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Selection of MR sequences in diagnosis of fetal spinal cord malformation / 中国医学影像技术
Chinese Journal of Medical Imaging Technology ; (12): 111-115, 2020.
Artículo en Chino | WPRIM | ID: wpr-861122
ABSTRACT

Objective:

To explore the value and selection of MR sequences in diagnosis of fetal spinal cord and spinal deformity.

Methods:

Data of 30 fetuses underwent prenatal ultrasound screening and history abnormal fetal spinal cord and 48 h adept MRI (Haste, Trufi and SWI) targeted examination about fetal spine were retrospectively analyzed, and compared with postpartum (production or induced labor) within 6 months of follow-up

Results:

(image, surgery or autopsy). The diagnosis coincidence rate of MRI and ultrasound, and MR sequence selection for different pathological changes were compared.

Results:

Among 30 fetuses, 7 were proved without deformity, 15 were found with vertebral malformation (split vertebra, half vertebra, incomplete septa, sacrococcygeal vertebra dysplasia), 5 with lower spinal cord, 3 with spina bifida or myelomeningocele, 4 with spinal cord complex malformation. Prenatal MRI diagnosis were completely consistent with follow-up

Results:

. Two fetuses of vertebral deformity, 4 of low spinal cord and 1 of meningiocele, syenomyelia and longitudinal fissure were missed with prenatal ultrasound. MR Haste and Trufi sequences were satisfied in displaying the structure and deformity of fetal spinal cord, while SWI could clearly show fetal vertebral structure and deformity.

Conclusion:

MR Haste and Trufi are accurate in displaying abnormalities in fetal spinal cord and spinal canal, while SWI has unique value in describing vertebral deformity.

Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Tipo de estudio: Estudio diagnóstico Idioma: Chino Revista: Chinese Journal of Medical Imaging Technology Año: 2020 Tipo del documento: Artículo

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Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Tipo de estudio: Estudio diagnóstico Idioma: Chino Revista: Chinese Journal of Medical Imaging Technology Año: 2020 Tipo del documento: Artículo