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. 2024 Aug 15;15(9):5251-5271.
doi: 10.1364/BOE.529779. eCollection 2024 Sep 1.

Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks

Arash Aghigh  1 , Jysiane Cardot  2 , Melika Saadat Mohammadi  1 , Gaëtan Jargot  1 , Heide Ibrahim  1 , Isabelle Plante  2 , François Légaré  1
Affiliations

Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks

Arash Aghigh et al. Biomed Opt Express. .

Abstract

Polarization second harmonic generation (P-SHG) imaging is a powerful technique for studying the structure and properties of biological and material samples. However, conventional whole-sample P-SHG imaging is time consuming and requires expensive equipment. This paper introduces a novel approach that significantly improves imaging resolution under conditions of reduced imaging time and resolution, utilizing enhanced super-resolution generative adversarial networks (ESRGAN) to upscale low-resolution images. We demonstrate that this innovative approach maintains high image quality and analytical accuracy, while reducing the imaging time by more than 95%. We also discuss the benefits of the proposed method for reducing laser-induced photodamage, lowering the cost of optical components, and increasing the accessibility and applicability of P-SHG imaging in various fields. Our work significantly advances whole-sample mammary gland P-SHG imaging and opens new possibilities for scientific discovery and innovation.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Imaging configuration for SHG and P-SHG setups. The motorized half-wave plate was removed during SHG imaging and added during the P-SHG imaging.
Fig. 2.
Fig. 2.
Comparative analysis of upscaled models for P-SHG imaging. This figure illustrates the side-by-side comparison of a) original high-quality and b) low-quality SHG images against images upscaled using various models including c) BSRGAN, d) Nomos2 K, e) Ultrasharp_4X, f) NMKD, g) guided upscaling via PixTransform, and h) uniscale.
Fig. 3.
Fig. 3.
Histological and SHG Images of both samples provide a comprehensive view of tissue microstructure.
Fig. 4.
Fig. 4.
This figure includes three categories of images: original high quality (1a,2a), original low quality (1b,2b) and upscaled images from two different samples (1c,2c). The original high-quality images (1a,2a) had a resolution of 1800 × 800 pixels, low-quality images (1b,2b) had a resolution of 225 × 100 pixels, and upscaled images (1c,2c) had a resolution of 3600 × 1600 pixels.
Fig. 5.
Fig. 5.
Comparative analysis using CurveAlign on samples: original high-quality (1a, 2a), low-quality (1b, 2b), and GAN-upscaled images (1c, 2c). CurveAlign accurately identifies the collagen fiber orientation in high-quality images (1a, 2a). In low-quality images (1b, 2b), the performance diminishes, with only larger recognizable fibers. However, the upscaled images (1c, 2c) show significantly improved analysis, with fiber orientation discernibility comparable to that of the original high-quality images. This demonstrates the efficacy of GAN-based upscaling in enhancing image analysis for CurveAlign.
Fig. 6.
Fig. 6.
P-SHG imaging of collagen fiber orientation in mammary glands. Panels (a) and (b) display the SHG signals of two distinct tissues, visualized in a range of colors corresponding to the collagen fiber orientations relative to the polarization angle of the incident light. The color wheel insets map these orientations, with each color representing a specific angle of polarization, illustrating the complex and heterogeneous arrangement of the fibers within the samples. Notably, both images contained dark regions inside the fibers, which were attributed to areas where the intensity of the SHG signal remained static, indicating a uniform orientation of collagen fibers over the polarization states captured. Owing to this uniformity, the spatial fast Fourier transform algorithm cannot discern variations, resulting in no color assignment in these specific regions.
Fig. 7.
Fig. 7.
Comparative P-SHG Analysis Across Three ROIs. Each row represents a distinct region of interest (ROI) from different samples, showcasing original high-quality images (20X objective), low-quality images initially captured with a 10X objective then digitally zoomed and cropped, and their GAN-upscaled counterparts. Despite the initial lower resolution, upscaling restores detail and smoothness, yielding a fiber orientation analysis comparable to the original high-quality images. Normalized intensity vs. laser input angle graphs for each set illustrate the consistency of P-SHG responses across all imaging modalities, affirming the accuracy of collagen fiber orientation details in the upscaled images.
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