This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

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Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study’s protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

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Over the last few years, awareness of autism spectrum disorder (ASD) in adults has increased. The precise etiology of ASD is still unresolved. Animal research, genetic and postmortem studies suggest that the glutamate (Glu) system has an important role, possibly related to a cybernetic imbalance between neuronal excitation and inhibition. To clarify the possible disruption of Glu metabolism in adults with high-functioning autism, we performed a magnetic resonance spectroscopy (MRS) study investigating the anterior cingulate cortex (ACC) and the cerebellum in adults with high-functioning ASD. Twenty-nine adult patients with high-functioning ASD and 29 carefully matched healthy volunteers underwent MRS scanning of the pregenual ACC and the left cerebellar hemisphere. Metabolic data were compared between groups and were correlated with psychometric measures of autistic features. We found a significant decrease in the cingulate N-acetyl-aspartate (NAA) and the combined Glu and glutamine (Glx) signals in adults with ASD, whereas we did not find other metabolic abnormalities in the ACC or the cerebellum. The Glx signal correlated significantly with psychometric measures of autism, particularly with communication deficits. Our data support the hypothesis that there is a link between disturbances of the cingulate NAA and Glx metabolism, and autism. The findings are discussed in the context of the hypothesis of excitatory/inhibitory imbalance in autism. Further research should clarify the specificity and dynamics of these findings regarding other neuropsychiatric disorders and other brain areas.

Accurate and reliable quantification of brain metabolites measured in vivo using 1 H magnetic resonance spectroscopy (MRS) is a topic of continued interest in the field. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, we analyze spectrally edited -aminobutyric acid (GABA) MRS data and quantify GABA levels relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using standard GABA+ editing. Unsuppressed water acquisitions from the same volume of interest were acquired for signal referencing. Whole-brain T1-weighted structural images were acquired and tissue-segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA+ measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17%, which was largely driven by vendor-related differences according to a linear mixed-effects analysis. The mean within-site coefficient of variation was 9%. Vendor differences contributed 53% to the total variance in the data, while the remaining variance was attributed to site-(11%) and participant-level (36%) effects. Results from an exploratory analysis suggested that the vendor differences were related to the water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA+ measurements exhibit levels of variance similar to creatine-referenced GABA+ measurements. It is concluded that quantification using internal tissue water referencing remains a viable and reliable method for the in vivo quantification of GABA+ levels.

Localised two-dimensional J-resolved spectroscopy (JPRESS) is optimised for the in vivo detection of J-coupled metabolites using magnetic resonance spectroscopy at 3 T. The acquisition of echo signals starts as early as possible (i.e. maximum-echo sampling). This sampling scheme increases sensitivity and decreases overlap of peak tails, hence alleviating baseline problems. Reconstruction issues are discussed and the sensitivity is compared analytically with that of 1D PRESS. The qualitative behaviour of eddy currents in JPRESS is outlined and a 2D eddy current correction procedure based on the 1D phase deconvolution method is proposed.