Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation

Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection

Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.

Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling

OBJECTIVE

Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design.

METHODS

Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution.

RESULT

Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography.

CONCLUSION

We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice.

SIGNIFICANCE

Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

Noninvasive Visualization of Amyloid-Beta Deposits in Alzheimer's Amyloidosis Mice via Fluorescence Molecular Tomography Using Contrast Agent

Alzheimer's disease is pathologically featured by the accumulation of amyloid-beta (Aβ) plaque and neurofibrillary tangles. Compared to small animal positron emission tomography, optical imaging features nonionizing radiation, low cost, and logistic convenience. Optical detection of Aβ deposits is typically implemented by 2D macroscopic imaging and various microscopic techniques assisted with Aβ-targeted contrast agents. Here, we introduce fluorescence molecular tomography (FMT), a macroscopic 3D fluorescence imaging technique, convenient for in vivo longitudinal monitoring of the animal brain without the involvement of cranial window opening operation. This chapter aims to provide the protocols for FMT in vivo imaging of Aβ deposits in the brain of rodent model of Alzheimer's disease. The materials, stepwise method, notes, limitations of FMT, and emerging opportunities for FMT techniques are presented.

Monitoring mouse brain perfusion with hybrid magnetic resonance optoacoustic tomography

Progress in brain research critically depends on the development of next-generation multi-modal imaging tools capable of capturing transient functional events and multiplexed contrasts noninvasively and concurrently, thus enabling a holistic view of dynamic events in vivo. Here we report on a hybrid magnetic resonance and optoacoustic tomography (MROT) system for murine brain imaging, which incorporates an MR-compatible spherical matrix array transducer and fiber-based light illumination into a 9.4 T small animal scanner. An optimized radiofrequency coil has further been devised for whole-brain interrogation. System's utility is showcased by acquiring complementary angiographic and soft tissue anatomical contrast along with simultaneous dual-modality visualization of contrast agent dynamics in vivo.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Non-invasive visualization of amyloid-beta deposits in Alzheimer amyloidosis mice using magnetic resonance imaging and fluorescence molecular tomography

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer's disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load have been predominantly based on macroscopic fluorescence reflectance imaging (FRI) and confocal or two-photon microscopy using Aβ-specific imaging agents. However, the former method lacks depth resolution, whereas the latter is restricted by the limited field of view preventing a full coverage of the large brain region. Here, we utilized a fluorescence molecular tomography (FMT)-magnetic resonance imaging (MRI) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition, whereas a customized software platform enabled the integration of MRI-derived anatomical information as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to those from conventional planar FRI recorded under similar physiological conditions, yielding comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region-of-interest comprising the brain. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI strategy. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in arcAβ mouse models, which can also facilitate monitoring the efficacy of putative treatments targeting Aβ.

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.

A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space

The traditional finite element method-based fluorescence molecular tomography (FMT)/ X-ray computed tomography (XCT) imaging reconstruction suffers from complicated mesh generation and dual-modality image data fusion, which limits the application of in vivo imaging. To solve this problem, a novel standardized imaging space reconstruction (SISR) method for the quantitative determination of fluorescent probe distributions inside small animals was developed. In conjunction with a standardized dual-modality image data fusion technology, and novel reconstruction strategy based on Laplace regularization and L1-fused Lasso method, the in vivo distribution can be calculated rapidly and accurately, which enables standardized and algorithm-driven data process. We demonstrated the method's feasibility through numerical simulations and quantitatively monitored in vivo programmed death ligand 1 (PD-L1) expression in mouse tumor xenografts, and the results demonstrate that our proposed SISR can increase data throughput and reproducibility, which helps to realize the dynamically and accurately in vivo imaging.

Correntropy-induced metric with Laplacian kernel for robust fluorescence molecular tomography

Fluorescence molecular tomography (FMT), which is used to visualize the three-dimensional distribution of fluorescence probe in small animals via the reconstruction method, has become a promising imaging technique in preclinical research. However, the classical reconstruction criterion is formulated based on the squared l ₂-norm distance metric, leaving it prone to being influenced by the presence of outliers. In this study, we propose a robust distance based on the correntropy-induced metric with a Laplacian kernel (CIML). The proposed metric satisfies the conditions of distance metric function and contains first and higher order moments of samples. Moreover, we demonstrate important properties of the proposed metric such as nonnegativity, nonconvexity, and boundedness, and analyze its robustness from the perspective of M-estimation. The proposed metric includes and extends the traditional metrics such as l ₀-norm and l ₁-norm metrics by setting an appropriate parameter. We show that, in reconstruction, the metric is a sparsity-promoting penalty. To reduce the negative effects of noise and outliers, a novel robust reconstruction framework is presented with the proposed correntropy-based metric. The proposed CIML model retains the advantages of the traditional model and promotes robustness. However, the nonconvexity of the proposed metric renders the CIML model difficult to optimize. Furthermore, an effective iterative algorithm for the CIML model is designed, and we present a theoretical analysis of its ability to converge. Numerical simulation and in vivo mouse experiments were conducted to evaluate the CIML method's performance. The experimental results show that the proposed method achieved more accurate fluorescent target reconstruction than the state-of-the-art methods in most cases, which illustrates the feasibility and robustness of the CIML method.

Noninvasive multimodal fluorescence and magnetic resonance imaging of whole-organ intervertebral discs

Low back pain (LBP) is a commonly experienced symptom posing a tremendous healthcare burden to individuals and society at large. The LBP pathology is strongly linked to degeneration of the intervertebral disc (IVD), calling for development of early-stage diagnostic tools for visualizing biomolecular changes in IVD. Multimodal measurements of fluorescence molecular tomography (FMT) and magnetic resonance imaging (MRI) were performed on IVD whole organ culture model using an in-house built FMT system and a high-field MRI scanner. The resulted multimodal images were systematically validated through epifluorescence imaging of the IVD sections at a microscopic level. Multiple image contrasts were exploited, including fluorescence distribution, anatomical map associated with T1-weighted MRI contrast, and water content related with T2 relaxation time. The developed multimodality imaging approach may thus serve as a new assessment tool for early diagnosis of IVD degeneration and longitudinal monitoring of IVD organ culture status using fluorescence markers.

Multimodal imaging combining time-domain near-infrared optical tomography and continuous-wave fluorescence molecular tomography

Fluorescence molecular tomography (FMT) emerges as a powerful non-invasive imaging tool with the ability to resolve fluorescence signals from sources located deep in living tissues. Yet, the accuracy of FMT reconstruction depends on the deviation of the assumed optical properties from the actual values. In this work, we improved the accuracy of the initial optical properties required for FMT using a new-generation time-domain (TD) near-infrared optical tomography (NIROT) system, which effectively decouples scattering and absorption coefficients. We proposed a multimodal paradigm combining TD-NIROT and continuous-wave (CW) FMT. Both numerical simulation and experiments were performed on a heterogeneous phantom containing a fluorescent inclusion. The results demonstrate significant improvement in the FMT reconstruction by taking the NIROT-derived optical properties as prior information. The multimodal method is attractive for preclinical studies and tumor diagnostics since both functional and molecular information can be obtained.