Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ∼4  ×  4  mm² subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value  <  0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of f_x  =  1.37  mm^−  1, optical wavelength of λ  =  490  nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign–malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm²) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.

Structured light imaging (SLI) with high spatial frequency (HSF) illumination provides a method to amplify native tissue scatter contrast and better differentiate superficial tissues. This was investigated for margin analysis in breast-conserving surgery (BCS) and imaging gross clinical tissues from 70 BCS patients, and the SLI distinguishability was examined for six malignancy subtypes relative to three benign/normal breast tissue subtypes. Optical scattering images recovered were analyzed with five different color space representations of multispectral demodulated reflectance. Excluding rare combinations of invasive lobular carcinoma and fibrocystic disease, SLI was able to classify all subtypes of breast malignancy from surrounding benign tissues (p-value  <  0.05) based on scatter and color parameters. For color analysis, HSF illumination of the sample generated more statistically significant discrimination than regular uniform illumination. Pathological information about lesion subtype from a presurgical biopsy can inform the search for malignancy on the surfaces of specimens during BCS, motivating the focus on pairwise classification analysis. This SLI modality is of particular interest for its potential to differentiate tissue classes across a wide field-of-view (∼100  cm²) and for its ability to acquire images of macroscopic tissues rapidly but with microscopic-level sensitivity to structural and morphological tissue constituents.

The potential to image subsurface fluorescent contrast agents at high spatial resolution has facilitated growing interest in short-wave infrared (SWIR) imaging for biomedical applications. The early but growing literature showing improvements in resolution in small animal models suggests this is indeed the case, yet to date, images from larger animal models that more closely recapitulate humans have not been reported. We report the first imaging of SWIR fluorescence in a large animal model. Specifically, we imaged the vascular kinetics of an indocyanine green (ICG) bolus injection during open craniotomy of a mini-pig using a custom SWIR imaging instrument and a clinical-grade surgical microscope that images ICG in the near-infrared-I (NIR-I) window. Fluorescence images in the SWIR were observed to have higher spatial and contrast resolutions throughout the dynamic sequence, particularly in the smallest vessels. Additionally, vessels beneath a surface pool of blood were readily visualized in the SWIR images yet were obscured in the NIR-I channel. These first-in-large-animal observations represent an important translational step and suggest that SWIR imaging may provide higher spatial and contrast resolution images that are robust to the influence of blood.

Mapping the optical absorption and scattering properties of tissues using spatial frequency-domain imaging (SFDI) enhances quantitative fluorescence imaging of protoporphyrin IX (PpIX) in gliomas in the preclinical setting. The feasibility of using SFDI in the operating room was investigated here. A benchtop SFDI system was modified to mount directly to a commercial operating microscope. A digital light processing module imposed a selectable spatial light pattern from a broad-band xenon arc lamp to illuminate the surgical field. White light excitation and a liquid crystal-tunable filter allowed the diffuse reflectance images to be recorded at discrete wavelengths from 450 to 720 nm on a sCMOS camera. The performance was first tested in tissue-simulating phantoms, and data were then acquired intraoperatively during brain tumor resection surgery. The optical absorption and transport scattering coefficients could be estimated with average errors of 3.2% and 4.5% for the benchtop and clinical systems, respectively, with spatial resolution of better than 0.7 mm. These findings suggest that SFDI can be implemented in a clinically relevant configuration to achieve accurate mapping of the optical properties in the surgical field that can then be applied to achieve quantitative imaging of the fluorophore.