OBJECTIVE:We studied the validity and reproducibility of a new abdominal ultrasound protocol for the assessment of intraabdominal adipose tissue. MEASUREMENTS: Intra-abdominal adipose tissue was assessed by CT, MRI, anthropometry and ultrasonography on a single day. By ultrasonography the distance between peritoneum and lumbar spine was measured using a strict protocol, including the location of the measurements, pressure on the transducer and respiration. All measurements were repeated after 3 months. RESULTS: The study population consisted of 19 overweight patients with a body mass index (BMI) of 32.9 kg=m 2 (s.d. 3.7), intra-abdominal adipose tissue on CT 140.1 cm 2 (s.d. 55.9), and a mean ultrasound distance of 9.8 cm (s.d. 2.5). There was a strong association between the CT and ultrasonographic measures: Pearson correlation coefficient was 0.81 (P < 0.001). The correlation between ultrasound and waist circumference was 0.74 (P < 0.001), the correlation between CT and waist circumference was 0.57 (P ¼ 0.01). Ultrasound appeared a good method to diagnose intra-abdominal obesity: the area under the ROC curve was 0.98. During the follow-up period of 3 months, the patients lost on average almost 3 kg of body weight. The correlation coefficient between changes in intra-abdominal adipose tissue assessed by CT and ultrasound was 0.74 (P < 0.001). The correlation coefficient of the mean ultrasound distance assessed by two different sonographers at baseline was 0.94 (P < 0.001), the mean difference 0.4 cm (s.d. 0.9), and the coefficient of variation 5.4%, indicating good reproducibility of the ultrasound measurements. CONCLUSIONS: The results of this validation study show that abdominal ultrasound, using a strict protocol, is a reliable and reproducible method to assess the amount of intra-abdominal adipose tissue and to diagnose intra-abdominal obesity.

A method is presented that uses a vectorial multiscale feature image for wave front propagation between two or more user defined points to retrieve the central axis of tubular objects in digital images. Its implicit scale selection mechanism makes the method more robust to overlap and to the presence of adjacent structures than conventional techniques that propagate a wave front over a scalar image representing the maximum of a range of filters. The method is shown to retain its potential to cope with severe stenoses or imaging artifacts and objects with varying widths in simulated and actual two-dimensional angiographic images.

A method is presented which aids the clinician in obtaining quantitative measures and a three-dimensional (3-D) representation of vessels from 3-D angiographic data with a minimum of user interaction. Based on two user defined starting points, an iterative procedure tracks the central vessel axis. During the tracking process, the minimum diameter and a surface rendering of the vessels are computed, allowing for interactive inspection of the vasculature. Applications of the method to CTA, contrast enhanced (CE)-MRA and phase contrast (PC)-MRA images of the abdomen are shown. In all applications, a long stretch of vessels with varying width is tracked, delineated, and visualized, in less than 10 s on a standard clinical workstation.

A method is introduced to automatically find the coronary axis based on two or more user-defined points, even in the presence of a severe stenosis. The coronary axis is determined by finding a minimum cost path (MCP) in a feature image in which the tubular-like structures are enhanced. The results of the proposed method were compared with manually drawn central axes to estimate the accuracy. In 32 3D TFE-EPI acquisitions of patients and volunteers, 14 right coronary arteries (RCAs), 15 left anterior descending arteries (LADs), and eight left circumflex arteries (LCXs) were manually tracked twice by two operators to determine a reference axis and to assess the interand intra-user variability. On average, the maximum distance to the reference axis, based on only two user-defined points, is less than 1.5 mm; the average distance is around 0.65 mm, which is less than the average in-plane resolution. In the last few years several magnetic resonance angiography (MRA) acquisition methods have been introduced which provide 3D images of the coronary arteries with good signal-to-noise (SNR) and contrast-to-noise (CNR) ratios (1-6). However, because of the tortuous nature of the coronaries, it is not possible to capture a long stretch of vessel in a single plane. Postprocessing is therefore required to obtain a proper visualization of the coronaries in 3D space. Although a large variety of approaches have been proposed to facilitate the diagnosis of vessel segments (e.g., 7-15), relatively few have addressed the problems that occur in the case of a (severe) stenosis or in the presence of image artifacts, in which case there is hardly any image evidence to guide the algorithm. Especially iterative tracking and region-growing procedures experience difficulties in these cases (see Fig. 1a).Determining a minimum cost path (MCP) (e.g., 16 -23) between two or more user-defined points is an alternative way to handle these situations (see Fig. 1b).In this article it is investigated whether the latter approach, in combination with a filter that enhances tubularlike structures, is suitable for tracking the coronary arteries automatically between two or more user-defined points. The result of the MCP approach is an estimate of the central coronary axis, which can serve as input for a subsequent visualization and quantification procedure. The method is applied to a number of 3D MRA images, and the results are compared with those of two human operators to determine the accuracy and applicability of the method. METHODS Image AcquisitionThe images are acquired on a 1.5 Tesla Philips Gyroscan ACS-NT MR scanner, using a navigator gated and corrected ECG triggered ultra-fast 3D interleaved gradient turbo field echo-echo planar imaging (TFE-EPI) sequence, preceded by fat-and muscle-suppressing pulses. For a detailed description of the imaging sequence, see Botnar et al. (6). The images consist of 20 slices of 512 ϫ 512 pixels, and a reconstructed slice thickness of 1.5 mm. The field of view (FOV) is either 360 or 370 mm. A total of 32 image...