Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6 years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6 years of life.

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than [Formula: see text] and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than [Formula: see text]. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.

PURPOSE: To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. METHODS: Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. RESULTS: DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. CONCLUSIONS: The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

BACKGROUND: Studies have shown that tiotropium once daily reduces lung hyperinflation and dyspnea during exercise and improves exercise tolerance in patients with COPD. Mechanisms underlying the effects of the muscarinic receptor antagonist tiotropium on COPD have not been fully understood. OBJECTIVE: In this study, we investigated whether improvement in neural respiratory drive is responsible for reducing dyspnea during exercise and improving exercise tolerance in COPD. METHODS: Twenty subjects with severe COPD were randomized into two groups: no treatment (Control, n = 10, 63.6 ± 4.6 years, FEV1 29.6 ± 13.3%pred) or inhaled tiotropium 18 μg once daily for 1 month (n = 10, 66.5 ± 5.4 years, FEV1 33.0 ± 11.1%pred). All subjects were allowed to continue their daily medications other than anti-cholinergics during the study. Constant cycle exercise with 75% of maximal workload and spirometry were performed before and 1 month after treatment. Diaphragmatic EMG (EMGdi) and respiratory pressures were recorded with multifunctional esophageal catheter. Efficiency of neural respiratory drive, defined as the ratio of minute ventilation (VE) and diaphragmatic EMG (VE/EMGdi%max), was calculated. Modified British Medical Research Council Dyspnea Scale (mMRC) was used for the evaluation of dyspnea before and after treatment. RESULTS: There was no significant difference in spirometry before and after treatment in both groups. Diaphragmatic EMG decreased significantly at rest (28.1 ± 10.9% vs. 22.6 ± 10.7%, P  0.05) and mean efficiency of neural respiratory drive at the later stage of exercise increased (39.8 ± 2.9 vs. 45.2 ± 3.9, P  0.01) after 1-month treatment with tiotropium. There were no remarkable changes in resting EMGdi and mean efficiency of neural respiratory drive post-treatment in control group. The score of mMRC decreased significantly (2.5 ± 0.5 vs. 1.9 ± 0.7, P  0.05) after 1-month treatment with tiotropium, but without significantly difference in control group. CONCLUSION: Tiotropium significantly reduces neural respiratory drive at rest and improves the efficiency of neural respiratory drive during exercise, which might account for the improvement in exercise tolerance in COPD.

Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).