Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.

Gold nanoparticles reduce inflammation in cerebral microvessels of mice with sepsis.

BACKGROUND: Sepsis is an emergency medical condition that can lead to death and it is defined as a life-threatening organ dysfunction caused by immune dysregulation in response to an infection. It is considered the main killer in intensive care units. Sepsis associated-encephalopathy (SAE) is mostly caused by a sepsis-induced systemic inflammatory response. Studies report SAE in 14-63% of septic patients. Main SAE symptoms are not specific and usually include acute impairment of consciousness, delirium and/or coma, along with electroencephalogram (EEG) changes. For those who recover from sepsis and SAE, impaired cognitive function, mobility and quality of life are often observed months to years after hospital discharge, and there is no treatment available today to prevent that. Inflammation and oxidative stress are key players for the SAE pathophysiology. Gold nanoparticles have been demonstrated to own important anti-inflammatory properties. It was also reported 20 nm citrate-covered gold nanoparticles (cit-AuNP) reduce oxidative stress. In this context, we tested whether 20 nm cit-AuNP could alleviate the acute changes caused by sepsis in brain of mice, with focus on inflammation. Sepsis was induced in female C57BL/6 mice by cecal ligation and puncture (CLP), 20 nm cit-AuNP or saline were intravenously (IV) injected 2 h after induction of sepsis and experiments performed 6 h after induction. Intravital microscopy was used for leukocyte and platelet adhesion study in brain, blood brain barrier (BBB) permeability carried out by Evans blue assay, cytokines measured by ELISA and real time PCR, cell adhesion molecules (CAMs) by flow cytometry and immunohistochemistry, and transcription factors, by western blotting. RESULTS: 20 nm cit-AuNP treatment reduced leukocyte and platelet adhesion to cerebral blood vessels, prevented BBB failure, reduced TNF- concentration in brain, and ICAM-1 expression both in circulating polymorphonuclear (PMN) leukocytes and cerebral blood vessels of mice with sepsis. Furthermore, 20 nm cit-AuNP did not interfere with the antibiotic effect on the survival rate of mice with sepsis. CONCLUSIONS: Cit-AuNP showed important anti-inflammatory properties in the brain of mice with sepsis, being a potential candidate to be used as adjuvant drug along with antibiotics in the treatment of sepsis to avoid SAE.