R 2 * Impact on Hepatic Fat Quantification With a Commercial Single Voxel Technique at 1.5 and 3.0 T.
Véronique FortierAhmed MohamedEvan McNabbJérémy DanaRita ZakarianIves R LevesqueCaroline ReinholdPublished in: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes (2024)
Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T 2 -corrected multi-echo (HISTO) technique and its robustness to R 2 * variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO at 3.0 T and sought to reproduce results at 1.5 T. Methods: Phantoms were prepared with a range of fat content and R 2 *. Data were acquired at 1.5 T and 3.0 T, using HISTO and a Dixon technique. Fat quantification accuracy was evaluated as a function of R 2 *. The patient study included 239 consecutive patients. Data were acquired at 1.5 T or 3.0 T, using HISTO and Dixon techniques. The techniques were compared using Bland-Altman plots. Bias significance was evaluated using a one-sample t -test. Results: In phantoms, HISTO was accurate within 10% up to a R 2 * of 100 s -1 at both field strengths, while Dixon was accurate within 10% where R 2 * was accurately quantified (up to 350 s -1 at 1.5 T, and 550 s -1 at 3.0 T). In patients, where R 2 * was <100 s -1 , fat quantification from both techniques agreed at 1.5 T ( P = .71), but not at 3.0 T ( P = .007), with a bias <1%. Conclusion: Results suggest that HISTO is reliable when R 2 * is <100 s -1 , corresponding to patients with at most mild liver iron overload, and that it should be used with caution when R 2 * is >100 s -1 . Dixon should be preferred for hepatic fat quantification due to its robustness to R 2 * variations.
Keyphrases
- adipose tissue
- end stage renal disease
- high speed
- newly diagnosed
- ejection fraction
- chronic kidney disease
- fatty acid
- prognostic factors
- magnetic resonance
- peritoneal dialysis
- clinical trial
- magnetic resonance imaging
- machine learning
- case report
- patient reported outcomes
- deep learning
- data analysis
- patient reported
- contrast enhanced