Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping

TL;DR

This work tackles the slow data acquisition of mid-infrared hyperspectral photothermal imaging (MIRSI) for histology-like tissue analysis. It introduces a sparse, interleaved sampling strategy along the Y-axis and a curvelet-based reconstruction to recover high-resolution, 27-band hyperspectral images from undersampled data, achieving about a 10× speedup. On a tissue microarray of 100 ovarian cancer patients, the approach enables accurate tissue subtype classification using Random Forest and CNN models, with CNN achieving around 95.7% overall accuracy and per-class gains (epithelium, stroma, necrosis), outperforming RF. The method preserves spectral fidelity while delivering high spatial detail, supporting label-free, quantitative histopathology and potential faster gynecologic cancer diagnostics and subtyping.

Abstract

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of high-resolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This method enables the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by ROC curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%.

Figures (11)

• Figure 1: Schematic illustration of the O-PTIR optical configuration showing both the IR and green (532 nm) laser paths. Pulsed QCL at point (a) causes a photothermal expansion in the sample. A Continuous Wave (CW) green laser, indicated by (b), is collinearly directed onto the sample to serve as a probe beam. A dichroic mirror (c) merges the green and QCL beams, focusing them onto the sample (e) through a reflective Cassegrain objective (d). The resulting modulation in the intensity of the green light (f), scattered back from the sample, facilitates the measurement of its IR absorbance.
• Figure 2: Comparison of High-Definition FT-IR and O-PTIR images of a cancerous Core. This figure illustrates the significant advantage of O-PTIR over FT-IR, showcasing its ability to overcome the diffraction limit, which results in enhanced spatial resolution. The improved image quality of O-PTIR is evident.
• Figure 3: Microarray of ovarian cancer cores imaged by O-PTIR at the 1660cm band. The data encompasses samples from 100 ovarian cancer patients. Variations in tissue biochemistry are highlighted by the color differences, demonstrating the rich biochemical information at the 1660cm band, chosen for high-resolution reconstruction due to its significance in the fingerprint region. Scale bar: 1.5 mm.
• Figure 4: Schematic for the data reconstruction algorithm used to enhance data acquisition speed. The figure illustrates how rectangular pixel-spaced data (0.5X5) acquired from the O-PTIR system is used to reconstruct 27 high-resolution, diffraction-limited band images. This method increases the data acquisition speed by 10X, yielding high-resolution images that offer more detailed information for improved segmentation of different cell types. The algorithm fuses spatial features from a high-resolution Amide I image with the linearly interpolated rectangular image via curvelet transform. This fusion preserves the biochemical information of each band image while accurately translating the spatial features of biological samples.
• Figure 5: Method for interpolating low-resolution band images. We begin by performing a Fourier transform, followed by padding zeros along the Y-axis, and then applying a Gaussian filter to isolate lower frequencies. The interpolated image is obtained by taking the inverse Fourier transform.