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Introduction: As prevalence of patients with steatotic liver diseases increases throughout the world, it is necessary to have accurate and accessible methods to estimate liver fat content. Using quantitative ultrasound parameters, such as attenuation and backscatter, it is possible to estimate liver fat, with MRI proton density fat fraction as the reference standard. Velacur determined fat fraction (VDFF) is a new output measurement on Velacur (Sonic Incytes Medical Corp, Vancouver, BC).
Methods: This study described the results of parameter fitting and validation of VDFF, which is a combination of quantitative ultrasound parameters. Patients were recruited from sites within the US and Canada. All patients had contemporaneous Velacur and MRI proton density fat fraction scans. The quantitative ultrasound parameter fitting was completed using linear regression on a random sub-sample approach, and a separate cohort was used for validation. The AUC for detection of 5% liver fat based on MRI-PDFF and the correlation between MRI-PDFF and VDFF was measured in both cohorts.
Results: VDFF had an AUROC of 0.97 for the detection of MRI-PDFF > 5% in the parameter fitting cohort, and 0.99 in the validation cohort. The correlation [95% CI] between MRI-PDFF and VDFF was r = 0.84 [0.78 - 0.89] for the parameter fitting cohort and r = 0.90 [0.82 - 0.95] for the validation cohort.
Conclusion: The Velacur Determined Fat Fraction (VDFF) is an accurate and accessible way to estimate steatosis as measured by MRI-PDFF. Velacur VDFF can fill the unmet need of an accurate means to diagnosis hepatic steatosis and serve as a potential alternative to biopsy or MRI-PDFF.
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http://dx.doi.org/10.1016/j.wfumbo.2024.100061 | DOI Listing |
Front Endocrinol (Lausanne)
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Objectives: To evaluate whether q-Dixon sequence-based fat fraction (FF) values of the lumbar spine can predict osteoporotic vertebral compression fracture (OVCF) risk in older adult(s) osteoporosis patients.
Materials & Methods: Thirty OVCF patients and 15 osteoporosis patients were enrolled. Areas of interest (ROIs) were manually drawn using the post-processing workstation, and FF values of the patient's L1-L4 vertebrae (except the fractured vertebrae) were measured.
Abdom Radiol (NY)
September 2025
Department of Gastroenterology department, Bishan Hospital of Chongqing Medical University, Chongqing, China.
Objective: This study aimed to create and validate a nomogram to predict early recurrence (ER) in Colorectal cancer (CRC) patients by combining CT-derived abdominal fat parameters with clinical and pathological characteristics.
Methods: We conducted a retrospective analysis of 206 CRC patients, dividing them into training (n = 146) and validation (n = 60) cohorts. We quantified abdominal fat parameters, including subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI), using semi-automatic software on CT images at the level of the third lumbar vertebra (L3).
J Obes
September 2025
School of Natural Sciences, University of Lincoln, Lincoln, UK.
To investigate the genetic determinants of fat distribution across anatomical sites and their implications for health outcomes. We analyzed neck-to-knee MRI data from the UK Biobank ( = 37,589) to measure fat at various locations and used Mendelian randomization to assess effects on 26 obesity-related diseases and 94 biomarkers from FinnGen and other consortia. We identified genetic loci associated with 10 fat depots: abdominal subcutaneous adipose tissue ( = 2 loci), thigh subcutaneous adipose tissue (25), thigh intermuscular adipose tissue (15), visceral adipose tissue (7), liver proton density fat fraction (PDFF) (8), pancreas PDFF (11), paraspinal adipose tissue (9), pelvic bone marrow fat (28), thigh bone marrow fat (27), and vertebrae bone marrow fat (5).
View Article and Find Full Text PDFMagn Reson Med
September 2025
Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France.
Purpose: Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and assessing disease severity in neuromuscular diseases. Undersampling of MRI acquisitions is commonly used to reduce scanning time. The present paper introduces novel unrolled neural networks for the reconstruction of undersampled MRI acquisitions.
View Article and Find Full Text PDFMagn Reson Lett
May 2025
GE Healthcare, Beijing, 100176, China.
This study explored the application value of iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) technology in the early diagnosis of ageing osteoporosis (OP). 172 participants were enrolled and underwent magnetic resonance imaging (MRI) examinations on a 3.0T scanner.
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