Changes in tissue composition and cellular architecture have been associated with neurological disease, and these in turn can affect biomechanical properties. Natural biological factors such as aging and an individual’s sex also affect underlying tissue biomechanics in different brain regions. Understanding the normal changes is necessary before determining the efficacy of stiffness imaging for neurological disease diagnosis and therapy monitoring. The objective of this study was to evaluate global and regional changes in brain stiffness as a function of age and sex, using improved MRE acquisition and processing that has been shown to provide median stiffness values that are typically reproducible to within 1% in global measurements and within 2% for regional measurements. Furthermore, this is the first study to report the effects of age and sex over the entire cerebrum volume and over the full frontal, occipital, parietal, temporal, deep gray matter/white matter (insula, deep gray nuclei and white matter tracts), and cerebellum volumes. In 45 volunteers, we observed a significant linear correlation between age and brain stiffness in the cerebrum (P<.0001), frontal lobes (P<.0001), occipital lobes (P=.0005), parietal lobes (P=.0002), and the temporal lobes (P<.0001) of the brain. No significant linear correlation between brain stiffness and age was observed in the cerebellum (P=.74), and the sensory-motor regions (P=.32) of the brain, and a weak linear trend was observed in the deep gray matter/white matter (P=.075). A multiple linear regression model predicted an annual decline of 0.011±0.002 kPa in cerebrum stiffness with a theoretical median age value (76 years old) of 2.56±0.08 kPa. Sexual dimorphism was observed in the temporal (P=.03) and occipital (P=.001) lobes of the brain, but no significant difference was observed in any of the other brain regions (P>.20 for all other regions). The model predicted female occipital and temporal lobes to be 0.23 kPa and 0.09 kPa stiffer than males of the same age, respectively. This study confirms that as the brain ages, there is softening; however, the changes are dependent on region. In addition, stiffness effects due to sex exist in the occipital and temporal lobes.

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This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since image noise cannot be fully characterized on the sole basis of the noise level at computed tomography (CT). Noise spatial correlation (or noise texture) is closely related to the detection and characterization of low-contrast objects and may be quantified by analyzing the noise power spectrum. High-contrast spatial resolution can be measured using the modulation transfer function and section sensitivity profile and is generally unaffected by noise reduction. Detectability of low-contrast lesions can be evaluated subjectively at varying dose levels using phantoms containing low-contrast objects. Clinical applications with inherent high-contrast abnormalities (eg, CT for renal calculi, CT enterography) permit larger dose reductions with denoising techniques. In low-contrast tasks such as detection of metastases in solid organs, dose reduction is substantially more limited by loss of lesion conspicuity due to loss of low-contrast spatial resolution and coarsening of noise texture. Existing noise reduction strategies for dose reduction have a substantial impact on lowering the radiation dose at CT. To preserve the diagnostic benefit of CT examination, thoughtful utilization of these strategies must be based on the inherent lesion-to-background contrast and the anatomy of interest. The authors provide an overview of existing noise reduction strategies for low-dose abdominopelvic CT, including analytic reconstruction, image and projection space denoising, and iterative reconstruction; review qualitative and quantitative tools for evaluating these strategies; and discuss the strengths and limitations of individual noise reduction methods.

Purpose To noninvasively evaluate gliomas with magnetic resonance elastography (MRE) to characterize the relationship of tumor stiffness with tumor grade and mutations in the IDH1 gene. Materials and Methods With institutional review board approval and following written, informed consent, tumor stiffness properties were prospectively quantified in 18 patients (mean age 42, 6 female) with histologically proven gliomas using MRE from 2014–2016. Images were acquired on a 3T MR unit with a vibration frequency of 60 Hz. Tumor stiffness was compared with unaffected contralateral white matter, across tumor grade and by IDH1 mutation status. The performance of the use of tumor stiffness to predict tumor grade and IDH1 mutation was evaluated by using Wilcoxon rank sum, one-way ANOVA and Tukey-Kramer tests. Results Gliomas were softer than healthy brain parenchyma, 2.2 kPa compared to 3.3 kPa (p < .0001) with grade IV tumors softer than grade II. Tumors with an IDH1 mutation were significantly stiffer than those with wild-type IDH1, 2.5 kPa vs. 1.6 kPa respectively (p = .007). Conclusions MRE demonstrated that gliomas were not only softer than normal brain but the degree of softening was directly correlated with tumor grade and IDH1 mutation status. Noninvasive determination of tumor grade and IDH1 mutation may result in improved stratification of patients for different treatment options and the evaluation of novel therapeutics. This work reports on the emerging field of mechanogenomics – the identification of genetic features such as IDH1 mutation using intrinsic biomechanical information.