分享:
分享到微信朋友圈
X
技术研究
磁共振IDEAL-IQ与mDixon Quant技术对腹部、椎体脂肪定量的对比分析
刘娜 张浩南 张煜堃 苗延巍 宋清伟

Cite this article as: Liu N, Zhang HN, Zhang YK, et al. A comparation analysis between IDEAL-IQ and mDixon Quant techniques in fat quantification of abdomen and vertebrae[J]. Chin J Magn Reson Imaging, 2022, 13(3): 49-53.本文引用格式:刘娜, 张浩南, 张煜堃, 等. 磁共振IDEAL-IQ与mDixon Quant技术对腹部、椎体脂肪定量的对比分析[J]. 磁共振成像, 2022, 13(3): 49-53. DOI:10.12015/issn.1674-8034.2022.03.010.


[摘要] 目的 探讨不同平台3.0 T MR设备上非对称回波最小二乘估算法迭代水脂分离序列(iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation sequence,IDEAL-IQ)和魔镜成像(mDixon Quant)序列对肝脏、胰腺与腰椎椎体脂肪含量(fat fraction,FF)定量评估的差异。材料与方法 前瞻性纳入36名健康志愿者[男15名,女21名;年龄(24.39±2.28)岁],分别在两个不同平台3.0 T MR上对腹部与腰椎行IDEAL-IQ和mDixon Quant序列扫描。两名观察者测量所有志愿者肝脏、胰腺和腰椎(L1~L5)椎体的FF值并进行两序列间对比分析。结果 两名观察者所测数据一致性良好(组内相关系数>0.75)。IDEAL-IQ与mDixon Quant序列定量测量肝脏FF值为3.74±0.89、3.69±0.80;胰腺FF值为4.66±1.37、4.63±1.35;腰椎各节椎体FF值如下:L1为32.29±7.98、32.32±7.85;L2为35.08±9.15、35.08±9.20;L3为37.75±9.93、37.61±9.82;L4为37.15±9.82、37.26±9.84;L5为37.79±9.58、37.72±9.54,差异无统计学意义(P值均>0.05)。结论 IDEAL-IQ与mDixon Quant序列均可定量测量肝脏、胰腺和腰椎椎体FF值,其测量值具有高度的一致性。
[Abstract] Objective To explore the differences in the quantitative assessment of fat fraction (FF) of liver, pancreas and lumbar vertebral body on the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation sequence (IDEAL-IQ) and mDixon Quant sequence on different platforms of 3.0 T MR device.Materials and Methods Prospectively included 36 healthy volunteers (15 males and 21 females; age 24.39±2.28 years), IDEAL-IQ and mDixon Quant sequences were performed on two different platforms of the 3.0 T MR to scan the epigastrium and lumbar vertebral body. Two observers measured the FF values of liver, pancreas, and lumbar (L1-L5) vertebral bodies in all volunteers and performed a comparative analysis between the two sequences.Results The data measured by the two observers were consistently good (intra-class correlation coefficients>0.75). IDEAL-IQ and mDixon Quant sequence showed that, the FF values of liver were 3.74±0.89, 3.69±0.80; FF values of pancreas were 4.66±1.37, 4.63±1.35; FF values of lumbar vertebral body L1 were 32.29±7.98, 32.32±7.85; L2 were 35.08±9.15, 35.08±9.20; L3 were 37.75±9.93, 37.61±9.82; L4 were 37.15±9.82, 37.26±9.84; L5 were 37.79±9.58, 37.72±9.54, there was no significant difference (P>0.05).Conclusions Both IDEAL-IQ and mDixon Quant sequences can quantitatively measure FF values of liver, pancreas, and lumbar vertebral body, its measurements are highly consistent.
[关键词] 腰椎;胰腺;肝脏;脂肪定量;磁共振成像
[Keywords] lumbar vertebrae;pancreas;liver;fat quantification;magnetic resonance imaging

刘娜    张浩南    张煜堃    苗延巍    宋清伟 *  

大连医科大学附属第一医院放射科,大连 116011

宋清伟,E-mail:songqw1964@163.com

作者利益冲突声明:全部作者均声明无利益冲突。


基金项目: 辽宁省教育厅基金项目 LJKZ0856 横向课题基金项目 2021HZ006
收稿日期:2021-09-01
接受日期:2022-02-11
中图分类号:R445.2 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.03.010
本文引用格式:刘娜, 张浩南, 张煜堃, 等. 磁共振IDEAL-IQ与mDixon Quant技术对腹部、椎体脂肪定量的对比分析[J]. 磁共振成像, 2022, 13(3): 49-53. DOI:10.12015/issn.1674-8034.2022.03.010.

       脂肪组织是人体最大的隔室之一,广泛分布于皮下、内脏及周围和椎体等部位。脂肪在肝脏、胰腺中的异常积累会引发非酒精性肝病、胰腺炎等系列疾病,加快脏器的坏死程度和癌症进展,导致致死率增高[1];脂肪在椎体中增多可能导致骨质疏松、压缩骨折、椎体退行性病变等疾病。对人体各脏器和骨骼内脂肪进行定量分析有助于疾病预测和早期评估[2]。目前用于脂肪定量的技术主要有病理活检、超声、计算机断层成像(computed tomography,CT)、磁共振成像(magnetic resonance imaging,MRI)等。病理活检作为脂肪定量的金标准,其创伤性较大,无法作为临床常规检查;超声受主观影响较大,敏感度和精确度欠佳;CT检查电离辐射无法避免且后处理较为复杂;相较之下,MRI能提供一种无创、简单、精准的影像学检查方法,是目前公认的用于脂肪定量的最佳技术[3, 4, 5]。基于MRI测量脏器、骨骼的脂肪分数(fat fraction,FF)是人体脂肪定量的主要方法之一。患者可能会在不同的设备下进行成像,因此,了解不同脉冲序列和测量结果的精确度非常重要[6],GE平台的非对称回波最小二乘估算法迭代水脂分离序列(iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation sequence, IDEAL-IQ)和Philips平台的魔镜成像(mDixon Quant)是近年来不断发展和完善的两种脂肪定量技术,目前国内对同场强下两种序列的FF值对比鲜有研究,国外研究较少且测量部位较为单一,测量方法不够完善,本研究创新性地采用半自动分割容积提取法测量肝脏和胰腺FF值,旨在探讨两个平台不同技术对肝脏、胰腺及腰椎椎体多部位脂肪定量的差异。

1 材料与方法

1.1 一般资料

       前瞻性纳入健康志愿者36名(男15名,女21名),年龄21~29 (24.39±2.28)岁,BMI 18.07~29.59 (21.83±2.69) kg/m2。所有志愿者均签署知情同意书。纳入标准:(1)无MRI检查禁忌证,如体内有心脏起搏器、支架、金属植入物等;(2)无幽闭恐惧症;(3)常规MR检查肝脏、胰腺及椎体无形态及信号异常者;(4)无肝脏、胰腺和腰椎手术史。上述36名健康志愿者分别接受上腹和腰椎IDEAL-IQ与mDixon Quant序列扫描。本研究经本院伦理委员会审批通过(批准文号:PJ-KS-KY-2021-10)。

1.2 MRI检查仪器与方法

       所有志愿者均在检查当天1~4 h内完成上腹轴位以及腰椎矢状位IDEAL-IQ、mDixon Quant序列扫描。检查期间禁食水。

1.2.1 IDEAL-IQ序列

       使用GE Signa HDxt 3.0 T MR扫描仪。上腹采用8通道腹部相控阵线圈,检查者取仰卧位,足先进,双臂上举。腰椎椎体采用8通道颈胸腰线圈,仰卧位,头先进,双上臂自然放于身体两侧。所有志愿者均行基于并行采集空间敏感编码技术(array spatial sensitivity encoding technique,ASSET)的IDEAL-IQ序列,扫描参数详见表1

表1  上腹部脂肪定量序列参数
Tab. 1  Upper abdominal fat quantification sequence parameters

1.2.2 mDixon Quant序列

       使用Philips Ingenia CX 3.0 T MR (Philips Healthcare, Best, the Netherlands)扫描仪。上腹采用32通道腹部相控阵线圈,检查者取仰卧位,头先进,双臂自然放于身体两侧;腰椎椎体使用12通道内置线圈。所有志愿者均行基于压缩感知技术(compressed sense,CS)加速因子(acceleration factor,AF)等于2的mDixon序列,扫描参数详见表2

表2  腰椎椎体脂肪定量序列参数
Tab. 2  Lumbar body fat quantification sequence parameters

1.3 图像分析与测量

       IDEAL-IQ序列经GE AW4.6工作站重建出6组图像,包括水像、脂像、同相位(in phase,IP)、反相位(out phase,OP)图像、R2*弛豫图像和脂肪分数图像。mDixon Quant序列扫描后系统自动生成上述6组图像。两名观察者(具有5年以上工作经验的放射科诊断医师)分别在两种序列的脂肪分数图像测量肝脏、胰腺及腰椎各节段椎体的FF值。肝脏与胰腺:将IDEAL-IQ序列及mDixon Quant序列的FF像导入Intelli Space Portall Version 7 (ISP)工作站,两名观察者通过3D容积提取tumor tracking软件进行半自动分割,各层面图像包含全部肝脏和胰腺,手动勾画调整边缘,得全肝脏、全胰腺FF值(图1)。腰椎:选取L1~L5椎体矢状位最大层面,两名观察者手动放置长方形ROI,面积为200~230 mm2,避开椎体头尾侧的终板、软骨和椎体前后缘骨皮质,得各椎体FF值(图2)。

图1  男,24岁,BMI为20.43 kg/m2。1A~1D为利用半自动分割容积提取法在脂肪分数图上勾画肝脏和胰腺3D ROI示意图。1A:IDEAL-IQ序列肝脏;1B:mDixon Quant序列胰腺;1C:mDixon Quant序列肝脏;1D:mDixon Quant序列胰腺。1E~1H为1A~1D对应伪彩图。
图2  女,24岁,BMI为17.15 kg/m2。2A~2D为在脂肪分数图上勾画腰椎ROI示意图。2A为IDEAL-IQ序列,所测L1~L5 FF值为L1:26.66%;L2:25.7%;L3:30.14%;L4:28.5%;L5:31.22%。2B为2A对应伪彩图。2C为mDixon Quant序列,所测L1~L5 FF值为L1:26.71%;L2:27.78%;L3:30.2%;L4:28.01%;L5:31.64%。2D为2C对应伪彩图。
Fig. 1  Male, 24 years old, BMI of 20.43 kg/m2. 1A-1D is a schematic diagram of liver and pancreas 3D ROI on the fat fractional graph by semi-automatic fractional volume extraction. 1A: liver of IDEAL-IQ sequence; 1B: pancreas of mDixon Quant sequence; 1C: liver of mDixon Quant sequence; 1D: pancreas of mDixon Quant sequence. 1E-1H corresponds to 1A-1D pseudo-color chart.
Fig. 2  Female, 24 years old, BMI of 17.15 kg/m2. 2A to 2D is a schematic diagram of lumbar SPINE ROI on the fat fraction plot. 2A was IDEAL-IQ sequence, and L1-L5 FF value was L1: 26.66%; L2: 25.7%; L3: 30.14%; L4: 28.5%; L5: 31.22%. 2B was 2A corresponding to the pseudo-color map. 2C was mDixon Quant sequence, and L1-L5 FF value was L1: 26.71%; L2: 27.78%; L3: 30.2%; L4: 28.01%; L5: 31.64%. 2D was 2C corresponding to the pseudo-color map.

1.4 统计学方法

       采用SPSS 24.0软件对数据进行统计学分析,计量资料采用x¯±s表示。采用组内相关系数(intra-class correlation coefficients, ICC)检验两名观察者所测得的数据的一致性,ICC>0.75表明一致性良好。若一致性良好,取两观察者测量数据平均值,比较两种不同定量序列的FF值。符合正态分布的采用配对样本t检验,不符合正态分布的采用Wilcoxon符号秩检验,P<0.05表示差异有统计学意义。

2 结果

2.1 两名观察者测量FF值的一致性检验

       两名观察者测得FF值一致性良好(表3)。

表3  两名观察者测量FF值结果一致性检验
Tab. 3  The consistency test of FF values measurement results between two observers

2.2 肝脏、胰腺和腰椎椎体FF值序列间比较

       IDEAL-IQ和mDixon Quant序列扫描所获得的肝脏、胰腺及腰椎椎体(L1~L5)FF值差异均无统计学意义(P>0.05;表4图3)。

图3  3A为IDEAL-IQ和mDIXON Quant序列上腹部FF值箱图。3B为IDEAL-IQ和mDIXON Quant序列腰椎FF值箱图。
Fig. 3  3A was box plot of FF values in the upper abdomen of the IDEAL-IQ and mDIXON Quant sequences. 3B was box plot of FF values in the lumbar vertebrae of the IDEAL-IQ and mDIXON Quant sequences.
表4  不同定量序列肝脏、胰腺及腰椎各椎体间FF值对比
Tab. 4  Comparison of interbody FF values of liver, pancreas and lumbar spine in different quantitative sequences

3 讨论

       本研究通过测量肝脏、胰腺与腰椎的FF值,对比不同平台IDEAL-IQ与mDixon Quant两种技术FF测量值准确性,国内首次进行多部位对比两种技术,且创新性地采用半自动分割容积提取法测量肝脏和胰腺FF值,结果表明3.0 T两平台不同序列对肝脏、胰腺及腰椎椎体脂肪定量无明显差异,不同成像技术有高度的可重复性。

3.1 IDEAL-IQ与mDixon Quant序列原理

       传统的水脂分离技术基于快速自旋回波或梯度回波序列,利用水和脂肪之间进动频率的差异,通过调整回波时间得到水、脂、IP、OP图像,对水与脂肪的和相与差相进行运算,最终得到单纯的水像与脂像[7]。但其得出的FF值是水和脂肪信号强度的差值,并不是真正意义上的质子密度脂肪分数(proton density fat fraction,PDFF)。GE平台的IDEAL-IQ和Philips平台的mDixon Quant都是基于化学位移编码的3D扫描技术,通过计算脂肪内质子密度在水分子和脂肪分子密度总和中所占的百分比得到PDFF[8]。脂肪定量的准确性依赖于脂肪峰值模型的准确性,典型的脂肪分子在脂肪谱上具有9个不同的峰值,但是目前1.5 T和3.0 T主磁场不能完全分辨出9个峰值。IDEAL-IQ采用回波不对称的三点Dixon法采集方式和最小二乘估算法对水和脂肪进行迭代分解并成像,根据不同脂质在化学位移中脂质峰的差异,通过6峰脂肪模型精确模拟甘油三酯的多共振峰,并采用fly-back对K空间进行填充[9]。mDixon Quant采用“两点法”水脂分离技术对信号进行采集,通过脂肪7峰模型将采集到的信号数据拟合成数学模型,分析脂肪质子峰的组成并进行定量计算[10],Wang等[11]对多谱峰模型(1、3、5、6、7、9个脂肪峰)进行脂肪定量,证明不同多谱峰模型均能准确计算FF值。

       此外,两种技术均采用小翻转角度(3°)使T1偏倚最小化,采用多个回波信号(6回波)来校正T2*效应和降低噪声偏倚[12, 13],从而实现了对脂肪的精准定量。Fukui等[14]发现IDEAL-IQ技术所测FF值与组织学胰腺脂肪分数成像显著相关(r=0.802)。胡磊等[15]证明IDEAL-IQ测量的椎体脂肪含量比与脂肪细胞计数之间呈高度正相关(r=0.925)。

3.2 相关研究比较

       本文旨在探讨相同场强条件下,不同平台IDEAL-IQ与mDixon Quant两种技术对FF值的测量是否有差异。宋宇等[16]通过对比敏感度编码和CS不同加速因子对3D mDixon Quant技术腰椎椎体脂肪定量的影响,证明3D mDixon Quant序列结合CS技术评估腰椎椎体脂肪含量是稳定可靠的。本研究选取AF=2,可在保证图像质量的同时,实现脂肪含量的快速、精准测量。以往研究[6,14,17, 18, 19, 20, 21, 22]分别对不同成像平台、不同技术、不同场强、不同观察者之间的脂肪定量值进行比较,均在体模、肝脏和腰椎间进行比较,差异均无统计学意义,有高度可重复性。本研究与以往结果相一致,除肝脏与腰椎外,还创新性地对胰腺FF值进行了比较,结果显示差异同样无统计学意义。传统肝脏与胰腺测量通常采用ROI法,需在9个肝段和胰腺钩突、胰头、胰体和胰尾部分别放置ROI以准确测量全肝和全胰腺FF值,操作复杂、耗时,受扫描角度和主观因素影响较大。此外,由于呼吸和胃肠运动易导致图像变形,影响肝左叶PDFF的评估[23]。本文创新性地采用半自动分割容积提取法测量肝脏和胰腺FF值,所得3D ROI更加准确、客观,且较传统方法便利[24],尤亚茹等[25]研究表明半自动分割技术与传统ROI法所测胰腺FF值差异无统计学意义,可在保证结果准确的情况下明显缩短测量时间。但腰椎在采用容积提取法测量时边缘骨皮质难以去除,为使测量结果更加准确,腰椎依旧采用二维ROI测法。

3.3 本研究的局限性

       局限性:(1)志愿者人数较少且年龄集中在20~30岁,未考虑年龄因素对FF测量值的影响。(2)两种技术的肝脏与胰腺图像均在ISP工作站进行测量,腰椎分别在各自工作站测量,不同平台间软件测量方法能否通用尚未研究。有待后续针对不同平台扩大样本量与年龄范围开展深入研究。

       综上所述,3.0 T磁共振IDEAL-IQ与mDixon Quant序列对肝脏、胰腺及腰椎椎体脂肪定量无明显差异,这两个序列测量FF值结果有高度一致性,证明其在多中心研究中的潜力。两种技术对人体无创无辐射,操作精准简便,在临床中有望应用于更多部位FF值的测量。

[1]
Kühn JP, Berthold F, Mayerle J, et al. Pancreatic steatosis demonstrated at MR imaging in the general population: clinical relevance[J]. Radiology, 2015, 276(1): 129-136. DOI: 10.1148/radiol.15140446.
[2]
Lee SS, Park SH. Radiologic evaluation of nonalcoholic fatty liver disease[J]. World J Gastroenterol, 2014, 20(23): 7392-7402. DOI: 10.3748/wjg.v20.i23.7392.
[3]
Samet JD, Deng J, Schafernak K, et al. Quantitative magnetic resonance imaging for determining bone marrow fat fraction at 1.5 T and 3.0 T: a technique to noninvasively assess cellularity and potential malignancy of the bone marrow[J]. Pediatr Radiol, 2021, 51(1): 94-102. DOI: 10.1007/s00247-020-04809-8.
[4]
Hyodo T, Yada N, Hori M, et al. Multimaterial decomposition algorithm for the quantification of liver fat content by using fast-kilovolt-peak switching dual-energy CT: clinical evaluation[J]. Radiology, 2017, 283(1): 108-118. DOI: 10.1148/radiol.2017160130.
[5]
Tang A, Desai A, Hamilton G, et al. Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease[J]. Radiology, 2015, 274(2): 416-425. DOI: 10.1148/radiol.14140754.
[6]
Zhao RY, Hernando D, Harris DT, et al. Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom[J]. Med Phys, 2021, 48(8): 4375-4386. DOI: 10.1002/mp.15038.
[7]
Zhan CY, Olsen S, Zhang HC, et al. Detection of hepatic steatosis and iron content at 3 Tesla: comparison of two-point Dixon, quantitative multi-echo Dixon, and MR spectroscopy[J]. Abdom Radiol (NY), 2019, 44(9): 3040-3048. DOI: 10.1007/s00261-019-02118-9.
[8]
Hamilton G, Yokoo T, Bydder M, et al. In vivo characterization of the liver fat ¹H MR spectrum[J]. NMR Biomed, 2011, 24(7): 784-790. DOI: 10.1002/nbm.1622.
[9]
Chen YD, Long LL, Jiang ZJ, et al. Quantification of pancreatic proton density fat fraction in diabetic pigs using MR imaging and IDEAL-IQ sequence[J]. BMC Med Imaging, 2019, 19(1): 38. DOI: 10.1186/s12880-019-0336-2.
[10]
Hwang SN. Editorial for "practical approaches to bone marrow fat fraction quantification across magnetic resonance imaging platforms"[J]. J Magn Reson Imaging, 2020, 52(1): 307-308. DOI: 10.1002/jmri.27177.
[11]
Wang XK, Hernando D, Reeder SB. Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum[J]. Magn Reson Med, 2016, 75(2): 845-851. DOI: 10.1002/mrm.25681.
[12]
Noureddin M, Lam J, Peterson MR, et al. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials[J]. Hepatology, 2013, 58(6): 1930-1940. DOI: 10.1002/hep.26455.
[13]
Hines CDG, Yu HZ, Shimakawa A, et al. Quantification of hepatic steatosis with 3-T MR imaging: validation in ob/ob mice[J]. Radiology, 2010, 254(1): 119-128. DOI: 10.1148/radiol.09090131.
[14]
Fukui H, Hori M, Fukuda Y, et al. Evaluation of fatty pancreas by proton density fat fraction using 3-T magnetic resonance imaging and its association with pancreatic cancer[J]. Eur J Radiol, 2019, 118: 25-31. DOI: 10.1016/j.ejrad.2019.06.024.
[15]
胡磊, 查云飞, 林苑, 等. IDEAL-IQ定量评价兔糖尿病模型椎体骨髓脂肪含量的可行性研究[J]. 磁共振成像, 2015, 6(12): 941-946. DOI: 10.3969/j.issn.1674-8034.2015.12.012.
Hu L, Zha YF, Lin Y, et al. The feasibility of IDEAL-IQ quantitative evaluation of vertebral fat fraction content in rabbit models of diabetes mellitus[J]. Chin J Magn Reson Imaging, 2015, 6(12): 941-946. DOI: 10.3969/j.issn.1674-8034.2015.12.012.
[16]
宋宇, 宋清伟, 张浩南, 等. 基于压缩感知的3DmDixonQuant对腰椎椎体脂肪含量的定量研究[J]. 磁共振成像, 2021, 12(4): 51-56. DOI: 10.12015/issn.1674-8034.2021.04.010.
Song Y, Song QW, Zhang HN, et al. 3D mDixon Quant based on compressed SENSE for quantitative study of lumbar vertebral body fat content[J]. Chin J Magn Reson Imaging, 2021, 12(4): 51-56. DOI: 10.12015/issn.1674-8034.2021.04.010.
[17]
Hernando D, Sharma SD, Aliyari Ghasabeh M, et al. Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5 T and 3 T using a fat-water phantom[J]. Magn Reson Med, 2017, 77(4): 1516-1524. DOI: 10.1002/mrm.26228.
[18]
Serai SD, Dillman JR, Trout AT. Proton density fat fraction measurements at 1.5- and 3-T hepatic MR imaging: same-day agreement among readers and across two imager manufacturers[J]. Radiology, 2017, 284(1): 244-254. DOI: 10.1148/radiol.2017161786.
[19]
Athithan L, Gulsin GS, House MJ, et al. A comparison of liver fat fraction measurement on MRI at 3 T and 1.5 T[J]. PLoS One, 2021, 16(7): e0252928. DOI: 10.1371/journal.pone.0252928.
[20]
Schmeel FC, Vomweg T, Träber F, et al. Proton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms[J]. J Magn Reson Imaging, 2019, 50(6): 1762-1772. DOI: 10.1002/jmri.26748.
[21]
Hernando D, Sharma SD, Aliyari Ghasabeh M, et al. Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom[J]. Magn Reson Med, 2017, 77(4): 1516-1524. DOI: 10.1002/mrm.26228.
[22]
Schneider E, Remer EM, Obuchowski NA, et al. Long-term inter-platform reproducibility, bias, and linearity of commercial PDFF MRI methods for fat quantification: a multi-center, multi-vendor phantom study[J]. Eur Radiol, 2021, 31(10): 7566-7574. DOI: 10.1007/s00330-021-07851-8.
[23]
Vu KN, Gilbert G, Chalut M, et al. MRI-determined liver proton density fat fraction, with MRS validation: comparison of regions of interest sampling methods in patients with type 2 diabetes[J]. J Magn Reson Imaging, 2016, 43(5): 1090-1099. DOI: 10.1002/jmri.25083.
[24]
张楠, 宋清伟, 刘爱连, 等. 阈值提取法用于压缩感知3D mDIXON肝脏脂肪定量的可行性[J]. 中国医学影像技术, 2020, 36(11): 1667-1670. DOI: 10.13929/j.issn.1003-3289.2020.11.018.
Zhang N, Song QW, Liu AL, et al. Feasibility of threshold extraction method for compressed sensing 3D mDIXON liver fat quantification[J]. Chin J Med Imaging Technol, 2020, 36(11): 1667-1670. DOI: 10.13929/j.issn.1003-3289.2020.11.018.
[25]
尤亚茹, 张钦和, 刘爱连, 等. 半自动分割技术对全胰腺脂肪定量的可行性: 与传统ROI方法的比较性研究[J]. 磁共振成像, 2020, 11(12): 1124-1128. DOI: 10.12015/issn.1674-8034.2020.12.009.
You YR, Zhang QH, Liu AL, et al. The feasibility of semi-automatic segmentation technology for quantification of pancreatic fat: comparative study with traditional ROI methods[J]. Chin J Magn Reson Imaging, 2020, 11(12): 1124-1128. DOI: 10.12015/issn.1674-8034.2020.12.009.

上一篇 基于DTI的人类与猕猴脑区连通性方法研究
下一篇 阿尔茨海默病默认网络和海马功能连接研究:一项基于SDM的Meta分析
  
诚聘英才 | 广告合作 | 免责声明 | 版权声明
联系电话:010-67113815
京ICP备19028836号-2