Novel Radiofluorinated Nanobody PET Tracer for Preclinical Studies of TIM3 Expression.

T-cell immunoglobulin and mucin domain-3 (TIM3) is an inhibitory checkpoint glycoprotein expressed on immune cells, particularly tumor-infiltrating lymphocytes (TILs), and plays a critical role in suppressing antitumor immune responses. While dual blockade of TIM3 and programmed cell death protein 1 (PD1) has shown promising results in enhancing immune responses in advanced cancers, the lack of reliable, noninvasive methods for detecting TIM3 expression in tumors remains a major challenge. To address this, we developed and characterized a novel positron emission tomography (PET) tracer, [F]AlF-RESCA-HVCR2N2, based on a TIM3-specific nanobody labeled via [F]AlF radiochemistry.

The Impact of PET Imaging on Translational Medicine: Insights from Large-Animal Disease Models.

Large-animal models are playing a pivotal role in bridging the translational research gap. Positron emission tomography (PET) imaging is preferred in disease research involving large-animal models. Its ability to non-invasively monitor metabolic activity, receptor-ligand interactions, and pharmacokinetics in real time makes PET imaging an essential tool for evaluating therapeutic efficacy and advancing the development of targeted treatments.

VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image.

Source-free unsupervised domain adaptation (SFUDA) methods aim to address the challenge of domain shift while preserving data privacy. Existing SFUDA approaches construct reliable and confident pseudo-labels for target-domain data through denoising methods, thereby guiding the training of the target-domain model. The effectiveness of denoising approaches is influenced by the degree of domain gap between the source and target domains.

Accelerated Molecular Transportation in the Brain Extracellular Space with 755-nm Light Attenuates Post-Stroke Cognitive Impairment in Rats.

Ischemic stroke exacts a heavy toll in death and disability worldwide. After ischemic stroke, the accumulation of pathobiomolecules in the brain extracellular space (ECS) will exacerbate neurological damage and cognitive impairment. Photobiomodulation (PBM) has been demonstrated to improve cognitive function in Alzheimer's disease mouse models by accelerating molecular transportation in the brain ECS.

Smart Organic-Inorganic Copolymer Nanoparticles Distinguish Between Microglia and Cancer Cells for Synergistic Immunotherapy in Glioma.

The stimulator of interferon genes (STING) pathway has emerged as a new immunotherapy strategy with potent local stimulation specificity, showing promising potential to counteract the immunosuppression in glioma. Herein, a tumor microenvironment (TME) responsive nanoagonists are developed based on an organic-inorganic copolymer composed of the polymer PC6AB coupled with manganous phosphate ionic oligomers (MnP). The degradation of nanoagonists into PC6AB and MnP in the acidic TME enables spatiotemporal control of their delivery to tumor cells and immune cells, respectively.

Don't fear peculiar activation functions: EUAF and beyond.

In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industrial and image datasets, including CIFAR-10, Tiny-ImageNet, and ImageNet. The models utilizing PEUAF achieve the best performance across several baseline industrial datasets.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results.

Purpose: Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging.

This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics.We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis.

Efficacy of Citicoline Delivered via Brain Extracellular Space against Experimental Acute Ischemic Stroke in Rats.

Citicoline can be used to reduce acute ischemic stroke injury via venous infusion, however, its protective effects in the brain extracellular space remain largely unknown. Herein, we investigated the brain protective effects of citicoline administered via the brain extracellular space and sought precise effective dosage range that can protect against ischemic injury after experimental ischemic stroke in rats. : Fifty-six Sprague-Dawley rats were randomly divided into control, intraperitoneal (IP), caudate-putamen (CPu)-25, CPu-40, CPu-50, CPu-60 and CPu-75 groups based on the infusion site and concentration of citicoline.

PCNet: Prior Category Network for CT Universal Segmentation Model.

Accurate segmentation of anatomical structures in Computed Tomography (CT) images is crucial for clinical diagnosis, treatment planning, and disease monitoring. The present deep learning segmentation methods are hindered by factors such as data scale and model size. Inspired by how doctors identify tissues, we propose a novel approach, the Prior Category Network (PCNet), that boosts segmentation performance by leveraging prior knowledge between different categories of anatomical structures.

Suppressing label noise in medical image classification using mixup attention and self-supervised learning.

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators.

Super-resolution reconstruction of -ray CT images for PET-enabled dual-energy CT imaging.

Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy -ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient results.

X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan.

Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning.

Background: Ischemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) within 1 year after its occurrence, both of which have manifestations of ischemia and hypoxia of the small vessels in the early phase of the condition. The fundus is a collection of capillaries, while the retina responds differently to light of different wavelengths. Predicting the risk of IS occurring secondary to AF, based on subtle differences in fundus images of different wavelengths, is yet to be explored.

Unsupervised deep learning framework for data-driven gating in positron emission tomography.

Background: Physiological motion, such as respiratory motion, has become a limiting factor in the spatial resolution of positron emission tomography (PET) imaging as the resolution of PET detectors continue to improve. Motion-induced misregistration between PET and CT images can also cause attenuation correction artifacts. Respiratory gating can be used to freeze the motion and to reduce motion induced artifacts.

A Hybrid Imaging Platform(CT/PET/FMI) for Evaluating Tumor Necrosis and Apoptosis in Real-Time.

Multimodality imaging is an advanced imaging tool for monitoring tumor behavior and therapy . In this study, we have developed a novel hybrid tri-modality system that includes two molecular imaging methods: positron emission computed tomography (PET) and fluorescence molecular imaging (FMI) and the anatomic imaging modality X-ray computed tomography (CT). The following paper describes the system development.

RelatingF-FDG image signal-to-noise ratio to time-of-flight noise-equivalent count rate in total-body PET using the uEXPLORER scanner.

This work assessed the relationship between image signal-to-noise ratio (SNR) and total-body noise-equivalent count rate (NECR)-for both non-time-of-flight (TOF) NECR and TOF-NECR-in a long uniform water cylinder and 14 healthy human subjects using the uEXPLORER total-body PET/CT scanner.A TOF-NEC expression was modified for list-mode PET data, and both the non-TOF NECR and TOF-NECR were compared using datasets from a long uniform water cylinder and 14 human subjects scanned up to 12 h after radiotracer injection.The TOF-NECR for the uniform water cylinder was found to be linearly proportional to the TOF-reconstructed image SNRin the range of radioactivity concentrations studied, but not for non-TOF NECR as indicated by the reducedvalue.

Anatomically aided PET image reconstruction using deep neural networks.

Purpose: The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction.

Methods: We used a pretrained deep neural network to represent PET images.

HRSPET: a 0.5 mm resolution high-sensitivity small-animal PET scanner, a simulation study.

With the goal of developing a total-body small-animal PET system with a high spatial resolution of ∼0.5 mm and a high sensitivity >10% for mouse/rat studies, we simulated four scanners using the graphical processing unit-based Monte Carlo simulation package (gPET) and compared their performance in terms of spatial resolution and sensitivity. We also investigated the effect of depth-of-interaction (DOI) resolution on the spatial resolution.

Generative adversarial network based regularized image reconstruction for PET.

Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been applied to regularized image reconstruction.

An applicable method for PET/CT image quality assessment and comparison among three PET/CT systems with similar physical performance in cancer patients.

Purpose: This research proposes a method with specific procedure guideline for clinical PET/CT image quality assessment according to physicians' behavior of image interpretation and explore the relationship between image quality and image systems with similar physical performance.

Methods: Clinical PET/CT were divided according to body location: brain, chest, abdomen and pelvic cavity. We explored the lesions and suspicious regions where radiologists concerned most through eye-tracker and behavior observation study to generate an assessment checklist.

Subsecond total-body imaging using ultrasensitive positron emission tomography.

A 194-cm-long total-body positron emission tomography/computed tomography (PET/CT) scanner (uEXPLORER), has been constructed to offer a transformative platform for human radiotracer imaging in clinical research and healthcare. Its total-body coverage and exceptional sensitivity provide opportunities for innovative studies of physiology, biochemistry, and pharmacology. The objective of this study is to develop a method to perform ultrahigh (100 ms) temporal resolution dynamic PET imaging by combining advanced dynamic image reconstruction paradigms with the uEXPLORER scanner.

Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER.

The world's first 194-cm-long total-body PET/CT scanner (uEXPLORER) has been built by the EXPLORER Consortium to offer a transformative platform for human molecular imaging in clinical research and health care. Its total-body coverage and ultra-high sensitivity provide opportunities for more accurate tracer kinetic analysis in studies of physiology, biochemistry, and pharmacology. The objective of this study was to demonstrate the capability of total-body parametric imaging and to quantify the improvement in image quality and kinetic parameter estimation by direct and kernel reconstruction of the uEXPLORER data.