Fluorescence molecular tomography (FMT) has gained prominence in recent years as a viable optical imaging technique for non-invasive, high-sensitivity, tomographic imaging of the brain. While optical imaging methods have demonstrated promising results for quantitative imaging of functional changes in the brain, they are still limited in their abilities to achieve high spatial and temporal resolution. To address these challenges, we present here a deep learning solution for FMT reconstructions, which implements a neural network with our novel asymptotic sparse function from our previously introduced sensitivity equation-based non-iterative sparse optical reconstruction (SENSOR) code to achieve highresolution and sparse reconstructions using only learned parameters. We evaluate the proposed network through numerical phantom experiments. Furthermore, once the network is trained, the total reconstruction time is independent of the number of sources and wavelengths used.
SignificanceImaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning.AimWe used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch.ApproachOur model “Unrolled-DOT” uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers.ResultsIn experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers.ConclusionWe demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.
Time-domain tomographic image reconstruction is typically based on an iterative process that requires repeated solving of the forward model of time-dependent light propagation in tissue. As a result, image reconstruction times remain relatively high. This has been one of the main obstacles in the practical use of time-domain data, for example, for realtime monitoring of brain function, in which case results have to be displayed in less than a second. To overcome this problem, we have developed a neural-network-based approach that promises to deliver image reconstructions in the subseconds range. The inputs to this network are parameterized data derived from the Mellin and Laplace transforms of the time of flight (ToF) distribution. In this study, we specifically focused on three data types: the integrated intensity (E), the mean time of flight (<t<), and the exponential feature (L). The network tested consisted of an input layer, three hidden layers, and an output layer that represents the spatial distribution of absorption values for the medium. We trained the parameters of the network with simulated brain diffuse optical tomography data. The inverse problem is then solved with a single-feed forward pass through the network. We demonstrate that this network, once trained, can recover single and multiple inclusions in a 3D medium with accurate localization within milliseconds and outperforms constrained iterative reconstruction methods.
Optical scattering parameters were correlated with markers of apoptosis and proliferation in preclinical tumor models, and outperformed tumor volume and functional parameters in treatment response prediction.
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