Experimental Cerenkov luminescence tomography of the mouse model with SPECT imaging validation

Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

Logarithmic total variation regularization via preconditioned conjugate gradient method for sparse reconstruction of bioluminescence tomography

BACKGROUND AND OBJECTIVE

Bioluminescence Tomography (BLT) is a powerful optical molecular imaging technique that enables the noninvasive investigation of dynamic biological phenomena. It aims to reconstruct the three-dimensional spatial distribution of bioluminescent sources from optical measurements collected on the surface of the imaged object. However, BLT reconstruction is a challenging ill-posed problem due to the scattering effect of light and the limitations in detecting surface photons, which makes it difficult for existing methods to achieve satisfactory reconstruction results. In this study, we propose a novel method for sparse reconstruction of BLT based on a preconditioned conjugate gradient with logarithmic total variation regularization (PCG-logTV).

METHOD

This PCG-logTV method incorporates the sparsity of overlapping groups and enhances the sparse structure of these groups using logarithmic functions, which can preserve edge features and achieve more stable reconstruction results in BLT. To accelerate the convergence of the algorithm solution, we use the preconditioned conjugate gradient iteration method on the objective function and obtain the reconstruction results. We demonstrate the performance of our proposed method through numerical simulations and in vivo experiment.

RESULTS AND CONCLUSIONS

The results show that the PCG-logTV method obtains the most accurate reconstruction results, and the minimum position error (LE) is 0.254mm, which is 26%, 31% and 34% of the FISTA (0.961), IVTCG (0.81) and L1-TV (0.739) methods, and the root mean square error (RMSE) and relative intensity error (RIE) are the smallest, indicating that it is closest to the real light source. In addition, compared with the other three methods, the PCG-logTV method also has the highest DICE similarity coefficient, which is 0.928, which means that this method can effectively reconstruct the three-dimensional spatial distribution of bioluminescent light sources, has higher resolution and robustness, and is beneficial to the preclinical and clinical studies of BLT.

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem.Approach.To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well asin vivoexperiments.Main results.The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution.Siginificance.These findings indicate that the SCAD-GML method has the potential to advance the application of FMT inin vivobiological research.

Sparse reconstruction based on dictionary learning and group structure strategy for cone-beam X-ray luminescence computed tomography

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.

FISTA-NET: Deep Algorithm Unrolling for Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging technique that can be used to reconstruct the three-dimensional distribution of radioactive probes in living animals. However, the accuracy of CLT reconstruction is limited by the simplified radiative transfer equation and ill-conditioned inverse problem. To address this issue, we propose a model-based deep learning network that combines the neural network with a model-based approach to enhance the performance of CLT reconstruction. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA), a traditional model-based approach, is expanded into a deep network (known as FISTA-NET). Each layer in the network represents an iteration of the algorithm steps, and connecting these layers can form a deep neural network. In addition, different from the traditional FISTA, the key parameters in FISTA, such as gradient step size and threshold value, can be learned through training data without manual production. To evaluate the performance of FISTA-NET, numerical simulation experiments were conducted, which demonstrate its excellent positioning and shape recovery abilities.Clinical Relevance-This indicates that FISTA-NET strategy can significantly improve the quality of CLT reconstruction, which is further beneficial to the assessment of disease activity and treatment effect based on CLT.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L₂ or L₁ norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L₂ or L₁ norm and achieve good reconstruction performance.

Practical Guidance for Developing Small-Molecule Optical Probes for In Vivo Imaging

The WMIS Education Committee (2019-2022) reached a consensus that white papers on molecular imaging could be beneficial for practitioners of molecular imaging at their early career stages and other scientists who are interested in molecular imaging. With this consensus, the committee plans to publish a series of white papers on topics related to the daily practice of molecular imaging. In this white paper, we aim to provide practical guidance that could be helpful for optical molecular imaging, particularly for small molecule probe development and validation in vitro and in vivo. The focus of this paper is preclinical animal studies with small-molecule optical probes. Near-infrared fluorescence imaging, bioluminescence imaging, chemiluminescence imaging, image-guided surgery, and Cerenkov luminescence imaging are discussed in this white paper.

Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.

A novel weighted auxiliary set matching pursuit method for glioma in Cerenkov luminescence tomography reconstruction

Cerenkov luminescence tomography (CLT) is a promising three-dimensional imaging technology that has been actively investigated in preclinical studies. However, because of the ill-posedness in the inverse problem of CLT reconstruction, the reconstruction performance is still not satisfactory for broad biomedical applications. In this study, a novel weighted auxiliary set matching pursuit (WASMP) method was explored to enhance the accuracy of CLT reconstruction. The numerical simulations and in vivo imaging studies using tumor-bearing mice models were conducted to evaluate the performance of the WASMP method. The results of the above experiments proved that the WASMP method achieved superior reconstruction performance than other approaches in terms of positional accuracy and shape recovery. It further demonstrates that the atom selection strategy proposed in this study has a positive effect on improving the accuracy of atoms. The proposed WASMP improves the accuracy for CLT reconstruction for biomedical applications.

Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging

Glioma grading during surgery can help clinical treatment planning and prognosis, but intraoperative pathological examination of frozen sections is limited by the long processing time and complex procedures. Near-infrared fluorescence imaging provides chances for fast and accurate real-time diagnosis. Recently, deep learning techniques have been actively explored for medical image analysis and disease diagnosis. However, issues of near-infrared fluorescence images, including small-scale, noise, and low-resolution, increase the difficulty of training a satisfying network. Multi-modal imaging can provide complementary information to boost model performance, but simultaneously designing a proper network and utilizing the information of multi-modal data is challenging. In this work, we propose a novel neural architecture search method DLS-DARTS to automatically search for network architectures to handle these issues. DLS-DARTS has two learnable stems for multi-modal low-level feature fusion and uses a modified perturbation-based derivation strategy to improve the performance on the area under the curve and accuracy. White light imaging and fluorescence imaging in the first near-infrared window (650-900 nm) and the second near-infrared window (1,000-1,700 nm) are applied to provide multi-modal information on glioma tissues. In the experiments on 1,115 surgical glioma specimens, DLS-DARTS achieved an area under the curve of 0.843 and an accuracy of 0.634, which outperformed manually designed convolutional neural networks including ResNet, PyramidNet, and EfficientNet, and a state-of-the-art neural architecture search method for multi-modal medical image classification. Our study demonstrates that DLS-DARTS has the potential to help neurosurgeons during surgery, showing high prospects in medical image analysis.

Elastic net-based non-negative iterative three-operator splitting strategy for Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) provides a powerful optical molecular imaging technique for non-invasive detection and visualization of radiopharmaceuticals in living objects. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the location accuracy and shape recovery of CLT reconstruction results are unsatisfactory for clinical application. Here, to improve the reconstruction spatial location accuracy and shape recovery ability, a non-negative iterative three operator splitting (NNITOS) strategy based on elastic net (EN) regularization was proposed. NNITOS formalizes the CLT reconstruction as a non-convex optimization problem and splits it into three operators, the least square, L_1/2-norm regularization, and adaptive grouping manifold learning, then iteratively solved them. After stepwise iterations, the result of NNITOS converged progressively. Meanwhile, to speed up the convergence and ensure the sparsity of the solution, shrinking the region of interest was utilized in this strategy. To verify the effectiveness of the method, numerical simulations and in vivo experiments were performed. The result of these experiments demonstrated that, compared to several methods, NNITOS can achieve superior performance in terms of location accuracy, shape recovery capability, and robustness. We hope this work can accelerate the clinical application of CLT in the future.

PET/NIR-II fluorescence imaging and image-guided surgery of glioblastoma using a folate receptor α-targeted dual-modal nanoprobe

PURPOSE

The surgery of glioblastoma (GBM) requires a maximal resection of the tumor when it is safe and feasible. The infiltrating growth property of the GBM makes it a challenge for neurosurgeons to identify the tumor tissue even with the assistance of the surgical microscope. This highlights the urgent requirement for imaging techniques that can differentiate tumor tissues during surgery in real time. Fluorescence image-guided surgery of GBM has been investigated using several non-specific fluorescent probes that emit light in the visible and the first near-infrared window (NIR-I, 700-900 nm), which limit the detection accuracy because of the non-specific targeting mechanism and spectral characteristics. Targeted NIR-II (1000-1700 nm) fluorescent probes for GBM are thus highly desired. The folate receptor (FR) has been reported to be upregulated in GBM, which renders it to be a promising target for specific tumor imaging.

METHODS

In this study, the folic acid (FA) that can target the FR was conjugated with the clinically approved indocyanine green (ICG) dye and DOTA chelator for radiolabeling with ⁶⁴Cu to achieve targeted positron emission tomography (PET) and fluorescence imaging of GBM.

RESULTS

Surprisingly it was found that the resulted bioconjugate, DOTA-FA-ICG and non-radioactive ^natCu-DOTA-FA-ICG, were both self-assembled into nanoparticles with NIR-II emission signal. The radiolabeled DOTA-FA-ICG, ⁶⁴Cu-DOTA-FA-ICG, was found to specifically accumulate in the orthotopic GBM models using in vivo PET, NIR-II, and NIR-I fluorescence imaging. The best time window of fluorescence imaging was demonstrated to be 24 h after DOTA-FA-ICG injection. NIR-II fluorescence image-guided surgery was successfully conducted in the orthotopic GBM models using DOTA-FA-ICG. All the fluorescent tissue was removed and proved to be GBM by the H&E examination.

CONCLUSION

Overall, our study demonstrates that the probes, ⁶⁴Cu-DOTA-FA-ICG and DOTA-FA-ICG, hold promise for preoperative PET examination and intraoperative NIR-II fluorescence image-guided surgery of GBM, respectively.

Cherenkov Luminescence in Tumor Diagnosis and Treatment: A Review

Malignant tumors rank as a leading cause of death worldwide. Accurate diagnosis and advanced treatment options are crucial to win battle against tumors. In recent years, Cherenkov luminescence (CL) has shown its technical advantages and clinical transformation potential in many important fields, particularly in tumor diagnosis and treatment, such as tumor detection in vivo, surgical navigation, radiotherapy, photodynamic therapy, and the evaluation of therapeutic effect. In this review, we summarize the advances in CL for tumor diagnosis and treatment. We first describe the physical principles of CL and discuss the imaging techniques used in tumor diagnosis, including CL imaging, CL endoscope, and CL tomography. Then we present a broad overview of the current status of surgical resection, radiotherapy, photodynamic therapy, and tumor microenvironment monitoring using CL. Finally, we shed light on the challenges and possible solutions for tumor diagnosis and therapy using CL.

Total Variation Constrained Graph Manifold Learning Strategy for Cerenkov Luminescence Tomography

Harnessing the power and flexibility of radiolabeled molecules, Cerenkov luminescence tomography (CLT) provides a novel technique for non-invasive visualisation and quantification of viable tumour cells in a living organism. However, owing to the photon scattering effect and the ill-posed inverse problem, CLT still suffers from insufficient spatial resolution and shape recovery in various preclinical applications. In this study, we proposed a total variation constrained graph manifold learning (TV-GML) strategy for achieving accurate spatial location, dual-source resolution, and tumour morphology. TV-GML integrates the isotropic total variation term and dynamic graph Laplacian constraint to make a trade-off between edge preservation and piecewise smooth region reconstruction. Meanwhile, the tetrahedral mesh-Cartesian grid pair method based on the k-nearest neighbour, and the adaptive and composite Barzilai-Borwein method, were proposed to ensure global super linear convergence of the solution of TV-GML. The comparison results of both simulation experiments and in vivo experiments further indicated that TV-GML achieved superior reconstruction performance in terms of location accuracy, dual-source resolution, shape recovery capability, robustness, and in vivo practicability. Significance: We believe that this novel method will be beneficial to the application of CLT for quantitative analysis and morphological observation of various preclinical applications and facilitate the development of the theory of solving inverse problem.

Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a novel and highly sensitive imaging technique, which could obtain the three-dimensional distribution of radioactive probes to achieve accurate tumor detection. However, the simplified radiative transfer equation and ill-conditioned inverse problem cause a reconstruction error. In this study, a novel attention mechanism based locally connected (AMLC) network was proposed to reduce barycenter error and improve morphological restorability. The proposed AMLC network consisted of two main parts: a fully connected sub-network for providing a coarse reconstruction result, and a locally connected sub-network based on an attention matrix for refinement. Both numerical simulations and in vivo experiments were conducted to show the superiority of the AMLC network in accuracy and stability over existing methods (MFCNN, KNN-LC network). This method improved CLT reconstruction performance and promoted the application of machine learning in optical imaging research.

Harnessing the Power of Hybrid Light Propagation Model for Three-Dimensional Optical Imaging in Cancer Detection

Optical imaging is an emerging technology capable of qualitatively and quantitatively observing life processes at the cellular or molecular level and plays a significant role in cancer detection. In particular, to overcome the disadvantages of traditional optical imaging that only two-dimensionally and qualitatively detect biomedical information, the corresponding three-dimensional (3D) imaging technology is intensively explored to provide 3D quantitative information, such as localization and distribution and tumor cell volume. To retrieve these information, light propagation models that reflect the interaction between light and biological tissues are an important prerequisite and basis for 3D optical imaging. This review concentrates on the recent advances in hybrid light propagation models, with particular emphasis on their powerful use for 3D optical imaging in cancer detection. Finally, we prospect the wider application of the hybrid light propagation model and future potential of 3D optical imaging in cancer detection.

Prior Compensation Algorithm for Cerenkov Luminescence Tomography From Single-View Measurements

Cerenkov luminescence tomography (CLT) has attracted much attention because of the wide clinically-used probes and three-dimensional (3D) quantification ability. However, due to the serious morbidity of 3D optical imaging, the reconstructed images of CLT are not appreciable, especially when single-view measurements are used. Single-view CLT improves the efficiency of data acquisition. It is much consistent with the actual imaging environment of using commercial imaging system, but bringing the problem that the reconstructed results will be closer to the animal surface on the side where the single-view image is collected. To avoid this problem to the greatest extent possible, we proposed a prior compensation algorithm for CLT reconstruction based on depth calibration strategy. This method takes full account of the fact that the attenuation of light in the tissue will depend heavily on the depth of the light source as well as the distance between the light source and the detection plane. Based on this consideration, a depth calibration matrix was designed to calibrate the attenuation between the surface light flux and the density of the internal light source. The feature of the algorithm was that the depth calibration matrix directly acts on the system matrix of CLT reconstruction, rather than modifying the regularization penalty items. The validity and effectiveness of the proposed algorithm were evaluated with a numerical simulation and a mouse-based experiment, whose results illustrated that it located the radiation sources accurately by using single-view measurements.

A deep unsupervised clustering-based post-processing framework for high-fidelity Cerenkov luminescence tomography

Cerenkov Luminescence Tomography (CLT) is a promising optical molecular imaging technology. It involves the three-dimensional reconstruction of the distribution of radionuclide probes inside a single object to indicate a tumor's localization and distribution. However, reconstruction using CLT suffers from severe ill-posedness, resulting in numerous artifacts within the reconstructed images. These artifacts influence the visual effect and may misguide the medical professional (diagnostician), resulting in a wrong diagnosis. Here, we proposed a deep unsupervised clustering-based post-processing framework to eliminate artifacts and facilitate high-fidelity CLT. First, an initial reconstructed image was obtained by a specific reconstruction method. Second, voxel data were generated based on the initial reconstructed result. Third, these voxels were divided into three groups, and only the group with the highest mean intensity was chosen as the final reconstructed result. A group of numerical simulation and in vivo mouse-based experiments were conducted to assess the presented framework's feasibility and potential. The results indicated that the proposed framework could reduce the number of artifacts effectively. The reconstructed image's shape and distribution were more similar to the actual light source than those obtained without the proposed framework.

Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography

Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.

Cerenkov luminescence imaging is an effective preclinical tool for assessing colorectal cancer PD-L1 levels in vivo

BACKGROUND

Preclinical and clinical studies have demonstrated that immunotherapy has effectively delayed tumor progression, and the clinical outcomes of anti-PD-1/PD-L1 therapy were related to PD-L1 expression level in the tumors. A 131I-labeled anti-PD-L1 monoclonal antibody tracer, 131I-PD-L1-Mab, was developed to study the target ability of noninvasive Cerenkov luminescence imaging in colorectal cancer xenograft mice.

METHOD

Anti-PD-L1 monoclonal antibody labeled with 131I (131I-PD-L1-Mab), and in vitro binding assays were used to evaluate the affinity of 131I-PD-L1-Mab to PD-L1 and their binding level to different colorectal cancer cells, and compared with flow cytometry, Western blot analysis, and immunofluorescence staining. The clinical application value of 131I-PD-L1-Mab was evaluated through biodistribution and Cerenkov luminescence imaging, and different tumor-bearing models expressing PD-L1 were evaluated.

RESULTS

131I-PD-L1-Mab showed high affinity to PD-L1, and the equilibrium dissociation constant was 1.069 × 10-9 M. The competitive inhibition assay further confirmed the specific binding ability of 131I-PD-L1-Mab. In four different tumor-bearing models with different PD-L1 expression, the biodistribution and Cerenkov luminescence imaging showed that the RKO tumors demonstrated the highest uptake of the tracer 131I-PD-L1-Mab, with a maximum uptake of 1.613 ± 0.738% IA/g at 48 h.

CONCLUSIONS

There is a great potential for 131I-PD-L1-Mab noninvasive Cerenkov luminescence imaging to assess the status of tumor PD-L1 expression and select patients for anti-PD-L1 targeted therapy.

A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer

Intraoperative Cerenkov luminescence imaging (CLI) can effectively improve the performance of tumor surgery. Nevertheless, the existing approaches are still unsatisfying to the clinical demands of open surgery. This study develops a novel intraoperative in vivo CLI approach to investigate the potential and value of Cerenkov luminescence (CL) image-guided surgery. A system characterized with high sensitivity (19.61 kBq mL^{-1 18} F-FDG) and desirable spatial resolution (88.34 μm) is developed. CL image-guided surgery is performed on colorectal cancer (CRC) models of mice and swine. Tumor surgery is guided by the static CL images, and the resection quality is evaluated quantitatively and contrasted with other imaging modalities exemplified by bioluminescence imaging (BLI). The in vivo results demonstrated the effectiveness of the proposed intraoperative CLI approach for removing primary and metastatic CRC. Safety of performing in vivo CL image-guided surgery is verified as well through radiation measurements of related staffs. Overall, the developed intraoperative in vivo CLI approach can efficiently improve the cancer treatment.

A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network

Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT reconstruction approach based on a multilayer fully connected neural network (MFCNN). Monte Carlo simulation data was employed to train the MFCNN, and the complex relationship between the surface signals and the true sources was effectively learned by the network. Both simulation and in vivo experiments were performed to validate the performance of MFCNN CLT, and it was further compared with the typical radiative transfer equation (RTE) based method. The experimental data showed the superiority of MFCNN CLT in terms of accuracy and stability. This promising approach for CLT is expected to improve the performance of optical tomography, and to promote the exploration of machine learning in biomedical applications.

Theoretical and experimental characterization of emission and transmission spectra of Cerenkov radiation generated by 177Lu in tissue

Cerenkov radiation (CR) is the emission of UV-vis light generated by the de-excitation of the molecules in the medium, after being polarized by an excited particle traveling faster than the speed of light. When β particles travel through tissue with energies greater than 219 keV, CR occurs. Tissues possess a spectral optical window of 600 to 1100 nm. The CR within this range can be useful for quantitative preclinical studies using optical imaging and for the in-vivo evaluation of Lu177-radiopharmaceuticals (β-particle emitters). The objective of our research was to determine the experimental emission light spectrum of Lu177-CR and evaluate its transmission properties in tissue as well as the feasibility to applying CR imaging in the preclinical studies of Lu177-radiopharmaceuticals. The theoretical and experimental characterizations of the emission and transmission spectra of Lu177-CR in tissue, in the vis-NIR region (350 to 900 nm), were performed using Monte Carlo simulation and UV-vis spectroscopy. Mice Lu177-CR images were acquired using a charge-coupled detector camera and were quantitatively analyzed. The results demonstrated good agreement between the theoretical and the experimental Lu177-CR emission spectra. Preclinical CR imaging demonstrated that the biokinetics of Lu177-radiopharmaceuticals in the main organs of mice can be acquired.

Estimation of the fractions of luminescence of water at higher energy than Cerenkov-light threshold for various types of radiation

Although the luminescence of water at a lower energy than the Cerenkov-light (CL) threshold has been found for various types of radiation, the fractions of the luminescence of water to the total produced light have not been obvious for radiations at a higher energy than the CL threshold because it is difficult to separate these two types of light. Thus, we used a Monte Carlo simulation to estimate the fractions of the luminescence of water for various types of radiation at a higher energy than the CL threshold to confirm the major component of the produced light. After we confirmed that the estimated light production of the luminescence of water could adequately simulate the experimental results, we calculated the produced light photons of this luminescence and the CL from water for protons (170 MeV), carbon ions (330 MeV/n), high-energy x-ray (6 MV) from a linear accelerator (LINAC), high-energy electrons (9 MeV) from LINAC, positrons (F-18, C-11, O-15, and N-13), and high-energy gamma photon radionuclides (Co-60). For protons, the major fraction of the produced light was the luminescence of water in addition to the CL from the prompt gamma photons produced by the nuclear interactions. For carbon ions, the major fraction of the produced light was the luminescence of water and the CL produced by the secondary electrons in addition to the prompt gamma photons produced by the nuclear interactions. For high-energy x-ray and electrons from LINAC, the fractions of luminescence of water were ∼0.1  %   to 0.2%. The fractions of luminescence of water for positrons were 0.2% to 1.5% and that for Co-60 was 0.4%. We conclude that the major fractions of light produced from x-ray and electrons from LINAC, positron radionuclides, and the Co-60 source are CL, with fractions of the luminescence of water from <0.1  %   to 1.5%.

A Monte Carlo study of pinhole collimated Cerenkov luminescence imaging integrated with radionuclide treatment

Cerenkov luminescence imaging (CLI) is an emerging optical imaging technique, which has been widely investigated for biological imaging. In this study, we proposed to integrate the CLI technique with the radionuclide treatment as a "see-and-treat" approach, and evaluated the performance of the pinhole collimator-based CLI technique. The detection of Cerenkov luminescence during radionuclide therapy was simulated using the Monte Carlo technique for breast cancer treatment as an example. Our results show that with the pinhole collimator-based configuration, the location, size and shape of the tumors can be clearly visualized on the Cerenkov luminescence images of the breast phantom. In addition, the CLI of multiple tumors can reflect the relative density of radioactivity among tumors, indicating that the intensity of Cerenkov luminescence is independent of the size and shape of a tumor. The current study has demonstrated the high-quality performance of the pinhole collimator-based CLI in breast tumor imaging for the "see-and-treat" multi-modality treatment.

Endoscopic Cerenkov luminescence imaging and image-guided tumor resection on hepatocellular carcinoma-bearing mouse models

Detecting deep tumors inside living subject is still challenging for Cerenkov luminescence imaging (CLI). In this study, a high-sensitivity endoscopic CLI (ECLI) system was developed with a dual-mode deep cooling approach to improve the imaging sensitivity. System was characterized through a series of ex vivo studies. Furthermore, subcutaneous and orthotropic human hepatocellular carcinoma (HCC) mouse models were established for ECLI guided tumor resection in vivo. The results showed that the ECLI system had spatial resolution (62.5 μm) and imaging sensitivity (6.29 × 10^-2 kBq/μl ¹⁸F-FDG). The in vivo experimental data from the HCC mouse models demonstrated that the system was effective to intraoperatively guide the surgery of deep tumors such as liver cancer. Overall, the developed system exhibits promising potential for the applications of tumor precise resection and novel nanoprobe based optical imaging.

Enhanced Cerenkov luminescence tomography analysis based on Y2O3:Eu3+ rare earth oxide nanoparticles

Cerenkov luminescence imaging offers a new diagnostic alternative to radiation imaging, but lacks intensity and penetration. In this study, a Cerenkov luminescence signal and its image quality were enhanced using rare earth oxide nanoparticles as a basis for Cerenkov luminescence excited fluorescence imaging and Cerenkov luminescence excited fluorescence tomography. The results also provided 3D-imaging and quantitative information. The approach was evaluated using phantom and mice models and 3D reconstruction and quantitative studies were performed in vitro, showing improved optical signal intensity, similarity, accuracy, signal-to-noise ratio, and spatial distribution information. The method offers benefits for both optical imaging research and radiopharmaceutical development.

Innovations in Nuclear Imaging Instrumentation: Cerenkov Imaging

Cerenkov luminescence (CL) is blue glow light produced by charged subatomic particles travelling faster than the phase velocity of light in a dielectric medium such as water or tissue. CL was first discovered in 1934, but for biomedical research it was recognized only in 2009 after advances in optical camera sensors brought the required high sensitivity. Recently, applications of CL from clinical radionuclides have been rapidly expanding to include not only preclinical and clinical biomedical imaging but also an approach to therapy. Cerenkov Luminescence Imaging (CLI) utilizes CL generated from clinically relevant radionuclides alongside optical imaging instrumentation. CLI is advantageous over traditional nuclear imaging methods in terms of infrastructure cost, resolution, and imaging time. Furthermore, CLI is a truly multimodal imaging method where the same agent can be detected by two independent modalities, with optical (CL) imaging and with positron emission tomography (PET) imaging. CL has been combined with small molecules, biomolecules and nanoparticles to improve diagnosis and therapy in cancer research. Here, we cover the fundamental breakthroughs and recent advances in reagents and instrumentation methods for CLI as well as therapeutic application of CL.

Gamma rays excited radioluminescence tomographic imaging

Background

Radionuclide-excited luminescence imaging is an optical radionuclide imaging strategy to reveal the distributions of radioluminescent nanophosphors (RLNPs) inside small animals, which uses radioluminescence emitted from RLNPs when excited by high energy rays such as gamma rays generated during the decay of radiotracers used in clinical nuclear medicine imaging. Currently, there is no report of tomographic imaging based on radioluminescence.

Methods

In this paper, we proposed a gamma rays excited radioluminescence tomography (GRLT) to reveal three-dimensional distributions of RLNPs inside a small animal using radioluminescence through image reconstruction from surface measurements of radioluminescent photons using an inverse algorithm. The diffusion equation was employed to model propagations of radioluminescent photons in biological tissues with highly scattering and low absorption characteristics.

Results

Phantom and artificial source-implanted mouse model experiments were employed to test the feasibility of GRLT, and the results demonstrated that the ability of GRLT to reveal the distribution of RLNPs such as Gd₂O₂S:Tb using the radioluminescent signals when excited by gamma rays produced from ^99mTc.

Conclusions

With the emerging of targeted RLNPs, GRLT can provide new possibilities for in vivo and noninvasive examination of biological processes at cellular levels. Especially, combining with Cerenkov luminescence imaging, GRLT can achieve dual molecular information of RLNPs and nuclides using single optical imaging technology.

A hybrid clustering algorithm for multiple-source resolving in bioluminescence tomography

Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications.

Forward-backward pursuit algorithm for Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a powerful imaging technique that allows dynamically and three-dimensionally resolving the metabolic process of radiopharmaceuticals. It uses optical method to detect radiopharmaceuticals with low cost and high sensitivity. However, because of the strong absorption and scatter of biological tissues, the reconstruction of CLT is always converted to an ill-posed linear system which is hard to solve. An accurate and fast reconstruct algorithm becomes a current issue. The traditional reconstruction algorithm based on l2 norm regularization is too smooth and with low accuracy. Some novel sparse reconstruction algorithm has satisfying accuracy and convergence rate, but lose its accuracy for multi-source situation. In this work, a novel CLT method based on forward-backward greedy algorithm is proposed to solve the ill-posed problem. Digital simulations and in vivo experiment were conducted to test the algorithm. The reconstruct results were compared with traditional orthogonal matching pursuit (OMP) algorithm and Tikhonov algorithm. Both the Digital simulations and in vivo experiment show that this approach can reconstruct the distribution of radiopharmaceuticals effectively and accurately.

Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography

Cerenkov luminescence tomography (CLT) provides a novel technique for 3-D noninvasive detection of radiopharmaceuticals in living subjects. However, because of the severe scattering of Cerenkov light, the reconstruction accuracy and stability of CLT is still unsatisfied. In this paper, a modified weight multispectral CLT (wmCLT) reconstruction strategy was developed which split the Cerenkov radiation spectrum into several sub-spectral bands and weighted the sub-spectral results to obtain the final result. To better evaluate the property of the wmCLT reconstruction strategy in terms of accuracy, stability and practicability, several numerical simulation experiments and in vivo experiments were conducted and the results obtained were compared with the traditional multispectral CLT (mCLT) and hybrid-spectral CLT (hCLT) reconstruction strategies. The numerical simulation results indicated that wmCLT strategy significantly improved the accuracy of Cerenkov source localization and intensity quantitation and exhibited good stability in suppressing noise in numerical simulation experiments. And the comparison of the results achieved from different in vivo experiments further indicated significant improvement of the wmCLT strategy in terms of the shape recovery of the bladder and the spatial resolution of imaging xenograft tumors. Overall the strategy reported here will facilitate the development of nuclear and optical molecular tomography in theoretical study.

Redshifted Cherenkov Radiation for in vivo Imaging: Coupling Cherenkov Radiation Energy Transfer to multiple Förster Resonance Energy Transfers

Cherenkov Radiation (CR), this blue glow seen in nuclear reactors, is an optical light originating from energetic β-emitter radionuclides. CR emitter ⁹⁰Y triggers a cascade of energy transfers in the presence of a mixed population of fluorophores (which each other match their respective absorption and emission maxima): Cherenkov Radiation Energy Transfer (CRET) first, followed by multiple Förster Resonance Energy transfers (FRET): CRET ratios were calculated to give a rough estimate of the transfer efficiency. While CR is blue-weighted (300–500 nm), such cascades of Energy Transfers allowed to get a) fluorescence emission up to 710 nm, which is beyond the main CR window and within the near-infrared (NIR) window where biological tissues are most transparent, b) to amplify this emission and boost the radiance on that window: EMT6-tumor bearing mice injected with both a radionuclide and a mixture of fluorophores having a good spectral overlap, were shown to have nearly a two-fold radiance boost (measured on a NIR window centered on the emission wavelength of the last fluorophore in the Energy Transfer cascade) compared to a tumor injected with the radionuclide only. Some CR embarked light source could be converted into a near-infrared radiation, where biological tissues are most transparent.

Nuclear medicine for photodynamic therapy in cancer: Planning, monitoring and nuclear PDT

Photodynamic therapy (PDT) is a modality with promising results for the treatment of various cancers. PDT is increasingly included in the standard of care for different pathologies. This therapy relies on the effects of light delivered to photosensitized cells. At different stages of delivery, PDT requires imaging to plan, evaluate and monitor treatment. The contribution of molecular imaging in this context is important and continues to increase. In this article, we review the contribution of nuclear medicine imaging in oncology to PDT for planning and therapeutic monitoring purposes. Several solutions have been proposed to plan PDT from nuclear medicine imaging. For instance, photosensitizer biodistribution has been evaluated with a radiolabeled photosensitizer or with conventional radiopharmaceuticals on positron emission tomography. The effects of PDT delivery have also been explored with specific SPECT or PET radiopharmaceuticals to evaluate the effects on cells (apoptosis, necrosis, proliferation, metabolism) or vascular damage. Finally, the synergy between photosensitizers and radiopharmaceuticals has been studied considering the Cerenkov effect to activate photosensitized cells.

Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences

Cerenkov luminescence imaging (CLI) is a novel imaging modality to study charged particles with optical methods by detecting the Cerenkov luminescence produced in tissue. This paper first describes the physical processes that govern the production and transport in tissue of Cerenkov luminescence. The detectors used for CLI and their most relevant specifications to optimize the acquisition of the Cerenkov signal are then presented, and CLI is compared with the other optical imaging modalities sharing the same data acquisition and processing methods. Finally, the scientific work related to CLI and the applications for which CLI has been proposed are reviewed. The paper ends with some considerations about further perspectives for this novel imaging modality.

Transition radiation at the boundary of a chiral isotropic medium

This study analyzes the radiation produced by a point charge intersecting the interface between a vacuum and a chiral isotropic medium. We deduce analytical expressions for the Fourier components of an electromagnetic field in both vacuum and medium for arbitrary charge velocity. The main focus is on investigating the far field in a vacuum. The distinguishing feature of the interface with a chiral isotropic medium is that the field in the vacuum area contains both copolarization (coinciding with the polarization of the self-field of a charge) and cross-polarization (orthogonal to the polarization of the self-field). Using a saddle-point approach, we obtain asymptotic representations for the field components in the far-field zone for typical frequency ranges of the Condon model of the chiral medium. We note that a so-called lateral wave is generated in a vacuum for certain parameters. The main contribution to the radiation at large distances is presented by two (co- and cross-) spherical waves of transition radiation. These waves are coherent and result in a total spherical wave with elliptical polarization, with the polarization coefficient being determined by the chirality of the medium. We present typical radiation patterns and ellipses of polarization.

In vivo pentamodal tomographic imaging for small animals

Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine.

Nanoparticle-mediated radiopharmaceutical-excited fluorescence molecular imaging allows precise image-guided tumor-removal surgery

Fluorescent molecular imaging technique has been actively explored for optical image-guided cancer surgery in pre-clinical and clinical research and has attracted many attentions. However, the efficacy of the fluorescent image-guided cancer surgery can be compromised by the low signal-to-noise ratio caused by the external light excitation. This study presents a novel nanoparticle-mediated radiopharmaceutical-excited fluorescent (REF) image-guided cancer surgery strategy, which employs the internal dual-excitation of europium oxide nanoparticles through both gamma rays and Cerenkov luminescence emitted from radiopharmaceuticals. The performance of the novel image-guided surgery technique was systematically evaluated using subcutaneous breast cancer 4 T1 tumor models, orthotropic and orthotropic-ectopic hepatocellular carcinoma tumor-bearing mice. The results reveal that the novel REF image-guided cancer surgery technique exhibits high performance of detecting invisible ultra-small size tumor (even less than 1 mm) and residual tumor tissue. Our study demonstrates the high potential of the novel image-guided cancer surgery for precise tumor resection.

Removing Noises Induced by Gamma Radiation in Cerenkov Luminescence Imaging Using a Temporal Median Filter

Cerenkov luminescence imaging (CLI) can provide information of medical radionuclides used in nuclear imaging based on Cerenkov radiation, which makes it possible for optical means to image clinical radionuclide labeled probes. However, the exceptionally weak Cerenkov luminescence (CL) from Cerenkov radiation is susceptible to lots of impulse noises introduced by high energy gamma rays generating from the decays of radionuclides. In this work, a temporal median filter is proposed to remove this kind of impulse noises. Unlike traditional CLI collecting a single CL image with long exposure time and smoothing it using median filter, the proposed method captures a temporal sequence of CL images with shorter exposure time and employs a temporal median filter to smooth a temporal sequence of pixels. Results of in vivo experiments demonstrated that the proposed temporal median method can effectively remove random pulse noises induced by gamma radiation and achieve a robust CLI image.

Cerenkov luminescence imaging (CLI) for image-guided cancer surgery

Cerenkov luminescence imaging (CLI) is a novel molecular optical imaging technique based on the detection of optical Cerenkov photons emitted by positron emission tomography (PET) imaging agents. The ability to use clinically approved tumour-targeted tracers in combination with small-sized imaging equipment makes CLI a particularly interesting technique for image-guided cancer surgery. The past few years have witnessed a rapid increase in proof-of-concept preclinical studies in this field, and several clinical trials are currently underway. This article provides an overview of the basic principles of Cerenkov radiation and outlines the challenges of CLI-guided surgery for clinical use. The preclinical and clinical trial literature is examined including applications focussed on image-guided lymph node detection and Cerenkov luminescence endoscopy, and the ongoing clinical studies and technological developments are highlighted. By intraoperatively guiding the oncosurgeon towards more accurate and complete resections, CLI has the potential to transform current surgical practice, and improve oncological and cosmetic outcomes for patients.

Cherenkov luminescence measurements with digital silicon photomultipliers: a feasibility study

Background

A feasibility study was done to assess the capability of digital silicon photomultipliers to measure the Cherenkov luminescence emitted by a β source. Cherenkov luminescence imaging (CLI) is possible with a charge coupled device (CCD) based technology, but a stand-alone technique for quantitative activity measurements based on Cherenkov luminescence has not yet been developed. Silicon photomultipliers (SiPMs) are photon counting devices with a fast impulse response and can potentially be used to quantify β-emitting radiotracer distributions by CLI.

Methods

In this study, a Philips digital photon counting (PDPC) silicon photomultiplier detector was evaluated for measuring Cherenkov luminescence. The PDPC detector is a matrix of avalanche photodiodes, which were read one at a time in a dark count map (DCM) measurement mode (much like a CCD). This reduces the device active area but allows the information from a single avalanche photodiode to be preserved, which is not possible with analog SiPMs. An algorithm to reject the noisiest photodiodes and to correct the measured count rate for the dark current was developed.

Results

The results show that, in DCM mode and at (10–13) °C, the PDPC has a dynamic response to different levels of Cherenkov luminescence emitted by a β source and transmitted through an opaque medium. This suggests the potential for this approach to provide quantitative activity measurements. Interestingly, the potential use of the PDPC in DCM mode for direct imaging of Cherenkov luminescence, as a opposed to a scalar measurement device, was also apparent.

Conclusions

We showed that a PDPC tile in DCM mode is able to detect and image a β source through its Cherenkov radiation emission. The detector’s dynamic response to different levels of radiation suggests its potential quantitative capabilities, and the DCM mode allows imaging with a better spatial resolution than the conventional event-triggered mode. Finally, the same acquisition procedure and data processing could be employed also for other low light levels applications, such as bioluminescence.

Electronic supplementary material

The online version of this article (doi:10.1186/s40658-015-0134-z) contains supplementary material, which is available to authorized users.

Cherenkov radiation fluence estimates in tissue for molecular imaging and therapy applications

Cherenkov radiation has recently emerged as an interesting phenomenon for a number of applications in the biomedical sciences. Its unique properties, including broadband emission spectrum, spectral weight in the ultraviolet and blue wavebands, and local generation of light within a given tissue, have made it an attractive new source of light within tissue for molecular imaging and phototherapy applications. While several studies have investigated the total Cherenkov light yield from radionuclides in units of [photons/decay], further consideration of the light propagation in tissue is necessary to fully consider the utility of this signal in vivo. Therefore, to help further guide the development of this novel field, quantitative estimates of the light fluence rate of Cherenkov radiation from both radionuclides and radiotherapy beams in a biological tissue are presented for the first time. Using Monte Carlo simulations, these values were found to be on the order of 0.01-1 nW cm(-2) per MBq g(-1) for radionuclides, and 1-100 μW cm(-2) per Gy s(-1) for external radiotherapy beams, dependent on the given waveband, optical properties, and radiation source. For phototherapy applications, the total light fluence was found to be on the order of nJ cm(-2) for radionuclides, and mJ cm(-2) for radiotherapy beams. The results indicate that diagnostic potential is reasonable for Cherenkov excitation of molecular probes, but phototherapy may remain elusive at such exceedingly low fluence values. The results of this study are publicly available for distribution online at www.dartmouth.edu/optmed/.

Review of biomedical Čerenkov luminescence imaging applications

Čerenkov radiation is a fascinating optical signal, which has been exploited for unique diagnostic biological sensing and imaging, with significantly expanded use just in the last half decade. Čerenkov Luminescence Imaging (CLI) has desirable capabilities for niche applications, using specially designed measurement systems that report on radiation distributions, radiotracer and nanoparticle concentrations, and are directly applied to procedures such as medicine assessment, endoscopy, surgery, quality assurance and dosimetry. When compared to the other imaging tools such as PET and SPECT, CLI can have the key advantage of lower cost, higher throughput and lower imaging time. CLI can also provide imaging and dosimetry information from both radioisotopes and linear accelerator irradiation. The relatively short range of optical photon transport in tissue means that direct Čerenkov luminescence imaging is restricted to small animals or near surface human use. Use of Čerenkov-excitation for additional molecular probes, is now emerging as a key tool for biosensing or radiosensitization. This review evaluates these new improvements in CLI for both medical value and biological insight.