Imaging Technology Research Articles

A Narrative Review on the Clinical Relevance of Imaging the Circumventricular Brain Organs and Performing Their Anatomical and Histopathological Examination in Acute and Postacute COVID-19.

Autopsy followed by histopathological examination is foundational in clinical and forensic medicine for discovering and understanding pathological changes in disease, their underlying processes, and cause of death. Imaging technology has become increasingly important for advancing clinical research and practice, given its noninvasive, in vivo and ex vivo applicability. Medical and forensic autopsy can benefit greatly from advances in imaging technology that lead toward minimally invasive, whole-brain virtual autopsy. Brain autopsy followed by histopathological examination is still the hallmark for understanding disease and a fundamental modus operandi in forensic pathology and forensic medicine, despite the fact that its practice has become progressively less frequent in medical settings. This situation is especially relevant with respect to new diseases such as COVID-19 caused by the SARS-CoV-2 virus, for which our neuroanatomical knowledge is sparse. In this narrative review, we show that ad hoc clinical autopsies and histopathological analyses combined with neuroimaging of the principal circumventricular organs are critical to gaining insight into the reconstruction of the pathophysiological mechanisms and the explanation of cause of death (ie, atrium mortis) related to the cardiovascular effects of SARS-CoV-2 infection in forensic and clinical medicine.

Meeting Report for the 2023 International Scientific Conference on Complex Lymphatic Anomalies

Objectives: Complex lymphatic anomalies (CLAs) are rare diseases with variable clinical manifestations caused by the abnormal development of lymphatic vessels. The Lymphatic Malformation Institute (LMI), Lymphangiomatosis & Gorham’s Disease Alliance (LGDA), and LGD Alliance Europe sponsored an international conference on CLAs so leaders in the field could discuss recent advances in research on CLAs. Methods: The conference occurred in Dallas, Texas, between September 29th and September 30th, 2023. The event had 22 distinguished speakers and 38 attendees. Additional researchers, clinicians, and patients attended the conference virtually. Results: In this article, we summarize the key takeaways from the meeting. The conference emphasized the crucial role of the patient voice in guiding research, and attendees heard stories from patients and parents of children with CLAs. The conference covered topics such as the genetic causes of CLAs, preclinical models, clinical advances, new technologies for CLA research, lymphatic imaging, and patient-centered research. Videos of all the presentations can be found on the LGDA website. Conclusions: The meeting revealed that while progress has been made, many challenges still exist surrounding the diagnosis and treatment of CLAs. Sustained research efforts are necessary to fill the gaps in knowledge and improve the care and quality of life of CLA patients.

Clinical Application of the 4K-3D Exoscope System in Cochlear Implantation.

To evaluate a system for otomicrosurgery based on 4K three-dimensional (3D) exoscope technology and apply it to cochlear implantation. An open stereoscopic vision-based surgical system, which differs from traditional surgical microscopes, was created by utilizing 4K stereo imaging technology and combining it with low-latency 4K ultra-high-definition 3D display. The system underwent evaluation based on 57 cochlear implantation operations, three designed microscopic manipulations, and a questionnaire survey. The surgical images displayed by the 4K-3D exoscope system (4K-3D-ES) are stereoscopic, clear, and smooth. The use of 4K-3D-ES in cochlear implantation is not inferior to traditional microscopes in terms of intraoperative bleeding and surgical complications, and the surgical duration is not slower or may even be faster than when using traditional microscopes. The results of micromanipulation experiments conducted on 16 students also confirmed this and demonstrated that 4K-3D-ES can be easily adapted. Furthermore, additional advantages of 4K-3D-ES were gathered. Significantly enlarged and high-definition stereoscopic images contribute to the visualization of finer anatomical microstructures such as chordae tympani, ensuring safer surgery. Users feel more comfortable in their necks, shoulders, waists, and backs. Real-time shared stereoscopic view for multiple people, convenient for collaboration and teaching. The ear endoscope and 4K-3D-ES enable seamless switching on the same screen. High-definition 3D images and videos can be saved with just one click, making future publication and communication convenient. The feasibility and safety of 4K-3D-ES for cochlear implantation surgery have been demonstrated. The 4K-3D-ES also offers numerous unique advantages and holds clinical application and promotional value.

Navigating the Complexities of Brain Metastases Management.

The management of brain metastases, a potentially devastating complication of advanced cancers, has become increasingly complex with advancements in local and systemic therapies. Improved outcomes and extended survival for patients with metastatic solid tumors have led to a surge in the prevalence and possibly incidence of brain metastases, affecting up to 40% of individuals with solid tumors. Enhanced imaging technologies contribute to more accurate and early detection, shaping the understanding of the intricate landscape of this condition. Traditionally, surgery and radiation stood as the mainstays of treatment because of the limited efficacy of systemic therapies within the brain. However, emerging clinical data, particularly in melanoma, lung, and breast cancers, reveal promising results with novel systemic treatments such as immunotherapy and targeted therapies. Despite the historical exclusion of patients with active brain metastases from clinical trials, a shift is occurring toward a more inclusive approach. This chapter delves into the multifaceted challenges associated with managing brain metastases, with a focus on the evolving landscape of systemic approaches as well as the intricacies of shared decision making, providing a comprehensive overview of the current state and future directions in navigating the complexities of brain metastases management.

Bacterial imaging in tumour diagnosis.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

Use of Microwave Tomography Technology for Water Seepage Investigation in Buildings

In metropolitan cities like Hong Kong, it is densely populated and most people live in privately owned premises of reinforced concrete residential buildings. Owing to material deterioration under (a) environmental exposure, (b) poor construction workmanship, (c) lack of proper maintenance and (d) alteration of premises into sub-divided flats with bathrooms, water may seep through the floor slabs of the premises and infiltrate into the subject premises below, and this create nuisance to the subject premises. In this paper, the causes of deterioration of concrete material are elaborated.

Evaluation of the correlation of wall elasticity between different sections of radial artery by velocity vector imaging.

This study aimed to evaluate the correlation between the elasticity of the anterior and posterior walls of the radial artery in different sections using velocity vector imaging. In this retrospective analysis, 30 healthy people who underwent physical examination in our hospital from January 2022 to January 2023 were analyzed offline by velocity vector imaging technology. The following parameters were assessed: peak systolic strain (Ss; %), peak diastolic strain (Sd; %), peak systolic strain rate (SRs; 1/s), and peak diastolic strain rate (SRd; 1/s). Elastic function was evaluated by analyzing the systolic and diastolic motion of the arterial walls. In the long-axis sections, there was a significant positive correlation between Ss, SRs, and SRd of both the anterior and posterior walls (r=0.531, r=0.803, and r=0.898, all p<0.01). Additionally, Sd showed a positive correlation (r=0.402, p<0.05). In the short-axis sections, there was a significant and positive correlation between SRs and SRd of both walls (r=0.762, r=0.667, both p<0.01). Furthermore, a positive correlation was found between SRd in the long-axis and short-axis sections of the anterior wall (r=0.382, p<0.05). Velocity vector imaging is a valuable tool for assessing the elasticity of the radial artery in different sections, and the longitudinal SRd in the long-axis section may serve as a highly sensitive and accurate parameter for assessing changes in wall elastic function during the occurrence of radial artery lesions.

Long-term effects of childhood single-parent family structure on brain connectivity and psychological well-being.

The high and increasing proportion of single-parent families is considered a risk factor associated with various childhood trauma experiences. Consequently, concerns have been raised regarding the potential long-term effects of the childhood single-parent family structure. In this study, we employed advanced magnetic resonance imaging technology, including morphometric similarity mapping, functional connectivity density, and network-based analysis, to investigate brain connectivity and behavioral differences among young adults who were raised in single-parent families. Our study also aimed to explore the relationship between these differences and childhood trauma experiences. The results showed that individuals who grew up in single-parent families exhibited higher levels of anxiety, depression, and harm-avoidant personality. The multimodal MRI analysis further showed differences in regional and network-based connectivity properties in the single-parent family group, including increased functional connectivity density in the left inferior parietal lobule, enhanced cortical structural connectivity between the left isthmus cingulate cortex and peri-calcarine cortex, and an increase in temporal functional connectivity. Moreover, elevated levels of anxiety and depression, along with heightened functional connectivity density in the left inferior parietal lobule and increased temporal functional connectivity, were found to be correlated with a greater number of childhood trauma experiences. Through analyzing multiple data patterns, our study provides objective neuropsychobiological evidence for the enduring impact of childhood single-parent family structure on psychiatric vulnerability in adulthood.

Nano-SiO2/polyurethane modified unsaturated polyester resin mortar for thin layer repairing on airport pavement

This paper introduces a novel toughened polymer mortar designed for runway repair, featuring outstanding impact resistance, high strength, and cost-effectiveness. To augment the toughness of unsaturated polyester (UP) polymer mortar, nano-SiO2 and polyurethane (PU) were incorporated. Firstly, the optimal dosages of nano-SiO2 and PU were determined through tensile test and impact test. Then, the modification mechanism of nano-SiO2/PU on UP resin was explored by microscopic image technology. In addition, the compression, flexural, fluidity, flexural bond strength and construction time tests were carried out to determine the optimal formula of nano-SiO2/PU modified UP resin mortar. Finally, the durability, impact resistance, impermeability resistance, skid resistance, and interlayer bond strength of modified UP resin mortar were studied and compared with those of pure UP resin mortar and epoxy (EP) resin mortar. Results show that the optimum proportion by mass for nano-SiO2/polyurethane modified UP resin is, UP resin: PU: dibutyltin dilaurate: nano-SiO2: initiator: accelerator: diluent: plasticizer = 1: 0.1: 0.002: 0.01: 0.01: 0.01:0.15:0.01. Furthermore, the mass proportion of modified UP resin to aggregate is 1: 1.2. In nano-SiO2/PU modification of UP resin, the -NCO groups of PU have been involved in chemical interaction with the -OH group of UP, while nano-SiO2 and UP resin are blended physically only. Compared with pure UP resin mortar, the nano-SiO2/PU modified UP resin mortar exhibited a 44% reduction in shrinkage rate, approximately 20% less corrosion loss, a 32% decrease in freeze-thaw loss, a 37% improvement in impact resistance, and increased shear bonding strength, flexural bonding strength, and tensile bonding strength by 44%, 33%, and 47%, respectively. Although the modified UP resin mortar demonstrated slightly lower performance in terms of dry shrinkage, corrosion resistance, skid resistance, and tensile bond strength compared to EP resin mortar, its freeze-thaw durability, impact resistance, flexural bond strength, and shear bond strength surpassed those of EP resin mortar. All three materials exhibited excellent impermeability. Leveraging the advantages of PU and nano-SiO2, the modified UP resin presents a promising repair material for airport pavement with enhanced toughness, high stability, and reduced cost.

Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection.

Near-infrared (NIR) hyperspectral imaging is a powerful technique that enables the capture of three-dimensional (3D) spectra-spatial information within the NIR spectral range, offering a wide array of applications. However, the high cost associated with InGaAs focal plane array (FPA) has impeded the widespread adoption of NIR hyperspectral imaging. Addressing this challenge, in this study, we adopt an alternative approach-single-pixel detection for NIR hyperspectral imaging. Our investigation reveals that single-pixel detection outperforms conventional FPA, delivering a superior signal-to-noise ratio (SNR) for both spectral and imaging reconstruction. To implement this strategy, we leverage self-assembled colloidal quantum dots (CQDs) and a digital micromirror device (DMD) for NIR spectral and spatial information multiplexing, complemented by single-pixel detection for simultaneous spectral and image reconstruction. Our experimental results demonstrate successful NIR hyperspectral imaging with a detection window about 600 nm and an average spectral resolution of 8.6 nm with a pixel resolution of 128 × 128. The resulting spectral and spatial data align well with reference instruments, which validates the effectiveness of our approach. By circumventing the need for expensive and bulky FPA and wavelength selection components, our solution shows promise in advancing affordable and accessible NIR hyperspectral imaging technologies, thereby expanding the range of potential applications.

Quantifying fetal heart health in gestational diabetes: a new approach with fetal heart quantification technology

ObjectiveThis study aimed to assess the impact of gestational diabetes mellitus (GDM) on fetal heart structure and function using a technique called fetal heart quantification (Fetal HQ), with a focus on mitochondrial dynamics, which employs advanced imaging technology for comprehensive analysis.MethodsA total of 180 fetuses with normal heart structures, aged 24–40 weeks of gestation, were examined. A 2–3 s cine loop in the standard four-chamber oblique view was captured and analyzed using the speckle-tracking technique with Fetal HQ. Various echocardiographic parameters were evaluated, including four-chamber view (4CV), global spherical index (GSI), global longitudinal strain (GLS), 24-segment spherical index (SI), ventricular fractional area change (FAC), cardiac output (CO), and stroke volume (SV). These parameters were compared between the GDM group and the control group during two gestational periods: 24+0 to 28+0 weeks and 28+1 to 40+1 weeks. Statistical analysis was performed using independent samples t-tests and Mann-Whitney U tests to identify significant differences.ResultsTwenty fetuses from mothers with GDM and 40 from the control group were recruited at 24+0 to 28+0 weeks. At 28+1 to 40+1 weeks, 40 fetuses from mothers with GDM and 80 from the control group were recruited. The fetal left ventricular global longitudinal function was similar between the GDM and control groups. However, compared to the controls, right ventricular function in the GDM group was lower only at 28+1 to 40+1 weeks. In the GDM group, the global spherical index (GSI) was lower than in the control group at 28+1 to 40+1 weeks (1.175 vs. 1.22; p = 0.001). There were significant decreases in ventricular FAC (38.74% vs. 42.83%; p &amp;lt; 0.0001) and 4CV GLS for the right ventricle (−22.27% vs. −26.31%; p = 0.005) at 28+1 to 40+1 weeks.ConclusionOur findings suggest that GDM is associated with decreased right ventricular function in the fetal heart, particularly during the later stages of pregnancy (28+1 to 40+1 weeks), compared to fetuses from healthy pregnancies. The Fetal HQ technique represents a valuable tool for evaluating the structure and function of fetal hearts affected by GDM during the advanced stages of pregnancy.

Diagnostic accuracy and clinical impact of renal biopsy cytology.

The role of fine needle biopsy cytology in the workup of renal mass lesions remains controversial. With advances in imaging technology and clinical management for renal masses, a critical reevaluation of the role of renal biopsy is needed. This study was designed to provide a comprehensive evaluation of the performance and clinical impact of fine needle biopsy in patients with renal masses. A 5-year retrospective study of ultrasound or computer tomography (CT)-guided fine needle biopsies of renal masses diagnosed via cytopathology was conducted. Overall diagnostic rate, sensitivity, and diagnostic accuracy were calculated. Further analysis of the impact of fine needle biopsy cytology on clinical management was performed. A total of 227 cases of fine-needle aspiration and/or biopsy (FNA/B) of renal masses were identified, including 76 with subsequent nephrectomies. Complications were rare (<1%). The diagnostic rate and sensitivity of FNA/B were 83.3% and 89.5%, respectively. Diagnostic accuracy was 98.7% at the major categorical level and 94.7% at the tumor subtype level. Subsequent clinical actions were associated with a definitive cytologic diagnosis of malignancy/neoplasia (p < .05) and were affected by tumor subtype (p < .05). This study demonstrates that FNA/B of renal masses is a safe and reliable minimally invasive diagnostic tool with excellent accuracy in confirmation of malignancy and subclassification of tumors. Diagnoses made on FNA/B play a key role in guiding a personalized clinical treatment plan.

An area‐effective and low‐power single‐slope ADC for DCG imaging CMOS image sensor

SummaryThis paper introduces an area‐effective and low‐power readout circuit structure tailored for high dynamic range (HDR) CMOS image sensors (CISs) utilizing dual conversion gain (DCG) imaging technology. A switched capacitor amplifier, which is applied at the comparator input port of the column‐parallel single‐slope analog‐to‐digital converter (SS ADC), is proposed to compensate for the charge injection and clock feed‐through introduced when the pixel switches between high conversion gain (HCG) and low conversion gain (LCG) modes. The proposed SS ADC is supplemented by the integration of a configurable counter, and the counter is specifically crafted for performing the A/D conversion of the readout voltage within a DCG pixel. Compared to the conventional DCG readout circuit, the proposed readout circuit use only one ADC for A/D conversion of HCG signals and LCG signals, which greatly reduces the area and power consumption of the readout circuit. Simulation results under 110 nm CMOS technology illustrate that the proposed 10‐bit SS ADC has a DNL of +0.78/−0.91 LSB and an INL of +1.91/−1.69 LSB, with a SNR of 57.2 dB and a SNDR of 55.95 dB, corresponding to an ENOB of 9 bits. The power consumption of the proposed SS ADC is 56.4 μW per column with a conversion time of 12 μs, and the simulation results show that the power consumption is 23.35% lower than the conventional structure and the area is saved by 24.69%.

Effect of electroacupuncture stimulation of "Sibai"(ST2) and "Quanliao"(SI18) on excitatory neurons of sensorimotor cortex in mice.

To observe the activation state and neuronal types of somatosensory cortex and the primary motor cortex induced by electroacupuncture (EA) stimulation of "Sibai" (ST2) and "Quanliao" (SI18) acupoints in mice. Male C57BL/6J mice were randomly divided into blank control and EA groups, with 6 mice in each group. Rats of the EA group received EA stimulation (2 Hz, 0.6 mA) at ST2 and SI18 for 30 minutes. Samples were collected after EA intervention, and immunofluorescence staining was performed to quantify the expression of the c-Fos gene (proportion of c-Fos positive cells) in the somatosensory cortex and primary motor cortex. The co-labelled cells of calcium/calmodulin-dependent protein kinase Ⅱ (CaMKⅡ) and gamma-aminobutyric acid (GABA) in the somatosensory cortex and primary motor cortex were observed and counted by using microscope after immunofluorescence staining. Another 10 mice were used to detect the calcium activity of excitatory neurons in the somatosensory cortex and primary motor cortex by fiber photometry. In comparison with the blank control group, the number of c-Fos positive cells, and the proportion of c-Fos and CaMKⅡ co-labelled cells in both the somatosensory cortex and primary motor cortex were significantly increased after EA stimulation (P<0.05). No significant changes were found in the proportion of c-Fos and GABA co-labeled cells in both the somatosensory cortex and primary motor cortex after EA. Results of fiber optic calcium imaging technology showed that the spontaneous calcium activity of excitatory neurons in both somatosensory cortex and primary motor cortex were obviously increased during EA compared with that before EA (P<0.01), and strikingly reduced after cessation of EA compared with that during EA (P<0.05). Under physiological conditions, EA of ST2 and SI18 can effectively activate excitatory neurons in the somatosensory cortex and primary motor cortex.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

IntroductionThe micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.MethodsThe recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.ResultsThe recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.DiscussionThis study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Single-sample image-fusion upsampling of fluorescence lifetime images.

Fluorescence lifetime imaging microscopy (FLIM) provides detailed information about molecular interactions and biological processes. A major bottleneck for FLIM is image resolution at high acquisition speeds due to the engineering and signal-processing limitations of time-resolved imaging technology. Here, we present single-sample image-fusion upsampling, a data-fusion approach to computational FLIM super-resolution that combines measurements from a low-resolution time-resolved detector (that measures photon arrival time) and a high-resolution camera (that measures intensity only). To solve this otherwise ill-posed inverse retrieval problem, we introduce statistically informed priors that encode local and global correlations between the two "single-sample" measurements. This bypasses the risk of out-of-distribution hallucination as in traditional data-driven approaches and delivers enhanced images compared, for example, to standard bilinear interpolation. The general approach laid out by single-sample image-fusion upsampling can be applied to other image super-resolution problems where two different datasets are available.

Advancing human-robot collaboration: Predicting operator trajectories through AI and infrared imaging

In industrial applications demanding close interaction between humans and robots, accurate perception of the robot's surroundings is vital to adjust its behavior appropriately. This need becomes especially pertinent when manipulators operate autonomously and execute both separate and common tasks with human operators. To ensure operator safety in such Human-Robot Collaboration (HRC) environments, the present paper proposes an innovative approach that leverages advanced collaborative robotic systems capable of sensing the operator's position to effectively prevent or mitigate potential contact. Our solution combines infrared-thermal imaging technology with a workspace monitoring system, utilizing thermal and depth cameras, to track the operator's current location and predict their future trajectory using a multi-layer Long Short-Term Memory (LSTM) neural network. We also introduce a technique for simulating both human and robot movements to generate a synthetic dataset. This data is shared with the motion planning system to generate trajectories that do not intersect with the operator's future paths, minimizing downtime due to safety stoppages. The solution is integrated and evaluated within an industrial high payload collaborative robot context, and the results have shown improvements in cycle time efficiency and operator approval. This approach offers a promising direction for enhancing safety and productivity in HRC workspaces.

Personnel Detection in Dark Aquatic Environments Based on Infrared Thermal Imaging Technology and an Improved YOLOv5s Model

This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images collected from diverse aquatic environments. This dataset was further expanded through synthetic image generation using CycleGAN and a newly developed color gamut transformation technique, which significantly improves the data variance and model training effectiveness. Furthermore, we integrated the Convolutional Block Attention Module (CBAM) at the end of the last encoder’s feedforward network. This integration maximizes the utilization of channel and spatial information to capture more intricate details in the feature maps. To decrease the computational demands of the network while maintaining model accuracy, Ghost convolution was employed, thereby boosting the inference speed as much as possible. Additionally, we applied hyperparameter evolution to refine the training parameters. The improved algorithm achieved an average detection accuracy of 85.49% on our proprietary dataset, significantly outperforming its predecessor, with a prediction speed of 23.51 FPS. The experimental outcomes demonstrate the proposed solution’s high recognition capabilities and robustness, fulfilling the demands of intelligent lifesaving missions.

Novel Near-Infrared Fluorescent Probe for Hepatocyte Growth Factor in Vivo Imaging in Surgical Navigation of Colorectal Cancer.

Despite recent advancements in colorectal cancer (CRC) treatment, the prognosis remains unfavorable primarily due to high recurrence and liver metastasis rates. Fluorescence molecular imaging technologies, combined with specific probes, have gained prominence in facilitating real-time tumor resection guided by fluorescence. Hepatocyte growth factor (HGF) is overexpressed in CRC, but the advancement of HGF fluorescent probes has been impeded by the absence of effective HGF-targeting small-molecular ligands. Herein, we present the targeted capabilities of the novel V-1-GGGK-MPA probe labeled with a near-infrared fluorescent dye, which targets HGF in CRC. The V-1-GGGK peptide exhibits high specificity and selectivity for HGF-positive in vitro tumor cells and in vivo tumors. Biodistribution analysis of V-1-GGGK-MPA revealed tumor-specific accumulation with low background uptake, yielding signal-to-noise ratio (SNR) values of tumor-to-colorectal >6 in multiple subcutaneous CRC models 12 h postinjection. Quantitative analysis confirmed the probe's high uptake in SW480 and HT29 orthotopic and liver metastatic models, with SNR values of tumor-to-colorectal and -liver being 5.6 ± 0.4, 4.6 ± 0.5, and 2.1 ± 0.3, 2.0 ± 0.5, respectively, enabling precise tumor visualization for surgical navigation. Pathological analysis demonstrated the excellent tumor boundaries discrimination capacity of the V-1-GGGK-MPA probe at the molecular level. With its rapid tumor targeting, sustained tumor retention, and precise tumor boundary delineation, V-1-GGGK-MPA merges as a promising HGF imaging agent, enriching the toolbox of intraoperative navigational fluorescent probes for CRC.

A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.