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Purpose we predict SAR during MRI exam using a 7 T 1H 298 MHz eight-channel degenerate birdcage coil1 combining SAR simulations with subject-specific measured (RF) B 1 + maps. Materials and Methods We simulated the coil1 in CST MW Suite, loaded by a model of human knee (Fig. 1, top). B 1 + was calculated in an axial slice crossing the patella. The maximum local SAR for an Axial “Zero” Time-of-Echo (ZTE) sequence “SILENT”2 was calculated. We acquired ∣ B 1 , map + ∣ maps of an adult (female) knee with a Bloch-Siegert sequence on 7 axial slices, centered on the same slice of the simulation, on a GE MR950 7T human system. For each slice a coefficient C, proportional to avg ( ∣ B 1 , map + ∣ ) / B 1 , nominal + , was used to scale the SAR simulated3. Results Fig. 1 shows: bottom left, simulated B 1 + magnitude; bottom center, local SAR for an input of 1 W per channel; bottom right, simulated B 1 , nominal + magnitude for a FA = 90° (length = 3.2 ms) sinc-pulse in the slice previously chosen. Fig. 2 shows the subject-specific measured ∣ B 1 , map + ∣ for a FA = 90° sinc-pulse. The predicted SAR obtained with scaled B 1 + maps are 0.50 W/kg (global) and 3.68 W/kg (maximum). Conclusions we obtained a good agreement between simulated and measured in vivo B 1 + maps, and we were able to calculate the distribution of SAR exposure, a safety MRI parameter not available in current exams, where only global SAR is provided, combining simulations and subject-specific measurements. Limits on global and local SAR (20 W/kg) were met for this sequence [1] , [2] , [3] .

Background Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible. Subjects A reference group composed of 144 AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24 month follow-up (MCI-non converters). All subjects came from the ADNI database. Methods We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm. Results We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC) = 0.97 (sensitivity ≃ 89% at specificity ≃ 94%) and Controls from MCI-converters with an AUC = 0.92 (sensitivity ≃ 89% at specificity ≃ 80%). MCI-converters are separated from MCI-non converters with AUC = 0.74(sensitivity ≃ 72% at specificity ≃ 65%). Findings The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population.

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by twelve morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with 6 FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.

Several quantities relevant to phenomenological studies of the mixing of neutral B mesons are computed on the lattice. Our main results are: f_{Bd} sqrt(B_{Bd})=206(28)(7) MeV, f_{Bs} sqrt(B_{Bs})/f_{Bd}sqrt(B_{Bd})=1.16(7). We also obtain the related quantities f_{Bs}sqrt(B{Bs})=237(18)(8) MeV, f_{Bd}= 174(22)(+7-0)(-4-0) MeV, f_{Bs}= 204(15)(+7-0)(+3-0) MeV, f_{Bs}/f_{Bd}=1.17(4)(+0-1), f_{Bd}/f_{Ds}=0.74(5). After combining our results with the experimental world average (Delta m_d), we predict (Delta m_s)=15.8(2.1)(3.3) ps^{-1}. We have also computed the relevant parameters for mixing of neutral D mesons which may be useful in some extensions of the Standard Model. All the quantities were obtained from a quenched simulation with a non-perturbatively improved Clover action at beta=6.2, corresponding to a lattice spacing 1/a=2.7(1) GeV, on a sample of 200 gauge-field configurations. A discussion of the main systematic errors is also presented.