Toward assessment of resection margins using hyperspectral diffuse reflection imaging (400–1,700 nm) during tongue cancer surgery

Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach

BACKGROUND

Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility.

OBJECTIVE

To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC).

METHODS

Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision.

RESULTS

This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system's ability to reliably detect tumor regions and showing high concordance with histopathological findings.

CONCLUSION

The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.

Toward real-time margin assessment in breast-conserving surgery with hyperspectral imaging

Margin assessment in breast-conserving surgery (BSC) remains a critical challenge, with 20-25% of cases resulting in inadequate tumor resection, increasing the risk of local recurrence and the need for additional treatment. In this study, we evaluate the diagnostic performance of hyperspectral imaging (HSI) as a non-invasive technique for assessing resection margins in ex vivo lumpectomy specimens. A dataset of over 200 lumpectomy specimens was collected using two hyperspectral cameras, and a classification algorithm was developed to distinguish between healthy and tumor tissue within margins of 0 and 2 mm. The proposed approach achieved its highest diagnostic performance at a 0 mm margin, with a sensitivity of 92%, specificity of 78%, accuracy of 83%, Matthews correlation coefficient of 68%, and an area under the curve of 89%. The entire resection surface could be imaged and evaluated within 10 minutes, providing a rapid and non-invasive alternative to conventional margin assessment techniques. These findings represent a significant advancement toward real-time intraoperative margin assessment, highlighting the potential of HSI to enhance surgical precision and reduce re-excision rates in BCS.

Endoscopic In Vivo Hyperspectral Imaging for Head and Neck Tumor Surgeries Using a Medically Approved CE-Certified Camera with Rapid Visualization During Surgery

Background: In vivo visualization of malignant tumors remains the main challenge during head and neck cancer surgery. This can result in inadequate tumor margin assessment and incomplete tumor resection, adversely affecting patient outcomes. Hyperspectral imaging (HSI) is a promising approach to address this issue. However, its application in surgery has been limited by the lack of medically approved HSI devices compliant with MDR regulations, as well as challenges regarding the integration into the surgical workflow. Methods: In this feasibility study, we employed endoscopic HSI during surgery to visualize the tumor sites of 12 head and neck cancer patients. We optimized the HSI workflow to minimize time required during surgery and to reduce the adaptation period needed for surgeons to adjust to the new workflow. Additionally, we implemented data processing to enable real-time classification and visualization of HSI within the intraoperative setting. HSI evaluation was conducted using principal component analysis and k-means clustering, with this clustering validated through comparison with expert annotations. Results: Our complete HSI workflow requires two to three minutes, with each HSI measurement-including evaluation and visualization-taking less than 10 s, achieving an accuracy of 79%, sensitivity of 72%, and specificity of 84%. Medical personnel became proficient with the HSI system after two surgeries. Conclusions: This study presents an HSI workflow for in vivo tissue differentiation during head and neck cancer surgery, providing accurate and visually accessible results within minimal time. This approach enhances the in vivo evaluation of tumor margins, leading to more clear margins and, consequently, improved patient outcomes.

Hyperspectral imaging and evaluation of surgical margins: where do we stand?

PURPOSE OF REVIEW

To highlight the recent literature on the use of hyperspectral imaging (HSI) for cancer margin evaluation ex vivo, for head and neck cancer pathology and in vivo during head and neck cancer surgery.

RECENT FINDINGS

HSI can be used ex vivo on unstained and stained tissue sections to analyze head and neck tissue and tumor cells in combination with machine learning approaches to analyze head and neck cancer cell characteristics and to discriminate the tumor border from normal tissue. Data on in vivo applications during head and neck cancer surgery are preliminary and limited. Even now an accuracy of 80% for tumor versus nonneoplastic tissue classification can be achieved for certain tasks, within the current in vivo settings.

SUMMARY

Significant progress has been made to introduce HSI for ex vivo head and neck cancer pathology evaluation and for an intraoperative use to define the tumor margins. To optimize the accuracy for in vivo use, larger HSI databases with annotations for head and neck cancer are needed.

Comparison of image quality of 3D ultrasound: motorized acquisition versus freehand navigated acquisition, a phantom study

PURPOSE

Intra-operative assessment of resection margins during oncological surgery is a field that needs improvement. Ultrasound (US) shows the potential to fulfill this need, but this imaging technique is highly operator-dependent. A 3D US image of the whole specimen may remedy the operator dependence. This study aims to compare and evaluate the image quality of 3D US between freehand acquisition (FA) and motorized acquisition (MA).

METHODS

Multiple 3D US volumes of a commercial phantom were acquired in motorized and freehand fashion. FA images were collected with electromagnetic navigation. An integrated algorithm reconstructed the FA images. MA images were stacked into a 3D volume. The image quality is evaluated following the metrics: contrast resolution, axial and elevation resolution, axial and elevation distance calibration, stability, inter-operator variability, and intra-operator variability. A linear mixed model determined statistical differences between FA and MA for these metrics.

RESULTS

The MA results in a statistically significant lower error of axial distance calibration (p < 0.0001) and higher stability (p < 0.0001) than FA. On the other hand, the FA has a better elevation resolution (p < 0.003) than the MA.

CONCLUSION

MA results in better image quality of 3D US than the FA method based on axial distance calibration, stability, and variability. This study suggests acquiring 3D US volumes for intra-operative ex vivo margin assessment in a motorized fashion.

Diffuse Reflectance Spectroscopy of the Oral Mucosa: In Vivo Experimental Validation of the Precancerous Lesions Early Detection Possibility

This article is devoted to the experimental validation of the possibility of early detection of precancerous lesions in the oral mucosa in vivo using diffuse reflectance spectroscopy in the wavelength range from 360 to 1000 nm. During the study, a sample of 119 patients with precancerous lesions has been collected and analyzed. As a result of the analysis, the most informative wavelength ranges were determined, in which the maximum differences in the backscattering spectra of lesions and intact tissues were observed, methods for automatic classification of backscattering spectra of the oral mucosa were studied, sensitivity and specificity values, achievable using diffuse reflectance spectroscopy for detecting hyperkeratosis on the tongue ventrolateral mucosa surface and buccal mucosa, were evaluated. As a result of preliminary experimental studies in vivo, the possibility of automatic detection of precancerous lesions of the oral mucosa surface using diffuse reflectance spectroscopy in the wavelength range from 500 to 900 nm with an accuracy of at least 75 percent has been shown.

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images

Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

Rapid and Quantitative Intraoperative Pathology-Assisted Surgery by Paired-Agent Imaging-Derived Confidence Map

PURPOSE

In nonmetastatic head and neck cancer treatment, surgical margin status is the most important prognosticator of recurrence and patient survival. Fresh frozen sectioning (FFS) of tissue margins is the standard of care for intraoperative margin assessment. However, FFS is time intensive, and its accuracy is not consistent among institutes. Mapping the epidermal growth factor receptor (EGFR) using paired-agent imaging (PAI) has the potential to provide more consistent intraoperative margin assessment in a fraction of the time as FFS.

PROCEDURES

PAI was carried out through IV injection of an anti-epidermal growth factor receptor (EGFR) affibody molecule (ABY-029, eIND 122,681) and an untargeted IRDye680LT carboxylate. Imaging was performed on 4 µm frozen sections from three oral squamous cell carcinoma xenograft mouse models (n = 24, 8 samples per cell line). The diagnostic ability and tumor contrast were compared between binding potential, targeted, and untargeted images. Confidence maps were constructed based on group histogram-derived tumor probability curves. Tumor differentiability and contrast by confidence maps were evaluated.

RESULTS

PAI outperformed ABY-029 and IRDye 680LT alone, demonstrating the highest individual receiver operating characteristic (ROC) curve area under the curve (PAI AUC: 0.91, 0.90, and 0.79) and contrast-to-noise ratio (PAI CNR: 1, 1.1, and 0.6) for FaDu, Det 562, and A253. PAI confidence maps (PAI CM) maintain high tumor diagnostic ability (PAI CMAUC: 0.91, 0.90, and 0.79) while significantly enhancing tumor contrast (PAI CMCNR: 1.5, 1.3, and 0.8) in FaDu, Det 562, and A253. Additionally, the PAI confidence map allows avascular A253 to be differentiated from a healthy tissue with significantly higher contrast than PAI. Notably, PAI does not require additional staining and therefore significantly reduces the tumor delineation time in a 5 [Formula: see text] 5 mm slice from ~ 35 min to under a minute.

CONCLUSION

This study demonstrated that PAI improved tumor detection in frozen sections with high diagnostic accuracy and rapid analysis times. The novel PAI confidence map improved the contrast in vascular tumors and differentiability in avascular tumors. With a larger database, the PAI confidence map promises to standardize fluorescence imaging in intraoperative pathology-assisted surgery (IPAS).

Image-guided surgery in oral cancer: toward improved margin control

PURPOSE OF REVIEW

The aim of this review is to discuss recent studies on the assessment of tumor extension and resection margins by different intraoperative techniques allowing for image-guided surgery of oral cancer.

RECENT FINDINGS

There are different in-vivo and ex-vivo intraoperative techniques to improve margin control of which intraoperative ultrasound and targeted fluorescence-guided resections have high potential clinical value and are closest to clinical implementation.

SUMMARY

In oral cancer surgery, resection margins, particularly deep margins, are often inadequate. Intraoperative frozen section does not improve resection margin control sufficiently. Specimen-driven intraoperative assessment for gross analysis of suspected margins reduces the amount of positive resection margins substantially but leaves still room for improvement. Mucosal staining methods, optical coherence tomography and narrow band imaging can only be used for superficial (mucosal) resection margin control. Spectroscopy is under investigation, but clinical data are scarce. Intraoperative ex-vivo imaging of the resection specimen by magnetic resonance and PET/computed tomography may be used to assess resection margins but needs more research. Intraoperative in-vivo ad ex-vivo ultrasound and targeted fluorescence imaging have high potential clinical value to guide oral cancer resections and are closest to clinical implementation for improved margin control.

Intraoperative imaging in pathology-assisted surgery

The pathological assessment of surgical specimens during surgery can reduce the incidence of positive resection margins, which otherwise can result in additional surgeries or aggressive therapeutic regimens. To improve patient outcomes, intraoperative spectroscopic, fluorescence-based, structural, optoacoustic and radiological imaging techniques are being tested on freshly excised tissue. The specific clinical setting and tumour type largely determine whether endogenous or exogenous contrast is to be detected and whether the tumour specificity of the detected biomarker, image resolution, image-acquisition times or penetration depth are to be prioritized. In this Perspective, we describe current clinical standards for intraoperative tissue analysis and discuss how intraoperative imaging is being implemented. We also discuss potential implementations of intraoperative pathology-assisted surgery for clinical decision-making.

Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures

Non-melanoma skin cancer, and basal cell carcinoma in particular, is one of the most common types of cancer. Although this type of malignancy has lower metastatic rates than other types of skin cancer, its locally destructive nature and the advantages of its timely treatment make early detection vital. The combination of multispectral imaging and artificial intelligence has arisen as a powerful tool for the detection and classification of skin cancer in a non-invasive manner. The present study uses hyperspectral images to discern between healthy and basal cell carcinoma hyperspectral signatures. Upon the combined use of convolutional neural networks, with a final support vector machine activation layer, the present study reaches up to 90% accuracy, with an area under the receiver operating characteristic curve being calculated at 0.9 as well. While the results are promising, future research should build upon a dataset with a larger number of patients.

Hyperspectral Imaging for Tissue Classification after Advanced Stage Ovarian Cancer Surgery-A Pilot Study

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665−975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.

Proposal of a new grading system for meningioma resection: the Copenhagen Protocol

INTRODUCTION

The extent of meningioma resection is the most fundamental risk factor for recurrence, and exact knowledge of extent of resection is necessary for prognostication and for planning of adjuvant treatment. Currently used classifications are the EANO-grading and the Simpson grading. The former comprises radiological imaging with contrast-enhanced MRI and differentiation between "gross total removal" and "subtotal removal," while the latter comprises a five-tiered differentiation of the surgeon's impression of the extent of resection. The extent of resection of tumors is usually defined via analyses of resection margins but has until now not been implemented for meningiomas. PET/MRI imaging with ⁶⁸Ga-DOTATOC allows more sensitive and specific imaging than MRI following surgery of meningiomas.

OBJECTIVE

To develop an objective grading system based on microscopic analyses of resection margins and sensitive radiological analyses to improve management of follow-up, adjuvant therapy, and prognostication of meningiomas. Based on the rationale of resection-margin analyses as gold standard and superior imaging performance of ⁶⁸Ga DOTATOC PET, we propose "Copenhagen Grading" for meningiomas.

RESULTS

Copenhagen Grading was described for six pilot patients with examples of positive and negative findings on histopathology and DOTATOC PET scanning. The grading could be traceably implemented and parameters of grading appeared complementary. Copenhagen Grading is prospectively implemented as a clinical standard at Rigshospitalet, Copenhagen.

CONCLUSION

Copenhagen Grading provided a comprehensive, logical, and reproducible definition of the extent of resection. It offers promise to be the most sensitive and specific imaging modality available for meningiomas. Clinical and cost-efficacy remain to be established during prospective implementation.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

BACKGROUND AND OBJECTIVES

Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer.

MATERIALS AND METHODS

Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance.

RESULTS

A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively.

CONCLUSION

It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis

Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal wavelengths for these purposes are yet to be conclusively determined. A visible-near infrared hyperspectral camera with an ad-hoc built platform was used for image acquisition in the present study. Robust statistical techniques were used to conclude an optimal range between 573.45 and 779.88 nm to distinguish between healthy and non-healthy skin. Wavelengths between 429.16 and 520.17 nm were additionally found to be optimal for the differentiation between cancer types.

Assessment of the deep resection margin during oral cancer surgery: A systematic review

The main challenge for radical resection in oral cancer surgery is to obtain adequate resection margins. Especially the deep margin, which can only be estimated based on palpation during surgery, is often reported inadequate. To increase the percentage of radical resections, there is a need for a quick, easy, minimal invasive method, which assesses the deep resection margin without interrupting or prolonging surgery. This systematic review provides an overview of technologies that are currently being studied with the aim of fulfilling this demand. A literature search was conducted through the databases Medline, Embase and the Cochrane Library. A total of 62 studies were included. The results were categorized according to the type of technique: 'Frozen Section Analysis', 'Fluorescence', 'Optical Imaging', 'Conventional imaging techniques', and 'Cytological assessment'. This systematic review gives for each technique an overview of the reported performance (accuracy, sensitivity, specificity, positive predictive value, negative predictive value, or a different outcome measure), acquisition time, and sampling depth. At the moment, the most prevailing technique remains frozen section analysis. In the search for other assessment methods to evaluate the deep resection margin, some technologies are very promising for future use when effectiveness has been shown in larger trials, e.g., fluorescence (real-time, sampling depth up to 6 mm) or optical techniques such as hyperspectral imaging (real-time, sampling depth few mm) for microscopic margin assessment and ultrasound (less than 10 min, sampling depth several cm) for assessment on a macroscopic scale.

Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000-1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.

Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation

OBJECTIVE

The utilization of hyperspectral imaging (HSI) in real-time tumor segmentation during a surgery have recently received much attention, but it remains a very challenging task.

METHODS

In this work, we propose semantic segmentation methods, and compare them with other relevant deep learning algorithms for tongue tumor segmentation. To the best of our knowledge, this is the first work using deep learning semantic segmentation for tumor detection in HSI data using channel selection, and accounting for more spatial tissue context, and global comparison between the prediction map, and the annotation per sample. Results, and Conclusion: On a clinical data set with tongue squamous cell carcinoma, our best method obtains very strong results of average dice coefficient, and area under the ROC-curve of [Formula: see text], and [Formula: see text], respectively on the original spatial image size. The results show that a very good performance can be achieved even with a limited amount of data. We demonstrate that important information regarding tumor decision is encoded in various channels, but some channel selection, and filtering is beneficial over the full spectra. Moreover, we use both visual (VIS), and near-infrared (NIR) spectrum, rather than commonly used only VIS spectrum; although VIS spectrum is generally of higher significance, we demonstrate NIR spectrum is crucial for tumor capturing in some cases.

SIGNIFICANCE

The HSI technology augmented with accurate deep learning algorithms has a huge potential to be a promising alternative to digital pathology or a doctors' supportive tool in real-time surgeries.

Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach

The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D-2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D-2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.

Laparoscopic system for simultaneous high-resolution video and rapid hyperspectral imaging in the visible and near-infrared spectral range

SIGNIFICANCE

Hyperspectral imaging (HSI) can support intraoperative perfusion assessment, the identification of tissue structures, and the detection of cancerous lesions. The practical use of HSI for minimal-invasive surgery is currently limited, for example, due to long acquisition times, missing video, or large set-ups.

AIM

An HSI laparoscope is described and evaluated to address the requirements for clinical use and high-resolution spectral imaging.

APPROACH

Reflectance measurements with reference objects and resected human tissue from 500 to 1000 nm are performed to show the consistency with an approved medical HSI device for open surgery. Varying object distances are investigated, and the signal-to-noise ratio (SNR) is determined for different light sources.

RESULTS

The handheld design enables real-time processing and visualization of HSI data during acquisition within 4.6 s. A color video is provided simultaneously and can be augmented with spectral information from push-broom imaging. The reflectance data from the HSI system for open surgery at 50 cm and the HSI laparoscope are consistent for object distances up to 10 cm. A standard rigid laparoscope in combination with a customized LED light source resulted in a mean SNR of 30 to 43 dB (500 to 950 nm).

CONCLUSIONS

Compact and rapid HSI with a high spatial- and spectral-resolution is feasible in clinical practice. Our work may support future studies on minimally invasive HSI to reduce intra- and postoperative complications.