SignificanceLabel-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information.AimWe aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images.ApproachTPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images.ResultsOptimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics.ConclusionsDenoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.
Fiber endoscopes capable of making two-photon (2P) autofluorescence measurements, time-resolved fluorescence decay measurements, and collagen second harmonic generation (SHG) measurements have been applied to animal models. Clinical translation of such devices to internal human organs has the potential to overhaul conventional methods of disease diagnosis and monitoring. Previous work by our lab has established the potential to diagnose high-grade cervical precancers using 2P autofluorescence measurements. Other groups have demonstrated that 2P-based fluorescence lifetime imaging microscopy (FLIM) measurements of nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and collagen SHG measurements have the potential to discriminate between cancerous and benign tissues. In this work, we demonstrate the potential to discern high-grade cervical precancerous lesions (HSILs) from benign tissues using fluorescence intensity measurements of NAD(P)H and oxidized flavoproteins, FLIM NAD(P)H measurements, and collagen SHG measurements. Consistent with previous results, benign tissues demonstrated increased depth-dependent heterogeneity in mitochondrial clustering, and increased overall and intrafield heterogeneity of oxido-reductive state relative to HSILs. FLIM phasor analysis demonstrated a relative decrease in NAD(P)H short and long lifetime, and a relative increase in NAD(P)H bound fraction for benign tissues compared to HSILs. Collagen SHG intensity in benign tissues was greater than that of HSIL tissues, along with overall intrafield variations in collagen fiber orientation. This work motivates the functionalization of a clinical 2P fiber endoscope capable of making SHG, autofluorescence intensity and lifetime measurements of metabolic coenzymes in the human cervix.
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