Search Results (140)

Objectives: Peptide receptor radionuclide therapy (PRRT) of neuroendocrine tumors (NETs) commonly relies on somatostatin receptor subtype 2 (SSTR2) agonists such as DOTA-TOC/TATE, which may show limited efficacy due to high hepatic uptake and therapy resistance in some patients. SSTR2 antagonists have demonstrated superior tumor targeting. This study aimed to establish the production and quality control of the Actinium-225-labeled SSTR2 antagonist [²²⁵Ac]Ac-DOTA-LM3 and to report in-human clinical experience with targeted alpha therapy (TAT). Methods: [²²⁵Ac]Ac-DOTA-LM3 was produced by radiolabeling DOTA-LM3 with Actinium-225 under validated conditions. Radiochemical conversion, purity, yield, and stability were assessed using radio-TLC, fractionated radio-HPLC combined with gamma spectroscopy, and in vitro serum stability testing. Clinical feasibility and therapeutic response were evaluated in a patient with metastatic neuroendocrine pancreatic neoplasm refractory to prior ¹⁷⁷Lu-based PRRT. Results: Radiolabeling achieved reproducibly high radiochemical purity (>97%) and decay-corrected yields exceeding 80%. The radiopharmaceutical showed high in vitro stability with minimal release of free Actinium-225 over five days. Fractionated radio-HPLC enabled indirect purity assessment. In the reported patient, [²²⁵Ac]Ac-DOTA-LM3 therapy resulted in partial remission without clinically relevant hematologic, renal, or hepatic toxicity and was associated with marked clinical improvement. Conclusions: [²²⁵Ac]Ac-DOTA-LM3 can be produced with high purity and stability using clinically applicable procedures. In-human results suggest promising efficacy and safety, supporting further clinical investigation of Actinium-225-labeled SSTR2 antagonists for advanced NETs. Full article

Background: Somatostatin receptors (SSTRs), especially subtype 2 (SSTR2), are increasingly recognized as valuable molecular targets in the imaging of chronic inflammatory and immune-mediated diseases. Their expression on activated immune and stromal cells enables specific, non-invasive detection of inflammatory activity using radio-labeled somatostatin analogs. Objective: This review aims to summarize current evidence on SSTR-targeted imaging across a range of chronic inflammatory and immune-mediated diseases, compare its diagnostic value with ¹⁸F-FDG PET/CT, and discuss biological mechanisms, clinical applications, and remaining challenges. Methods: A literature-based narrative review was conducted, integrating preclinical studies, clinical trials, and comparative imaging research involving SSTR PET/SPECT tracers such as ⁶⁸Ga-DOTATATE, ⁶⁸Ga-DOTANOC, ⁹⁹ᵐTc-HYNIC-TOC, and ¹¹¹In-pentetreotide in diseases including vasculitis, sarcoidosis, autoimmune myocarditis, rheumatoid arthritis, and thyroid-associated ophthalmopathy. Results: SSTR-targeted imaging has shown promising specificity for inflammatory lesions and provides favorable lesion-to-background contrast, particularly in tissues with high physiological FDG uptake such as the myocardium and brain. In vasculitis and sarcoidosis, SSTR-targeted tracers may complement FDG PET by improving diagnostic confidence and inter-observer consistency in selected small studies. Mechanistically, SSTR2 expression is closely associated with cytokine-driven immune activation, predominantly involving M1 macrophages. However, current evidence remains limited by heterogeneous receptor expression, variable myocardial uptake, and the lack of standardized imaging protocols. Conclusions: SSTR-targeted molecular imaging represents a biologically grounded and clinically promising complementary approach for assessing immune-mediated inflammation. Future developments in tracer design, quantitative standardization, and multicenter clinical validation are warranted to establish its role in precision diagnostics. Full article

Wideband spectrum sensing is a crucial technology for the efficient utilization of limited frequency resources in cognitive radio. While deep learning models have yielded promising results, they typically rely on either time-domain (I/Q) or frequency-domain (FFT) data alone, which can limit their performance. This study proposes IPFSCNN (IQ-Parallel FFT-Serial CNN), a novel asymmetric hybrid architecture that synergistically fuses both data representations. The key idea of its design is an asymmetric architecture that employs two specialized streams: a parallelized branch to efficiently capture temporal features from I/Q data, and a deep serial branch to extract spectral patterns from FFT data. These complementary features are fused to perform a multi-label classification task. Experiments on an LTE-M dataset demonstrate that the proposed IPFSCNN achieves a higher detection performance than state-of-the-art models, including DeepSense and ParallelCNN, particularly in low signal-to-noise ratio conditions. Furthermore, IPFSCNN achieves this superior accuracy while maintaining high computational efficiency, requiring 15% fewer parameters and only one-third of the multiply-accumulate (MAC) operations compared to the DeepSense model. Crucially, a comprehensive ablation study validates this asymmetric design, proving that the proposed ‘IQ-Parallel FFT-Serial’ combination is demonstrably superior to other hybrid configurations. Full article

Synthetic aperture radar (SAR), as a high-resolution microwave remote sensing imaging technology, plays an indispensable role in both military and civilian applications. However, in complex electromagnetic countermeasure environments, radio frequency interference (RFI) severely degrades SAR imaging quality. SAR anti-interference, as a countermeasure method, has significantly practical values. Although deep learning-based anti-interference techniques have demonstrated notable advantages, two key issues remain unresolved: 1. Strong coupling between interference suppression and SAR imaging—most existing methods rely on raw echo data, leading to a complex processing pipeline and error accumulation. 2. Scarcity of labeled data—the lack of high-quality labeled data severely restricts model deployment. To address these challenges, this work constructs a standardized dataset and conducts comprehensive validation experiments based on this dataset. The main contributions are as follows: Firstly, this work establishes the mathematical model for block-type interference, laying a theoretical foundation for the subsequent construction of RFI-polluted data. Secondly, this work constructs a block-type interference dataset, which includes the labeled data constructed by our laboratory and open-source data from the Sentinel-1 satellites, providing reliable data support for deep learning. Thirdly, this work proposes an image subspace-based interference suppression method, which eliminates the dependence on raw echo data and significantly simplifies the processing pipeline. Finally, this work makes a fair comparison of the current works, summarizes the existing problems, and looks forward to possible future research directions. Full article

The advent of nanotechnology has revolutionized the field of medicine, particularly in the development of targeted therapeutic strategies. Among these, radiolabeled nanomaterials have emerged as promising tools for both diagnostic and therapeutic applications, offering precise delivery of radiation to diseased tissues while minimizing damage to healthy ones. Notably, Lutetium-177 (¹⁷⁷Lu) has gained significant attention due to its favorable emission properties and availability that render it suitable for imaging and therapeutic purposes. When integrated with magnetic nano-formulations, ¹⁷⁷Lu-labeled systems combine the benefits of targeted radiation therapy (TRT) with the unique properties of magnetic nanoparticles (MNPs), such as magnetic resonance imaging (MRI) contrast enhancement and magnetically guided drug delivery to address challenges in diagnosis and treatment of diseases, such as cancer. By examining the latest advancements in their design, particularly surface functionalization and bioconjugation strategies, this study aims to highlight their efficacy in targeted therapy, imaging, and theranostic applications. Furthermore, we discuss the current challenges, such as scalability, biocompatibility, and regulatory hurdles, while proposing future directions to enhance their clinical translation. This comprehensive review underscores the transformative potential of ¹⁷⁷Lu-labeled magnetic nano-formulations in precision medicine and their role in shaping the future of therapeutic interventions. Full article

Radio frequency (RF)-based human activity sensing is an essential component of ubiquitous computing, with WiFi sensing providing a practical and low-cost solution for gesture and activity recognition. However, challenges such as manual data collection, multipath interference, and poor cross-domain generalization hinder real-world deployment. Existing data augmentation approaches often neglect the biomechanical structure underlying RF signals. To address these limitations, we present CM-GR, a cross-modal gesture recognition framework that integrates semantic learning with generative modeling. CM-GR leverages 3D skeletal points extracted from vision data as semantic priors to guide the synthesis of realistic WiFi signals, thereby incorporating biomechanical constraints without requiring extensive manual labeling. In addition, dynamic conditional vectors are constructed from inter-subject skeletal differences, enabling user-specific WiFi data generation without the need for dedicated data collection and annotation for each new user. Extensive experiments on the public MM-Fi dataset and our SelfSet dataset demonstrate that CM-GR substantially improves the cross-subject gesture recognition accuracy, achieving gains of up to 10.26% and 9.5%, respectively. These results confirm the effectiveness of CM-GR in synthesizing personalized WiFi data and highlight its potential for robust and scalable gesture recognition in practical settings. Full article

Gallium-68 is a widely used positron-emitting radionuclide in nuclear medicine, traditionally obtained from ⁶⁸Ge/⁶⁸Ga generators. However, increasing clinical demand has driven interest in alternative production methods, such as medical cyclotrons equipped with solid targets. This study evaluates the functional equivalence of gallium-68 chloride obtained from cyclotron solid target and formulated to be equivalent to the eluate from a germanium-gallium generator, aiming to determine whether this production method can serve as a reliable alternative for PET radiopharmaceutical applications. Preparations of [⁶⁸Ga]Ga-PSMA-11 and [⁶⁸Ga]Ga-DOTATOC, labeled with cyclotron-derived gallium-68 chloride, were subjected to quality control analysis using radio thin layer chromatography and radio high performance liquid chromatography. Subsequently, biodistribution studies were performed in mouse oncological models of expression of PSMA antigen and SSTR receptor to compare uptake of preparations produced with generator and cyclotron-derived isotopes. All tested formulations met the required radiochemical purity specifications. Moreover, tumor accumulation of the radiolabeled compounds was comparable regardless of the isotope source. The results support the conclusion that gallium-68 produced via cyclotron is functionally equivalent to that obtained from a generator, demonstrating its potential for interchangeable use in clinical and research radiopharmaceutical applications. Full article

Specific Emitter Identification (SEI) is a specialized technique for identifying different emitters by analyzing the unique characteristics embedded in received signals, known as Radio Frequency Fingerprints (RFFs), and SEI plays a crucial role in civilian applications. Recently, various SEI methods based on deep learning have been proposed. However, in real-world scenarios, the scarcity of accurately labeled data poses a significant challenge to these methods, which typically rely on large-scale supervised training. To address this issue, we propose a novel SEI framework based on self-supervised contrastive learning. Our approach comprises two stages: an unsupervised pretraining phase that uses contrastive loss to learn discriminative RFF representations from unlabeled data, and a supervised fine-tuning stage regularized through virtual adversarial training (VAT) to improve generalization under limited labels. This framework enables effective feature learning while mitigating overfitting. To validate the effectiveness of the proposed method, we collected real-world satellite navigation signals using a 40-meter antenna and conducted extensive experiments. The results demonstrate that our approach achieves outstanding SEI performance, significantly outperforming several mainstream SEI methods, thereby highlighting the practical potential of contrastive self-supervised learning in satellite transmitter identification. Full article

Modulation recognition, as one of the key technologies in the field of wireless communications, holds significant importance in applications such as spectrum resource management, interference suppression, and cognitive radio. While deep learning has substantially improved the performance of Automatic Modulation Recognition (AMR), it heavily relies on large amounts of labeled data. Given the high annotation costs and privacy concerns, researching semi-supervised AMR methods that leverage readily available unlabeled data for training is of great significance. This study constructs a semi-supervised AMR method based on dual-student. Specifically, we first adopt a dual-branch co-training architecture to fully exploit unlabeled data and effectively learn deep feature representations. Then, we develop a dynamic stability evaluation module using strong and weak augmentation strategies to improve the accuracy of generated pseudo-labels. Finally, based on the dual-student semi-supervised framework and pseudo-label stability evaluation, we propose a stability-guided consistency regularization constraint method and conduct semi-supervised AMR model training. The experimental results demonstrate that the proposed DualBranch-AMR method significantly outperforms traditional supervised baseline approaches on benchmark datasets. With only 5% labeled data, it achieves a recognition accuracy of 55.84%, reaching over 90% of the performance of fully supervised training. This validates the superiority of the proposed method under semi-supervised conditions. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

The application of self-supervised learning (SSL) is increasingly imperative for advancing wireless communication technologies, particularly in scenarios with limited labeled data. Traditional data-augmentation-based SSL methods have struggled to accurately capture the intricate properties of wireless signals. This letter introduces a novel self-supervised learning framework that leverages feature-level augmentation combined with contrastive learning to enhance wireless signal recognition. Extensive experiments conducted in various environments demonstrate that the proposed method achieves improvements of more than 2.56% over the existing supervised learning (SL) methods and SSL methods on the RadioML2016.10a and ADS-B datasets. Moreover, the experimental results show that the proposed SSL pre-training strategy improves performance by 4.67% compared to supervised approaches. These results validate that the proposed method offers stronger generalization capabilities and superior performance when handling different types of wireless signal tasks. Full article

Specific emitter identification (SEI) is a core technology for wireless device security that plays a crucial role in protecting wireless communication systems from various security threats. However, current deep learning-based SEI methods heavily rely on large amounts of labeled data for supervised training, facing challenges in non-cooperative communication scenarios. To address these issues, this paper proposes a novel contrastive asymmetric masked learning-based SEI (CAML-SEI) method, effectively solving the problem of SEI under scarce labeled samples. The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. This design promotes the learning of fine-grained local feature representations. To further enhance feature discriminability, a learnable non-linear mapping is introduced to compress high-dimensional encoded features into a compact low-dimensional space, accompanied by a contrastive loss function that simultaneously achieves feature aggregation of positive samples and feature separation of negative samples. Finally, the network is jointly optimized by combining signal reconstruction and feature contrast tasks. Experiments conducted on real-world ADS-B and Wi-Fi datasets demonstrate that the proposed method effectively learns generalized RFF features, and the results show superior performance compared with other SEI methods. Full article

Background/Objectives: Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are highly sensitive clinical imaging modalities, frequently employed in conjunction with magnetic resonance imaging (MRI) or computed tomography (CT) for diagnosing a wide range of disorders. Efficient and robust radiolabeling methods are needed to accommodate the increasing demand for PET and SPECT tracer development. Copper-mediated radiohalogenation (CMRH) reactions enable rapid late-stage preparation of radiolabeled arenes, yet synthetic challenges and radiolabeling precursors’ instability can limit the applications of CMRH approaches. Methods: A series of aryl-boronic acids were converted into their corresponding aryl-boronic acid 1,1,2,2-tetraethylethylene glycol esters [ArB(Epin)s] and aryl-boronic acid 1,1,2,2-tetrapropylethylene glycol esters [ArB(Ppin)s] as stable and versatile precursor building blocks for radiolabeling via CMRH. General protocols for the preparation of ¹⁸F-labeled and ¹²³I-labeled arenes utilizing CMRH of these substrates were developed and applied. The radiochemical conversions (RCC) were determined by radio-(U)HPLC. Results: Both ArB(Epin)s and ArB(Ppin)s-based radiolabeling precursors were prepared in a one-step synthesis with chemical yields of 49–99%. Radiolabeling of the aryl-boronic esters with fluorine-18 or iodine-123 via CMRH furnished the corresponding radiolabeled arenes with RCC of 7–99% and 10–99%, respectively. Notably, a radiohalogenated prosthetic group containing a vinyl sulfone motif was obtained with an activity yield (AY) of 18 ± 3%, and applied towards the preparation of two clinically relevant PET tracers. Conclusions: This approach enables the synthesis of stable radiolabeling precursors and thus provides increased versatility in the application of CMRH, thereby supporting the development of novel PET and SPECT radiotracers. Full article

Multiple sclerosis (MS) is a multifaceted inflammatory, demyelinating, and neurodegenerative disease typified by lesions with distinct hallmarks in the central nervous system. Dysregulation of micro-environmental factors, including extracellular matrix (ECM) remodelling and glial cell activation, has a decisive effect on lesion development and disease progression. Understanding the biological and pathological features of lesions would aid in prognosis and personalised treatment decision making. Positron emission tomography (PET) is an imaging technique that uses radio-labelled tracers to detect specific biological phenomena. Recent PET hardware developments enable high-resolution, quantitative imaging, which may allow biological characterisation of relatively small MS lesions. PET may complement MRI by offering objective, quantitative insights into lesion characteristics, including myelin density, inflammation and axonal integrity. Moreover, PET may provide information on lesion traits supporting decision making on upcoming therapeutic strategies for progressive MS, such as the availability of oligodendrocyte progenitor cells and ECM composition that affect remyelination and/or axon regeneration. This review explores the cellular and molecular ECM signatures and neuropathological processes of white matter MS lesions, discusses current and potential novel PET targets that may help characterise MS lesions in vivo, and addresses the potential of PET as a decision tool for selection and evaluation of therapeutic strategies, with a focus on remyelination. Full article

Radio mean labeling of a connected graph G is an assignment of distinct positive integers to the vertices of G satisfying a mathematical constraint called radio mean condition. The maximum label assigned to any vertex of G is called the $span$ of the radio mean labeling. The minimum $span$ of all feasible radio mean labelings of G is the radio mean number of G, denoted by $rmn\left(G\right)$. In our previous study, we proved that if G has order n, then $rmn\left(G\right)\in [n,rmn\left(P_n\right)]$ where $P_n$ is a path of order n. All graphs of diameters 1, 2 and 3 have the radio mean number equal to order n. However, they are not the only graphs on n vertices with radio mean number n. Graphs isomorphic to path $P_n$ are the graphs having the maximum diameter among the set of all graphs of order n and they possess the maximum feasible radio mean number. In this paper, we show that, for any integer in the range of achievable radio mean numbers, there always exists a graph of order n with the given integer as its radio mean number. This is approached by introducing a special type of tree whose construction is detailed in the article. The task of assigning radio mean labels to a graph can be considered as an optimization problem. This paper critiques the limitations of existing Integer Linear Programming (ILP) models for assigning radio mean labeling to graphs and proposes a new ILP model. The existing ILP model does not guarantee that the vertex labels are distinct, positive and satisfy the radio mean condition, prompting the need for an improved approach. We propose a new ILP model which involves $n^2$ constraints is the input graph’s order is n. We obtain a radio mean labeling of cycle of order 10 using the new ILP. In our previous study, we showed that, for any graph G, we can extend the radio mean labelings of its diametral paths to the vertex set of G and obtain radio mean labelings of G. This insight forms the basis for an algorithm presented in this paper to obtain radio mean labels for a given graph G with n vertices and diameter d. The correctness and complexity of this algorithm are analyzed in detail. Radio mean labelings have been proposed for cryptographic key generation in previous works, and the algorithm presented in this paper is general enough to support similar applications across various graph structures. Full article