Improved abdominal T1 weighted imaging at 0.55T.
Magn Reson Med
; 2024 Jul 12.
Article
en En
| MEDLINE
| ID: mdl-38997798
ABSTRACT
PURPOSE:
Breath-held fat-suppressed volumetric T1-weighted MRI is an important and widely-used technique for evaluating the abdomen. Both fat-saturation and Dixon-based fat-suppression methods are used at conventional field strengths; however, both have challenges at lower field strengths (<1.5T) due to insufficient fat suppression and/or inadequate resolution. Specifically, at lower field strengths, fat saturation often fails due to the short T1 of lipid; and Cartesian Dixon imaging provides poor spatial resolution due to the need for a long ∆TE, due to the smaller ∆f between water and lipid. The purpose of this work is to demonstrate a new approach capable of simultaneously achieving excellent fat suppression and high spatial resolution on a 0.55T whole-body system.METHODS:
We applied 3D stack-of-spirals Dixon imaging at 0.55T, with compensation of concomitant field phase during reconstruction. The spiral readouts make efficient use of the requisite ∆TE. We compared this with 3D Cartesian Dixon imaging. Experiments were performed in 2 healthy and 10 elevated liver fat volunteers.RESULTS:
Stack-of-spirals Dixon imaging at 0.55T makes excellent use of the required ∆TE, provided high SNR efficiency and finer spatial resolution (1.7 × 1.7 × 5 mm3) compared Cartesian Dixon (3.5 × 3.5 × 5 mm3), within a 17-s breath-hold. We observed successful fat suppression, and improved definition of structures such as the liver, kidneys, and bowel.CONCLUSION:
We demonstrate that high-resolution single breath-hold volumetric abdominal T1-weighted imaging is feasible at 0.55T using spiral sampling and concomitant field correction. This is an attractive alternative to existing Cartesian-based methods, as it simultaneously provides high-resolution and excellent fat-suppression.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos