Water and lipids are key participants in many biological processes, but there are few non-invasive methods that provide quantification of these components in vivo, and none that can isolate and quantify lipids in the blood. Here we develop a new imaging modality termed shortwave infrared meso-patterned imaging (SWIR-MPI) to provide label-free, non-contact, spatial mapping of water and lipid concentrations in tissue. The method utilizes patterned hyperspectral illumination to target chromophore absorption bands in the 900–1,300 nm wavelength range. We use SWIR-MPI to monitor clinically important physiological processes including edema, inflammation, and tumor lipid heterogeneity in preclinical models. We also show that SWIR-MPI can spatially map blood-lipids in humans, representing an example of non-invasive and contact-free measurements of in vivo blood lipids. Together, these results highlight the potential of SWIR-MPI to enable new capabilities in fundamental studies and clinical monitoring of major conditions including obesity, cancer, and cardiovascular disease.
This paper investigates a highly parallel extension of the single-pixel camera based on a focal plane array. It discusses the practical challenges that arise when implementing such an architecture and demonstrates that system-specific optical effects must be measured and integrated within the system model for accurate image reconstruction. Three different projection lenses were used to evaluate the ability of the system to accommodate varying degrees of optical imperfection. Reconstruction of binary and grayscale objects using system-specific models and Nesterov's proximal gradient method produced images with higher spatial resolution and lower reconstruction error than using either bicubic interpolation or a theoretical system model that assumes ideal optical behavior. The high-quality images produced using relatively few observations suggest that higher throughput imaging may be achieved with such architectures than with conventional single-pixel cameras. The optical design considerations and quantitative performance metrics proposed here may lead to improved image reconstruction for similar highly parallel systems.
Computational imaging based on compressed sensing (CS) has shown potential for outperforming conventional techniques in many applications, but challenges arise when translating CS theory to practical imaging systems. Here we examine such challenges in two physical architectures under coherent and incoherent illumination. We describe hardware alignment protocols that can be used to optimize system performance for each case. We found that an architecture using coded masks located at a conjugate image plane outperformed an identical architecture using masks at a Fourier plane, enabling recovery of images with up to 64 times more resolvable points than pixels in the image sensor. We demonstrate and explain the basis for the tradeoff between achievable resolution and dynamic range of reconstructed CS images. Finally, we demonstrate that these principles can be applied beyond binary test targets by reconstructing a 480 × 480 image of a human tissue section from a 120 × 120 pixel sensor. These results provide a basis to further develop compressive imaging architectures for biomedical imaging and we also anticipate that these findings may be useful to investigators focused on translating CS theory to other real-world imaging applications.
This letter presents a framework for computational imaging (CI) in fiber-bundle-based endoscopy systems. Multiple observations are acquired of objects spatially modulated with different random binary masks. Sparserecovery algorithms then reconstruct images with more resolved pixels than individual fibers in the bundle. Object details lying within the diameter of single fibers are resolved, allowing images with 41,663 resolvable points to be generated through a bundle with 2,420 fibers. Computational fiber bundle imaging of micro-and macro-scale objects is demonstrated using fluorescent standards and biological tissues, including in vivo imaging of a human fingertip. In each case, CI recovers detail that conventional endoscopy does not provide. http://dx.
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