A feasibility study of an integrated NIR/gamma/visible imaging system for endoscopic sentinel lymph node mapping

Radioactive nanosized colloids and indocyanine green identify the same sentinel lymph nodes in oral squamous cell carcinoma

PURPOSE

Near-infrared fluorescence imaging using indocyanine green (ICG) combined with radioactive markers has the potential to improve sentinel lymph-node (SLN) mapping in oral squamous cell carcinoma (OSCC). This study aimed to evaluate the ability of 99mTc and ICG in identifying the sentinel lymph nodes in patients with early stage OSCC.

METHODS

Data were collected prospectively, and a retrospective analysis of 15 patients with early stage OSCC and a cN0 neck was performed. All patients received peritumoral injection of 99mTc the day before surgery and ICG was administered intraoperatively. Intentionally, the application of the two different tracers were done by two different physicians with varying degrees of experience. The number of identified lymph nodes positive for 99mTc and ICG, the overlap or possible discrepancies of both methods, and the time until fluorescence signals of ICG were detected were noted.

RESULTS

In all patients, a 100% agreement in sentinel lymph-node identification was achieved, regardless of both the exact location of the peritumoral injection and the experience of the injecting surgeon. Time until ICG accumulation in the sentinel lymph node was consistently found to be between 1 and 3 min.

CONCLUSION

ICG constitutes a viable and useful addition to 99mTc for intraoperative sentinel lymph-node detection in this study.

A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea

We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60^th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.

Near-Infrared Inorganic Nanomaterials for Precise Diagnosis and Therapy

Traditional wavelengths (400–700 nm) have made tremendous inroads in vivo fluorescence imaging. However, the ability of visible light photon penetration hampered the bio-applications. With reduced photon scattering, minimal tissue absorption and negligible autofluorescence properties, near-infrared light (NIR 700–1700 nm) demonstrates better resolution, high signal-to-background ratios, and deep tissue penetration capability, which will be of great significance for in-vivo determination in deep tissue. In this review, we summarized the latest novel NIR inorganic nanomaterials and the emission mechanism including single-walled carbon nanotubes, rare-earth nanoparticles, quantum dots, metal nanomaterials. Subsequently, the recent progress of precise noninvasive diagnosis in biomedicine and cancer therapy utilizing near-infrared inorganic nanomaterials are discussed. In addition, this review will highlight the concerns, challenges and future directions of near-infrared light utilization.

SiPM-based gamma detector with a central GRIN lens for a visible/NIRF/gamma multi-modal laparoscope

Intraoperative imaging has been studied using conventional devices such as near infrared (NIR) optical probes and gamma probes. However, these devices have limited depth penetration and spatial resolution. In a previous study, we realized a multi-modal endoscopic system. However, charge-coupled device (CCD)-based gamma imaging required long acquisition times and lacked gamma energy information. A silicon photomultiplier (SiPM)-based gamma detector is implemented in a multi-modal laparoscope herein. A gradient index (GRIN) lens and CCD are used to transfer and readout visible and NIR photons. The feasibility of in-vivo sentinel lymph node (SLN) mapping was successfully performed with the proposed system.

Partial-ring PET image restoration using a deep learning based method

PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance of a deep learning (DL) based method for the recovery of partial-ring PET images. Twenty digital brain phantoms were used in the Monte Carlo simulation toolkit, SimSET, to simulate 15 min full-ring PET scans. Partial-ring PET data were generated from full-ring PET data by removing coincidence events that hit these specific detector blocks. A convolutional neural network based on the residual U-Net architecture was trained to predict full-ring data from partial-ring data in either the projection or image domain. The performance of the proposed DL-based method was evaluated by comparing with the PET images reconstructed using the full-ring projection data in terms of the mean squared error (MSE), structural similarity (SSIM) index and recovery coefficient (RC). The MSE results showed the superiority of the image-domain approach in reduction of 91.7% in contrast to 14.3% for the projection-domain approach. Therefore, the image-domain approach was used to study the influence of the number of detector block removal. The SSIM results were 0.998, 0.996 and 0.993 for 3, 5 and 7 detector block removals, respectively. The activity of gray and white matters could be fully recovered even with 7 detector block removal, while the RCs of two artificially inserted small lesions (3 pixels in diameter) in the testing data were 94%, 89% and 79% for 3, 5, and 7 detector block removals, respectively. Our simulation results suggest that DL has the potential to recover partial-ring PET images.

Characterization and validation of multimodal annihilation-gamma/near-infrared/visible laparoscopic system

Minimally invasive robotic surgery using fluorescence-guided images with a video laparoscope has been widely used because of its advantages of small incision, fast recovery time, and efficiency. However, the penetration depth limitation of fluorescence is a disadvantage caused by the absorption and scattering in tissues and blood cells. If this limitation can be overcome by additional imaging modalities, the surgical procedure can be quite efficient and precise. High-energy annihilation-gamma photons have a stronger penetration capability than visible and fluorescence photons. To characterize and validate a multimodal annihilation-gamma/near-infrared (NIR)/visible laparoscopic imaging system, an internal detector composed of an annihilation-gamma detector and an optical system was assembled inside a surgical stainless pipe with an outer diameter of 15.8 mm and an external detector with a dimension of 100  ×  100  mm2 placed at the opposite side of the internal detector. Integrated images of 511-keV gamma rays, NIR fluorescence, and visible light were obtained simultaneously. The 511-keV gamma image could be clearly seen with the acquisition of 5 s, while NIR and visible images could be presented in real time. This multimodal system has the potential for improving the surgery time and the quality of patient care.

Gradient regularized convolutional neural networks for low-dose CT image enhancement

The potential risks of x-ray to patients have transferred the public's attention from normal dose CT (NDCT) to low-dose CT (LDCT). However, simply lowering the radiation dose of the CT system will significantly degrade the quality of CT images such as noise and artifacts, which compromises the diagnostic performance. Hence, various methods have been proposed to solve this problem over the past decades. Although these methods have achieved impressive results, they also suffer from a drawback of smoothing image details after denoising, which makes it difficult for clinical diagnosis and treatment. To address this issue, this paper introduces a novel gradient regularization method for LDCT enhancement. Rather than common methods which only consider the pixel-wise gray value loss in the reconstruction procedure, we also take the image gradient loss into consideration to preserve image details. By combining the gradient regularization method and the convolutional neural network (CNN) framework, a gradient regularized convolutional neural network (GRCNN) is proposed to enhance LDCT images which has achieved promising performance in our experiments both visually and quantitatively.

Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT)

OBJECTIVE

To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute electrical impedance tomography (a-EIT) that pairs deep learning techniques with real-time robust D-bar methods and examine the influence of prior information on the reconstruction.

APPROACH

A D-bar method is paired with a trained convolutional neural network (CNN) as a post-processing step. Training data is simulated for the network using no knowledge of the boundary shape by using an associated nonphysical Beltrami equation rather than simulating the traditional current and voltage data specific to a given domain. This allows the training data to be boundary shape independent. The method is tested on experimental data from two EIT systems (ACT4 and KIT4) with separate training sets of varying prior information.

MAIN RESULTS

Post-processing the D-bar images with a CNN produces significant improvements in image quality measured by structural SIMilarity indices (SSIMs) as well as relative [Formula: see text] and [Formula: see text] image errors.

SIGNIFICANCE

This work demonstrates that more general networks can be trained without being specific about boundary shape, a key challenge in EIT image reconstruction. The work is promising for future studies involving databases of anatomical atlases.

ADMM-based deep reconstruction for limited-angle CT

In the real applications of computed tomography (CT) imaging, the projection data of the scanned objects are usually acquired within a limited-angle range because of the limitation of the scanning condition. Under these circumstances, conventional analytical algorithms, such as filtered back-projection (FBP), do not work because the projection data are incomplete. The regularization method has proven to be effective for tomographic reconstruction from under-sampled measurements. To deal with the limited-angle CT reconstruction problem, the regularization method is commonly used, but it is difficult to find a generic regularization term and choose the regularization parameters. Moreover, in some cases, the quality of reconstructed images is less than satisfactory. To solve this problem, we developed an alternating direction method of multipliers (ADMM)-based deep reconstruction (ADMMBDR) algorithm for limited-angle CT. First, we used the ADMM algorithm to decompose a regularization reconstruction model. Then, we utilized a deep convolutional neural network (CNN) to replace a part of the ADMM algorithm to reduce artifacts and avoid the choice of the regularization term and the regularization parameter. Furthermore, we conducted some numerical experiments to evaluate the feasibility and the advantages of the proposed algorithm. The results showed that the proposed algorithm had a better performance than several state-of-the-art algorithms; with respect to structure preservation and artifact reduction.

Intraoperative biophotonic imaging systems for image-guided interventions

Biophotonic imaging has revolutionized the operation room by providing surgeons intraoperative image-guidance to diagnose tumors more efficiently and to resect tumors with real-time image navigation. Among many medical imaging modalities, near-infrared (NIR) light is ideal for image-guided surgery because it penetrates relatively deeply into living tissue, while nuclear imaging provides quantitative and unlimited depth information. It is therefore ideal to develop an integrated imaging system by combining NIR fluorescence and gamma-positron imaging to provide surgeons with highly sensitive and quantitative detection of diseases, such as cancer, in real-time without changing the look of the surgical field. The focus of this review is to provide recent progress in intraoperative biophotonic imaging systems, NIR fluorescence imaging and intraoperative nuclear imaging devices, and their future perspectives for image-guided interventions.

Proof-of-concept of a multimodal laparoscope for simultaneous NIR/gamma/visible imaging using wavelength division multiplexing

An optical/nuclear hybrid surgical technique using ICG-^99mTc-nanocolloid can improve lesion detectability by detecting both fluorescence and gamma signals. However, a hybrid multimodal laparoscope that can obtain both NIR and gamma images is not available yet. In this work, we present a proof-of-concept study of a prototype multimodal laparoscope that can provide simultaneous NIR/gamma/visible imaging using wavelength division multiplexing. The performances of optical and gamma imaging were evaluated using a USAF 1951 negative resolution target and ^99mTc-filled tumor-like sources, respectively. Simultaneous NIR/gamma/visible images of two Eppendorf tubes containing a mixture of ^99mTc-ICG are presented.

Lens implementation on the GATE Monte Carlo toolkit for optical imaging simulation

Optical imaging techniques are widely used for in vivo preclinical studies, and it is well known that the Geant4 Application for Emission Tomography (GATE) can be employed for the Monte Carlo (MC) modeling of light transport inside heterogeneous tissues. However, the GATE MC toolkit is limited in that it does not yet include optical lens implementation, even though this is required for a more realistic optical imaging simulation. We describe our implementation of a biconvex lens into the GATE MC toolkit to improve both the sensitivity and spatial resolution for optical imaging simulation. The lens implemented into the GATE was validated against the ZEMAX optical simulation using an US air force 1951 resolution target. The ray diagrams and the charge-coupled device images of the GATE optical simulation agreed with the ZEMAX optical simulation results. In conclusion, the use of a lens on the GATE optical simulation could improve the image quality of bioluminescence and fluorescence significantly as compared with pinhole optics.

Development of a fiber-optic gamma endoscope to measure both optical and gamma images in a confined space

In nuclear medicine, obtaining information on the exact location, size, and dose of radiopharmaceuticals distributed on lesions is critically important. Therefore, we have fabricated a novel fiber-optic gamma endoscope (FOGE) to measure the shape and size of the radioisotope as well as the gamma-ray distribution simultaneously. To evaluate the performance of the novel FOGE, we obtained optical images and gamma images by using a USAF 1951 target and radioisotope sources, respectively. The experimental results demonstrated that the FOGE could be utilized to obtain both the location and the distribution of the radioactive isotope that emitted gamma-rays. Based on the results of this study, use of a flexible and thin FOGE would be valuable in nuclear medicine and nuclear safety technologies given the advantages of accurate dose-monitoring. Especially, improvements could be achieved in surgery technologies because the FOGE could be used in minimally invasive radioguided surgery owing to its thin form and flexibility.