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使用3D超短回波时间MRI进行分区定量分析髌腱病中的组织特异性T *生物标志物

Tissue-Specific T * Biomarkers in Patellar Tendinopathy by Subregional Quantification Using 3D Ultrashort Echo Time MRI.

作者信息

Breda Stephan J, Poot Dirk H J, Papp Dorottya, de Vries Bas A, Kotek Gyula, Krestin Gabriel P, Hernández-Tamames Juan A, de Vos Robert-Jan, Oei Edwin H G

机构信息

Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.

Department of Orthopedics and Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.

出版信息

J Magn Reson Imaging. 2020 Aug;52(2):420-430. doi: 10.1002/jmri.27108. Epub 2020 Feb 28.

DOI:10.1002/jmri.27108
PMID:32108398
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7496783/
Abstract

BACKGROUND

Quantitative MRI of patellar tendinopathy (PT) can be challenging due to spatial variation of T * relaxation times.

PURPOSE

  1. To compare T * quantification using a standard approach with analysis in specific tissue compartments of the patellar tendon. 2) To evaluate test-retest reliability of different methods for fitting ultrashort echo time (UTE)-relaxometry data.

STUDY TYPE

Prospective.

SUBJECTS

Sixty-five athletes with PT.

FIELD STRENGTH/SEQUENCE: 3D UTE scans covering the patellar tendon were acquired using a 3.0T scanner and a 16-channel surface coil.

ASSESSMENT

Voxelwise median T * was quantified with monoexponential, fractional-order, and biexponential fitting. We applied two methods for T * analysis: first, a standard approach by analyzing all voxels covering the proximal patellar tendon. Second, within subregions of the patellar tendon, by using thresholds on biexponential fitting parameter percentage short T * (0-30% for mostly long T *, 30-60% for mixed T *, and 60-100% for mostly short T *).

STATISTICAL TESTS

Average test-retest reliability was assessed in three athletes using coefficients-of-variation (CV) and coefficients-of-repeatability (CR).

RESULTS

With standard image analysis, we found a median [interquartile range, IQR] monoexponential T * of 6.43 msec [4.32-8.55] and fractional order T * 4.39 msec [3.06-5.78]. The percentage of short T * components was 52.9% [35.5-69.6]. Subregional monoexponential T * was 13.78 msec [12.11-16.46], 7.65 msec [6.49-8.61], and 3.05 msec [2.52-3.60] and fractional order T * 11.82 msec [10.09-14.44], 5.14 msec [4.25-5.96], and 2.19 msec [1.82-2.64] for 0-30%, 30-60%, and 60-100% short T *, respectively. Biexponential component short T * was 1.693 msec [1.417-2.003] for tissue with mostly short T * and long T * of 15.79 msec [13.47-18.61] for mostly long T *. The average CR (CV) was 2 msec (15%), 2 msec (19%) and 10% (22%) for monoexponential, fractional order and percentage short T *, respectively.

DATA CONCLUSION

Patellar tendinopathy is characterized by regional variability in binding states of water. Quantitative multicompartment T * analysis in PT can be facilitated using a voxel selection method based on using biexponential fitting parameters.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:420-430.

摘要

背景

由于T*弛豫时间的空间变化,髌腱病(PT)的定量MRI可能具有挑战性。

目的

1)比较使用标准方法与在髌腱特定组织区域进行分析的T*定量。2)评估不同方法拟合超短回波时间(UTE)弛豫测量数据的重测可靠性。

研究类型

前瞻性研究。

研究对象

65名患有PT的运动员。

场强/序列:使用3.0T扫描仪和16通道表面线圈获取覆盖髌腱的3D UTE扫描图像。

评估

采用单指数、分数阶和双指数拟合对体素中位数T进行定量。我们应用了两种T分析方法:第一,通过分析覆盖髌腱近端的所有体素的标准方法。第二,在髌腱的子区域内,根据双指数拟合参数短T百分比(大部分为长T时为0 - 30%,混合T时为30 - 60%,大部分为短T时为60 - 100%)设置阈值。

统计检验

使用变异系数(CV)和重复性系数(CR)评估三名运动员的平均重测可靠性。

结果

采用标准图像分析,我们发现单指数T的中位数[四分位间距,IQR]为6.43毫秒[4.32 - 8.55],分数阶T为4.39毫秒[3.06 - 5.78]。短T成分的百分比为52.9%[35.5 - 69.6]。子区域单指数T分别为13.78毫秒[12.11 - 16.46]、7.65毫秒[6.49 - 8.61]和3.05毫秒[2.52 - 3.60],分数阶T分别为11.82毫秒[10.09 - 14.44]、5.14毫秒[4.25 - 5.96]和2.19毫秒[1.82 - 2.64],对应短T为0 - 30%、30 - 60%和60 - 100%。对于大部分为短T的组织,双指数成分短T为1.693毫秒[1.417 - 2.003],对于大部分为长T的组织,长T为15.79毫秒[13.47 - 18.61]。单指数、分数阶和短T*百分比的平均CR(CV)分别为2毫秒(15%)、2毫秒(19%)和10%(22%)。

数据结论

髌腱病的特征是水结合状态的区域变异性。使用基于双指数拟合参数的体素选择方法可以促进PT中的定量多区域T*分析。

证据水平

1 技术效能阶段:1 《磁共振成像杂志》2020年;52:420 - 430。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/98c07e4d1c20/JMRI-52-420-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/d8a8df954081/JMRI-52-420-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/a43ca2157db0/JMRI-52-420-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/97b2bc1a21a8/JMRI-52-420-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/3dbee2cbd6f5/JMRI-52-420-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/98c07e4d1c20/JMRI-52-420-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/d8a8df954081/JMRI-52-420-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/a43ca2157db0/JMRI-52-420-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/97b2bc1a21a8/JMRI-52-420-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/3dbee2cbd6f5/JMRI-52-420-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ba3/7496783/98c07e4d1c20/JMRI-52-420-g005.jpg

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