Estimation of prenatal aorta intima-media thickness from ultrasound examination.

Prenatal events such as intrauterine growth restriction and increased cardiovascular risk in later life have been shown to be associated with an increased intima-media thickness (aIMT) of the abdominal aorta in the fetus. In order to assess and manage atherosclerosis and cardiovascular disease risk in adults and children, in recent years the measurement of abdominal and carotid artery thickness has gained a growing appeal. Nevertheless, no computer aided method has been proposed for the analysis of prenatal vessels from ultrasound data, yet. To date, these measurements are being performed manually on ultrasound fetal images by skilled practitioners. The aim of the presented study is to introduce an automatic algorithm that identifies abdominal aorta and estimates its diameter and aIMT from routine third trimester ultrasonographic fetal data.The algorithm locates the aorta, then segments it and, by modeling the arterial wall longitudinal sections by means of a gaussian mixture, derives a set of measures of the aorta diameter (aDiam) and of the intima-media thickness (aIMT). After estimating the cardiac cycle, the mean diameter and the aIMT at the end-diastole phase are computed.Considering the aIMT value for each subject, the correlation between automatic and manual end-diastolic aIMT measurements is 0.91 in a range of values 0.44-1.10 mm, corresponding to both normal and pathological conditions. The automatic system yields a mean relative error of 19%, that is similar to the intra-observer variability (14%) and much lower that the inter-observer variability (42%).The correlation between manual and automatic measurements and the small error confirm the ability of the proposed system to reliably estimate aIMT values in prenatal ultrasound sequences, reducing measurement variability and suggesting that it can be used for an automatic assessment of aIMT.

Combined diffusion imaging and MR spectroscopy in the diagnosis of human prion diseases.

BACKGROUND AND PURPOSE: The physiopathologic bases underlying the signal intensity changes and reduced diffusibility observed in prion diseases (TSEs) are still poorly understood. We evaluated the interest of MRS combined with DWI both as a diagnostic tool and a way to understand the mechanism underlying signal intensity and ADC changes in this setting. MATERIALS AND METHODS: We designed a prospective study of multimodal MR imaging in patients with suspected TSEs. Forty-five patients with a suspicion of TSE and 11 age-matched healthy volunteers were included. The MR imaging protocol included T1, FLAIR, and DWI sequences. MRS was performed on the cerebellum, pulvinar, right lenticular nucleus, and frontal cortex. MR images were assessed visually, and ADC values were calculated. RESULTS: Among the 45 suspected cases, 31 fulfilled the criteria for probable or definite TSEs (19 sCJDs, 3 iCJDs, 2 vCJDs, and 7 genetic TSEs); and 14 were classified as AltDs. High signals in the cortex and/or basal ganglia were observed in 26/31 patients with TSEs on FLAIR and 29/31 patients on DWI. In the basal ganglia, high DWI signals corresponded to a decreased ADC. Metabolic alterations, increased mIns, and decreased NAA were observed in all patients with TSEs. ADC values and metabolic changes were not correlated; this finding suggests that neuronal stress (vacuolization), neuronal loss, and astrogliosis do not alone explain the decrease of ADC. CONCLUSIONS: MRS combined with other MR imaging is of interest in the diagnosis of TSE and provides useful information for understanding physiopathologic processes underlying prion diseases.

Automatic labelling and BI-RADS characterisation of mammogram densities.

Intelligent management of medical data is an important field of research in clinical information and decision support systems. Such systems are finding increasing use in the management of patients known to have, or suspected of having, breast cancer. Different types of breast-tissue patterns convey semantic information which is reported by the radiologist when reading mammograms. In this paper, a novel method is presented for the automatic labelling and characterisation of mammographic densities. The presented method is first concerned with the identification of the prominent structures in each mammogram. Subsequently, "dense tissue" is labelled in a mammogram data set, and BI-RADS classification is performed based on a 2D pdf that is contracted from a "ground truth" data set as well as a shape analysis framework. The presented method can be used in large-scale epidemiological studies which involve mammographic measurements of tissue-pattern, especially since breast-tissue density has been linked to an increased risk of breast cancer.