npj Imaging最新文献

Renal cell carcinomas (RCCs) have multiple subtypes that are difficult to distinguish using imaging alone. This study characterises renal tumour microstructure using diffusion MRI (dMRI) and the Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT)-MRI framework. Patients were prospectively recruited from the RIM trial (ClinicalTrials.gov: NCT07173140, 20/11/2024). Fourteen patients with 17 renal tumours (including benign and various RCC subtypes) underwent dMRI using nine b-values (0-2500 s/mm²). A three-compartment VERDICT model was fitted with a self-supervised neural network. Compared to simpler dMRI models, VERDICT more accurately captured the diffusion data in tumour and healthy tissue. VERDICT revealed significant differences in intracellular volume fraction between cancerous and normal tissue, and in vascular volume fraction between vascular and non-vascular regions. A feature selection method identified a reduced 4 b-value protocol (b = [70, 150, 1000, 2000]), cutting scan time by over 30 min, enabling more efficient imaging in larger cohorts.

Current biomedical imaging focuses on spatial detail but overlooks time, limiting our understanding of disease progression. There is an unmet need for temporal atlases that align multiscale and multimodal data across defined timepoints, enabling dynamic mapping of pathophysiology. This framework will pave the way for more personalised, time-aware diagnostics and interventions.

Parkinson's disease (PD) diagnosis remains a substantial clinical challenge due to its heterogeneous symptomatology and the absence of reliable early-stage biomarkers. While molecular imaging offers promise, current methods are lengthy or have limited specificity. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of glutamate, mobile proteins, semisolid, and mobile macromolecules in an acute MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. The resulting quantitative parameter maps align well with histology and magnetic resonance spectroscopy (MRS) findings. Notably, the semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions emerged as promising PD biomarkers.

Anti-CD8 immunoPET facilitates non-invasive, whole-body visualization of immune responses, and syngeneic preclinical models are a crucial tool for studying tumor infiltration of T cells in response to cancer therapies. This study characterized longitudinal CD8⁺ T cell responses in an orthotopic mouse model of breast cancer treated with radiation and anti-CTLA4 by immunohistochemistry and anti-CD8 immunoPET, confirming an early but heterogeneous response induced by combination treatment that is detectable by imaging.

This perspective article aims to provide an update on current trends in the research of radiolabelled siderophores for molecular imaging of bacterial infections. It begins by explaining the importance of developing novel diagnostic tools for infections and addresses the limitations of contemporary methods, including molecular imaging. The discussion then shifts to compounds currently being studied for nuclear imaging, with a focus on radiolabelled siderophores and recent advances in their development. It also provides the latest insights into the structures of siderophores, their utilisation by bacteria and their role in bacterial metabolism, as well as potential for labelling with various radioisotopes. Additionally, it presents the use of radiolabelled siderophores, both naturally occurring and artificial siderophore derivates, for imaging of various bacterial infections.

Magnetic resonance microscopy (MRM) produces high spatial resolution proton images of biological tissues, plants, and porous media, revealing microstructural details and contrast unattainable by other means. A major challenge in MRM is the low signal-to-noise ratio at high spatial resolutions, as smaller voxels produce smaller MR signals. This necessitates the use of highly sensitive microcoils, high-performance gradient systems, and high magnetic fields. Here, we present a step-by-step prescription for fabricating a cost-effective, flexible microimaging probe system compatible with horizontal bore high-field MRI systems. We demonstrate performance at 15.2 T by acquiring high-resolution (15 μm isotropic voxels) images of ex vivo mouse spinal cord (gray matter SNR 38; 46 h scan) and hippocampus (SNR 67; 45 h scan), clearly resolving microstructural features. Shorter imaging times are possible using compressed sampling. The flexible probe design supports solenoid diameters ranging from < 1 mm up to 10 mm in diameter, offering flexibility for imaging a variety of biological samples at high resolution.

Cardiology is continually evolving towards increased personalization with targeted diagnostics and therapeutics. Peptide-based radiopharmaceuticals have emerged as a valuable tool for noninvasive, receptor-specific imaging, addressing limitations of traditional perfusion-based radiotracers like [¹⁵O]H₂O, [¹³N]NH₃, [⁸²Rb]RbCl and [^99mTc]Tc-Sestamibi, which lack molecular specificity. While these conventional tracers provide crucial insights into myocardial perfusion and ventricular function, receptor-targeted imaging can illuminate the molecular mechanisms underlying cardiovascular diseases. This, in turn, offers novel insights into disease progression, enhanced diagnostic accuracy, and a tool for companion diagnostics of molecularly targeted therapeutics. Beyond receptor-mediated targeting, recent advances in cell-penetrating peptides (CPPs), such as the development of the cardiac targeting peptide (CTP), offer new opportunities for the enhanced delivery of a therapeutic payload to the injured heart. Their biodistribution can be effectively monitored using radiolabeled analogs. This review explores the role of peptide-based radiopharmaceuticals in nuclear cardiology, highlighting their applications in receptor-mediated imaging and briefly discussing non-receptor-specific CPPs. Select examples illustrate how these innovations are advancing molecular characterization of cardiovascular diseases such as fibrosis, cardiac amyloidosis, atherosclerosis, and more, reshaping the nuclear cardiology landscape.

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization-based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and often compromise between model performance and its generalization. Researchers have to manually tune simulated training data parameters to resemble their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for super-resolution reconstruction of single-molecule localization microscopy data that are based on Deep-STORM and DeepSTORM3D. Our methods significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate comparable or superior performance of both methods compared to Deep-STORM, DeepSTORM3D, and other state-of-the-art methods, while significantly reducing the manual labor and computation time.

Research and development programs for adoptive cell therapies continue to expand, but few products make it to late-phase clinical trials, and even fewer receive FDA approval. Despite undergoing extensive validation before entering the trial phase, variable results may be observed in patients due to inherent differences between preclinical models and human subjects as well as heterogeneity between tumors. Moreover, the current clinical evaluation of cell therapies, including CAR-T cells, relies on limited or inconclusive approaches - usually blood sampling or tissue biopsies - lacking spatial and temporal information about their fate in the human body. Here we offer our perspective on how the application of PET imaging to track cell therapies in clinical studies could address these shortcomings and enhance our understanding of cell therapy biodistribution, patients and trial-level therapeutic success or failure, and safety considerations. We further address key challenges, from probe development to methodological, technical, and regulatory, and financial hurdles for integrating PET imaging of cell therapies into clinical studies.