Significance: After three decades, more than 75,000 publications, tens of companies being involved in its commercialization, and a global market perspective of about USD 1.5 billion in 2023, optical coherence tomography (OCT) has become one of the fastest successfully translated imaging techniques with substantial clinical and economic impacts and acceptance.

Aim: Our perspective focuses on disruptive forward-looking innovations and key technologies to further boost OCT performance and therefore enable significantly enhanced medical diagnosis.

Approach: A comprehensive review of state-of-the-art accomplishments in OCT has been performed.

Results: The most disruptive future OCT innovations include imaging resolution and speed (single-beam raster scanning versus parallelization) improvement, new implementations for dual modality or even multimodality systems, and using endogenous or exogenous contrast in these hybrid OCT systems targeting molecular and metabolic imaging. Aside from OCT angiography, no other functional or contrast enhancing OCT extension has accomplished comparable clinical and commercial impacts. Some more recently developed extensions, e.g., optical coherence elastography, dynamic contrast OCT, optoretinography, and artificial intelligence enhanced OCT are also considered with high potential for the future. In addition, OCT miniaturization for portable, compact, handheld, and/or cost-effective capsule-based OCT applications, home-OCT, and self-OCT systems based on micro-optic assemblies or photonic integrated circuits will revolutionize new applications and availability in the near future. Finally, clinical translation of OCT including medical device regulatory challenges will continue to be absolutely essential.

Conclusions: With its exquisite non-invasive, micrometer resolution depth sectioning capability, OCT has especially revolutionized ophthalmic diagnosis and hence is the fastest adopted imaging technology in the history of ophthalmology. Nonetheless, OCT has not been completely exploited and has substantial growth potential—in academics as well as in industry. This applies not only to the ophthalmic application field, but also especially to the original motivation of OCT to enable optical biopsy, i.e., the in situ imaging of tissue microstructure with a resolution approaching that of histology but without the need for tissue excision.

Significance: Beer–Lambert law (BLL) is a widely used tool for contact and remote determination of absorber concentration in various media, including living tissues. Originally proposed in the 18th century as a simple exponential expression, it has survived numerous modifications and updates. The basic assumptions of this law may not be fulfilled in real measurement conditions. This can lead to mistaken or misinterpreted results. In particular, the effects to be additionally taken into account in the tissue measurements include anisotropy, scattering, fluorescence, chemical equilibria, interference, dichroism, spectral bandwidth disagreements, stray radiation, and instrumental effects.

Aim: We review the current state of the art and the main limitations of remote tissue diagnostics using the BLL. Historical development of updating this law by taking into account specific additional factors such as light scattering and photon pathlengths in diffuse reflectance is described, along with highlighting the main risks to be considered by interpreting the measured data.

Approach: Literature data related to extension and modification of the BLL related to tissue assessment and concentration estimation of specific tissue molecules are collected and analyzed. The main emphasis here is put on the optical measurements of living tissue chromophore concentrations and estimation of physiological parameters, e.g., blood oxygen saturation.

Results: Modified expressions of the BLL suitable for several specific cases of living tissue characterization are presented and discussed.

Conclusions: Applications of updated/modified Beer–Lambert law (MBLL) with respect to particular measurement conditions are helpful for obtaining more reliable data on the target tissue physiological state and biochemical content. MBLL accounting for the role of scattering in several ways appears to be a successful approach. Extended MBLL and BLL in the time domain form could provide more accurate results, but this requires more time resources to be spent.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification.

Aim: To reduce the class bias caused by data imbalance.

Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings.

Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications.

Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Significance: Peripheral pitting edema is a clinician-administered measure for grading edema. Peripheral edema is graded 0, 1  +  , 2  +  , 3  +  , or 4  +  , but subjectivity is a major limitation of this technique. A pilot clinical study for short-wave infrared (SWIR) molecular chemical imaging (MCI) effectiveness as an objective, non-contact quantitative peripheral edema measure is underway.

Aim: We explore if SWIR MCI can differentiate populations with and without peripheral edema. Further, we evaluate the technology for correctly stratifying subjects with peripheral edema.

Approach: SWIR MCI of shins from healthy subjects and heart failure (HF) patients was performed. Partial least squares discriminant analysis (PLS-DA) was used to discriminate the two populations. PLS regression (PLSR) was applied to assess the ability of MCI to grade edema.

Results: Average spectra from edema exhibited higher water absorption than non-edema spectra. SWIR MCI differentiated healthy volunteers from a population representing all pitting edema grades with 97.1% accuracy (N  =  103 shins). Additionally, SWIR MCI correctly classified shin pitting edema levels in patients with 81.6% accuracy.

Conclusions: Our study successfully achieved the two primary endpoints. Application of SWIR MCI to monitor patients while actively receiving HF treatment is necessary to validate SWIR MCI as an HF monitoring technology.

Significance: A non-destructive technique for accurately characterizing the spatial distribution of optical properties of soft tissue membranes may give improved outcomes in many tissue engineering applications.

Aim: This study aimed to develop a non-destructive macroscopic imaging technique that is sensitive to optical anisotropy, typical of fibrous components in soft tissue membranes, and can address some of the difficulties caused by the complex turbid nature of these tissues.

Approach: A near-infrared Mueller matrix imaging polarimeter employing logarithm decomposition was developed and used to conduct transmission measurements of all the polarization properties across the full thickness of bovine pericardium tissue.

Results: The full Mueller matrix was measured across a 70  mm  ×  70  mm sample of calf bovine pericardium and revealed significant retardance (linear and circular) and depolarization in this tissue. Regions with a uniform axis of optical anisotropy were identified. Mueller matrix imaging demonstrated that the exhibited circular retardance was sufficient to lead to possible misinterpretation of apparent fiber orientation when using conventional polarization imaging techniques for such tissues.

Conclusions: Mueller matrix imaging can identify regional distributions of optical anisotropy in calf bovine pericardium. This new capability is a promising development in non-destructive imaging for tissue selection.

Significance: The early detection of dysplasia in patients with Barrett’s esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy.

Aim: We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract.

Approach: We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, k-nearest neighbor classification, and a neural network.

Results: Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and k-nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett’s esophagus and neoplasia based on majority decisions per-image.

Conclusions: MSI shows promise for disease classification in Barrett’s esophagus and merits further investigation as a tool in high-definition “chip-on-tip” endoscopes.

Significance: The Cherenkov emission spectrum overlaps with that of ambient room light sources. Choice of room lighting devices dramatically affects the efficient detection of Cherenkov emission during patient treatment.

Aim: To determine optimal room light sources allowing Cherenkov emission imaging in normally lit radiotherapy treatment delivery rooms.

Approach: A variety of commercial light sources and long-pass (LP) filters were surveyed for spectral band separation from the red to near-infrared Cherenkov light emitted by tissue. Their effects on signal-to-noise ratio (SNR), Cherenkov to background signal ratio, and image artifacts were quantified by imaging irradiated tissue equivalent phantoms with an intensified time-gated CMOS camera.

Results: Because Cherenkov emission from tissue lies largely in the near-infrared spectrum, a controlled choice of ambient light that avoids this spectral band is ideal, along with a camera that is maximally sensitive to it. An RGB LED light source produced the best SNR out of all sources that mimic room light temperature. A 675-nm LP filter on the camera input further reduced ambient light detected (optical density > 3), achieving maximal SNR for Cherenkov emission near 40. Reduction of the room light signal reduced artifacts from specular reflection on the tissue surface and also minimized spurious Cherenkov signals from non-tissue features such as bolus.

Conclusions: LP filtering during image acquisition for near-infrared light in tandem with narrow band LED illuminated rooms improves image quality, trading off the loss of red wavelengths for better removal of room light in the image. This spectral filtering is also critically important to remove specular reflection in the images and allow for imaging of Cherenkov emission through clear bolus. Beyond time-gated external beam therapy systems, the spectral separation methods can be utilized for background removal for continuous treatment delivery methods including proton pencil beam scanning systems and brachytherapy.

Significance: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.

Aim: We aim to reduce the chest wall’s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction.

Approach: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.

Results: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.

Conclusions: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.

Significance: Deranged metabolism and dysregulated growth factor signaling are closely associated with abnormal levels of proliferation, a recognized hallmark in tumorigenesis. Fluorescence lifetime imaging microscopy (FLIM) of endogenous nicotinamide adenine dinucleotide (NADH), a key metabolic coenzyme, offers a non-invasive, diagnostic indicator of disease progression, and treatment response. The model-independent phasor analysis approach leverages FLIM to rapidly evaluate cancer metabolism in response to targeted therapy.

Aim: We combined lifetime and phasor FLIM analysis to evaluate the influence of human epidermal growth factor receptor 2 (HER2) inhibition, a prevalent cancer biomarker, on both nuclear and cytoplasmic NAD(P)H of two squamous cell carcinoma (SCC) cultures. While better established, the standard lifetime analysis approach is relatively slow and potentially subject to intrinsic fitting errors and model assumptions. Phasor FLIM analysis offers a rapid, model-independent alternative, but the sensitivity of the bound NAD(P)H fraction to growth factor signaling must also be firmly established.

Approach: Two SCC cultures with low- and high-HER2 expression, were imaged using multiphoton-excited NAD(P)H FLIM, with and without treatment of the HER2 inhibitor AG825. Cells were challenged with mitochondrial inhibition and uncoupling to investigate AG825’s impact on the overall metabolic capacity. Phasor FLIM and lifetime fitting analyses were compared within nuclear and cytoplasmic compartments to investigate epigenetic and metabolic impacts of HER2 inhibition.

Results: NAD(P)H fluorescence lifetime and bound fraction consistently decreased following HER2 inhibition in both cell lines. High-HER2 SCC74B cells displayed a more significant response than low-HER2 SCC74A in both techniques. HER2 inhibition induced greater changes in nuclear than cytoplasmic compartments, leading to an increase in NAD(P)H intensity and concentration.

Conclusions: The use of both, complementary FLIM analysis techniques together with quantitative fluorescence intensity revealed consistent, quantitative changes in NAD(P)H metabolism associated with inhibition of growth factor signaling in SCC cell lines. HER2 inhibition promoted increased reliance on oxidative phosphorylation in both cell lines.

Significance: Fourier ptychography microscopy (FPM) is a computational optical imaging technology that employs angularly varying illuminations and a phase retrieval algorithm to achieve a wide field of view and high-resolution imaging simultaneously. In the FPM, LED position error will reduce the quality of the reconstructed high-resolution image. To correct the LED positions, current methods consider each of the LED positions as independent and use an optimization algorithm to get each of the positions. When the positional misalignment is large or the search position falls into a local optimal value, the current methods may lack stability and accuracy.

Aim: We improve the model of the LED position and propose an accurate and stable two-step correction scheme (tcFPM) to calibrate the LED position error.

Approach: The improved LED positions model combines the overall offset, which represents the relative deviation of the LED array and the optical axis, with the slight deviation of each LED’s independent position. In the tcFPM, the overall offset of the LED array is corrected at first, which obtains an approximate value of the overall offset of the LED array. Then the position of each LED is precisely adjusted, which obtains the slight offset of each LED.

Results: This LED position error model is more in line with the actual situation. The simulation and experimental results show that the method has high accuracy in correcting the LED position. Furthermore, the reconstruction process of tcFPM is more stable and significantly improves the quality of the reconstruction results, which is compared with some LED position error correction methods.

Conclusions: An LED position error correction technology is proposed, which has a stable iterative process and improves the reconstruction accuracy of complex amplitude.