Water-fat magnetic resonance imaging of adipose tissue compartments in the normal third trimester fetus

Giza, Stephanie A.; Koreman, Tianna; Sethi, Simran; Miller, Michael R.; Penava, Debbie; Eastabrook, Genevieve; McKenzie, Charles A.; Vrijer, Barbra de

doi:10.1007/s00247-020-04955-z

Cited by 6 publications

(15 citation statements)

References 28 publications

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Section: Discussionmentioning

confidence: 84%

“…Like the muscle, AT was separated into compartments since fetal fat fraction in AT compartments differs by location in the third trimester. 25 The lipid T 1 relaxation times of AT were not significantly different between compartments, suggesting that each AT location's lipid-containing vesicles' microstructure is similar at a given GA. The absence of water T 1 change suggests a similar interpretation for the water-dominated components of AT compartments.…”

Section: Discussionmentioning

confidence: 86%

“…The T 2 * values of AT compartments of seven participants are previously published, so those data were not included. The study found that the T 2 * values of the thorax, abdomen, and lower legs AT decreased significantly with GA. 25 Before the third trimester, many fetal tissues become well-differentiated and begin performing the functions they serve after birth, such as the liver producing bile and kidneys producing urine. 27,28 As a result, the consistency with adult values and the lack of change in T 1 suggest that these tissues have achieved structural maturity at the investigated GA. For instance, the lack of change of T 1 relaxation times of lungs and intraabdominal organs with GA and similarity to the corresponding published adult values correlates to a functional maturity of these tissues, which only need minimal support if the fetus is delivered prematurely in the third trimester.…”

Section: Discussionmentioning

confidence: 99%

“…A change in either the water or lipid T 1 could tell whether only the lipid‐containing vesicles are developing or only the water components. Like the muscle, AT was separated into compartments since fetal fat fraction in AT compartments differs by location in the third trimester 25 . The lipid T 1 relaxation times of AT were not significantly different between compartments, suggesting that each AT location's lipid‐containing vesicles' microstructure is similar at a given GA.…”

Section: Discussionmentioning

confidence: 99%

“…AT volumes were segmented on the PDFF maps for the following compartments: cheeks, upper arms, forearms, thorax, abdomen, thighs, and lower legs. A detailed description of how the AT compartments were divided is found in Giza et al 25 (Fig. 1c).…”

Section: Methodsmentioning

confidence: 99%

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Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Sethi

Giza

Goldberg

et al. 2021

Magnetic Resonance Imaging

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Background: Despite its many advantages, experience with fetal magnetic resonance imaging (MRI) is limited, as is knowledge of how fetal tissue relaxation times change with gestational age (GA). Quantification of fetal tissue relaxation times as a function of GA provides insight into tissue changes during fetal development and facilitates comparison of images across time and subjects. This, therefore, can allow the determination of biophysical tissue parameters that may have clinical utility. Purpose: To demonstrate the feasibility of quantifying previously unknown T 1 and T 2 * relaxation times of fetal tissues in uncomplicated pregnancies as a function of GA at 1.5 T. Study Type: Pilot. Population: Nine women with singleton, uncomplicated pregnancies (28-38 weeks GA). Field Strength/Sequence: All participants underwent two iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) acquisitions at different flip angles (6 and 20 ) at 1.5 T. Assessment: Segmentations of the lungs, liver, spleen, kidneys, muscle, and adipose tissue (AT) were conducted using water-only images and proton density fat fraction maps. Driven equilibrium single pulse observation of T 1 (DESPOT 1 ) was used to quantify the mean water T 1 of the lungs, intraabdominal organs, and muscle, and the mean water and lipid T 1 of AT. IDEAL T 2 * maps were used to quantify the T 2 * values of the lungs, intraabdominal organs, and muscle. Statistical Tests: F-tests were performed to assess the T 1 and T 2 * changes of each analyzed tissue as a function of GA. Results: No tissue demonstrated a significant change in T 1 as a function of GA (lungs [P = 0.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Sethi

Giza

Goldberg

et al. 2021

Magnetic Resonance Imaging

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Fetal MRI‐Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification

Rabinowich,

Avisdris,

Yehuda

et al. 2023

Magnetic Resonance Imaging

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BackgroundSmall for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification.HypothesisSmaller, leaner fetuses are malnourished and will experience unfavorable outcomes.Study TypeProspective.Population40 SGA fetuses, 26 (61.9%) females: 10/40 (25%) had obstetric interventions due to non‐reassuring fetal status (NRFS), and 17/40 (42.5%) experienced adverse neonatal events (CANO). Participants underwent MRI between gestational ages 30 + 2 and 37 + 2.Field Strength/Sequence3‐T, True Fast Imaging with Steady State Free Precession (TruFISP) and T1‐weighted two‐point Dixon (T1W Dixon) sequences.AssessmentTotal body volume (TBV), fat signal fraction (FSF), and the fat‐to‐body volumes ratio (FBVR) were extracted from TruFISP and T1W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded.Statistical TestsUnivariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. A P‐value <0.05 was considered statistically significant.ResultsFBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2–0.76) and FSF (OR 0.95, CI 0.91–0.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56–0.86) and FSF (OR 0.96, CI 0.93–0.99) were linked to CANO. The FBVR sensitivity/specificity for obstetric interventions was 85.7%/87.5%, and the TBV sensitivity/specificity for CANO was 82.35%/86.4%. The sonographic criteria sensitivity/specificity for obstetric interventions was 100%/33.3% and insignificant for CANO (P = 0.145).Data ConclusionReduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO.Evidence Level2Technical EfficacyStage 5

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Automatic Fetal Fat Quantification from MRI

Avisdris

Rabinowich

Daniel

et al. 2022

Lecture Notes in Computer Science

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Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive or depleted adiposity. Although previous studies showed a correlation between the amount of AT and perinatal outcome, prenatal assessment of AT is limited by lacking quantitative methods. Using magnetic resonance imaging (MRI), 3D fat-and water-only images of the entire fetus can be obtained from twopoint Dixon images to enable AT lipid quantification. This paper is the first to present a methodology for developing a deep learning (DL) based method for fetal fat segmentation based on Dixon MRI. It optimizes radiologists' manual fetal fat delineation time to produce annotated training dataset. It consists of two steps: 1) model-based semi-automatic fetal fat segmentations, reviewed and corrected by a radiologist; 2) automatic fetal fat segmentation using DL networks trained on the resulting annotated dataset. Segmentation of 51 fetuses was performed with the semi-automatic method. Three DL networks were trained. We show a significant improvement in segmentation times (3:38 hours → < 1 hour) and observer variability (Dice of 0.738 → 0.906) compared to manual segmentation. Automatic segmentation of 24 test cases with the 3D Residual U-Net, nn-UNet and SWIN-UNetR transformer networks yields a mean Dice score of 0.863, 0.787 and 0.856, respectively. These results are better than the manual observer variability, and comparable to automatic adult and pediatric fat segmentation. A radiologist reviewed and corrected six new independent cases segmented using the best performing network (3D Residual U-Net), resulting in a Dice score of 0.961 and a significantly reduced correction time of 15:20 minutes. Using these novel segmentation methods and short MRI acquisition time, whole body subcutaneous lipids can be quantified for individual fetuses in the clinic and large-cohort research.

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Water-fat magnetic resonance imaging of adipose tissue compartments in the normal third trimester fetus

Cited by 6 publications

References 28 publications

Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Fetal MRI‐Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification

Automatic Fetal Fat Quantification from MRI

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Water-fat magnetic resonance imaging of adipose tissue compartments in the normal third trimester fetus

Cited by 6 publications

References 28 publications

Quantification of 1.5 T T1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Quantification of 1.5 T T1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Fetal MRI‐Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification

Automatic Fetal Fat Quantification from MRI

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Product

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Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

Quantification of 1.5 T T₁ and T₂^* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies