title: Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates.
abstract: OBJECTIVE
  Overweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed. Chest fat estimation by routine computed tomography (CT) imaging may therefore be important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification and quality assessment based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of the paper is to seek answers to several key questions related to chest fat quantity and quality assessment based on a single slice CT (whether in the chest, abdomen, or thigh) versus a volumetric CT, which have not been addressed in the literature.
  
  
  METHODS
  Unenhanced chest CT image data sets from 40 adult lung transplant candidates (age 58 ± 12 yrs and BMI 26.4 ± 4.3 kg/m2), 16 with chronic obstructive pulmonary disease (COPD), 16 with idiopathic pulmonary fibrosis (IPF), and the remainder with other conditions were analyzed together with a single slice acquired for each patient at the L5 vertebral level and mid-thigh level. The thoracic body region and the interface between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the chest were consistently defined in all patients and delineated using Live Wire tools. The SAT and VAT components of chest were then segmented guided by this interface. The SAS approach was used to identify the corresponding anatomic slices in each chest CT study, and SAT and VAT areas in each slice as well as their whole volumes were quantified. Similarly, the SAT and VAT components were segmented in the abdomen and thigh slices. Key parameters of the attenuation (Hounsfield unit (HU) distributions) were determined from each chest slice and from the whole chest volume separately for SAT and VAT components. The same parameters were also computed from the single abdominal and thigh slices. The ability of the slice at each anatomic location in the chest (and abdomen and thigh) to act as a marker of the measures derived from the whole chest volume was assessed via Pearson correlation coefficient (PCC) analysis.
  
  
  RESULTS
  The SAS approach correctly identified slice locations in different subjects in terms of vertebral levels. PCC between chest fat volume and chest slice fat area was maximal at the T8 level for SAT (0.97) and at the T7 level for VAT (0.86), and was modest between chest fat volume and abdominal slice fat area for SAT and VAT (0.73 and 0.75, respectively). However, correlation was weak for chest fat volume and thigh slice fat area for SAT and VAT (0.52 and 0.37, respectively), and for chest fat volume for SAT and VAT and BMI (0.65 and 0.28, respectively). These same single slice locations with maximal PCC were found for SAT and VAT within both COPD and IPF groups. Most of the attenuation properties derived from the whole chest volume and single best chest slice for VAT (but not for SAT) were significantly different between COPD and IPF groups.
  
  
  CONCLUSIONS
  This study demonstrates a new way of optimally selecting slices whose measurements may be used as markers of similar measurements made on the whole chest volume. The results suggest that one or two slices imaged at T7 and T8 vertebral levels may be enough to estimate reliably the total SAT and VAT components of chest fat and the quality of chest fat as determined by attenuation distributions in the entire chest volume.
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Yubing
authors.initials: Y
authors.lastname: Tong
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Drew A
authors.initials: DA
authors.lastname: Torigian
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Dewey
authors.initials: D
authors.lastname: Odhner
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Caiyun
authors.initials: C
authors.lastname: Wu
authors.affiliation: Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Gargi
authors.initials: G
authors.lastname: Pednekar
authors.affiliation: Department of Medicine, Duke University, Durham, North Carolina, United States of America.
authors.forename: Scott
authors.initials: S
authors.lastname: Palmer
authors.affiliation: Department of Radiology, Columbia University, New York City, New York, United States of America.
authors.forename: Anna
authors.initials: A
authors.lastname: Rozenshtein
authors.affiliation: Department of Radiology, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Melissa A
authors.initials: MA
authors.lastname: Shirk
authors.affiliation: Department of Radiology, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: John D
authors.initials: JD
authors.lastname: Newell
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Hospital of the University of Pennsylvania & Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
authors.forename: Mary
authors.initials: M
authors.lastname: Porteous
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Hospital of the University of Pennsylvania & Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Diamond
authors.affiliation: Department of Radiology, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Jason D
authors.initials: JD
authors.lastname: Christie
authors.affiliation: Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York City, New York, United States of America.
authors.forename: David J
authors.initials: DJ
authors.lastname: Lederer
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D050154:Adiposity; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D005260:Female; D006801:Humans; D054990:Idiopathic Pulmonary Fibrosis / Q000000981:diagnostic imaging / Q000601:surgery; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000033:anatomy & histology*; D016040:Lung Transplantation; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D015588:Observer Variation; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging / Q000601:surgery; D013909:Thorax / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed