the stability of the fluorescent labels used and the sophisticated labeling strategies that have been developed for selectively labeling target molecules. More recently, two and three-dimensional optical imaging methods have also been applied to monitor biological processes in intact biological organisms such as animals or even humans. These whole body optical imaging approaches have to cope with the fact that biological tissue is a highly scattering and absorbing medium. As a consequence, light propagation in tissue is well described by a diffusion approximation and accurate reconstruction of spatial information is demanding. While in vivo optical imaging is a highly sensitive method, the signal is strongly surface weighted, i.e., the signal detected from the same light source will become weaker the deeper it is embedded in tissue, and strongly depends on the optical properties of the surrounding tissue. Derivation of quantitative information, therefore, requires tomographic techniques such as fluorescence molecular tomography (FMT), which maps the three-dimensional distribution of a fluorescent probe or protein concentration. The combination of FMT with a structural imaging method such as X-ray computed tomography (CT) or Magnetic Resonance Imaging (MRI) will allow mapping molecular information on a high definition anatomical reference and enable the use of prior information on tissue's optical properties to enhance both resolution and sensitivity. Today many of the fluorescent assays originally developed for studies in cellular systems have been successfully translated for experimental studies in animals. The opportunity of monitoring molecular processes non-invasively in the intact organism is highly attractive from a diagnostic point of view but even more so for the drug developer, who can use the techniques for proof-of-mechanism and proof-of-efficacy studies. This review shall elucidate the current status and potential of fluorescence tomography including recent advances in multimodality imaging approaches for preclinical and clinical drug development.