Bénédicte Mortamet

Automatic quality assessment in structural brain magnetic resonance imaging

MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer‐aided diagnosis. This work proposes a fully‐automatic method for measuring image quality of three‐dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T1‐weighted 1.5T and 3T head scans acquired at 36 Alzheimers Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore. Magn Reson Med, 2009.

Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier

Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimers disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimers Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of training samples and thereby improving performance of disease classifiers. Although small, a change in hardware could lead to a change of the decision value and thus diagnostic grouping. The findings of this study provide estimators for diagnostic accuracy of an automated disease diagnosis method involving scans acquired with different sets of hardware. Furthermore, we show that the level of confidence in the performance estimation significantly depends on the size of the training sample, and hence should be taken into account in a clinical setting.

The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography

Histological and magnetic resonance imaging studies have demonstrated that age-associated alterations of the human brain may be at least partially related to vascular alterations. Relatively little information has been published on vascular changes associated with healthy aging, however. The study presented in this paper examined vessels segmented from standardized, high-resolution, magnetic resonance angiograms (MRAs) of 100 healthy volunteers (50 males, 50 females), aged 18-74, without hypertension or other disease likely to affect the vasculature. The subject sample was divided into 5 age groups (n=20/group) with gender equally distributed per group. The anterior cerebral, both middle cerebral, and the posterior circulations were examined for vessel number, vessel radius, and vessel tortuosity. Males exhibited larger vessel radii regardless of age and across all anatomical regions. Both males and females displayed a lower number of MRA-discernible vessels with age, most marked in the posterior circulation. Age-associated tortuosity increases were relatively mild. Our multi-modal image database has been made publicly available for use by other investigators.

Effects of MRI scan acceleration on brain volume measurement consistency

To evaluate the effects of recent advances in magnetic resonance imaging (MRI) radiofrequency (RF) coil and parallel imaging technology on brain volume measurement consistency.

Magnetization transfer-based 3D visualization of foot peripheral nerves

To investigate magnetization transfer (MT) effects as a new source of contrast for imaging and tracking of peripheral foot nerves.