About

Viktor Gruev is a Professor in Electrical and Computer Engineering at UIUC, serving as the Wendell and Rita Dunning Faculty Scholar. He holds multiple professorships, including positions in the Carle Illinois School of Medicine and Bioengineering. As the Principal Investigator of the Biosensors Lab at UIUC, his research focuses on biosensors and related technologies. His work involves developing innovative imaging and sensing systems inspired by biological mechanisms, such as mantis shrimp-inspired cameras for cancer surgery and other bio-inspired detection methods. Dr. Gruev's contributions aim to advance biomedical imaging, underwater geolocalization, and the detection of unseen phenomena, leveraging interdisciplinary approaches to solve complex scientific and medical challenges.

Research topics

• Medicine
• Internal medicine
• Materials science
• Chemistry
• Nanotechnology

Selected publications

• Depth-resolved fluorescence via ratiometric imaging in NIR-I and NIR-II

  2026-03-05

  articleSenior author

  Fluorescence-guided surgery (FGS) has become increasingly popular in surgical procedures due to its ability to provide excellent contrast between malignant and healthy tissue in real time. One current limitation of FGS involves the inability to accurately assess the depth of malignant tissue, which may lead to the resection of excess healthy tissue. Although preoperative imaging modalities can accurately assess the location and depth of lesions, they do not provide real-time information during surgery. Therefore, it is common for ultrasound to be used for real-time depth measurements of tumors during surgery. However, ultrasound measurements require contact between the surface of interest and the probe. This study aims to introduce an alternative method for real-time, no contact depth detection of malignant tissue through ratiometric imaging utilizing NIR-I and NIR-II cameras. Given a single type of tissue, controlled experiments with indocyanine green (ICG) doped phantoms underneath different tissue thicknesses can be set up to develop linear or nonlinear models that relate tissue thickness parameters derived from camera images. We have defined four parameters to extract depth information: intensity, spread, delta intensity, and delta spread. One-dimensional line profiles across the ICG phantom in images taken with the NIR-I and NIR-II cameras have been constructed to calculate these parameters. Nonlinear monotonic relationships relating depth to these four parameters have been found. Intriguingly, obtaining a ratio between delta intensity and delta spread resulted in the highest sensitivity to depth measurements.

  PublisherDOI

• E-Cadherin Is an Accurate Target for Fluorescence-Guided Imaging of Lymph Nodes

  Current Issues in Molecular Biology · 2026-03-03

  articleOpen access

  Lymph node (LN) dissection is a necessary part of every oncologic surgery in order to provide important information for staging, predicting prognosis and improving survival. To do this, surgical oncologists strive to localize and dissect every pathologically positive LN while avoiding the increased morbidity of removing true negative LNs. The goal is to develop an imaging method to distinguish positive and negative LNs, but a specific biomarker is missing. Thus, our aim is to identify a reliable imaging marker for identifying LNs with lung cancer cells. After screening many epithelial markers, we identified E-cadherin, a membrane protein normally expressed in epithelial cells, including in the lung. To follow up on our potential target, we performed immunofluorescence staining on 48 human LNs with a conjugated anti-E-cadherin monoclonal antibody. Fluorescence was significantly higher in LNs with metastasis, as shown in 48 positive LNs from patients with resected primary lung cancer. There was high fluorescence in both hilar and mediastinal LNs, and in all primary tumor histologies. E-cadherin may be useful for the surgical oncologist for targeted imaging technologies for selecting positive LNs from lung cancer.

  PublisherOA PDFDOI

• Bioinspired ultraviolet-to-near-infrared imager for label-free intraoperative assessment of lymph node metastasis

  Optica · 2026-03-16

  articleOpen accessSenior author

  During cancer surgery, surgeons must decide which lymph nodes to biopsy or preserve. Current intraoperative tools localize draining nodes but lack a real-time readout of tumor involvement, leading to overtreatment, undertreatment, and lymphedema. We developed a bioinspired single-chip imager modeled on mantis shrimp vision that coregisters color, near-infrared (NIR) fluorescence from indocyanine green (ICG), and deep-ultraviolet (UV) autofluorescence on a single sensor within the same field of view. In the ex vivo workflow, NIR fluorescence first localizes draining nodes. After the lymph node is exposed, a deep-UV autofluorescence acquisition provides a label-free readout of nodal involvement. Using stacked photodiodes and pixel-level multiband filters, the device enables coregistered multispectral imaging from 300 to 1000 nm. In immediate ex vivo testing on breast cancer specimens (33 patients, 94 nodes), the UV channel distinguished metastatic from benign nodes (area under the receiver operating characteristic curve (AUC) 0.89; sensitivity 97%; specificity 89%) while preserving standard sentinel node localization with ICG. The results demonstrate a compact route to point-of-care nodal assessment.

  PublisherDOI

• Light detectors made from perovskite crystals see in full colour

  Nature · 2025-06-18

  articleSenior authorCorresponding
  PublisherDOI

• UV-Visible-NIR Image Sensor for Labeled and Label-free Intra-operative Imaging with Human Clinical Validation

  2025-05-25

  articleSenior author

  We present a single-chip imaging sensor capable of simultaneous ultraviolet (UV), visible (VIS), and near-infrared (NIR) imaging. The system integrates a checkerboard-patterned multispectral pixelated filter array with a three-layer stacked photodiode architecture, achieving six distinct spectral bands. Each photodiode layer is optimized for specific wavelengths, leveraging the wavelength-dependent absorption properties of silicon. With a power consumption of 250 mW and nearly 100% transmission in both UV and NIR spectra, the platform is optimized for intraoperative use without disrupting the surgical workflow. Clinical data from 33 patients with breast cancer demonstrated a positive predictive value of 100% for detecting primary tumors based on UV autofluorescence. These results highlight the platform’s potential for enhancing cancer detection in real-time surgical settings.

  PublisherDOI

• A 1280 by 720 by 3, 12-Band Multispectral Imager for Dual Near-Infrared Fluorophore Differentiation

  2025-05-25

  articleSenior author

  We present the design, fabrication, and optical validation of a single-chip multispectral imaging sensor spanning 400 nm to 1050 nm. The sensor integrates vertically stacked photodiodes with pixelated spectral filters to capture 12 spectral bands—three in the visible spectrum and nine in the near-infrared (NIR) spectrum. This enables simultaneous visualization of both tumor-targeted and lymph node-mapping probes using the same NIR excitation source. The system’s performance was validated through optical experiments, demonstrating accurate spectral differentiation between indocyanine green (ICG) and the folate-targeted probe Cytalux. This multispectral imaging approach addresses the limitations of current imaging systems, which are restricted to single-probe visualization, offering a powerful tool for enhancing cancer surgery outcomes.

  PublisherDOI

• Abstract 2528: Bioinspired multispectral imaging for intraoperative detection of metastatic lymph nodes: enhancing precision through UV and NIR fluorescence

  Cancer Research · 2025-04-21

  articleSenior author

  Intraoperative identification of metastatic lymph nodes (LNs) remains a clinical challenge, with current techniques often leading to unnecessary removal of healthy tissue or retention of cancer-positive nodes. To address this issue, we developed a bioinspired multispectral imaging system that integrates ultraviolet (UV) autofluorescence and near-infrared (NIR) fluorescence detection. This platform mimics the mantis shrimp's unique visual system, leveraging vertically stacked photodiodes and pixelated spectral filters to achieve simultaneous imaging across the UV, visible, and NIR spectral ranges without spatial misalignment or loss of resolution. The system uses indocyanine green (ICG) as an FDA-approved NIR fluorescent probe for LN localization and UV excitation to evaluate LN status via autofluorescence. The UV fluorescence leverages endogenous biomarkers, such as tryptophan, commonly enriched in tumor-associated tissues. This dual-modality imaging approach enables real-time identification of both the anatomical location and pathological status of LNs, reducing dependence on postoperative histopathological analysis. In a clinical study involving 33 breast cancer patients, the imaging system achieved a positive predictive value of 96% and a receiver operating characteristic (ROC) area under the curve (AUC) of 89% in distinguishing metastatic from non-metastatic lymph nodes. These results were corroborated by pathology, which served as the ground truth. By providing immediate and accurate feedback to surgeons, this system has the potential to improve surgical precision, reduce unnecessary excisions, and minimize the need for follow-up procedures. Unlike traditional multispectral imaging systems reliant on mechanically rotating filters or dichroic beamsplitters, our bioinspired sensor eliminates co-registration errors and significantly reduces system complexity. Its compact design and capability to perform high-resolution imaging under challenging surgical lighting conditions make it well-suited for widespread clinical adoption. Further studies are underway to validate the system’s efficacy across different cancer types and expand its applications to other surgical scenarios. This novel imaging platform offers a significant step forward in integrating real-time molecular and anatomical imaging into intraoperative workflows, addressing critical gaps in precision oncology. Citation Format: Zhongmin Zhu, Yifei Jin, Brianna Hajek, Shuming Nie, Viktor Gruev. Bioinspired multispectral imaging for intraoperative detection of metastatic lymph nodes: enhancing precision through UV and NIR fluorescence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2528.

  PublisherDOI

• Epithelial Cell Adhesion Molecule Is an Accurate Target for Fluorescence Guided Imaging of Lymph Nodes

  Molecular Imaging and Biology · 2025-10-14

  articleOpen access

  PURPOSE: Lymph node (LN) excision is critical in oncologic surgery to provide important therapeutic and diagnostic information. LN evaluation helps in staging cancers, predicting prognosis and improving survival. The ultimate wish of a surgical oncologist would be to localize and dissect all pathologically positive LNs while avoiding the morbidity of removing true negative LNs. The goal of our study was to identify a reliable marker for intraoperative molecular imaging of LNs with cancer cells from non-small cell lung cancer versus a LN without. PROCEDURES: We identified Epithelial Cell Adhesion Molecule (EpCAM), a membrane protein normally expressed in epithelial tissues including lung. We performed immunofluorescence staining on human specimens with a conjugated anti-EpCAM monoclonal antibody. RESULTS: Fluorescence was significantly higher in LNs with metastases as shown in 48 positive LNs from patients with resected primary lung cancer. There was high fluorescence in both hilar and mediastinal LNs, and in all primary tumor histologies. CONCLUSIONS: EpCAM may be useful for the surgical oncologist for intraoperative molecular imaging of positive LNs from lung cancer.

  PublisherOA PDFDOI

• 3D Tumor-Mimicking Phantom Models for Assessing NIR I/II Nanoparticles in Fluorescence-Guided Surgical Interventions

  ACS Nano · 2025-05-16 · 5 citations

  article

  Fluorescence image-guided surgery (FIGS) offers high spatial resolution and real-time feedback but is limited by shallow tissue penetration and autofluorescence from current clinically approved fluorophores. The near-infrared (NIR) spectrum, specifically the NIR-I (700-900 nm) and NIR-II (950-1700 nm), addresses these limitations with deeper tissue penetration and improved signal-to-noise ratios. However, biological barriers and suboptimal optical performance under surgical conditions have hindered the clinical translation of NIR-I/II nanoprobes. In vivo mouse models have shown promise, but these models do not replicate the complex optical scenarios encountered during real-world surgeries. Existing tissue-mimicking phantoms used to evaluate NIR-I/II imaging systems are useful but fall short when assessing nanoprobes in surgical environments. These phantoms often fail to replicate the tumor microenvironment, limiting their predictive assessment. To overcome these challenges, we propose developing tumor-mimicking phantom models (TMPs) that integrate key tumor features, such as tunable tumor cell densities, in vivo-like nanoparticle concentrations, biologically relevant factors (pH, enzymes), replicate light absorption components (hemoglobin), and light scattering components (intralipid). These TMPs enable more clinically relevant assessments of NIR-I/II nanoprobes, including optical tissue penetration profiling, tumor margin delineation, and ex vivo thoracic surgery on porcine lungs. The components of TMPs can be further modulated to closely match the optical profiles of in vivo and ex vivo tumors. Additionally, 3D bioprinting technology facilitates a high-throughput platform for screening nanoprobes under realistic conditions. This approach will identify high-performing NIR-I/II probes with superior surgical utility, bridging the gap between preclinical findings and clinical applications, and ensuring results extend beyond traditional in vivo mouse studies.

  PublisherDOI

• Hybrid CNN–transformer demosaicing for bioinspired single-chip color-near-infrared fluorescence imaging in oncologic surgery

  Journal of Biomedical Optics · 2025-10-28 · 1 citations

  articleOpen accessSenior authorCorresponding

  Significance: Single-chip multispectral imaging sensors with vertically stacked photodiodes and pixelated spectral filters enable advanced, real-time visualization for image-guided cancer surgery. However, their design inherently reduces spatial resolution. We present a convolutional neural network (CNN)-transformer demosaicing algorithm, validated on both clinical and preclinical datasets that effectively doubles spatial resolution and improves image quality-substantially enhancing intraoperative cancer visualization. Aim: We present a CNN-transformer-based demosaicing approach specifically optimized for reconstructing high-resolution color and NIR images acquired by a hexachromatic imaging sensor. Approach: A hybrid CNN-transformer demosaicing model was developed and trained on color-image datasets, then rigorously evaluated on color and NIR images to demonstrate superior reconstruction quality compared with conventional bilinear interpolation and residual CNN methods. Results: for color images and 76% for NIR images and improves structural dissimilarity by roughly 72% and 79%, respectively, compared with state-of-the-art CNN-based demosaicing algorithms in preclinical datasets. In clinical datasets, our approach similarly demonstrates significant reductions in MSE and structural dissimilarity, substantially outperforming existing CNN-based methods, particularly in reconstructing high-frequency image details. Conclusions: We demonstrate improvements in spatial resolution and image fidelity for color and NIR images obtained from hexachromatic imaging sensors, achieved by integrating convolutional neural networks with transformer architectures. Given recent advances in GPU computing, our CNN-transformer approach offers a practical, real-time solution for enhanced multispectral imaging during cancer surgery.

  PublisherOA PDFDOI

Recent grants

• Bioinspired Multispectral Imager for Near Infrared Fluorescence Image Guided Surgery

  NSF · $499k · 2016–2017

• Collaborative Research: Ultraviolet(UV)-MultiSpectral-Polarization 3D Imaging of the Underwater World

  NSF · $404k · 2016–2019

• Bioinspired Multispectral Imager for Near Infrared Fluorescence Image Guided Surgery

  NSF · $442k · 2016–2020

• Collaborative Research: Ultraviolet(UV)-MultiSpectral-Polarization 3D Imaging of the Underwater World

  NSF · $404k · 2016–2017

• Bioinspired Sensors for Image Guided Cancer Surgery

  NSF · $600k · 2020–2024

Frequent coauthors

• Missael Garcia

  University of Illinois Urbana-Champaign

  33 shared

• Steven Blair

  University of Illinois Urbana-Champaign

  23 shared

• Shuming Nie

  University of Illinois Urbana-Champaign

  17 shared

• Ralph Etienne‐Cummings

  Johns Hopkins University

  16 shared

• Zhongmin Zhu

  University of Illinois System

  15 shared

• Zuodong Liang

  13 shared

• Tyler S. Davis

  University of Utah

  12 shared

• Benjamin Lew

  University of Illinois Urbana-Champaign

  11 shared

Labs

• Biosensors Lab @ UIUCPI

Education

• PhD, Electrical and Computer Engineering

  Johns Hopkins University

  2005

Awards & honors

• IEEE ISCAS Best Student Paper Award (May 2017)
• IEEE ISCAS Best Paper in the Sensory Systems Technical Track…
• IEEE ISCAS Best Paper in Sensory Track (May 2015)
• IEEE ISCAS Best Live Demo (May 2015)
• IEEE ISCAS Best Paper Award (May 2011)