Different characteristics of fibrosis in NAFLD can be diagnosed and quantified by blood tests with excellent accuracy.

MRI, and in particular the MFGRE method, provides accurate and automatic quantification for the noninvasive evaluation of liver steatosis, either as a single measurement or in combination with blood variables.

The goal of this study was to put together several techniques of image segmentation to provide a reliable assessment of the left ventricular mass with short-axis cardiac MR images. No initial manual input was required for this process based on region growing, gradient detection, and adaptive thresholding. A comparison between actual mass and automatic assessment was implemented with 9 minipigs that underwent spin-echo MR imaging. Fifteen normal volunteers were studied with a fast-gradient-echo sequence. The automatic segmentation was then controlled by three trained observers. Actual mass and automatic segmentation were strongly correlated (r = .97 with P < .01). For normal volunteers, the standard error of estimation of the automatic assessment (12 g) compared well with the average myocardial mass (120 +/- 30 g) and the interobserver reproducibility of the manual assessment (9 g). These results allow the application of this method to the quantification of the left ventricular function and mass in clinical practice.

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International audienceWith medical imaging technologies growth, the question of their assessment on the impact and benefit on patient care is rising. Development and design of those medical imaging technologies should take into account the concept of image quality as it might impact the ability of practicians while they are using image information. Towards that goal, one should consider several human factors involved in image analysis and interpretation, e.g. image perception issues, decision process, image analysis pipeline (detection, localization, characterization...). While many efforts have been dedicated to objectively assess the value of imaging system in terms of ideal decision process, new trends have recently emerged to deal with human observer perfomances. This task effort is huge considering the variability of imaging acquisition methods and the possible pathologies. This paper proposes a survey of some key issues and results associated to this effort. We first outline the wide range of medical images with their own specific features. Next, we review the main methodologies to evaluate diagnostic quality of medical images from subjective assessment including ROC analysis, and diagnostic criteria quality analysis, to objective assessment including metrics based on the HVS, and model observers. At last, we present another evaluation method: eye-tracking studies to gain basic understanding of the visual search and decision-making process