Acquisition of thin coronal sectional dataset of cadaveric liver

• Published in: Surgical and radiologic anatomy (English ed.) Vol. 36; no. 3; pp. 225 - 229
• Main Authors: Lou, Li, Liu, Shu Wei, Zhao, Zhen Mei, Tang, Yu Chun, Lin, Xiang Tao
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.04.2014; Springer Nature B.V
• Subjects: Adult; Anatomy; Anatomy & physiology; Cadaver; Datasets as Topic; Freezing; Histological Techniques - methods; Humans; Imaging; Liver; Liver - anatomy & histology; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Microtomy; Original Article; Orthopedics; Photography; Radiology; Scientific method; Surgery; Tomography, X-Ray Computed; Sectional anatomy; Digital freezing milling technique; Liver; Dataset
• ISSN: 0930-1038; 1279-8517
• DOI: 10.1007/s00276-013-1169-2

Purpose To obtain the thin coronal sectional anatomic dataset of the liver by using digital freezing milling technique. Methods The upper abdomen of one Chinese adult cadaver was selected as the specimen. After CT and MRI examinations verification of absent liver lesions, the specimen was embedded with gelatin in stand erect position and frozen under profound hypothermia, and the specimen was then serially sectioned from anterior to posterior layer by layer with digital milling machine in the freezing chamber. The sequential images were captured by means of a digital camera and the dataset was imported to imaging workstation. Results The thin serial section of the liver added up to 699 layers with each layer being 0.2 mm in thickness. The shape, location, structure, intrahepatic vessels and adjacent structures of the liver was displayed clearly on each layer of the coronal sectional slice. CT and MR images through the body were obtained at 1.0 and 3.0 mm intervals, respectively. Conclusion The methodology reported here is an adaptation of the milling methods previously described, which is a new data acquisition method for sectional anatomy. The thin coronal sectional anatomic dataset of the liver obtained by this technique is of high precision and good quality.