Background: Among the novelties in the field of cardiovascular imaging, the construction of quantitative maps in a fast and efficient way is one of the most interesting aspects of the clinical research. Quantitative parametric maps are typically obtained by post processing dynamic images, that is, sets of images usually acquired in different temporal intervals, where several images with different contrasts are obtained. Magnetic resonance imaging, and emission tomography (positron emission and single photon emission) are the imaging techniques best suited for the formation of quantitative maps.
Methods: In this review article we present several methods that can be used for obtaining parametric maps, in a fast way, starting from the acquired raw data. We describe both methods commonly used in clinical research, and more innovative methods that build maps directly from the raw data, without going through the image reconstruction.
Results: We briefly described recently developed methods in magnetic resonance imaging that accelerate further the MR raw data generation, based on appropriate sub-sampling of k-space; then, we described recently developed methods for generating MR parametric maps. With regard to the emission tomography techniques, we gave an overview of both conventional methods, and more recently developed direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography data.
Conclusion: We have provided an overview of the possible approaches that can be followed to realize useful parametric maps from imaging raw data. We moved from the conventional approaches to more recent and efficient methods for accelerating the raw data generation and the of parametric maps formation.
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http://dx.doi.org/10.2174/1381612823666170328143348 | DOI Listing |
JMIR Med Inform
January 2025
INSERM U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 30 Bd Jean Monnet, Nantes, 44093, France, 33 2 40 08 74 10.
Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical "digital twin" has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations.
View Article and Find Full Text PDFForensic Sci Med Pathol
January 2025
Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Mushroom poisoning incidents happen infrequently, yet owing to the non-lethal nature of most toxins and the efficacy of timely treatment, fatalities from mushroom poisoning are uncommon, leading to a scarcity of pertinent clinical and pathological data. Here, we reported a case of death caused by the consumption of raw mushrooms, alongside detailed clinical data and multi-organs pathological alterations, which underscored its potential significant reference value in forensic practice. Futhermore, ibotenic acid, a type of mushroom toxin, was detected both in the patient's blood and gastric lavage fluid about 19 h after the consumption of mushrooms, and was successfully quantified at concentrations of 0.
View Article and Find Full Text PDFInsights Imaging
January 2025
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Zurich, Switzerland.
Objectives: To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.
Methods: In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner.
Sci Rep
January 2025
Nanotechnology Department, Faculty of Science, Urmia University, Urmia, Iran.
Today, active packaging has become essential to increase food safety and decrease food spoilage. In this study, the aim was to delay spoilage and increase the shelf life of rainbow fish fillets with a new hybrid nanocomposite active packaging. Packaging was fabricated with Ethylene vinyl acetate and active compounds such as rosemary extract, zinc oxide nanoparticles, and modified iron (Fe-MMT).
View Article and Find Full Text PDFNeural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
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