Computed tomography (CT) exams were conducted to determine the distribution of abdominal fat identified based on the CT number measured in Hounsfield Units (HU) and to measure the volume of the abdominal visceral and subcutaneous fat in minipigs. The relationship between the CT-based fat volumes of several vertebral levels and the entire abdomen and anthropometric data including the sagittal abdominal diameter and waist circumference were evaluated. Moreover, the total fat volumes at the T11, T13, L3, and L5 levels were compared with the total fat volume of the entire abdomen to define the landmark of abdominal fat distribution. Using a single-detector CT, six 6-month-old male minipigs were scanned under general anesthesia. Three radiologists then assessed the HU value of visceral and subcutaneous abdominal fat by drawing the region of interest manually at the T11, T13, L1, L3, and L5 levels. The CT number and abdominal fat determined in this way by the three radiologists was found to be correlated (intra-class coefficient = 0.9). The overall HU ranges for the visceral and subcutaneous fat depots were -147.47 to -83.46 and -131.62 to -90.97, respectively. The total fat volume of the entire abdomen was highly correlated with the volume of abdominal fat at the T13 level (r = 0.97, p < 0.0001). These findings demonstrate that the volume of abdominal adipose tissue measured at the T13 level using CT is a strong and reliable predictor of total abdominal adipose volume.
Animals ; *Body Composition ; Male ; Subcutaneous Fat, Abdominal/*radiography ; Swine ; Swine, Miniature/growth & development/*physiology ; Tomography, X-Ray Computed/*veterinary

OBJECTIVE: To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. MATERIALS AND METHODS: Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. RESULTS: Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). CONCLUSION: The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.
*Algorithms ; Animals ; Body Size ; Image Processing, Computer-Assisted/*methods ; *Phantoms, Imaging/standards ; Radiation Dosage ; Signal-To-Noise Ratio ; Subcutaneous Fat, Abdominal/*radiography ; Swine ; Tomography, X-Ray Computed/*methods