多发性硬化症患者非病变脑组织的超长 T2 加权成像。

IF 2.7 4区 医学 Q2 BIOPHYSICS NMR in Biomedicine Pub Date : 2024-12-01 Epub Date: 2024-07-31 DOI:10.1002/nbm.5235
Pietro Bontempi, Sabrina Marangoni, Lucia Cazzoletti, Albulena Bajrami, Bruno Giometto, Paolo Farace, Umberto Rozzanigo
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引用次数: 0

摘要

本研究旨在证明超长回波时间(TE > 300 ms)的 T2 加权成像可为神经退行性疾病/炎症性疾病提供相关信息。20名病程稳定的复发性多发性硬化症患者接受了1.5 T三维FLAIR、三维T1加权和32次回波(TE = 10-320毫秒)的多回波序列检查。在 FLAIR 上确定病灶(FL)。对T1图像进行处理,以分割深部灰质(dGM)、白质(WM)、T1低强度的FL亚体积(T1FL)和dGM体积(萎缩)。临床放射学参数包括残疾状况扩展量表(EDSS)、病程、患者年龄、T1FL和dGM萎缩。在不同TE下计算的非病变dGM和WM的平均信号强度(SI)与临床放射学参数之间进行了相关性分析。对数据进行了多变量线性回归拟合,以评估因变量 EDSS 与自变量 T1FL 病变负荷以及不同 TE 下 dGM 和 WM 的平均 SI 之间的关联。可以观察到一个明显的趋势,即在较长的 TE 下,所有临床放射学参数之间的相关性都有系统性的加强,变得显著(p
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Very-long T2-weighted imaging of the non-lesional brain tissue in multiple sclerosis patients.

The purpose of this study is to demonstrate that T2-weighted imaging with very long echo time (TE > 300 ms) can provide relevant information in neurodegenerative/inflammatory disorder. Twenty patients affected by relapsing-remitting multiple sclerosis with stable disease course underwent 1.5 T 3D FLAIR, 3D T1-weighted, and a multi-echo sequence with 32 echoes (TE = 10-320 ms). Focal lesions (FL) were identified on FLAIR. T1-images were processed to segment deep gray matter (dGM), white matter (WM), FL sub-volumes with T1 hypo-intensity (T1FL), and dGM volumes (atrophy). Clinical-radiological parameters included Expanded Disability Status Scale (EDSS), disease duration, patient age, T1FL, and dGM atrophy. Correlation analysis was performed between the mean signal intensity (SI) computed on the non-lesional dGM and WM at different TE versus the clinical-radiological parameters. Multivariable linear regressions were fitted to the data to assess the association between the dependent variable EDSS and the independent variables obtained by T1FL lesion load and the mean SI of dGM and WM at the different TE. A clear trend is observed, with a systematic strengthening of the significance of the correlation at longer TE for all the relationships with the clinical-radiological parameters, becoming significant (p < 0.05) for EDSS, T1FL volumes, and dGM atrophy. Multivariable linear regressions show that at shorter TE, the SI of the T2-weighted sequences is not relevant for describing the EDSS variability while the T1FL volumes are relevant, and vice versa, at very-long TEs (around 300 ms); the SI of the T2-weighted sequences significantly (p < 0.05) describes the EDSS variability. By very long TE, the SI primarily originates from water with a T2 longer than 250 ms and/or free water, which may be arising from the perivascular space (PVS). Very-long T2-weighting might detect dilated PVS and represent an unexplored MR approach in neurofluid imaging of neurodegenerative/inflammatory diseases.

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来源期刊
NMR in Biomedicine
NMR in Biomedicine 医学-光谱学
CiteScore
6.00
自引率
10.30%
发文量
209
审稿时长
3-8 weeks
期刊介绍: NMR in Biomedicine is a journal devoted to the publication of original full-length papers, rapid communications and review articles describing the development of magnetic resonance spectroscopy or imaging methods or their use to investigate physiological, biochemical, biophysical or medical problems. Topics for submitted papers should be in one of the following general categories: (a) development of methods and instrumentation for MR of biological systems; (b) studies of normal or diseased organs, tissues or cells; (c) diagnosis or treatment of disease. Reports may cover work on patients or healthy human subjects, in vivo animal experiments, studies of isolated organs or cultured cells, analysis of tissue extracts, NMR theory, experimental techniques, or instrumentation.
期刊最新文献
The effect of fat model variation on muscle fat fraction quantification in a cross-sectional cohort. Improvement of MRS at ultra-high field using a wireless RF array. Very-long T2-weighted imaging of the non-lesional brain tissue in multiple sclerosis patients. Simultaneous whole-liver water T 1 and T 2 mapping with isotropic resolution during free-breathing. Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values.
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