Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

We have performed single-and two-channel high transition temperature (high-T c ) superconducting quantum interference device (SQUID) magnetoencephalography (MEG) recordings of spontaneous brain activity in two healthy human subjects. We demonstrate modulation of two well-known brain rhythms: the occipital alpha rhythm and the mu rhythm found in the motor cortex. We further show that despite higher noise-levels compared to their low-T c counterparts, high-T c SQUIDs can be used to detect and record physiologically relevant brain rhythms with comparable signal-to-noise ratios. These results indicate the utility of high-T c technology in MEG recordings of a broader range of brain activity. V C 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3698152]The first magnetic recordings of human brain activity were made with an induction coil 1 and led to a significant leap in neuroscience research. A few years later, the invention of the low transition temperature (low-T c ) superconducting quantum interference device (SQUID) revolutionized the field by improving the sensitivity of magnetic recordings by orders of magnitude. 2 In modern magnetoencephalography (MEG) systems, hundreds of low-T c SQUID sensors are enclosed in a helmet that surrounds the subject's head and map the magnetic field emanating from the brain. Low-T c SQUIDs are preferred because of their high fabrication yield and superior noise performance. A typical noise figure for such a SQUID is 1-5 fT/HHz at 10 Hz, 3,4 roughly one order of magnitude better than a similar high-T c device. However, in order to keep the low-T c SQUIDs operating at 4 K, thermal insulation limits the separation between the cold sensors and the room temperature environment to 18 mm at best (Elekta, Neuromag V R ). The possibility to operate high-T c SQUIDs at 77 K has enabled some researchers to reduce this distance to just a few hundred microns. [5][6][7] The first MEG recordings with high-T c SQUIDs were accomplished some years after the discovery of high-T c superconductivity when Zhang et al. 8 recorded the brain's response to auditory stimuli in 1993. Similar studies have proven high-T c technology is sensitive enough to record such well-understood evoked MEG sources by averaging hundreds or thousands of stimulus-response signals. 9-11 However, recordings of spontaneous brain activity have yet to be demonstrated, perhaps because many spontaneous rhythms present themselves at frequencies below 20 Hz, where 1/f noise can be problematic for high-T c SQUID technology. Furthermore, it is not possible to average spontaneous brain activity in the time-domain due to its inherently spontaneous nature.Herein, we present MEG recordings of spontaneous brain activity in humans with single-and two-channel high-T c SQUID magnetometer systems. We demonstrate time resolved modulation of the occipital alpha rhythm via visual stimulation as well as modulation of the mu rhythm in the motor cortex via muscle activation, both of which are wellcharacterized phenomena and are present ...

Purpose An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water–fat MRI scans of the abdomen was investigated, using 2 different neural network architectures. Methods The 2 fully convolutional network architectures U‐Net and V‐Net were trained, evaluated, and compared using the water–fat MRI data. Data of the study Tellus with 90 scans from a single center was used for a 10‐fold cross‐validation in which the most successful configuration for both networks was determined. These configurations were then tested on 20 scans of the multicenter study beta‐cell function in JUvenile Diabetes and Obesity (BetaJudo), which involved a different study population and scanning device. Results The U‐Net outperformed the used implementation of the V‐Net in both cross‐validation and testing. In cross‐validation, the U‐Net reached average dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the multicenter test data, the U‐Net performs only slightly worse, with average dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80% (VAT) and 1.65% (SAT). Conclusion The segmentations generated by the U‐Net allow for reliable quantification and could therefore be viable for high‐quality automated measurements of VAT and SAT in large‐scale studies with minimal need for human intervention. The high performance on the multicenter test data furthermore shows the robustness of this approach for data of different patient demographics and imaging centers, as long as a consistent imaging protocol is used.

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