Quantitative bioluminescence tomography using spectral derivative data

Design and validation of a reporter mouse to study the dynamic regulation of TFEB and TFE3 activity through in vivo imaging techniques

TFEB and TFE3 belong to the MiT/TFE family of transcription factors that bind identical DNA responsive elements in the regulatory regions of target genes. They are involved in regulating lysosomal biogenesis, function, exocytosis, autophagy, and lipid catabolism. Precise control of TFEB and TFE3 activity is crucial for processes such as senescence, stress response, energy metabolism, and cellular catabolism. Dysregulation of these factors is implicated in various diseases, thus researchers have explored pharmacological approaches to modulate MiT/TFE activity, considering these transcription factors as potential therapeutic targets. However, the physiological complexity of their functions and the lack of suitable in vivo tools have limited the development of selective MiT/TFE modulating agents. Here, we have created a reporter-based biosensor, named CLEARoptimized, facilitating the pharmacological profiling of TFEB- and TFE3-mediated transcription. This innovative tool enables the measurement of TFEB and TFE3 activity in living cells and mice through imaging and biochemical techniques. CLEARoptimized consists of a promoter with six coordinated lysosomal expression and regulation motifs identified through an in-depth bioinformatic analysis of the promoters of 128 TFEB-target genes. The biosensor drives the expression of luciferase and tdTomato reporter genes, allowing the quantification of TFEB and TFE3 activity in cells and in animals through optical imaging and biochemical assays. The biosensor's validity was confirmed by modulating MiT/TFE activity in both cell culture and reporter mice using physiological and pharmacological stimuli. Overall, this study introduces an innovative tool for studying autophagy and lysosomal pathway modulation at various biological levels, from individual cells to the entire organism.Abbreviations: CLEAR: coordinated lysosomal expression and regulation; MAR: matrix attachment regions; MiT: microphthalmia-associated transcription factor; ROI: region of interest; TBS: tris-buffered saline; TF: transcription factor; TFE3: transcription factor binding to IGHM enhancer 3; TFEB: transcription factor EB; TH: tyrosine hydroxylase; TK: thymidine kinase; TSS: transcription start site.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent ona priorinformation, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. 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 ona prioriinformation, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable 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 andin vivoexperiment, 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.

Improved intraoperative identification of close margins in oral squamous cell carcinoma resections using a dual aperture fluorescence ratio approach: first in-human results

Significance

Surgical excision is the main treatment for solid tumors in oral squamous cell carcinomas, where wide local excision (achieving a healthy tissue margin of > 5    mm around the excised tumor) is the goal as it results in reduced local recurrence rates and improved overall survival.

Aim

No clinical methods are available to assess the complete surgical margin intraoperatively while the patient is still on the operating table; and while recent intraoperative back-bench fluorescence-guided surgery approaches have shown promise for detecting "positive" inadequate margins (< 1    mm ), they have had limited success in the detection of "close" inadequate margins (1 to 5 mm). Here, a dual aperture fluorescence ratio (dAFR) approach was evaluated as a means of improving detection of close margins.

Approach

The approach was evaluated on surgical specimens from patients who were administered a tumor-specific fluorescent imaging agent (cetuximab-800CW) prior to surgery. The dAFR approach was compared directly against standard wide-field fluorescence imaging and pathology measurements of margin thickness in specimens from three patients and a total of 12 margin locations (1 positive, 5 close, and 6 clear margins).

Results

The area under the receiver operating characteristic curve, representing the ability to detect close compared to clear margins (> 5    mm ) was found to be 1.0 and 0.57 for dAFR and sAF, respectively. Improvements in dAFR were found to be statistically significant (p < 0.02 ).

Conclusions

These results provide evidence that the dAFR approach potentially improves detection of close surgical margins.

Characterization of a commercial bioluminescence tomography-guided system for pre-clinical radiation research

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.

ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography

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 Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, 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 permission source region, which further improves the performance of ABPO-TVSCAD.Main results.The results of numerical simulations andin vivoxenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source.Significance.ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography.

Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction

Bioluminescence tomography (BLT) is a promising pre-clinical imaging technique for a wide variety of biomedical applications, which can non-invasively reveal functional activities inside living animal bodies through the detection of visible or near-infrared light produced by bioluminescent reactions. Recently, reconstruction approaches based on deep learning have shown great potential in optical tomography modalities. However, these reports only generate data with stationary patterns of constant target number, shape, and size. The neural networks trained by these data sets are difficult to reconstruct the patterns outside the data sets. This will tremendously restrict the applications of deep learning in optical tomography reconstruction. To address this problem, a self-training strategy is proposed for BLT reconstruction in this paper. The proposed strategy can fast generate large-scale BLT data sets with random target numbers, shapes, and sizes through an algorithm named random seed growth algorithm and the neural network is automatically self-trained. In addition, the proposed strategy uses the neural network to build a map between photon densities on surface and inside the imaged object rather than an end-to-end neural network that directly infers the distribution of sources from the photon density on surface. The map of photon density is further converted into the distribution of sources through the multiplication with stiffness matrix. Simulation, phantom, and mouse studies are carried out. Results show the availability of the proposed self-training strategy.

Mobile bioluminescence tomography-guided system for pre-clinical radiotherapy research

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.

Quantitative molecular bioluminescence tomography

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.

Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography

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.

A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT

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 (SP_N), a hybrid light transport model needs to be built. DE and SP_N models are first-order and higher-order approximations of RTE, respectively. Due to the discontinuity of the regions using the DE and SP_N 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.

Bioluminescence tomography system for in vivo irradiation guidance

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.

Quantitative Bioluminescence Tomography for In Vivo Volumetric-Guided Radiotherapy

Several groups, including ours, have initiated efforts to develop small-animal irradiators that mimic radiation therapy (RT) for human treatment. The major image modality used to guide irradiation is cone-beam computed tomography (CBCT). While CBCT provides excellent guidance capability, it is less adept at localizing soft tissue targets growing in a low image contrast environment. In contrast, bioluminescence imaging (BLI) provides strong image contrast and thus is an attractive solution for soft tissue targeting. However, commonly used 2D BLI on an animal surface is inadequate to guide irradiation, because optical transport from an internal bioluminescent tumor is highly susceptible to the effects of optical path length and tissue absorption and scattering. Recognition of these limitations led us to integrate 3D bioluminescence tomography (BLT) with the small animal radiation research platform (SARRP). In this chapter, we introduce quantitative BLT (QBLT) with the advanced capabilities of quantifying tumor volume for irradiation guidance. The detail of system components, calibration protocol, and step-by-step procedure to conduct the QBLT-guided irradiation are described.

Quantitative Bioluminescence Tomography-guided Conformal Irradiation for Preclinical Radiation Research

PURPOSE

Widely used cone beam computed tomography (CBCT)-guided irradiators in preclinical radiation research are limited to localize soft tissue target because of low imaging contrast. Knowledge of target volume is a fundamental need for radiation therapy (RT). Without such information to guide radiation, normal tissue can be overirradiated, introducing experimental uncertainties. This led us to develop high-contrast quantitative bioluminescence tomography (QBLT) for guidance. The use of a 3-dimensional bioluminescence signal, related to cell viability, for preclinical radiation research is one step toward biology-guided RT.

METHODS AND MATERIALS

Our QBLT system enables multiprojection and multispectral bioluminescence imaging to maximize input data for the tomographic reconstruction. Accurate quantification of spectrum and dynamic change of in vivo signal were also accounted for the QBLT. A spectral-derivative method was implemented to eliminate the modeling of the light propagation from animal surface to detector. We demonstrated the QBLT capability of guiding conformal RT using a bioluminescent glioblastoma (GBM) model in vivo. A threshold was determined to delineate QBLT reconstructed gross target volume (GTV_QBLT), which provides the best overlap between the GTV_QBLT and CBCT contrast labeled GBM (GTV), used as the ground truth for GBM volume. To account for the uncertainty of GTV_QBLT in target positioning and volume delineation, a margin was determined and added to the GTV_QBLT to form a QBLT planning target volume (PTV_QBLT) for guidance.

RESULTS

The QBLT can reconstruct in vivo GBM with localization accuracy within 1 mm. A 0.5-mm margin was determined and added to GTV_QBLT to form PTV_QBLT, largely improving tumor coverage from 75.0% (0 mm margin) to 97.9% in average, while minimizing normal tissue toxicity. With the goal of prescribed dose 5 Gy covering 95% of PTV_QBLT, QBLT-guided 7-field conformal RT can effectively irradiate 99.4 ± 1.0% of GTV.

CONCLUSIONS

The QBLT provides a unique opportunity for investigators to use biologic information for target delineation, guiding conformal irradiation, and reducing normal tissue involvement, which is expected to increase reproducibility of scientific discovery.

In vivo bioluminescence tomography-guided radiation research platform for pancreatic cancer: an initial study using subcutaneous and orthotopic pancreatic tumor models

Genetically engineered mouse model(GEMM) that develops pancreatic ductal adenocarcinoma(PDAC) offers an experimental system to advance our understanding of radiotherapy(RT) for pancreatic cancer. Cone beam CT(CBCT)-guided small animal radiation research platform(SARRP) has been developed to mimic the RT used for human. However, we recognized that CBCT is inadequate to localize the PDAC growing in low image contrast environment. We innovated bioluminescence tomography(BLT) to guide SARRP irradiation for in vivo PDAC. Before working on the complex PDAC-GEMM, we first validated our BLT target localization using subcutaneous and orthotopic pancreatic tumor models. Our BLT process involves the animal transport between the BLT system and SARRP. We inserted a titanium wire into the orthotopic tumor as the fiducial marker to track the tumor location and to validate the BLT reconstruction accuracy. Our data shows that with careful animal handling, minimum disturbance for target position was introduced during our BLT imaging procedure(<0.5mm). However, from longitudinal 2D bioluminescence image(BLI) study, the day-to-day location variation for an abdominal tumor can be significant. We also showed that the 2D BLI in single projection setting cannot accurately capture the abdominal tumor location. It renders that 3D BLT with multiple-projection is needed to quantify the tumor volume and location for precise radiation research. Our initial results show the BLT can retrieve the location at 2mm accuracy for both tumor models, and the tumor volume can be delineated within 25% accuracy. The study for the subcutaneous and orthotopic models will provide us valuable knowledge for BLT-guided PDAC-GEMM radiation research.

Simultaneous diffuse optical and bioluminescence tomography to account for signal attenuation to improve source localization

Photonics based pre-clinical imaging is an extensively used technique to allow for the study of biologically relevant activity typically within a small-mouse model. Namely, bioluminescent tomography (BLT) attempts to tomographically reconstruct the 3-dimensional spatial light distribution of luminophores within a small animal given surface light measurements and known underlying optical parameters. Often it is the case where these optical parameters are unknown leading to the use of a 'best' guess approach or to direct measurements using either a multi-modal or dedicated system. Using these conventional approaches can lead to both inaccurate results and extending periods of imaging time. This work introduces the development of an algorithm that is used to accurately localize the spatial light distribution from a bioluminescence source within a subject by simultaneously reconstructing both the underlying optical properties and source spatial distribution and intensity from the same set of surface measurements. Through its application in 2- and 3-dimensional, homogeneous and heterogenous numerical models, it is demonstrated that the proposed algorithm is capable of replicating results as compared to 'gold' standard where the absolute optical properties are known. Additionally, the algorithm has been applied to experimental data using a tissue mimicking block phantom, recovering a spatial light distribution that has a localization error of ∼1.53 mm, which is better than previously published results without the need of assumptions regarding the underlying optical properties or source distribution.

Exploring the feasibility of wavelength modulated near-infrared spectroscopy

SIGNIFICANCE

The application of near-infrared spectroscopy (NIRS) to determine the concentrations of tissue chromophores has typically relied on three alternative technological approaches: continuous-wave, frequency-domain, and time-domain. It is often the case that uncertain and variable coupling of light into and out of the skin surface renders absolute measurements unreliable, and NIRS methods are mostly used to measure changes of chromophore concentrations and of physiological parameters such as blood volume and oxygenation.

AIM

The aim has been to investigate whether an approach based on a wavelength-modulated source may enable measurements to be acquired, which are independent of surface coupling and may facilitate derivation of absolute values of tissue parameters.

APPROACH

An analysis is performed using the modified Beer-Lambert law.

RESULTS

It is shown that the relative modulation in detected intensity resulting from a wavelength-modulated source could be used to estimate absolute concentrations of chromophores if unknown surface coupling losses and geometrical factors are insensitive to small changes in wavelength.

CONCLUSIONS

Wavelength modulated NIRS could be an effective tool for quantitative in vivo analysis of tissues, although it may be technically challenging.

Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy

The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500-3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including 'raw' unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features.

Restarted primal-dual Newton conjugate gradient method for enhanced spatial resolution of reconstructed cone-beam x-ray luminescence computed tomography images

Cone-beam x-ray luminescence computed tomography (CB-XLCT) has been proposed as a promising imaging tool, which enables three-dimensional imaging of the distribution of nanophosphors (NPs) in small animals. However, the reconstruction performance is usually unsatisfactory in terms of spatial resolution due to the ill-posedness of the CB-XLCT inverse problem. To alleviate this problem and to achieve high spatial resolution, a reconstruction method consisting of inner and outer iterations based on a restarted strategy is proposed. In this method, the primal-dual Newton conjugate gradient method (pdNCG) is adopted in the inner iterations to get fast reconstruction, which is used for resetting the permission region and increasing the convergence speed of the outer iteration. To assess the performance of the method, both numerical simulation and physical phantom experiments were conducted with a CB-XLCT system. The results demonstrate that compared with conventional reconstruction methods, the proposed re-pdNCG method can accurately and efficiently resolve the adjacent NPs with the least relative error.

Development of an embedded multimodality imaging platform for onco-pharmacology using a smart anticancer prodrug as an example

Increasingly, in vivo imaging holds a strategic position in bio-pharmaceutical innovation. We will present the implementation of an integrated multimodal imaging setup enabling the assessment of multiple, complementary parameters. The system allows the fusion of information provided by: Near infrared fluorescent biomarkers, bioluminescence (for tumor proliferation status), Photoacoustic and Ultrasound imaging. We will study representative applications to the development of a smart prodrug, delivering a highly cytotoxic chemotherapeutic agent to cancer tumors. The results realized the ability of this embedded, multimodality imaging platform to firstly detect bioluminescent and fluorescent signals, and secondly, record ultrasound and photoacoustic data from the same animal. This study demonstrated that the prodrug was effective in three different models of hypoxia in human cancers compared to the parental cytotoxic agent and the vehicle groups. Monitoring by photoacoustic imaging during the treatments revealed that the prodrug exhibits an intrinsic capability to prevent the progression of tumor hypoxia. It is essential for onco-pharmacology studies to precisely document the hypoxic status of tumors both before and during the time course of treatments. This approach opens new perspectives for exploitation of preclinical mouse models of cancer, especially when considering associations between hypoxia, neoangiogenesis and antitumor activity.

In Vivo Bioluminescence Tomography Center of Mass-Guided Conformal Irradiation

PURPOSE

The cone-beam computed tomography (CBCT)-guided small animal radiation research platform (SARRP) has provided unique opportunities to test radiobiologic hypotheses. However, CBCT is less adept to localize soft tissue targets growing in a low imaging contrast environment. Three-dimensional bioluminescence tomography (BLT) provides strong image contrast and thus offers an attractive solution. We introduced a novel and efficient BLT-guided conformal radiation therapy and demonstrated it in an orthotopic glioblastoma (GBM) model.

METHODS AND MATERIALS

A multispectral BLT system was integrated with SARRP for radiation therapy (RT) guidance. GBM growth curve was first established by contrast CBCT/magnetic resonance imaging (MRI) to derive equivalent sphere as approximated gross target volume (aGTV). For BLT, mice were subject to multispectral bioluminescence imaging, followed by SARRP CBCT imaging and optical reconstruction. The CBCT image was acquired to generate anatomic mesh for the reconstruction and RT planning. To ensure high accuracy of the BLT-reconstructed center of mass (CoM) for target localization, we optimized the optical absorption coefficients μ_a by minimizing the distance between the CoMs of BLT reconstruction and contrast CBCT/MRI-delineated GBM volume. The aGTV combined with the uncertainties of BLT CoM localization and target volume determination was used to generate estimated target volume (ETV). For conformal irradiation procedure, the GBM was first localized by the predetermined ETV centered at BLT-reconstructed CoM, followed by SARRP radiation. The irradiation accuracy was qualitatively confirmed by pathologic staining.

RESULTS

Deviation between CoMs of BLT reconstruction and contrast CBCT/MRI-imaged GBM is approximately 1 mm. Our derived ETV centered at BLT-reconstructed CoM covers >95% of the tumor volume. Using the second-week GBM as an example, the ETV-based BLT-guided irradiation can cover 95.4% ± 4.7% tumor volume at prescribed dose. The pathologic staining demonstrated the BLT-guided irradiated area overlapped well with the GBM location.

CONCLUSIONS

The BLT-guided RT enables 3-dimensional conformal radiation for important orthotopic tumor models, which provides investigators a new preclinical research capability.

Single pixel hyperspectral bioluminescence tomography based on compressive sensing

Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ∼3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications.