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

Minimally invasive surgery (MIS) has expanded broadly in the field of abdominal and pelvic surgery. Laparoscopic and robotic surgery has improved surgeon ergonomics, instrument precision, operative time, and postoperative recovery across various abdominal procedures. The goal of this study is to establish the feasibility of implementing high-speed hyperspectral imaging into a standard laparoscopic setup and exploring its benefit to common intracorporeal procedures. A hyperspectral laparoscopic imaging system was constructed using a customized hyperspectral camera alongside a standard rigid laparoscope and was validated for both spectral and spatial accuracy. Demosaicing methods were investigated for improved full-resolution visualization. Hyperspectral cameras with different spectral ranges were considered and compared with one another alongside two different light sources to determine the most effective configuration. Finally, different porcine tissues were imaged ex-vivo to test the capabilities of the system and spectral footprints of the various tissues were extracted. The tissue was also imaged in a phantom to simulate the system's use in MIS. The results demonstrated a hyperspectral laparoscopic imaging system that could provide quantitative, diagnostic information while not disrupting normal workflow nor adding excessive weight to the laparoscopic setup. The high-speed hyperspectral laparoscopic imaging system can have immediate applications in image-guided surgery.

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“…In order to measure spatial contrast, a Michelson contrast cutoff of 20% was used as shown in Equation (2) [45]:…”

Section: Evaluation Of Image Qualitymentioning

confidence: 99%

A high-speed hyperspectral laparoscopic imaging system

Pruitt

Johnson

Gahan

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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“…Some of the main reasons why spectral imaging has not yet found its way into surgical practice are related to image acquisition time, processing time and size of the available devices 13 . In fact, many available MSI/HSI camera systems are large (14-50 cm) and/or take several seconds (2-8 s) to record and process one image [32][33][34][35][36][37][38][39] . To the best of our knowledge, the only laparoscopic spectral device proposed for clinical use so far 34 takes around five seconds to record one hyperspectral image, which prevents real-time application.…”

Section: Mainmentioning

confidence: 99%

“…Furthermore, all relevant changes made compared to the standard clinical setup concern components that are attached to the laparoscope and as such never touch the patient, which minimizes the patient risk introduced by a new imaging device. Previous work on clinical spectral imaging has focused on systems for open surgery with acquisition times of several seconds [32][33][34][35][36][37][38][39] . Preclinical multispectral or hyperspectral systems proposed in the specific context of laparoscopy have typically been based on sequential image acquisition 11,[44][45][46] , e.g.…”

Section: Msi System Designmentioning

confidence: 99%

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

Ayala

Adler

Seidlitz

et al. 2022

Preprint

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Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. Spectral imaging takes advantage of the fact that different tissue components have unique optical properties to recover relevant information on tissue function such as ischemia. However, clinical success stories for advancing laparoscopic surgery with spectral imaging are lacking to date. To address this bottleneck, we developed the first laparoscopic real-time multispectral imaging (MSI) system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional RGB (Red, Green, and Blue) surgical view of the patient with functional information at a video rate of 25 Hz. To account for the high inter-patient variability of human tissue, we phrase the problem of ischemia detection as an out-of-distribution (OoD) detection problem that does not rely on data from any other patient. Using an ensemble of invertible neural networks (INNs) as a core component, our algorithm computes the likelihood of ischemia based on a short (several seconds) video sequence acquired at the beginning of each surgery. A first-in-human trial performed on 10 patients undergoing partial nephrectomy demonstrates the feasibility of our approach for fully-automatic live ischemia monitoring during laparoscopic surgery. Compared to the clinical state-of-the-art approach based on indocyanine green (ICG) fluorescence, the proposed MSI-based method does not require the injection of a contrast agent and is repeatable if the wrong segment has been clamped. Spectral imaging combined with advanced deep learning-based analysis tools could thus evolve as an important tool for fast, efficient, reliable and safe functional imaging in minimally invasive surgery.

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“…The absorption spectra in the NIR region convey fingerprint data owing to the overtone or combined vibrations of the chemical bonds 13 – 15 This permits the investigation of the distribution of the chemical composition within a sample 16 . Hyperspectral imaging provides a three-dimensional dataset (two spatial and one spectral) that allows a spectral curve at each pixel in the acquired images to be obtained 17 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens

Mitsui,

Mori,

Takamatsu

et al. 2023

J. Biomed. Opt.

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. Significance Determining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently. Aim We investigated the ability of near-infrared hyperspectral imaging (NIR-HSI) to identify GC areas, including exposed and unexposed using surgical specimens, and explored the identifiable characteristics of the GC. Approach Our study examined 10 patients with diagnosed GC who underwent surgery between 2020 and 2021. Specimen images were captured using NIR-HSI. For the specimens, the exposed area was defined as an area wherein the cancer was exposed on the surface, the unexposed area as an area wherein the cancer was present although the surface was covered by normal tissue, and the normal area as an area wherein the cancer was absent. We estimated the GC (including the exposed and unexposed areas) and normal areas using a support vector machine, which is a machine-learning method for classification. The prediction accuracy of the GC region in every area and normal region was evaluated. Additionally, the tumor thicknesses of the GC were pathologically measured, and their differences in identifiable and unidentifiable areas were compared using NIR-HSI. Results The average prediction accuracy of the GC regions combined with both areas was 77.2%; with exposed and unexposed areas was 79.7% and 68.5%, respectively; and with normal regions was 79.7%. Additionally, the areas identified as cancerous had a tumor thickness of . Conclusions NIR-HSI identified the GC regions with high rates. As a feature, the exposed and unexposed areas with tumor thicknesses of were identified using NIR-HSI.

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

Cited by 14 publications

References 34 publications

A high-speed hyperspectral laparoscopic imaging system

A high-speed hyperspectral laparoscopic imaging system

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens

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