title: Adipose segmentation in small animals at 7T: a preliminary study.
abstract: BACKGROUND
  Small animal MRI at 7 Tesla (T) provides a useful tool for adiposity research. For adiposity researchers, separation of fat from surrounding tissues and its subsequent quantitative or semi- quantitative analysis is a key task. This is a relatively new field and a priori it cannot be known which specific biological questions related to fat deposition will be relevant in a specific study. Thus it is impossible to predict what accuracy and what spatial resolution will be required in all cases and even difficult what accuracy and resolution will be useful in most cases. However the pragmatic time constraints and the practical resolution ranges are known for small animal imaging at 7T. Thus we have used known practical constraints to develop a method for fat volume analysis based on an optimized image acquisition and image post processing pair.
  
  
  METHODS
  We designed a fat segmentation method based on optimizing a variety of factors relevant to small animal imaging at 7T. In contrast to most previously described MRI methods based on signal intensity of T1 weighted image alone, we chose to use parametric images based on Multi-spin multi-echo (MSME) Bruker pulse sequence which has proven to be particularly robust in our laboratory over the last several years. The sequence was optimized on a T1 basis to emphasize the signal. T2 relaxation times can be calculated from the multi echo data and we have done so on a pixel by pixel basis for the initial step in the post processing methodology. The post processing consists of parallel paths. On one hand, the weighted image is precisely divided into different regions with optimized smoothing and segmentation methods; and on the other hand, a confidence image is deduced from the parametric image according to the distribution of relaxation time relationship of typical adipose. With the assistance of the confidence image, a useful software feature was implemented to which enhances the data and in the end results in a more reliable and flexible method for adipose evaluation.
  
  
  RESULTS
  In this paper, we describe how we arrived at our recommended procedures and key aspects of the post-processing steps. The feasibility of the proposed method is tested on both simulated and real data in this preliminary research. A research tool was created to help researchers segment out fat even when the anatomical information is of low quality making it difficult to distinguish between fat and non-fat. In addition, tool is designed to allow the operator to make adjustments to many of the key steps for comparison purposes and to quantitatively assess the difference these changes make. Ultimately our flexible software lets the researcher define key aspects of the fat segmentation and quantification.
  
  
  CONCLUSIONS
  Combining the full T2 parametric information with the optimized first echo image information, the research tool enhances the reliability of the results while providing more flexible operations than previous methods. The innovation in the method is to pair an optimized and very specific image acquisition technique to a flexible but tuned image post processing method. The separation of the fat is aided by the confidence distribution of regions produced on a scale relevant to and dictated by practical aspects of MRI at 7T.
authors.affiliation: Department of Radiology, University of Southern California, Childrens Hospital Los Angeles, Los Angeles, USA. ytang@chla.usc.edu
authors.forename: Yang
authors.initials: Y
authors.lastname: Tang
authors.forename: Susan
authors.initials: S
authors.lastname: Lee
authors.forename: Marvin D
authors.initials: MD
authors.lastname: Nelson
authors.forename: Simerly
authors.initials: S
authors.lastname: Richard
authors.forename: Rex A
authors.initials: RA
authors.lastname: Moats
mesh_terms: D000273:Adipose Tissue / Q000033:anatomy & histology*; D050154:Adiposity; D000818:Animals; D050218:Body Fat Distribution; D001330:Electronic Data Processing; D008279:Magnetic Resonance Imaging; D051379:Mice; D012984:Software