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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.

In this work, we present a significant step toward in vivo ophthalmic optical coherence tomography and angiography on a photonic integrated chip. The diffraction gratings used in spectral-domain optical coherence tomography can be replaced by photonic integrated circuits comprising an arrayed waveguide grating. Two arrayed waveguide grating designs with 256 channels were tested, which enabled the first chip-based optical coherence tomography and angiography in vivo three-dimensional human retinal measurements. Design 1 supports a bandwidth of 22 nm, with which a sensitivity of up to 91 dB (830 µW) and an axial resolution of 10.7 µm was measured. Design 2 supports a bandwidth of 48 nm, with which a sensitivity of 90 dB (480 µW) and an axial resolution of 6.5 µm was measured. The silicon nitride-based integrated optical waveguides were fabricated with a fully CMOS-compatible process, which allows their monolithic co-integration on top of an optoelectronic silicon chip. As a benchmark for chip-based optical coherence tomography, tomograms generated by a commercially available clinical spectral-domain optical coherence tomography system were compared to those acquired with on-chip gratings. The similarities in the tomograms demonstrate the significant clinical potential for further integration of optical coherence tomography on a chip system.Optical coherence tomography: chip promiseThe goal of an optical coherence tomography (OCT) imaging system that is integrated on a photonic chip has taken a step closer to reality. Elisabet Rank and coworkers from Austria have shown that arrayed waveguide gratings (AWGs), integrated-optical devices commonly used to separate different wavelength channels in an optical communications system, can be used to replace diffraction gratings in an OCT system. Several designs of silicon nitride AWGs with 256 channels in the near-infrared were fabricated and then tested in an OCT system which was able to capture in-vivo tomograms and angiography of the human eye’s retina, with comparable quality to a conventional system. The next stage of the OCT-on-a-chip research will focus on exploring the use of multimode interference structures, integrated photodiodes, and compact light sources.

Optical coherence tomography (OCT) enables early detection of vision-threatening diabetic retinopathy (DR) and retinal fluid accumulation, both major complications of diabetes. Despite high classification performance, deep learning models face limited clinical adoption due to poor interpretability. While attribution methods are effective for explaining predictions in the natural image domain, their applicability to medical imaging remains underexplored. To bridge this gap, our work explores how well these strong results transfer to the medical imaging domain. This study evaluates three cutting-edge attribution methods— DeepLIFT, AGI, and AttEXplore —for explaining predictions of a VGG16-based deep learning model in DR classification using widefield OCTA en face images and fluid detection in OCT B-scans. We assess attribution methods’ ability to highlight clinically relevant regions using quantitative (insertion and deletion scores) and qualitative (heatmap visual analysis) measures. Although the VGG16 model achieves high classification accuracy (94% for DR and 98% for fluid), attribution methods yield markedly different qualitative results due to variations in underlying assumptions and hyperparameter sensitivity. Additionally, high insertion or low deletion scores do not necessarily correlate with clinically meaningful visual attributions. In particular, insertion- and deletion-based behaviour can be more informative in pathological cases, where localized lesions can drive predictions, but tends to be less informative in normal cases, where confirming the absence of pathology requires global contextual evidence. Comparing the three approaches, we find that AGI and AttEXplore achieve similarly strong quantitative performance, whereas AttEXplore more consistently highlights clinically meaningful structures in pathological cases than AGI and DeepLIFT, making it the preferred option in our DR and fluid detection settings. However, our results also show that in any potential clinical usage, all three methods must be interpreted alongside clinical expertise, and even the result of the best-performing attribution approach cannot serve as an automatic proxy for clinical relevance. This study provides critical insights into challenges of applying attribution methods to medical imaging, using ophthalmic data, laying a foundation for improving transparency and trust in AI-assisted diagnostics.

Ocular aberrometry with a wide dynamic range for assessing vision performance and anterior segment imaging that provides anatomical details of the eye are both essential for vision research and clinical applications. Defocus error is a major limitation of digital wavefront aberrometry (DWA), as the blurring of the detected point spread function (PSF) significantly reduces the signal-to-noise ratio (SNR) beyond the ±3 D range. With the aid of Badal-like precompensation of defocus, the dynamic defocus range of the captured aberrated PSFs can be effectively extended. We demonstrate a dual-modality MHz VCSEL-based swept-source OCT (SS-OCT) system with easy switching between DWA and OCT imaging modes. The system is capable of measuring aberrations with defocus dynamic range of 20 D as well as providing fast anatomical imaging of the anterior segment at an A-scan rate of 1.6 MHz.

Optical coherence tomography (OCT) is a non-contact method for imaging the topological and internal microstructure of samples in three dimensions. OCT can be configured as a conventional microscope, as an ophthalmic scanner, or using endoscopes and small diameter catheters for accessing internal biological organs. In this Primer, we describe the principles underpinning the different instrument configurations that are tailored to distinct imaging applications and explain the origin of signal, based on light scattering and propagation. Although OCT has been used for imaging inanimate objects, we focus our discussion on biological and medical imaging. We examine the signal processing methods and algorithms that make OCT exquisitely sensitive to reflections as weak as just a few photons and that reveal functional information in addition to structure. Image processing, display and interpretation, which are all critical for effective biomedical imaging, are discussed in the context of specific applications. Finally, we consider image artifacts and limitations that commonly arise and reflect on future advances and opportunities.

Organoid and spheroid technology provide valuable insights into developmental biology and oncology. Optical coherence tomography (OCT) is a label-free technique that has emerged as an excellent tool for monitoring the structure and function of these samples. However, mature organoids are often too opaque for OCT. Access to multi-angle views is highly desirable to overcome this limitation, preferably with non-contact sample handling. To fulfil these requirements, we present an ultrasound-induced reorientation method for multi-angle-OCT, which employs a 3D-printed acoustic trap inserted into an OCT imaging system, to levitate and reorient zebrafish larvae and tumor spheroids in a controlled and reproducible manner. A model-based algorithm was developed for the physically consistent fusion of multi-angle data from a priori unknown angles. We demonstrate enhanced penetration depth in the joint 3D-recovery of reflectivity, attenuation, refractive index, and position registration for zebrafish larvae, creating an enabling tool for future applications in volumetric imaging. Accessing multi-angle views of organoids is important for biology and oncology. The authors propose ultrasound-induced reorientation for multi-angle optical coherence tomography, using a 3D-printed acoustic trap to levitate and rotate samples with a model-based algorithm for reconstruction.

Maximal safe tumor resection remains the key prognostic factor for improved prognosis in brain tumor patients. Despite 5-aminolevulinic acid-based fluorescence guidance the neurosurgeon is, however, not able to visualize most low-grade gliomas (LGG) and infiltration zone of high-grade gliomas (HGG). To overcome the need for a more sensitive visualization, we investigated the potential of macroscopic, wide-field fluorescence lifetime imaging of nicotinamide adenine dinucleotide (NADH) and protoporphyrin IX (PPIX) in selected human brain tumors. For future intraoperative use, the imaging system offered a square field of view of 11 mm at 250 mm free working distance. We performed imaging of tumor tissue ex vivo, including LGG and HGG as well as brain metastases obtained from 21 patients undergoing fluorescence-guided surgery. Half of all samples showed visible fluorescence during surgery, which was associated with significant increase in PPIX fluorescence lifetime. While the PPIX lifetime was significantly different between specific tumor tissue types, the NADH lifetimes did not differ significantly among them. However, mainly necrotic areas exhibited significantly lower NADH lifetimes compared to compact tumor in HGG. Our pilot study indicates that combined fluorescence lifetime imaging of NADH/PPIX represents a sensitive tool to visualize brain tumor tissue not detectable with conventional 5-ALA fluorescence.

By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction. In this work, we propose a low complexity neural network for denoising, directly incorporated into the image reconstruction pipeline of a microscope-integrated 4D-OCT prototype with an A-scan rate of 1.2 MHz. For this purpose, we trained a blind-spot network on unpaired OCT images using a self-supervised learning approach. With an optimized U-Net, only a few milliseconds of additional latency were introduced. Simultaneously, these architectural adaptations improved the numerical denoising performance compared to the basic setup, outperforming non-local filtering algorithms. Layers and edges of anatomical structures in B-scans were better preserved than with Gaussian filtering despite comparable processing time. By comparing scenes with and without denoising employed, we show that neural networks can be used to improve visual appearance of volumetric renderings in real time. Enhancing the rendering quality is an important step for the clinical acceptance and translation of 4D-OCT as an intra-surgical guidance tool.

The cutaneous vasculature is involved in many diseases. Current clinical examination techniques, however, cannot resolve the human vasculature with all plexus in a non-invasive manner. By combining an optical coherence tomography system with angiography extension and an all optical photoacoustic tomography system, we can resolve in 3D the blood vessels in human skin for all plexus non-invasively. With a customized imaging unit that permits access to various parts of patients’ bodies, we applied our multimodality imaging system to investigate several different types of skin conditions. Quantitative vascular analysis is given for each of the dermatological conditions to show the potential diagnostic value of our system in non-invasive examination of diseases and physiological processes. Improved performance of our system over its previous generation is also demonstrated with an updated characterization.

Diabetic retinopathy (DR), a pathologic change of the human retinal vasculature, is the leading cause of blindness in working-age adults with diabetes mellitus. Optical coherence tomography angiography (OCTA), a functional extension of optical coherence tomography, has shown potential as a tool for early diagnosis of DR through its ability to visualize the retinal vasculature in all spatial dimensions. Previously introduced deep learning-based classifiers were able to support the detection of DR in OCTA images, but require expert labeling at the pixel level, a labor-intensive and expensive process. We present a multiple instance learning-based network, MIL-ResNet,14 that is capable of detecting biomarkers in an OCTA dataset with high accuracy, without the need for annotations other than the information whether a scan is from a diabetic patient or not. The dataset we used for this study was acquired with a diagnostic ultra-widefield swept-source OCT device with a MHz A-scan rate. We were able to show that our proposed method outperforms previous state-of-the-art networks for this classification task, ResNet14 and VGG16. In addition, our network pays special attention to clinically relevant biomarkers and is robust against adversarial attacks. Therefore, we believe that it could serve as a powerful diagnostic decision support tool for clinical ophthalmic screening.