Whole-body MRI for metastatic cancer detection using T2 -weighted imaging with fat and fluid suppression.
Magn Reson Med
; 80(4): 1402-1415, 2018 10.
Article
en En
| MEDLINE
| ID: mdl-29446127
ABSTRACT
PURPOSE:
To develop a whole-body MRI technique at 3T with improved lesion conspicuity for metastatic cancer detection using fast, high-resolution and high SNR T2 -weighted (T2 W) imaging with simultaneous fat and fluid suppression. THEORY ANDMETHODS:
The proposed dual-echo T2 -weighted acquisition for enhanced conspicuity of tumors (DETECT) acquires 4 images, in-phase (IP) and out-of-phase (OP) at a short and a long TE using single-shot turbo spin echo. The IP/OP images at the short and long TEs are reconstructed using the standard Dixon and shared-field-map Dixon reconstruction respectively, for robust fat-water separation. An adaptive complex subtraction between the 2 TE water-only images achieves fluid attenuation. DETECT imaging was optimized and evaluated in whole-body imaging of 5 healthy volunteers, and compared against diffusion-weighted imaging with background suppression (DWIBS) in 5 patients with known metastatic renal cell carcinoma.RESULTS:
Robust fat-water separation and fluid attenuation were achieved using the shared-field-map Dixon reconstruction and adaptive complex subtraction, respectively. DETECT imaging technique generated co-registered T2 W images with and without fat suppression, heavily T2 W, and fat and fluid suppressed T2 W whole-body images in <7 min. Compared to DWIBS acquired in 17 min, the DETECT imaging achieved better detection and localization of lesions in patients with metastatic cancer.CONCLUSION:
DETECT imaging technique generates T2 W images with high resolution, high SNR, minimal geometric distortions, and provides good lesion conspicuity with robust fat and fluid suppression in <7 min for whole-body imaging, demonstrating efficient and reliable metastatic cancer detection at 3T.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Procesamiento de Señales Asistido por Computador
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Imagen por Resonancia Magnética
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Imagen de Cuerpo Entero
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Neoplasias Renales
Tipo de estudio:
Diagnostic_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos