A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors
arxiv(2024)
摘要
Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided
brain tumor surgery has enabled detection and classification of tumor regions
invisible to the human eye. Prior unmixing work has focused on determining a
minimal set of viable fluorophore spectra known to be present in the brain and
effectively reconstructing human data without overfitting. With these
endmembers, non-negative least squares regression (NNLS) was used to compute
the abundances. However, HSI images are heterogeneous, so one small set of
endmember spectra may not fit all pixels well. Additionally, NNLS is the
maximum likelihood estimator only if the measurement is normally distributed,
and it does not enforce sparsity, which leads to overfitting and unphysical
results. Here, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo
measurements of patients with brain tumors to show that a Poisson distribution
models the measured data 82
Kullback-Leibler divergence and that the endmember abundance vectors are
sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra,
(2) a sparse, non-negative Poisson regression algorithm to perform
physics-informed unmixing with this library without overfitting, and (3) a
highly realistic spectral measurement simulation with known endmember
abundances. The new unmixing method was then tested on the human and simulated
data and compared to four other candidate methods. It outperforms previous
methods with 25
than NNLS, lower reconstruction error on human data, beUer sparsity, and 31
times faster runtime than state-of-the-art Poisson regression. This method and
library of endmember spectra can enable more accurate spectral unmixing to
beUer aid the surgeon during brain tumor resection.
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