Theodoor J. M. Ruers

This paper presents the design and performance evaluation of an illumination-based hyperspectral imaging system for laparoscopic surgery, referred to as the HyperScope. Hyperspectral imaging holds significant promise in minimally invasive surgery due to its ability to capture detailed spectral signatures that can enhance intraoperative tissue characterization and improve surgical decision-making without the need for exogenous contrast agents. The presented HyperScope system integrates a supercontinuum laser source, a laser line tunable filter, and a laparoscope-coupled short-wave infrared camera, enabling spectral imaging across a broad wavelength range (450–1500 nm) with high spectral resolution and sufficient spatial resolution quantified through several metrics. The system’s hardware and software components are optimized for clinical usability and spectral fidelity. Performance was assessed through a series of technical experiments, including spectral response characterization, a modulation transfer function measurement using the slanted-edge method, and an evaluation of distance compensation via pre-processing. The system achieves a spectral deviation of 0.16 nm (450–975 nm) and −1.23 nm (1000–1675 nm), with mean spatial resolutions of 0.02 mm in the visible range (450–800 nm) and 0.14 mm in the SWIR range (800–1500 nm). The results demonstrate consistent spectral accuracy, acceptable spatial resolution across the spectral range, and effective correction of distance-induced spectral variability using standard normal variate (SNV) processing. These findings support the potential of the HyperScope system for future integration into clinical MIS workflows, with applications in tumor detection, perfusion assessment, and real-time optical biopsies.

SignificanceMinimally invasive surgery (MIS) offers substantial benefits to patients, including reduced trauma and faster recovery. However, it limits visual and tactile feedback, which can affect surgical decision-making. Hyperspectral and multispectral imaging (HSI/MSI) are imaging technologies with the potential to provide detailed, real-time tissue characterization, enhancing minimally invasive intraoperative guidance.AimThis perspective aims to summarize the current state-of-the-art in the use of HSI/MSI in laparoscopy and endoscopy. It focuses on both technological development and clinical implementation, providing an overview of performance characteristics and translational state.ApproachA structured literature search was conducted to identify relevant studies. These were analyzed for key system specifications (spectral range, resolution, and acquisition speed) and clinical applications. The studies were further categorized using a “bench-to-bedside” framework to evaluate their level of translational progress.ResultsThe majority of studies fall within early translational stages (T1 to T2), with no reports of large-scale clinical trials. Most systems operate in the visible spectrum (450 to 650 nm), optimized for blood-related imaging. Applications include perfusion assessment, nerve visualization using short-wave infrared wavelengths, and tumor detection.ConclusionsHSI/MSI for MIS is a rapidly developing field with demonstrated potential across a range of applications. Continued research and validation are essential to transition these technologies from experimental use to routine surgical practice.

Hyperspectral imaging captures detailed spectral information for each pixel, enabling analysis of material properties through volume reflection; however, accurate interpretation is often hindered by challenges such as the need for reproducible calibration and the impact of surface reflections (glare), particularly on rough surfaces. To address these limitations, we introduce an algorithmic implicit calibration (IC) technique that leverages predictable differences in the spatial distribution of surface and volume reflections to achieve accurate calibration directly from the acquired data. By exploiting the invariance of surface reflection spectra as a surrogate reference, this approach circumvents the need for traditional reference tiles and enables calibration under complex illumination and geometric conditions. The proposed method was evaluated using simulations, optical phantoms, and hyperspectral images of surgical specimens, where it successfully isolated volume reflections while effectively managing surface reflections. In simulations, the method achieved a median relative difference of 2.07%, and a difference of 7.83% compared to conventional calibration on phantoms, demonstrating robust performance. Overall, IC provides a practical solution for improving hyperspectral imaging accuracy when conventional calibration is not feasible or reliable. By simplifying the calibration process and maintaining data quality in variable settings, this approach has broad potential to enhance diagnostic performance, particularly in biomedical applications such as surgical guidance and tissue classification, where calibration at the same time and location as the measurement improves robustness and facilitates use in sterile environments.

Optical phantoms are widely used to characterize diffuse optical setups and data analysis methods for in-vivo/ex-vivo measurements. Coconut oil is an interesting compound to use in phantoms, because it could be used to model lipidic tissues, such as the one in breast tissue. In this paper, we measure the absorption and scattering spectra of coconut oil from 600 to 1600 nm, encompassing the so-called “therapeutic window”. To cover the entire range, we exploit a supercontinuum pulsed laser and a superconducting nanowire single photon detector operating in the time domain. Finally, we demonstrate the use of a homogeneous coconut oil phantom to characterize a hyperspectral continuous-wave (CW) setup.

Accurate endoscope pose estimation and 3D tissue surface reconstruction significantly enhances monocular minimally invasive surgical procedures by enabling accurate navigation and improved spatial awareness. However, monocular endoscope pose estimation and tissue reconstruction face persistent challenges, including depth ambiguity, physiological tissue deformation, inconsistent endoscope motion, limited texture fidelity, and a restricted field of view. To overcome these limitations, a unified framework for monocular endoscopic tissue reconstruction that integrates scale-aware depth prediction with temporally-constrained perceptual refinement is presented. This framework incorporates a novel MAPIS-Depth module, which leverages Depth Pro for robust initialisation and Depth Anything for efficient per-frame depth prediction, in conjunction with L-BFGS-B optimisation, to generate pseudo-metric depth estimates. These estimates are temporally refined by computing pixel correspondences using RAFT and adaptively blending flow-warped frames based on LPIPS perceptual similarity, thereby reducing artefacts arising from physiological tissue deformation and motion. To ensure accurate registration of the synthesised pseudo-RGBD frames from MAPIS-Depth, a novel WEMA-RTDL module is integrated, optimising both rotation and translation. Finally, truncated signed distance function-based volumetric fusion and marching cubes are applied to extract a comprehensive 3D surface mesh. Evaluations on HEVD and SCARED, with ablation and comparative analyses, demonstrate the framework's robustness and superiority over state-of-the-art methods.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Optical technologies revolutionized the field of oncologic surgery in recent years by providing non-invasive and innovative ways to monitor the assessment of resection margins during surgical procedures. By providing real-time visualization of tissue characteristics, optical technologies, such as diffuse reflectance spectroscopy (DRS),^1,2 fluorescence lifetime imaging,³ and hyperspectral imaging,^4,5 can help to assess if all cancerous tissues are removed while minimizing damage to surrounding healthy tissues. This will lower the number of positive resection margins and thereby reduce the need for additional treatments, such as surgical re-excision or radiotherapy. However, the accuracy and reliability of these technologies are crucial to ensure safety and efficacy during surgical procedures. Therefore, validation of optical technologies is essential to establish their clinical significance and provide healthcare professionals with the confidence to use them in their practice. This involves assessing the clinical outcomes of these technologies with the current ground truth.⁶