Comparison of fast field-cycling magnetic resonance imaging methods and future perspectives

This article is based upon work from COST Action CA15209, supported by COST (European Cooperation in Science and Technology). M. Bödenler, C. Gösweiner and H. Scharfetter acknowledge the financial support by the European Commission in the frame of the H2020 Future and Emerging Technologies (FET-open) under grant agreement 665172, project ‘CONQUER’. L. de Rochefort acknowledges the France Life Imaging network (Grant ANR-11-INBS-0006) that partially funded the small animal FFC-MRI system. D.J. Lurie, L.M. Broche and P.J. Ross acknowledge funding from the European Union’s H2020 research and innovation programme under grant agreement No 668119, project ‘IDentIFY’.Peer reviewedPublisher PD

Federated Learning with Dynamic Model Exchange

Large amounts of data are needed to train accurate robust machine learning models, but the acquisition of these data is complicated due to strict regulations. While many business sectors often have unused data silos, researchers face the problem of not being able to obtain a large amount of real-world data. This is especially true in the healthcare sector, since transferring these data is often associated with bureaucratic overhead because of, for example, increased security requirements and privacy laws. Federated Learning should circumvent this problem and allow training to take place directly on the data owner’s side without sending them to a central location such as a server. Currently, there exist several frameworks for this purpose such as TensorFlow Federated, Flower, or PySyft/PyGrid. These frameworks define models for both the server and client since the coordination of the training is performed by a server. Here, we present a practical method that contains a dynamic exchange of the model, so that the model is not statically stored in source code. During this process, the model architecture and training configuration are defined by the researchers and sent to the server, which passes the settings to the clients. In addition, the model is transformed by the data owner to incorporate Differential Privacy. To trace a comparison between central learning and the impact of Differential Privacy, performance and security evaluation experiments were conducted. It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres

High‐Field Detection of biomarkers with Fast Field‐Cycling MRI: The Example of Zinc Sensing

International audienceMany smart MRI probes provide response to a biomarker based on modulation of their rotational correlation time. The magnitude of such MRI signal changes is highly dependent on the magnetic field and the response decreases dramatically at high fields (> 2 T). To overcome the loss of efficiency of responsive probes at high field, with FFC‐MRI we exploit field‐dependent information rather than the absolute difference in the relaxation rate measured in the absence and in the presence of the biomarker at a given imaging field. We report here the application of fast field‐cycling techniques combined with the use of a molecular probe for the detection of Zn2+ to achieve 166% MRI signal enhancement at 3 T, while the same agent provides no detectable response using conventional MRI. This approach can be generalized to any biomarker provided the detection is based on variation of the rotational motion of the probe

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres

R1 dispersion contrast at high field with fast field-cycling MRI

Contrast agents with a strong R1dispersion have been shown to be effective in generating target-specific contrast in MRI. The utilization of this R1field dependence requires the adaptation of an MRI scanner for fast field-cycling (FFC). Here, we present the first implementation and validation of FFC-MRI at a clinical field strength of 3 T. A field-cycling range of ±100 mT around the nominal B0field was realized by inserting an additional insert coil into an otherwise conventional MRI system. System validation was successfully performed with selected iron oxide magnetic nanoparticles and comparison to FFC-NMR relaxometry measurements. Furthermore, we show proof-of-principle R1dispersion imaging and demonstrate the capability of generating R1dispersion contrast at high field with suppressed background signal. With the presented ready-to-use hardware setup it is possible to investigate MRI contrast agents with a strong R1dispersion at a field strength of 3 T

Tuning nuclear quadrupole resonance:a novel approach for the design of frequency-selective MRI contrast agents

Abstract The interaction between water protons and suitable quadrupolar nuclei (QN) can lead to quadrupole relaxation enhancement (QRE) of proton spins, provided the resonance condition between both spin transitions is fulfilled. This effect could be utilized as a frequency selective mechanism in novel, responsive T₁ shortening contrast agents (CAs) for magnetic resonance imaging (MRI). In particular, the proposed contrast mechanism depends on the applied external flux density—a property that can be exploited by special field-cycling MRI scanners. For the design of efficient CA molecules, exhibiting narrow and pronounced peaks in the proton T₁ relaxation dispersion, the nuclear quadrupole resonance (NQR) properties, as well as the spin dynamics of the system QN−¹H, have to be well understood and characterized for the compounds in question. In particular, the energy-level structure of the QN is a central determinant for the static flux densities at which the contrast enhancement appears. The energy levels depend both on the QN and the electronic environment, i.e., the chemical bonding structure in the CA molecule. In this work, the NQR properties of a family of promising organometallic compounds containing ²⁰⁹Bi as QN have been characterized. Important factors like temperature, chemical structure, and chemical environment have been considered by NQR spectroscopy and ab initio quantum chemistry calculations. The investigated Bi-aryl compounds turned out to fulfill several crucial requirements: NQR transition frequency range applicable to clinical 1.5- and 3 T MRI systems, low temperature dependency, low toxicity, and tunability in frequency by chemical modification