PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 22, 2019BMC Medical Imaging9 citationsOpen Access

Half-body MRI volumetry of abdominal adipose tissue in patients with obesity

NLNicolas LinderKSKilian SoltyAHAnna Hartmann

Key Result

Half-body MRI volumetry accurately predicted total abdominal subcutaneous (R2 > 0.99) and visceral (R2 ≥ 0.97) adipose tissue volumes compared to full-body MRI in patients with obesity.

Study Design

Type

Cross-Sectional (n=26)

Multicenter

No

Structured PICO

Does half-body MRI volumetry accurately predict total abdominal subcutaneous and visceral adipose tissue volumes in patients with obesity?

P
Population
26 adults with obesity (mean age 50 years, 50% female) underwent abdominal MRI to evaluate the accuracy of half-body volumetry for predicting total abdominal adipose tissue.
E
Exposure
Half-body MRI volumetry (left or right side) of abdominal adipose tissue
C
Comparator
Full-body abdominal MRI volumetry (reference standard)
O
Outcome
Agreement and correlation (coefficient of determination R2) between half-body and full-body volumes of abdominal subcutaneous (ASAT) and visceral adipose tissue (VAT)surrogate

Half-body MRI volumetry can reliably estimate total abdominal subcutaneous and visceral fat volumes in patients with obesity, providing a practical solution when the imaging field of view is insufficient.

Main Result

Effect estimate: R2 > 0.99

p-value: p=<0.01

Limitations

  • Small sample size
  • Effective BMI range limited to 30-41 kg/m2, which may not hold for higher degrees of obesity
  • Semi-automatic segmentation requires more processing time than fully-automated approaches
  • Data analyzed by one operator only
  • Retrospective analysis not validated against an independent method such as DEXA

Abstract

Abstract Background The purpose of this study was to determine to what extent the whole volumes of abdominal subcutaneous (ASAT) and visceral adipose tissue (VAT) of patients with obesity can be predicted by using data of one body half only. Such a workaround has already been reported for dual-energy x-ray absorption (DEXA) scans and becomes feasible whenever the field of view of an imaging technique is not large enough. Methods Full-body abdominal MRI data of 26 patients from an obesity treatment center (13 females and 13 males, BMI range 30.8–41.2 kg/m 2 , 32.6–61.5 years old) were used as reference (REF). MRI was performed with IRB approval on a clinical 1.5 T MRI (Achieva dStream, Philips Healthcare, Best, Netherlands). Segmentation of adipose tissue was performed with a custom-made Matlab software tool. Statistical measures of agreement were the coefficient of determination R 2 of a linear fit. Results Mean ASAT REF was 12,976 (7812–24,161) cm 3 and mean VAT REF was 4068 (1137–7518) cm 3 . Mean half-body volumes relative to the whole-body values were 50.8% (48.2–53.7%) for ASAT L and 49.2% (46.3–51.8%) for ASAT R . Corresponding volume fractions were 56.4% (51.4–65.9%) for VAT L and 43.6% (34.1–48.6%) for VAT R . Correlations of ASAT REF with ASAT L as well as with ASAT R were both excellent ( R 2 > 0.99, p < 0.01). Corresponding correlations of VAT REF were marginally lower ( R 2 = 0.98 for VAT L , p < 0.01, and R 2 = 0.97 for VAT R , p < 0.01). Conclusions In conclusion, abdominal fat volumes can be reliably assessed by half-body MRI data, in particular the subcutaneous fat compartment.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Linder et al. (2019) conducted a cross-sectional in Obesity (n=26). Half-body MRI volumetry vs. Full-body MRI volumetry was evaluated on Coefficient of determination (R2) between half-body and full-body abdominal subcutaneous adipose tissue (ASAT) volumes (R2 > 0.99, p=<0.01). Half-body MRI volumetry accurately predicted total abdominal subcutaneous (R2 > 0.99) and visceral (R2 ≥ 0.97) adipose tissue volumes compared to full-body MRI in patients with obesity.

synapsesocial.com/papers/6a51948eac10e4e6224a77a4https://doi.org/10.1186/s12880-019-0383-8
Ask AI
Helpful
Bookmark
Share
View Full Paper