Validation of a free software for unsupervised assessment of abdominal fat in MRI. (May 2017)

• Record Type: Journal Article
• Authors: Maddalo, Michele; Zorza, Ivan; Zubani, Stefano; Nocivelli, Giorgio; Calandra, Giulio; Soldini, Pierantonio; Mascaro, Lorella; Maroldi, Roberto
• Is Part Of: Physica medica. Volume 37(2017)
• Page Start: 24
• Page End: 31
• Publication Date: 2017-05
• Subjects: Magnetic resonance imaging -- 2-point Dixon sequence -- Visceral adipose tissue -- Subcutaneous adipose tissue -- Segmentation -- Unsupervised software; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2017.04.002
• Languages: English
• ISSNs: 1120-1797

Abstract

Graphical abstract: Highlights: A novel and freeware software (FATCALC) for automatic quantification of fat is proposed. The accuracy of FATCALC is determined against a supervised method of reference. It is freeware solution which will be distributed on the WEB. A user-editable parameter could be used to (re)calibrate the fat inclusion. Abstract: Purpose: To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies. Methods: The lumbar part of the abdomen (19 cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21–46 years, BMI range: 21.7–31.6 kg/m 2 ) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD% ). Results: Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue – SAT (R = 0.9996, p < 0.001) and visceral adipose tissue – VAT (R = 0.995, p < 0.001). Bland-Altman plots showed the absence of systematic errors and a limited spread of the differences. In the single-slice analysis, VD% were (1.6 ± 2.9)% for SAT and (4.9 ± 6.9)% for VAT. In the volumetric analysis, VD% were (1.3 ± 0.9)% for SAT and (2.9 ± 2.7)% for VAT. Conclusions: The developed software is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases.