## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Overcoming Technological Limitations  
ML offers a promising solution to address certain inherent limitations in SWIR techniques. A notable restriction of SWIRF imaging is the lack of FDA-approved dyes tailored for efficient emission in the SWIR band [8]. Currently, Indocyanine Green (ICG) stands as the lone FDA-approved option, primarily emitting in the NIR-I region (700-900 nm), albeit with a relatively weak emission tail in the SWIR spectrum [8]. A study aimed to harness the power of DNN to convert SWIRF images captured within the NIR-I/IIa window (900-1300 nm) using ICG dyes into comparable images captured in the NIR-IIb (1500-1700 nm) range with SWIR-specialized dyes (see Figure 9) [51]. Results showcased significant enhancement in the signal-to-base ratio (>100) for in vivo lymph node imaging, along with notable improvements in tumor-to-normal tissue ratios (>20) and tumor margin detection through imaging epidermal growth factor receptor after processing ICG images by the trained DNN. Another challenge in fluorescent imaging pertains to accurately reconstructing a 3D map of fluorophore distribution, a task constrained by complicated inverse modeling problems. DNNs demonstrated capability to directly render the 3D distribution of fluorophores from raw data, eliminating the need for complex inverse modeling calculations [53]. Increasing the imaging depth in SWIRF has also become possible with DNN models. While the typical maximum penetration depth in soft tissues in the SWIR range spans from 4 to 6 mm, penetration depth decreases to 1.4 mm for brain imaging due to higher tissue scattering. To overcome this limitation, a DNN was trained using images acquired through a two-photon illumination technique. Reconstructing SWIRF images with this net  
Figure 9: Generativemodel of NIR-IIb from NIR-IIa. Contrast enhancement of SWIRF by generating images with a GAN network to resemble a NIR-IIb image from a NIR-IIa input image (scale bar, 5 mm). Figure adapted with permission from [51]. work resulted in enhanced SNR in deeper tissue layers [54]. This advancement facilitated 3D volume reconstruction of brain tissues with enhanced details without compromising temporal resolution. ML models have also addressed some systemic limitations of OCT imaging, such as enhancing image contrast, extending the imaging range, and correcting for degradation of spatial resolution with depth. For instance, dual DNNs have directly enhanced axial resolution from raw interference fringe signals and subsequently reconstructed B-scans to reduce speckle noise [106]. Likewise, DNNs with GAN structures have been trained with different variations of the OCT system to produce speckle-free images [107; 108]. For OCT retinal images, speckle noise was effectively removed using CNN [109] and GAN [110] deep networks that autonomously learned from training data, eliminating the need for manual parameter selection. OCT's challenge of balancing lateral resolution and depth of focus was successfully met by a DNN, which reconstructed out-of-focus en-face images through a GAN structure [111]. ML models have also accelerated OCT systems by reducing required spectral datapoints, followed by DNN-based reconstruction to eliminate aliasing artifacts resulting from undersampling [112; 113]. Additionally, to combat axial resolution degradation due to light dispersion in tissues, a modified U-Net architecture has proven effective in compensating for chromatic dispersion in OCT [67]. Beyond conventional OCT, ML models have helped overcome technological limitations of functional extensions of OCT. For instance, in OCT-Angiography (OCTA), the quality of results and the field of view have an inverse relationship. To address the issue, a DNN was employed to transform low-quality outcomes obtained from a 6 mm by 6 mm field of view into high-quality results acquired from a 3 mm by 3 mm field of view [114]. ML has also been used to improve the performance of OCT phase-tied modalities, such as PT-OCT.