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The total expected and recovered bioluminescence intensity (AU) using different reconstruction algorithms
Source publication
Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new tr...
Citations
... Due to the non-contact imaging geometry, there is inevitable deviation between the actual surface fluence rate and BLI measurement . The spectral derivative(SD) method utilizing the ratio of neighboring wavelengths as the input for BLT reconstruction can minimize the deviation and eliminate complicated system modeling for noncontact imaging geometry 22 . The relationship between the and is assumed as = , where is an unknown factor and assumed to be spectrally invariant. ...
CBCT-guided small animal irradiators encounter challenges in localizing soft-tissue targets due to low imaging contrast. Bioluminescence tomography (BLT) offers a promising solution, but they have largely remained in laboratorial development, limiting accessibility for researchers. In this work, we develop a universal, commercial-graded BLT-guided system (MuriGlo) designed to seamlessly integrate with commercial irradiators and empower researchers for translational studies. We demonstrate its capabilities in supporting in vitro and in vivo studies. The MuriGlo comprises detachable mouse bed, thermostatic control, mirrors, filters, and CCD, enabling multi-projection and multi-spectral imaging. We evaluate that the thermostatic control effectively sustains animal temperature at 37{\deg}C throughout imaging, and quantify that the system can detect as few as 61 GL261-AkaLuc cells in vitro. To illustrate how the MuriGlo can be utilized for in vivo image-guided research, we present 3 strategies, BLT-guided 5-arc, 2-field box, and BLI-guided single-beam, ranging from complicated high-conformal to simplest high-throughput plans. The high conformal BLT-guided 5-arc plan fully covers the gross tumor volume (GTV) at prescribed dose with minimal normal tissue exposure (3.9%), while the simplified, high-throughput BLT-guided 2-field box achieves 100% GTV coverage but results in higher normal tissue exposure (13.1%). Moreover, we demonstrate that the localization accuracy of MuriGlo for both widely-used SARRP and SmART irradiators is within1 mm, and the tumor coverage reaches over 97% with 0.75mm margin. The universal BLT-guided system offers seamless integration with commercial irradiators, achieving comparable localization accuracy, expected to supporting high-precision radiation research.
... In the past decades, several efforts are devoted to alleviate such ill-posedness, such as the use of a permissible region strategy (Naser andPatterson 2001, Feng et al 2008), adding a regularization term (Li et al 2010, Cai et al 2020a, using multi-spectral measurements (Dehghani et al 2018, Zhang et al 2021a, etc. Generally, the combination of using the permissible region strategy and regularization terms is an efficient strategy. ...
Objective: The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems. Approach: This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence on a priori information, a moveable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this moveable region and the results are used as the basis for its next movement. Main Results: The performance of the proposed framework is evaluated with a series of simulations and in vivo experiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility. Significance: The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.
... In non-contact imaging geometry, one major challenging is accounting for the light propagation from animal surface to the optical detector (e.g., CCD). We have developed a SD method, 27 in which the SD of that data (the ratio of the surface images at adjacent wavelengths) is used, as bioluminescence at similar wavelengths encounters a near-identical system response. The system response can be expressed by rewriting Φ = b n, where n is a measurement point specific angular dependent offset to account for the difference between actual surface fluence rate Φ and BLI measurement b , and n is assumed to be spectrally invariant. ...
... We had proposed the theory that the SD approach can eliminate the geometric dependence of the free-spacing light detection. 27 In this work, we further examined its ability in alleviating the image intensity variation due to focusing (Figure 4a,c vs. Figure 4d). Because of the chromatic aberration, as illustrated in Figure 4b,c, we showed the focusing position is function of wavelengths and it can also affect the measured intensity away from the focal plane. ...
Background
Widely used Cone‐beam computed tomography (CBCT)‐guided irradiators have limitations in localizing soft tissue targets growing in a low‐contrast environment. This hinders small animal irradiators achieving precise focal irradiation.
Purpose
To advance image‐guidance for soft tissue targeting, we developed a commercial‐grade bioluminescence tomography‐guided system (BLT, MuriGlo) for pre‐clinical radiation research. We characterized the system performance and demonstrated its capability in target localization. We expect this study can provide a comprehensive guideline for the community in utilizing the BLT system for radiation studies.
Methods
MuriGlo consists of four mirrors, filters, lens, and charge‐coupled device (CCD) camera, enabling a compact imaging platform and multi‐projection and multi‐spectral BLT. A newly developed mouse bed allows animals imaged in MuriGlo and transferred to a small animal radiation research platform (SARRP) for CBCT imaging and BLT‐guided irradiation. Methods and tools were developed to evaluate the CCD response linearity, minimal detectable signal, focusing, spatial resolution, distortion, and uniformity. A transparent polycarbonate plate covering the middle of the mouse bed was used to support and image animals from underneath the bed. We investigated its effect on 2D Bioluminescence images and 3D BLT reconstruction accuracy, and studied its dosimetric impact along with the rest of mouse bed. A method based on pinhole camera model was developed to map multi‐projection bioluminescence images to the object surface generated from CBCT image. The mapped bioluminescence images were used as the input data for the optical reconstruction. To account for free space light propagation from object surface to optical detector, a spectral derivative (SD) method was implemented for BLT reconstruction. We assessed the use of the SD data (ratio imaging of adjacent wavelength) in mitigating out of focusing and non‐uniformity seen in the images. A mouse phantom was used to validate the data mapping. The phantom and an in vivo glioblastoma model were utilized to demonstrate the accuracy of the BLT target localization.
Results
The CCD response shows good linearity with < 0.6% residual from a linear fit. The minimal detectable level is 972 counts for 10 × 10 binning. The focal plane position is within the range of 13–18 mm above the mouse bed. The spatial resolution of 2D optical imaging is < 0.3 mm at Rayleigh criterion. Within the region of interest, the image uniformity is within 5% variation, and image shift due to distortion is within 0.3 mm. The transparent plate caused < 6% light attenuation. The use of the SD imaging data can effectively mitigate out of focusing, image non‐uniformity, and the plate attenuation, to support accurate multi‐spectral BLT reconstruction. There is < 0.5% attenuation on dose delivery caused by the bed. The accuracy of data mapping from the 2D bioluminescence images to CBCT image is within 0.7 mm. Our phantom test shows the BLT system can localize a bioluminescent target within 1 mm with an optimal threshold and only 0.2 mm deviation was observed for the case with and without a transparent plate. The same localization accuracy can be maintained for the in vivo GBM model.
Conclusions
This work is the first systematic study in characterizing the commercial BLT‐guided system. The information and methods developed will be useful for the community to utilize the imaging system for image‐guided radiation research.
... In contrast to other mathematically-driven solutions (Dehghani et al 2018, Deng et al 2020, Rapic et al 2022, our efforts have mainly been focused on deep learning (DL) based solutions. Previously, we proposed a 3D convolutional neural network (CNN) to predict the tumor's center of mass (CoM) and to construct a spherical volume around the CoM as the targeting volume (Rezaeifar et al 2022). ...
A novel solution is required for accurate 3D Bioluminescence Tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the X-ray imaging dose imposed by high-resolution micro cone-beam CT.
Approach: A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models.
Significance: Bioluminescence Imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI's value in preclinical radiobiology research.
Results: The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases.
Conclusion: The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.
... Specifically, BLT can recover the spatial information of the bioluminescent sources through complex reconstruction algorithms, which makes the positioning of internal bioluminescent sources and quantitative analysis of bioluminescent density more accurate (Wang et al 2004, Klose et al 2010, Darne et al 2013. BLT employs the forward photon propagation model, and combined with the optimization algorithm, the bioluminescent flux on the surface of the organism is used to model-based inverse reconstruct the source distribution (Dehghani et al 2018). However, the light scattering and the limitation of detected photons result in the high ill-posedness of the BLT, so it is extremely challenging to reconstruct the source distribution (Qin et al 2014, Feng et al 2018, Guo et al 2018. ...
Objective:
Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT.
Approach:
An alternating Breg man proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT recon struction. TVSCAD combines the anisotropic total variation (TV) regularization con straints and the non-convex smoothly clipped absolute deviation (SCAD) penalty con straints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permis sion source region, which further improves the performance of ABPO-TVSCAD. Main re sults: The re sults of numerical simulations and in vivo xenograft mouse experiment show that our pro posed method achieved superior accuracy in spatial localization and morphological recon struction of bioluminescent source.
Significance:
ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the devel opment of optical molecular tomography.
... For the registration of system coordinates, we modeled the assembly of 2D optical image acquisition as a pinhole camera model to precisely map the surface bioluminescent data at any projection angle to the mesh. To eliminate the errors of free-space light propagation from the imaged object surface to the optical detector, we proposed a spectral derivative (SD) algorithm [26] and applied it for this study. ...
... In the non-contact imaging geometry, as shown in Fig. 1(a), a major challenge is accounting for light propagation from subject surface to the optical detector (CCD camera in our system). Instead of directly using surface BLIs acquired at different wavelengths (φ 590 , φ 610 , φ 630 , φ 650 ), we mapped the ratio images at adjacent wavelengths (φ 610 /φ 590 , φ 630 /φ 610 , φ 650 /φ 630 ), which is called the SD data [26], as the input for BLT reconstruction. Our assumption is the BLIs at adjacent wavelengths encountering a near-identical system response, i.e. light emitted from a surface point at the adjacent wavelengths passing approximately same optical path toward the detector. ...
Due to low imaging contrast, a widely-used cone-beam computed tomography-guided small animal irradiator is less adept at localizing in vivo soft tissue targets. Bioluminescence tomography (BLT), which combines a model of light propagation through tissue with an optimization algorithm, can recover a spatially resolved tomographic volume for an internal bioluminescent source. We built a novel mobile BLT system for a small animal irradiator to localize soft tissue targets for radiation guidance. In this study, we elaborate its configuration and features that are indispensable for accurate image guidance. Phantom and in vivo validations show the BLT system can localize targets with accuracy within 1 mm. With the optimal choice of threshold and margin for target volume, BLT can provide a distinctive opportunity for investigators to perform conformal biology-guided irradiation to malignancy.
... To account for this, it is possible to use a free-space model, 30 however this can be time-consuming and complex. To overcome this problem, it was shown that by using spectral derivative data, which utilizes the "logarithm of intensity," it is possible to improve the quantitative error of BLT from 49% to 4%, without the need to collect additional data or make any modifications to existing imaging systems, 28 which has been utilized in this work. The importance of having accurate knowledge of the underlying optical properties to account for light propagation in model-based optimization is shown in Figs. 4 and 5. Figure 4 shows tomographic reconstructions using the surface fluence rate data of the same GBM model, using two different assumed concentrations of total hemoglobin (cTHb) as the optical properties used for model-based reconstruction. ...
Significance:
Bioluminescence imaging and tomography (BLT) are used to study biologically relevant activity, typically within a mouse model. A major limitation is that the underlying optical properties of the volume are unknown, leading to the use of a "best" estimate approach often compromising quantitative accuracy.
Aim:
An optimization algorithm is presented that localizes the spatial distribution of bioluminescence by simultaneously recovering the optical properties and location of bioluminescence source from the same set of surface measurements.
Approach:
Measured data, using implanted self-illuminating sources as well as an orthotopic glioblastoma mouse model, are employed to recover three-dimensional spatial distribution of the bioluminescence source using a multi-parameter optimization algorithm.
Results:
The proposed algorithm is able to recover the size and location of the bioluminescence source while accounting for tissue attenuation. Localization accuracies of <1 mm are obtained in all cases, which is similar if not better than current "gold standard" methods that predict optical properties using a different imaging modality.
Conclusions:
Application of this approach, using in-vivo experimental data has shown that quantitative BLT is possible without the need for any prior knowledge about optical parameters, paving the way toward quantitative molecular imaging of exogenous and indigenous biological tumor functionality.
... Diffusion approximation was applied to model light propagation in tissue. We used spectral derivative of the BLIs for BLT reconstruction to bypass free spacing light propagation modeling [4]. The tumor distribution (GTV BLT ) is iteratively solved by applying a compressive sensing optimization algorithm. ...
We constructed a bioluminescence tomography(BLT) to localize soft tissue targets for preclinical radiotherapy study. With the threshold and margin designed for target volume, BLT can provide opportunity to perform conformal irradiation to malignancy.
... Bioluminescence tomography (BLT) employs threedimensional (3D) reconstruction of bioluminescent sources to more accurately locate and quantify tumors compared with BLI (5). The basic idea of BLT is to utilize a "forward" model of light propagation through the tissue to the skin surface, along with an "inversion" algorithm to reconstruct the underlying bioluminescence source distribution (6,7). In the process, the accuracy of the BLT reconstruction is significantly affected by the forward modeling errors in the simplified photon propagation model, the measurement noise in data acquisition, and the inherent ill-posedness of the inverse problem. ...
... However, such implementations led to insufficient spatial sampling and field of view, and poorer resolution and signal-to-noise ratio (17). Therefore, at present, all BLI systems are based on a noncontact configuration (7), which makes data collection more flexible but also has some disadvantages and limitations. It has been demonstrated that a change in position of the imaging subject can result in a differently measured signal, and due to the impact of charge-coupled device (CCD) noise and environmental background noise, the measurement accuracy of the optical signal is affected. ...
... Further, graph convolutional networks and dictionary learning techniques for hyperspectral can also be used to overcome measurement noise and improve the accuracy of acquired spectral images (22)(23)(24). Moreover, the importance of the domain geometry and imaging subject position on the measured bioluminescence fluence was studied, and an image reconstruction algorithm based on the spectral derivative (SD) of the measured spectral data was proposed to overcome the measurement noise from the surface of the imaging subject to the CCD (7). For the spectral-derivative method, the ratio of the BLIs at adjacent wavelengths was used as input data for the source reconstruction, as bioluminescence at similar wavelengths encounters a near-identical system response (25). ...
Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels. However, the accuracy of the BLT reconstruction is significantly affected by the forward modeling errors in the simplified photon propagation model, the measurement noise in data acquisition, and the inherent ill-posedness of the inverse problem. In this paper, we present a new multispectral differential strategy (MDS) on the basis of analyzing the errors generated from the simplification from radiative transfer equation (RTE) to diffusion approximation and data acquisition of the imaging system. Through rigorous theoretical analysis, we learn that spectral differential not only can eliminate the errors caused by the approximation of RTE and imaging system measurement noise but also can further increase the constraint condition and decrease the condition number of system matrix for reconstruction compared with traditional multispectral (TM) reconstruction strategy. In forward simulations, energy differences and cosine similarity of the measured surface light energy calculated by Monte Carlo (MC) and diffusion equation (DE) showed that MDS can reduce the systematic errors in the process of light transmission. In addition, in inverse simulations and in vivo experiments, the results demonstrated that MDS was able to alleviate the ill-posedness of the inverse problem of BLT. Thus, the MDS method had superior location accuracy, morphology recovery capability, and image contrast capability in the source reconstruction as compared with the TM method and spectral derivative (SD) method. In vivo experiments verified the practicability and effectiveness of the proposed method.
... where A is a sensitivity matrix at a given wavelength l, and F m is the measurable photon fluence rate (on the surface) at the same wavelength. As the imaging problem is known to be non-unique, it has been shown that measuring at multiple wavelengths can help overcome this issue caused by the unique spectrally varying attenuation of biological tissue (38). Assuming there are two wavelengths l in Eq. (19), Eq. (20) is deduced: ...
X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality in optical molecular imaging, which has attracted more attention and has been widely studied. In XLCT, the accuracy and operational efficiency of an optical transmission model play a decisive role in the rapid and accurate reconstruction of light sources. For simulation of optical transmission characteristics in XLCT, considering the limitations of the diffusion equation (DE) and the time and memory costs of simplified spherical harmonic approximation equation (SPN ), a hybrid light transport model needs to be built. DE and SPN models are first-order and higher-order approximations of RTE, respectively. Due to the discontinuity of the regions using the DE and SPN models and the inconsistencies of the system matrix dimensions constructed by the two models in the solving process, the system matrix construction of a hybrid light transmission model is a problem to be solved. We provided a new finite element mesh regrouping strategy-based hybrid light transport model for XLCT. Firstly, based on the finite element mesh regrouping strategy, two separate meshes can be obtained. Thus, for DE and SP N models, the system matrixes and source weight matrixes can be calculated separately in two respective mesh systems. Meanwhile, some parallel computation strategy can be combined with finite element mesh regrouping strategy to further save the system matrix calculation time. Then, the two system matrixes with different dimensions were coupled though repeated nodes were processed according to the hybrid boundary conditions, the two meshes were combined into a regrouping mesh, and the hybrid optical transmission model was established. In addition, the proposed method can reduce the computational memory consumption than the previously proposed hybrid light transport model achieving good balance between computational accuracy and efficiency. The forward numerical simulation results showed that the proposed method had better transmission accuracy and achieved a balance between efficiency and accuracy. The reverse simulation results showed that the proposed method had superior location accuracy, morphological recovery capability, and image contrast capability in source reconstruction. In-vivo experiments verified the practicability and effectiveness of the proposed method.
