分享:
分享到微信朋友圈
X
综述
体素内不相干运动扩散加权成像在肺部的应用进展
吕四强 秦文恒 孙占国

Cite this article as: Lü SQ, Qin WH, Sun ZG. Application progress of intravoxel incoherent motion diffusion weighted imaging in lungs[J]. Chin J Magn Reson Imaging, 2022, 13(2): 141-144.本文引用格式:吕四强, 秦文恒, 孙占国. 体素内不相干运动扩散加权成像在肺部的应用进展[J]. 磁共振成像, 2022, 13(2): 141-144. DOI:10.12015/issn.1674-8034.2022.02.035.


[摘要] 扩散加权成像(diffusion weighted imaging,DWI)技术无需对比剂即可获得组织的水分子扩散情况。然而单指数DWI无法区分组织内单纯水分子扩散和微循环灌注信息,体素内不相干运动扩散加权成像(intravoxel incoherent motion-diffusion weighted imaging,IVIM-DWI)通过双指数曲线拟合,可以分别获得单纯水分子扩散和微循环灌注参数,能够更全面、准确地反映肿瘤组织微观结构的复杂性。近年来,IVIM-DWI在肺部的应用研究逐渐增多,已成为肺功能磁共振成像研究的热点。笔者就肺部IVIM-DWI相关技术参数、IVIM-DWI对肺良恶性病变的鉴别及其在肺癌评估中的应用进展进行综述。
[Abstract] The diffusion weighted imaging (DWI) can reflect the degree of diffusion of water molecules in tissues without contrast enhancement. However, DWI is a mono-exponential model, which cannot separate the pseudo-diffusion from pure molecular diffusion. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) utilizes a double-exponential model to obtain parameters of pure water molecule diffusion and microcirculatory perfusion-related diffusion, more comprehensively and accurately reflects the complexity of the microstructure of the tumor tissue. In recent years, the application of IVIM-DWI in lung has been gradually increasing and become the focus of lung functional magnetic resonance imaging research. In this paper, we mainly reviewed the technical parameters of IVIM-DWI in lung and related research progress in the diagnosis and treatment of the lung cancer.
[关键词] 肺癌;体素内不相干运动;磁共振成像;扩散加权成像
[Keywords] lung cancer;intravoxel incoherent motion;magnetic resonance imaging;diffusion weighted imaging

吕四强 1   秦文恒 2   孙占国 2*  

1 济宁医学院临床医学院,济宁 272013

2 济宁医学院附属医院医学影像科,济宁 272029

孙占国,E-mail:yingxiangszg@163.com

作者利益冲突声明:全体作者均声明无利益冲突。


基金项目: 山东省医药卫生科技发展计划项目 202009011151 济宁医学院附属医院“苗圃”科研计划项目 MP-ZD-2020-003
收稿日期:2021-10-08
接受日期:2022-02-07
中图分类号:R445.2  R734.2 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.02.035
本文引用格式:吕四强, 秦文恒, 孙占国. 体素内不相干运动扩散加权成像在肺部的应用进展[J]. 磁共振成像, 2022, 13(2): 141-144. DOI:10.12015/issn.1674-8034.2022.02.035

       单指数扩散加权成像(diffusion weighted imaging,DWI)通过表观扩散系数(apparent diffusion coefficient,ADC)来定量反映水分子在病变组织中的扩散运动,然而单指数DWI无法区分组织内单纯水分子扩散和微循环灌注信息,影响了定量评估的准确性。而基于双指数拟合模型的体素内不相干运动加权成像(intravoxel incoherent motion diffusion weighted imaging,IVIM-DWI)可以通过多b值拟合分别获得病变内水分子真实扩散和微循环灌注参数,能够更加准确、客观地反映病变组织的结构和功能信息。近年来,随着磁共振设备和采集技术的发展,IVIM-DWI在肺部病变评估中的应用潜力逐渐被重视[1, 2, 3]。本文就肺部IVIM-DWI相关技术参数、IVIM-DWI对肺良恶性病变的鉴别及其在肺癌评估中的应用进展作一综述。

1 IVIM-DWI的基本原理及其应用现状

       IVIM-DWI成像信号衰减的双指数模型可用以下公式表示[4]:Sb/S0=(1-f)·exp (-bD)+f·exp [-b (D+D*)]。其中Sb代表不同b值(b≠0 s/mm2)时的信号强度,S0代表b=0 s/mm2时的信号强度;D是真性扩散系数,也称慢扩散系数(Dslow),反映的是感兴趣区纯水分子的扩散运动;D*是伪扩散系数,也称快速表观扩散系数(Dfast),代表感兴趣区的毛细血管网的微循环灌注;f是灌注分数,表示局部微循环灌注相关效应与总扩散效应的体积比,可用于确定感兴趣区的血容量。D、D*单位均为mm2/s,f值无单位。

       近年来,国内外学者已将IVIM-DWI广泛应用于头颈部、乳腺、肝脏、肾脏等良恶性病变的鉴别及疗效的评估中,研究结果表明IVIM-DWI不仅可以鉴别肿瘤的良恶性,对于肿瘤性病变的分级和分期也具有指导作用[5, 6, 7, 8, 9]。但由于肺实质氢质子密度较小、呼吸运动伪影较大及病变-肺界面磁场不均质性等的影响,IVIM-DWI在肺部的应用依然面临着诸多挑战,目前相关应用研究报道较少。但IVIM-DWI作为一种无创功能学检查方法,能够弥补肺部病变单纯形态学评估的局限性,具有重要的临床应用前景。

2 影响肺部 IVIM-DWI成像的相关技术参数

       除了扫描仪主磁体系统、梯度系统等硬件设施外,b值的选择、图像采集序列、扫描时呼吸方式以及数据测量感兴趣区域(region of interest,ROI)的选取都可以直接或间接地影响肺部IVIM-DWI的图像质量和数据拟合的准确性。

2.1 b值的选择对IVIM-DWI成像的影响

       IVIM-DWI需使用多个b值才能将组织扩散信息与灌注信息分开。理论上来说,b值的数量越多,获得的图像质量越好,数据拟合的准确性也就越高,但相应的采集时间也会延长[10]。多个b值中,低b值获得的数据稳定性和重复性较差,所以应适当增加b<200 s/mm2的b值数量,且b<50 s/mm2的数量至少设置两个,才能有效提高灌注评估的准确性[11, 12]。而在目前已发表的关于IVIM-DWI在肺部应用的文献中,b值的数量从4到13个不等,大多数学者常采用10个左右的b值进行数据拟合,最小b值均设定b=0 s/mm2,最大b值往往采用b=800 s/mm2或b=1000 s/mm2,其中b<200 s/mm2的个数约占b值总数的三分之二且分布较密集,而b>200 s/mm2的个数约占b值总数的三分之一且分布较稀疏[3,13, 14, 15]

2.2 采集序列对IVIM-DWI成像的影响

       既往研究中,学者们多采用平面回波成像(echo-planar imaging,EPI)序列进行肺部IVIM-DWI成像[16]。EPI序列可以缩短扫描时间,减少呼吸运动伪影,但是磁敏感伪影可导致图像失真,一些肺部小病灶的成像质量更是难以保证[16]。针对这一问题,有学者尝试采用快速自旋回波成像(turbo spin-echo,TSE)序列进行IVIM-DWI成像。TSE序列采用多重射频重聚脉冲纠正磁场的不均质性,可以很大程度上降低磁敏感伪影,减少图像变形及失真[17, 18]。Wan等[16]研究发现,对比EPI和TSE两种IVIM-DWI采集方式在肺癌中的应用,与EPI序列相比,TSE序列成像几乎无失真,且ADC值、D值重复测量的稳定性明显提高,但伴随图像质量提升的是采集时间的明显延长,限制了TSE序列IVIM-DWI的常规应用。近年来,有学者尝试将梯度-自旋回波(gradient and spin echo,GRASE)序列应用于磁共振胰胆管成像及颅脑DWI[19, 20],结果表明GRASE序列既可以保证图像的质量,还可以缩短扫描时间,具有较大的临床应用潜力。目前尚未见将GRASE序列用于肺部IVIM-DWI成像的文献报道,其应用的可行性及成像效果亟待尝试和探索。

2.3 扫描时呼吸方式对IVIM-DWI成像的影响

       胸部MRI图像采集时常用呼吸方式包括自由呼吸,呼吸触发及屏气扫描三种,而目前尚未见关于三种呼吸采集方式对IVIM成像质量及定量参数影响的专题报道。既往胸部单指数DWI研究[21]表明,呼吸方式的选择对于成像质量具有一定影响,以自由呼吸下采集的图像质量最高。Swerkersson等[22]发现,对于体积较小的病灶或位于肺下叶的病灶,自由呼吸比呼吸触发获得的图像质量更稳定。对于IVIM-DWI扫描,屏气呼吸扫描和呼吸触发均容易受患者呼吸的影响,从而影响图像的信噪比和扫描时间[23],而自由呼吸IVIM-DWI扫描的采集时间和图像质量都更易被临床接受,因而被大多数学者采用[14, 15,23]

2.4 ROI的选取对IVIM-DWI数据测量的影响

       目前,IVIM数据测量时ROI的选取标准亦不统一,多数文献采用最大层面勾勒法(避开坏死、囊变等区域),但由于肿瘤的异质性及血管分布差异,勾勒法测量无法全面、客观地反映病灶整体的结构特点[11,24]。全肿瘤容积直方图分析(包含囊变、坏死等区域)已用于甲状腺肿瘤[25]及肝脏肿瘤[26]的IVIM-DWI成像中,其可获取病灶内所有体素的参数特征,而这一测量方法在肺部IVIM-DWI中的研究应用较少[27, 28]。Yuan等[29]研究发现,容积直方图分析可提高观察者间的短期重复性[组内相关系数(intra-class correlation coefficients,ICC)为0.89~0.95],能够更准确地反映肿瘤的特征。因此肺部病变IVIM-DWI的研究设计中,应尽量选用容积直方图分析法获取定量参数,以取得更准确的研究结果。

3 IVIM-DWI在鉴别肺部肿瘤良恶性中的应用

       恶性肿瘤细胞增殖速度快,细胞排列紧密,水分子扩散受限程度高于良性肿瘤[30]。因此,单指数DWI或IVIM-DWI均能对肺肿瘤良恶性的鉴别提供依据,其中IVIM-DWI可通过D值反映真实的水分子扩散,理论上应较ADC值具有更高的诊断效能。Yuan等[31]的一项IVIM-DWI鉴别肺良恶性肿瘤的研究发现,D值鉴别肺肿瘤良恶性的准确率(72.2%)和敏感度(91.3%)均显著高于ADC(准确率66.7%,敏感度81.2%)。Wan等[11]的研究结果与Yuan等相仿,发现D值鉴别肺肿瘤良恶性的诊断效能[曲线下面积(area under the curve,AUC)为0.884]高于ADC值(AUC为0.832)。一篇Meta分析[2]也得出D值对肺肿瘤良恶性的鉴别诊断效能最佳(AUC=0.90),其次是ADC值(AUC=0.86)。Jiang等[32]发现IVIM-DWI在肺肿块(直径>3 cm)的良恶性鉴别中,D值的诊断效能高于ADC值,其敏感度和特异度分别为90.57%和89.47%,然而在肺结节(直径≤3 cm)的良恶性鉴别中却发现ADC值的诊断效能最高。可见,IVIM-DWI成像对肺部小病灶良恶性的鉴别价值尚不明确,这可能与小病灶更易受到呼吸运动、磁敏感伪影和部分容积效应的影响有关。

       目前,IVIM-DWI成像中,f和D*值对肺部病变良恶性的鉴别诊断价值尚存在较大的争议。Wang等[33]和Deng等[15]研究发现,f值可以鉴别肺癌和炎性病变。然而,更多的文献认为f和D*不能作为鉴别肺肿瘤良恶性的参考指标,一方面因为部分良恶性肺肿瘤的微循环灌注状态在一定程度上较为类似[23];另一方面回波时间的设定、组织的T2弛豫时间均对f值有较大影响,进而降低了f值的稳定性[11,24]。虽然f和D*值目前在肺肿瘤良恶性鉴别中的价值仍存在争议,但它们作为反映病变灌注信息的参数,仍具有重要的应用潜力,有待规范扫描参数、扩大样本量对其进一步探讨。

4 IVIM-DWI在肺癌精准评估中的应用

       IVIM-DWI不仅可以鉴别肺肿瘤的良恶性,在鉴别肺癌不同亚型中也具有一定的潜力。侯月娇等[34]研究发现D值鉴别小细胞肺癌和非小细胞肺癌的诊断效能最高(AUC为0.874),而D*和f值的鉴别价值有限。Zheng等[24]研究也发现小细胞肺癌的ADC、D值显著低于非小细胞肺癌,且以D值的诊断效能最高(AUC为0.82)。小细胞肺癌D值更低的现象主要与小细胞肺癌增殖速度快,细胞外间隙较非小细胞肺癌更小,进而导致肿瘤内水分子扩散明显受限有关。上述两项研究均表明D*和f值不能有效区分肺癌的病理亚型。然而,Peng等[30]却发现腺癌组的f值显著高于鳞癌组和小细胞肺癌组,f值对不同细胞类型的肺癌具有一定的鉴别意义。Liu等[1]把44例肺腺癌的患者分成低级别组(原位癌、微浸润癌或贴壁为主腺癌)、中级别组(乳头状或腺泡状为主腺癌)和高级别组(微乳头或实体为主腺癌),结果发现f值在肺腺癌的病理分级评估中也具有一定价值。

       此外,IVIM-DWI在肺癌的分化、分期及增殖状态的评估中也具有一定的潜能。理论上分化程度低的非小细胞肺癌增殖能力强、细胞致密、血供丰富。余芬芬等[35]的研究表明低分化的非小细胞肺癌D*和f值均高于高分化者,但D值的意义没有得到体现,可能与其纳入的样本量较小有关。此外,Ye等[36]研究发现D值可用于术前评估肺癌纵隔淋巴结转移的检出,其诊断敏感度及特异度分别为70.0%、84.1%,对肺癌术前N分期具有一定的价值。还有研究发现ADC和D值与肺癌患者Ki-67的表达状态呈负相关,均能够反映肿瘤细胞的增殖情况,其中D值的诊断效能最高(AUC为0.85)[24]。另有学者报道,D值与肺癌表皮因子生长受体的突变状态也有一定相关性[37]。可见,IVIM在肺癌的精准诊断中具有重要的临床应用潜力,有待进一步探索。

5 IVIM在肺癌疗效评估中的应用

       由于多数肺癌确诊时已是晚期,对于晚期非小细胞肺癌临床主要以药物化疗和分子靶向治疗为主。既往研究发现,IVIM-DWI参数与肿瘤细胞密度、微血管密度及增殖指数存在一定的相关性,其参数的变化可以反映肿瘤治疗前后的病理改变[38, 39]。研究表明,IVIM-DWI参数D和ADC值均具有较好的组内重复性及组间一致性[ICC为0.918~0.944;组间变异系数(within coefficient of variation,WCV)为5.04%~6.64%],其对于肺癌治疗前后的变化及随访的评估是可行的[22]。江建芹等[40]把联合化疗一周期的26例晚期非小细胞肺癌患者根据实体瘤评价标准分为治疗有效组和无效组,对比治疗前后IVIM-DWI参数的变化,发现有效组治疗前ADC、D和f值均明显低于化疗后,无效组治疗前仅f值低于化疗后,而ADC和D值治疗前后无变化。对比常规化疗药物,抗血管生成药物可以选择性抑制肿瘤血管生成、诱导细胞凋亡,毒副作用更小,可明显改善患者预后。Shi等[41]对荷瘤小鼠进行抗血管药物CA4P治疗,应用IVIM-DWI评估治疗前后肿瘤的变化并与病理对照,结果发现f和D*能够在非小细胞肺癌出现形态学变化之前无创性地反映出CA4P的治疗效果。还有学者应用IVIM-DWI评估纳米药物治疗小鼠非小细胞肺癌的疗效,发现D值与肿瘤组织Ki-67的表达水平呈负相关,可通过D值变化反映肿瘤细胞的增殖状态,进而预测非小细胞肺癌的治疗效果[42]。综上,IVIM-DWI可以对肺癌早期治疗的效果进行评估,预测抗肿瘤药物治疗应答的可靠性,从而区分出潜在的治疗有效或者无效的人群,避免无效人群的过度医疗。

6 总结与展望

       综上所述,IVIM-DWI可以无创地反映组织中水分子的扩散和微循环灌注有关的信息,在肺部病变良恶性的鉴别诊断、肺癌的精准评估及肺癌疗效的早期评估中具有重要的应用前景。目前IVIM-DWI扫描技术的标准化设定(如b值数量及分布的选择、ROI的选取、扫描技术的选择等)仍没有统一的标准,需要在后续的研究中不断地完善和优化。此外,由于D*和f值的不稳定性,其在肺部病变评估中的应用价值仍需进一步探索。相信随着MR技术的不断创新和进步,IVIM-DWI成像技术将会在未来肺部病变的诊断和治疗的评估中发挥更重要的作用。

[1]
Liu H, Zheng L, Shi G, et al. Pulmonary Functional Imaging for Lung Adenocarcinoma: Combined MRI Assessment Based on IVIM-DWI and OE-UTE-MRI[J]. Front Oncol, 2021, 11: 677942. DOI: 10.3389/fonc.2021.677942.
[2]
Liang J, Li J, Li Z, et al. Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis[J]. BMC Cancer, 2020, 20(1): 799. DOI: 10.1186/s12885-020-07308-z.
[3]
Ercolani G, Capuani S, Antonelli A, et al. IntraVoxel Incoherent Motion (IVIM) MRI of fetal lung and kidney: Can the perfusion fraction be a marker of normal pulmonary and renal maturation?[J]. Eur J Radiol, 2021, 139: 109726. DOI: 10.1016/j.ejrad.2021.109726.
[4]
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging[J]. Radiology, 1988, 168(2): 497-505. DOI: 10.1148/radiology.168.2.3393671.
[5]
Li K, Machireddy A, Tudorica A, et al. Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study[J]. Tomography, 2020, 6(2): 148-159. DOI: 10.18383/j.tom.2019.00028.
[6]
Martens RM, Koopman T, Lavini C, et al. Early Response Prediction of Multiparametric Functional MRI and (18)F-FDG-PET in Patients with Head and Neck Squamous Cell Carcinoma Treated with (Chemo) Radiation[J]. Cancers (Basel), 2022, 14(1): 216. DOI: 10.3390/cancers14010216.
[7]
Van Baalen S, Froeling M, Asselman M, et al. Mono, bi- and tri-exponential diffusion MRI modelling for renal solid masses and comparison with histopathological findings[J]. Cancer Imaging, 2018, 18(1): 44. DOI: 10.1186/s40644-018-0178-0.
[8]
Becker AS, Wurnig MC, Finkenstaedt T, et al. Non-parametric intravoxel incoherent motion analysis of the thyroid gland[J]. Heliyon, 2017, 3(1): e00239. DOI: 10.1016/j.heliyon.2017.e00239.
[9]
Kovač JD, Daković M, Janković A, et al. The role of quantitative diffusion-weighted imaging in characterization of hypovascular liver lesions: A prospective comparison of intravoxel incoherent motion derived parameters and apparent diffusion coefficient[J]. PLoS One, 2021, 16(2): e0247301. DOI: 10.1371/journal.pone.0247301.
[10]
Karki K, Hugo GD, Ford JC, et al. Estimation of optimal b-value sets for obtaining apparent diffusion coefficient free from perfusion in non-small cell lung cancer[J]. Phys Med Biol, 2015, 60(20): 7877-7891. DOI: 10.1088/0031-9155/60/20/7877.
[11]
Wan Q, Deng YS, Zhou JX, et al. Intravoxel incoherent motion diffusion-weighted MR imaging in assessing and characterizing solitary pulmonary lesions[J]. Sci Rep, 2017, 7: 43257. DOI: 10.1038/srep43257.
[12]
Cohen AD, Schieke MC, Hohenwalter MD, et al. The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver[J]. Magn Reson Med, 2015, 73(1): 306-311. DOI: 10.1002/mrm.25109.
[13]
Fang T, Meng N, Feng P, et al. A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes[J]. J Magn Reson Imaging, 2021. DOI: 10.1002/jmri.27977.
[14]
Karayama M, Yoshizawa N, Sugiyama M, et al. Intravoxel incoherent motion magnetic resonance imaging for predicting the long-term efficacy of immune checkpoint inhibitors in patients with non-small-cell lung cancer[J]. Lung Cancer, 2020, 143: 47-54. DOI: 10.1016/j.lungcan.2020.03.013.
[15]
Deng Y, Li X, Lei Y, et al. Use of diffusion-weighted magnetic resonance imaging to distinguish between lung cancer and focal inflammatory lesions: a comparison of intravoxel incoherent motion derived parameters and apparent diffusion coefficient[J]. Acta Radiol, 2016, 57(11): 1310-1317. DOI: 10.1177/0284185115586091.
[16]
Wan Q, Lei Q, Wang P, et al. Intravoxel Incoherent Motion Diffusion-Weighted Imaging of Lung Cancer: Comparison Between Turbo Spin-Echo and Echo-Planar Imaging[J]. J Comput Assist Tomogr, 2020, 44(3): 334-340. DOI: 10.1097/rct.0000000000001004.
[17]
Mikayama R, Yabuuchi H, Sonoda S, et al. Comparison of intravoxel incoherent motion diffusion-weighted imaging between turbo spin-echo and echo-planar imaging of the head and neck[J]. Eur Radiol, 2018, 28(1): 316-324. DOI: 10.1007/s00330-017-4990-x.
[18]
Panyarak W, Chikui T, Yamashita Y, et al. Image Quality and ADC Assessment in Turbo Spin-Echo and Echo-Planar Diffusion-Weighted MR Imaging of Tumors of the Head and Neck[J]. Acad Radiol, 2019, 26(10): e305-e316. DOI: 10.1016/j.acra.2018.11.016.
[19]
廖振洪, 明兵, 马春, 等. 基于GRASE-DWI序列的颅脑扩散加权成像技术[J]. 磁共振成像, 2020, 11(6): 433-437.
Liao ZH, Ming B, Ma C, et al. Diffusion weighted imaging of head based on GRADE-DWI sequence[J]. Chin J Magn Reson Imaging, 2020, 11(6): 433-437. DOI: 10.12015/issn.1674-8034.2020.06.007.
[20]
Yoshida M, Nakaura T, Inoue T, et al. Magnetic resonance cholangiopancreatography with GRASE sequence at 3.0T: does it improve image quality and acquisition time as compared with 3D TSE?[J]. Eur Radiol, 2018, 28(6): 2436-2443. DOI: 10.1007/s00330-017-5240-y.
[21]
蔡荣芳, 崔磊, 江建芹, 等. 3种呼吸采集方式下肺癌肺门纵隔淋巴结扩散加权成像图像质量的比较[J]. 实用放射学杂志, 2018, 34(11): 1783-1787. DOI: 10.3969/j.issn.1002-1671.2018.11.035.
Cai RF, Cui L, Jiang JQ, et al. Comparison of DWI image quality with three breath collection techniques for hilar and mediastinal lymph nodes in lung cancer[J]. J Pract Radiol, 2018, 34(11): 1783-1787. DOI: 10.3969/j.issn.1002-1671.2018.11.035.
[22]
Swerkersson S, Grundberg O, Kölbeck K, et al. Optimizing diffusion-weighted magnetic resonance imaging for evaluation of lung tumors: A comparison of respiratory triggered and free breathing techniques[J]. Eur J Radiol Open, 2018, 5: 189-193. DOI: 10.1016/j.ejro.2018.10.003.
[23]
Wan Q, Deng YS, Lei Q, et al. Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?[J]. Eur Radiol, 2019, 29(3): 1607-1615. DOI: 10.1007/s00330-018-5714-6.
[24]
Zheng Y, Huang W, Zhang X, et al. A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging[J]. J Cancer, 2021, 12(1): 190-197. DOI: 10.7150/jca.48589.
[25]
Song M, Yue Y, Jin Y, et al. Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T[J]. Cancer Imaging, 2020, 20(1): 9. DOI: 10.1186/s40644-020-0289-2.
[26]
Ai Z, Han Q, Huang Z, et al. The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions[J]. Ann Transl Med, 2020, 8(18): 1128. DOI: 10.21037/atm-20-5109.
[27]
Usuda K, Iwai S, Yamagata A, et al. Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection[J]. Cancers (Basel), 2021, 13(11): 2720. DOI: 10.3390/cancers13112720.
[28]
Tsuchiya N, Doai M, Usuda K, et al. Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion[J]. PLoS One, 2017, 12(2): e0172433. DOI: 10.1371/journal.pone.0172433.
[29]
Yuan M, Zhong Y, Zhang YD, et al. Volumetric analysis of intravoxel incoherent motion imaging for assessment of solitary pulmonary lesions[J]. Acta Radiol, 2017, 58(12): 1448-1456. DOI: 10.1177/0284185117698863.
[30]
彭琴, 黄遥, 唐威, 等. 不同病理亚型肺癌的体素内不相干运动扩散加权成像模型参数比较[J]. 中华肿瘤杂志, 2018, 40(11): 824-828. DOI: 10.3760/cma.j.issn.0253-3766.2018.11.005.
Peng Q, Huang Y, Tang W, et al. Comparison of parameters for diffusion-weighted intravoxel incoherent motion imaging in lung cancer patients with different histopathological subtypes[J]. Chin J Oncol, 2018, 40(11): 824-828. DOI: 10.3760/cma.j.issn.0253-3766.2018.11.005.
[31]
Yuan M, Zhang YD, Zhu C, et al. Comparison of intravoxel incoherent motion diffusion-weighted MR imaging with dynamic contrast-enhanced MRI for differentiating lung cancer from benign solitary pulmonary lesions[J]. J Magn Reson Imaging, 2016, 43(3): 669-679. DOI: 10.1002/jmri.25018.
[32]
Jiang J, Fu Y, Hu X, et al. The value of diffusion-weighted imaging based on monoexponential and biexponential models for the diagnosis of benign and malignant lung nodules and masses[J]. Br J Radiol, 2020, 93(1110): 20190400. DOI: 10.1259/bjr.20190400.
[33]
Wang LL, Lin J, Liu K, et al. Intravoxel incoherent motion diffusion-weighted MR imaging in differentiation of lung cancer from obstructive lung consolidation: comparison and correlation with pharmacokinetic analysis from dynamic contrast-enhanced MR imaging[J]. Eur Radiol, 2014, 24(8): 1914-1922. DOI: 10.1007/s00330-014-3176-z.
[34]
侯月娇, 靳先文, 陈婧娴, 等. 体素内不相干运动扩散加权成像在诊断肺癌中的初步应用[J]. 实用放射学杂志, 2016, 32(8): 1194-1197, 1217. DOI: 10.3969/j.issn.1002-1671.2016.08.008.
Hou YJ, Jin XW, Chen JX, et al. Application of intravoxel incoherent motion diffusion-weighted imaging in diagnosis of lung cancer[J]. J Pract Radiol, 2016, 32(8): 1194-1197, 1217. DOI: 10.3969/j.issn.1002-1671.2016.08.008.
[35]
余芬芬, 谢磊, 王禹博, 等. 单、双指数模型DWI与实性非小细胞肺癌分化程度的相关性研究[J]. 临床放射学杂志, 2019, 38(12): 2303-2306.
Yu FF, Xie L, Wang YB, et al. The Application of Diffusion Weighted Magnetic Resonance Imaging with Single and Double Index Models in Evaluating the Differentiation Degree of Solid Non-Small Cell Lung Cancer[J]. J Clin Radiol, 2019, 38(12): 2303-2306. DOI: 10.13437/j.cnki.jcr.2019.12.018.
[36]
Ye X, Chen S, Tian Y, et al. A preliminary exploration of the intravoxel incoherent motion applied in the preoperative evaluation of mediastinal lymph node metastasis of lung cancer[J]. J Thorac Dis, 2017, 9(4): 1073-1080. DOI: 10.21037/jtd.2017.03.110.
[37]
Yuan M, Pu XH, Xu XQ, et al. Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image[J]. J Magn Reson Imaging, 2017, 46(1): 281-289. DOI: 10.1002/jmri.25572.
[38]
Yuan Z, Niu XM, Liu XM, et al. Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient[J]. Transl Lung Cancer Res, 2021, 10(8): 3671-3681. DOI: 10.21037/tlcr-21-610.
[39]
Wan Q, Bao Y, Xia X, et al. Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting and Monitoring the Response of Anti-Angiogenic Treatment in the Orthotopic Nude Mouse Model of Lung Adenocarcinoma[J]. J Magn Reson Imaging, 2021. DOI: 10.1002/jmri.27920.
[40]
江建芹, 崔磊, 蔡荣芳, 等. 单、双指数MR扩散加权成像预测非小细胞肺癌化疗疗效的临床研究[J]. 中华放射学杂志, 2018, 52(11): 829-835. DOI: 10.3760/cma.j.issn.1005-1201.2018.11.004.
Jiang JQ, Cui L, Cai RF, et al. The value of diffusion-weighted imaging based on monoexponential and biexponential model in predicting the response of chemotherapy in non-small cell lung cancer patients[J]. Chin J Radiol, 2018, 52(11): 829-835. DOI: 10.3760/cma.j.issn.1005-1201.2018.11.004.
[41]
Shi C, Liu D, Xiao Z, et al. Monitoring Tumor Response to Antivascular Therapy Using Non-Contrast Intravoxel Incoherent Motion Diffusion-Weighted MRI[J]. Cancer Res, 2017, 77(13): 3491-3501. DOI: 10.1158/0008-5472.Can-16-2499.
[42]
Huang C, Liang J, Ma M, et al. Evaluating the Treatment Efficacy of Nano-Drug in a Lung Cancer Model Using Advanced Functional Magnetic Resonance Imaging[J]. Front Oncol, 2020, 10: 563932. DOI: 10.3389/fonc.2020.563932.

上一篇 心脏磁共振在肥厚型心肌病预后评估及危险分层中的应用进展
下一篇 多参数乳腺MRI技术的研究现状及潜力
  
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