Dataset Information

Intelligent Metamaterials Based on Nonlinearity for Magnetic Resonance Imaging.

ABSTRACT: Metamaterials provide a powerful platform to probe and enhance nonlinear responses in physical systems toward myriad applications. Herein, the development of a coupled nonlinear metamaterial (NLMM) featuring a self-adaptive response that selectively amplifies the magnetic field is reported. The resonance of the NLMM is suppressed in response to higher degrees of radio-frequency excitation strength and recovers during a subsequent low excitation strength phase, thereby exhibiting an intelligent, or nonlinear, behavior by passively sensing excitation signal strength and responding accordingly. The nonlinear response of the NLMM enables us to boost the signal-to-noise ratio during magnetic resonance imaging to an unprecedented degree. These results provide insights into a new paradigm to construct NLMMs consisting of coupled resonators and pave the way toward the utilization of NLMMs to address a host of practical technological applications.

SUBMITTER: Zhao X

PROVIDER: S-EPMC7108751 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Intelligent Metamaterials Based on Nonlinearity for Magnetic Resonance Imaging.

Zhao Xiaoguang X Duan Guangwu G Wu Ke K Anderson Stephan W SW Zhang Xin X

Advanced materials (Deerfield Beach, Fla.) 20191030 49

Metamaterials provide a powerful platform to probe and enhance nonlinear responses in physical systems toward myriad applications. Herein, the development of a coupled nonlinear metamaterial (NLMM) featuring a self-adaptive response that selectively amplifies the magnetic field is reported. The resonance of the NLMM is suppressed in response to higher degrees of radio-frequency excitation strength and recovers during a subsequent low excitation strength phase, thereby exhibiting an intelligent, ...[more]

PMID: 31663651

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Project description:In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

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Intelligent Metamaterials Based on Nonlinearity for Magnetic Resonance Imaging.

Publications

Intelligent Metamaterials Based on Nonlinearity for Magnetic Resonance Imaging.

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