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.
A novel algorithm to simultaneously recover bioluminescence source location and optical parameters is developed. In-vivo studies show 30% improvement in localization error while also providing local total hemoglobin concentration.
Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time.
Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy.
Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated.
Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of <10 % for the recovered optical property maps.
Conclusion: The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.
Bioluminescent Imaging (BLI) is a widely utilised technique for the investigation of biological functions within preclinical biomedical studies. Its aim is to image distributed (biologically informative) visible and near infrared light sources, such as luciferase-tagged cells that are located within a living animal. Images are used to estimate the concentration and spatial distribution of reporters and therefore infer biological activity from measurements taken at the surface of the animal. Quantitative accuracy of the measurements can be improved by considering the highly attenuating and scattering nature of biological tissue, modelling the transport of the light through tissue to tomographically reconstruct a 3D image of the light source within the animal. This accuracy can be improved further by collecting spectral data of the bioluminescent signal. Compressive Sensing (CS) is a method of signal processing that utilises the sparse nature of real-world signals in order for them to be compressed in some domain. This in turn means that a sparse signal of length n can be represented by k<<n nonzero coefficients with high accuracy. Due to the localisation of bioluminescent sources, which are in sparse in nature, measurements can be collected using a CS based method. This work introduces the development of a CS based hyperspectral bioluminescent imaging system that can be used to collect compressed hyperspectral fluence data of an internal light source at the surface of an animal model. Effects of the number of measurements collected on image reconstruction quality are also investigated.
Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. It is a sensitive and non-invasive technique that is able to detect distributed (biologically informative) visible and nearinfrared light sources providing information about biological 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 can be 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.
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