Abstract
目的
磁共振扩散加权成像(diffusion-weighted imaging,DWI)对子宫内膜癌的诊断和疗效评估具有重要的临床价值,如何提高DWI对子宫内膜微小病灶的检出率是目前MRI技术的研究热点。本研究旨在分析小视野MRI ZOOMit-DWI序列和常规单次激发平面回波成像(single-shot echo-planar imaging,SS-EPI)DWI序列在子宫内膜癌扫描中的图像质量,探讨ZOOMit-DWI序列的临床应用价值。
方法
收集2019年7月至2021年5月在中南大学湘雅二医院经手术病理确诊的37例子宫内膜癌患者,所有患者术前均行MRI ZOOMit-DWI序列和常规SS-EPI DWI序列扫描,由两名放射科医师分别对两组图像的解剖细节显示、伪影、几何变形程度、病灶清晰度进行主观评价;同时测量病灶等的信号强度并计算两种图像的信号噪声比(signal-to-noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和表观扩散系数(apparent diffusion coefficient,ADC)以进行客观评价,分析两组DWI序列的主观评分、客观评分及ADC的差异。
结果
ZOOMit-DWI组的SNR显著高于SS-EPI DWI组(301.96±141.85 vs 94.66±41.26,P<0.05);ZOOMit-DWI组的CNR显著高于SS-EPI DWI组(185.05±105.45 vs 57.91±31.54,P<0.05);ZOOMit-DWI组的噪声标准差与SS-EPI DWI组比较,差异无统计学意义(P>0.05)。ZOOMit-DWI组主观评分中的解剖细节和病灶清晰度均显著高于SS-EPI DWI组(均P<0.05),伪影和几何变形均显著低于SS-EPI DWI组(均P<0.05)。ZOOMit-DWI组的ADC与SS-EPI DWI组比较差异无统计学意义(P>0.05)。
结论
ZOOMit-DWI的图像质量明显高于常规SS-EPI DWI。在子宫内膜癌MRI DWI中,ZOOMit-DWI可以有效减少图像的几何变形和伪影,更有利于临床诊疗工作。
Keywords: 子宫内膜癌, ZOOMit-DWI, 常规DWI, 表观扩散系数
Abstract
Objective
Magnetic resonance diffusion-weighted imaging (DWI) has important clinical value in diagnosis and curative effect evaluation on endometrial carcinoma. How to improve the detection rate of endometrial small lesions by DWI is the research focus of MRI technology. This study aims to analyze the image quality of small field MRI ZOOMit-DWI sequence and conventional single-shot echo-planar imaging (SS-EPI) DWI sequence in the scanning of endometrial carcinoma, and to explore the clinical value of ZOOMit-DWI sequence.
Methods
A total of 37 patients with endometrial carcinoma diagnosed by operation and pathology in the Second Xiangya Hospital of Central South University from July 2019 to May 2021 were collected. All patients were scanned with MRI ZOOMit-DWI sequence and SS-EPI DWI sequence before operation. Two radiologists subjectively evaluated the anatomical details, artifacts, geometric deformation and focus definition of the 2 groups of DWI images. At the same time, the signal intensity were measured and the signal-to-noise ratio (SNR), contrast to noise ratio (CNR), and apparent diffusion coefficient (ADC) of the 2 DWI sequences were calculated for objective evaluation. The differences of subjective score, objective score and ADC value of the 2 DWI sequences were analyzed.
Results
The SNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (301.96±141.85 vs 94.66±41.26), and the CNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (185.05±105.45 vs 57.91±31.54, P<0.05). There was no significant difference in noise standard deviation between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05). The subjective score of anatomical detail and focus definition in the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (both P<0.05). The subjective score of artifacts and geometric deformation of ZOOMit-DWI group was significantly lower than that of the SS-EPI DWI group (both P<0.05). ADC had no significant difference between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05).
Conclusion
The image quality of ZOOMit-DWI is significantly higher than that of conventional SS-EPI DWI. In the MRI DWI examination of endometrial carcinoma, ZOOMit-DWI can effectively reduce the geometric deformation and artifacts of the image, which is more conducive to clinical diagnosis and treatment.
Keywords: endometrial cancer, ZOOMit-DWI, conventional DWI, apparent diffusion coefficient
子宫内膜癌发生于子宫内膜上皮,是常见的女性生殖系统肿瘤,占女性妇科恶性肿瘤死亡的第3位,仅次于卵巢癌和宫颈癌,好发于围绝经期和绝经后女性[1]。在我国,子宫内膜癌的发病率逐年升高,目前仅次于宫颈癌,居女性生殖系统恶性肿瘤的第2位。子宫内膜癌的早期诊断和治疗,对于提高患者的生存质量和生存率具有重要的临床意义。磁共振成像(magnetic resonance imaging,MRI)软组织分辨率高,是目前临床上子宫内膜癌诊断和分期的重要检查方法[2]。获得高质量的子宫内膜的MRI图像是妇科医生和人工智能(artificial intelligence,AI)辅助诊断系统准确识别影像特征的根本保证[3]。
MR扩散加权成像(diffusion-weighted imaging,DWI)以其较高的诊断准确率、操作便利性及图像多样性,获得广大科研及临床工作者的青睐。它可以检测活体内水分子的扩散运动。子宫内膜癌细胞排列致密导致水分子活动受限,引起DWI图像上信号增强而被识别;然而,常规扩散采用单次激发平面回波成像(single-shot echo-planar imaging,SS-EPI),磁敏感伪影较明显,对子宫内膜的解剖细节和微小病灶的显示受限[4]。随着MRI技术的发展,新的小视野DWI(reduced field-of-view DWI, r-FOV DWI)扫描技术ZOOMit-DWI出现,其利用二维选择性激励射频技术仅激发小范围感兴趣区(region of interest,ROI),可以有效去除伪影,获得较高质量和分辨率的DWI图像。而常规DWI(conventional DWI,C-DWI)是使用SS-EPI技术采集全视野腹部激发信号,这种全视野的激发技术可能会由于主磁场的不均、非常短的采集时间及涡流等导致图像的解剖变形、较重的磁敏感伪影及较低的分辨率[5]。本研究拟比较ZOOMit-DWI和SS-EPI DWI两种序列的子宫内膜癌图像质量和表观扩散系数(apparent diffusion coefficient,ADC),同时优化子宫内膜癌的MRI扫描方案,提高影像诊断准确率。
1. 对象与方法
1.1. 对象
收集2019年7月至2021年5月在中南大学湘雅二医院放射科MRI中心进行子宫内膜MRI检查的37例患者,年龄44~61(52.9±8.6)岁。纳入标准:1)患者均经手术病理证实为子宫内膜癌;2)患者均未进行放射治疗和化学治疗(以下简称放化疗);3)患者子宫内膜的MRI图像能够显示出可分辨的病灶。排除标准:1)骨盆骨质术后有金属伪影的患者;2)患者不配合导致运动伪影过大。3)肿瘤无法手术的放化疗患者。患者的所有临床资料均来自医院电子病历。本研究经中南大学湘雅二医院医学伦理委员会批准(审批号:2020105),患者均签署知情同意书。
1.2. 扫描设备和参数
采用Siemens MAGNETOM Skyra 3.0T超导MRI扫描仪,选用线圈为标准相控阵18通道体部线圈和32通道脊柱线圈,患者采取仰卧位、足先进,扫描范围包含整个盆腔。所有患者均进行了常规T2WI、T1WI、SS-EPI DWI、ZOOMit-DWI及增强扫描。扫描序列如下:
1)横断位TSE blade(刀锋)T2WI强抑脂序列,重复时间(repetition time,TR)为5 460.0 ms,回波时间(echo time,TE)为79.0 ms,层厚4.0 mm;层间距0.4 mm,视野(field of view,FOV)为320 mm×320 mm,翻转角度(flip angle,FA)为141°;加速因子为2,轨迹选用blade,矩阵为384×320,28层。
2)矢状位TSE blade T2WI强抑脂序列,TR为4 300.0 ms,TE为85.0 ms,层厚4.0 mm,层间距
0.4 mm,FOV为260 mm×260 mm,矩阵为320×256,FA为147°;信号平均次数为2,轨迹选用blade,带宽为300 Hz/Px,24层。
3)冠状位TSE blade T2WI抑脂序列,TR为4 740.0 ms,TE为79.0 ms,层厚4.0 mm,层间距
0.4 mm,FOV为280 mm×280 mm,矩阵为320×256,FA为143°,带宽为300 Hz/Px,28层。
4)横断位TSE T1WI(快速自旋回波T1加权序列),TR为550.0 ms,TE为12.0 ms,层厚4.0 mm,层间距0.4 mm,FOV为300 mm×300 mm,带宽为320×320,FA为160°,相位方向为左右,28层。
5)常规横断位抑脂DWI扫描,TR为5 200.0 ms,TE为64.0 ms,层厚4.0 mm,层间距0.4 mm,FOV为320 mm×288 mm,b值分别为50、1 000 s/mm2,带宽为1 776 Hz/Px,矩阵为224×192,扩散方向3个。扩散模型为3平面扫描跟踪(3-scan trace)。
6)ZOOMit横断位抑脂DWI,TR为5 200.0 ms,TE为64.0 ms,层厚4.0 mm,层间距0.4 mm,FOV为300 mm×180 mm,b值分别为50、1 000 s/mm2,带宽为1 776 Hz/Px,矩阵为224×192,梯度激发模式为Zoomit,图像增强采用迭代降噪技术,噪声水平为10,相位方向为前后,强抑脂模式,扩散方向为4个。扩散模型为4平面扫描跟踪(4-scan trace)。
7)动态增强T1容积式内插值法呼吸门控(volume interpolate breathhold examination,VIBE)序列,体素大小1.0 mm×1.0 mm×1.0 mm,TE为1.97 ms,TR为4.03 ms,FOV为300 mm×300 mm,带宽为450 Hz/Px,FA为9°,相位方向为前后。脂肪抑制方法为Q-fatsat,动态扫描4期。
8)冠状位压脂快速自旋回波T1增强,TE为
9.0 ms,TR为500.0 ms,信号平均次数为3,FA为180°,FOV为260 mm×260 mm,矩阵为320×320,加速因子为2,层厚4 mm,层间距0.4 mm,带宽为240 Hz/Px,体素大小为 0.8 mm×0.8 mm×4.5 mm,28层。
9)横断位压脂快速自旋回波T1增强,TE为
12.0 ms,TR为507.0 ms,信号平均次数为3,FA为160°,FOV为300 mm×300 mm,矩阵为320×320,加速因子为2,层厚 4 mm,层间距0.4 mm,带宽为170 Hz/Px,体素大小为0.7 mm×0.8 mm×4.5 mm,28层。
10)矢状位压脂快速自旋回波T1增强,TE为 9.0 ms,TR为582.0 ms,信号平均次数为3,FA为180°,FOV为260 mm×260 mm,矩阵为320×320,加速因子为2,层厚4 mm,层间距0.4 mm,带宽为240 Hz/Px,体素大小为 0.7 mm×0.8 mm×4.0 mm,24层,相位方向为头足。
1.3. 主观评价
进行图像的主观评价评分时,图像以SS-EPI DWI与ZOOMit-DWI两两配对方式呈现,由2位5年以上工作经验的放射科医师采用双盲法阅读后独立进行评价。参照Hellms等[6]的Likert评分法,分别从图像的几何变形程度、图像模糊度、伪影、病灶清晰度4方面进行评分,均采用4分制。具体评分标准为解剖细节显示(1=差,2=中等,3=良好,4=优秀);变形程度(1=严重,2=中度,3=轻微,4=缺失);伪影(1=严重,2=中度,3=轻微,4=缺失);病变清晰度 (1=差,被认为未识别;2=中等,大部分轮廓不清楚;3=好,小部分轮廓不清晰;4=优秀,轮廓清晰)。
1.4. 客观评价
所有图像的ADC、信号噪声比(singnal-to-noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)测量,以及子宫内膜癌术前MRI图像分期均在Siemens 3.0T Skyra MR工作站上进行,软件版本Syngo MR D13。进行SNR和CNR测量时,隐去患者信息后选取两组DWI图像中b为1 000 s/mm2的图像,结合T2WI的横断位手动勾画ROI。采用双盲法测量所需的值,测量内容包括子宫内膜病灶区和子宫肌层信号强度、图像背景的标准差(在盆腔外子宫内膜位置相同的相位编码方向上选取)。每次选取3个ROI测量后取平均值,ROI的选取避开坏死、出血及囊变区。
ADC测量:利用西门子后处理工作站,结合T2WI和DWI图像,确定ADC图像病变范围,在病灶最大截面的实质区域手动勾画ROI,选取ROI时避开出血、囊变和坏死区域及血流伪影,每个病灶重复测量3次平均表观扩散系数(mean apparent diffusion coefficient, mADC),计算其平均值。
SNR=SI病灶/SD背景 [7],公式中SI病灶表示病灶的信号强度,SD背景指背景噪声信号强度的标准差。SI病灶的ROI与测量ADC值的ROI一致,选取3个ROI测量SI病灶,取平均值为最终值。
CNR=(SI病灶-SI子宫肌层)/SD背景 [7],公式中SI病灶、SI子宫肌层分别表示病灶及子宫肌层的信号强度,SD背景指背景噪声信号强度的标准差。选取的子宫肌层ROI尽量与病灶ROI大小相同,且在同一层面上,同时避开伪影,选取3个ROI测量SI子宫肌层,取平均值为最终值。
1.5. 统计学处理
采用SPSS 23.0统计学软件分析数据,计量资料用均数±标准差( ±s)表示,SS-EPI DWI和ZOOMit-DWI客观和主观评价指标比较采用配对t检验。P<0.05为差异有统计学意义。
2. 结 果
2.1. 诊断治疗结果
37例子宫内膜癌患者均采用腹腔镜下次广泛子宫切除+双附件切除+盆腔淋巴结、腹主动脉旁淋巴结及骶前淋巴结清扫术+盆腔粘连松解术。子宫内膜癌病理结果:腺癌33例,腺磷癌2例,浆液性乳头状腺癌2例;高分化28例,高中分化5例,低分化3例,低中分化1例;侵犯浅肌层(小于1/2肌层)20例,侵犯深肌层17例。子宫内膜癌FIGO2009分期:IA期20例,IB期10例,II期5例,IIIA期2例。术后化疗采用多西他赛(艾素)加奈达铂(奥先达)方案,术后3周返院进行第1次化疗。
2.2. 子宫内膜癌病灶的影像特点及ADC
ZOOMit-DWI组的SNR显著高于SS-EPI DWI组(301.96±141.85 vs 94.66±41.26,P<0.05);ZOOMit-DWI组的CNR显著高于SS-EPI DWI组(185.05±105.45 vs 57.91±31.54,P<0.05);ZOOMit-DWI组的噪声标准差与SS-EPI DWI组比较,差异无统计学意义(P>0.05)。ZOOMit-DWI组主观评分中的解剖细节和病灶清晰度均显著高于SS-EPI DWI组(P<0.05),伪影和几何变形均显著低于SS-EPI DWI组(均P<0.05)。ZOOMit-DWI组的ADC与SS-EPI DWI组比较差异无统计学意义(P>0.05;表1,2)。
表1.
常规DWI和ZOOMit客观评价指标比较
Table 1 Comparison of objective evaluation indexes between single-shot echo-planar imaging (SS-EPI) DWI and ZOOMit-DWI
| DWI | SI病灶 | SD背影 | SI肌层 | SNR | CNR | ADC/(×10-3 mm2·s-1) |
|---|---|---|---|---|---|---|
| SS-EPI | 170.74±57.32 | 2.07±1.20 | 66.10±26.73 | 94.66±41.26 | 57.91±31.54 | 0.71±0.07 |
| ZOOMit | 432.20±97.42 | 1.80±0.92 | 184.66±39.87 | 301.96±141.85 | 185.05±105.45 | 0.68±0.09 |
| t | 14.070 | 1.060 | 15.021 | 8.535 | 7.032 | 1.203 |
| P | <0.001 | 0.293 | <0.001 | <0.001 | 0.010 | 0.233 |
SI:信号强度;SD:信号强度的标准差;SNR:信号噪声比;CNR:对比噪声比;ADC:表观扩散系数。
表2.
常规DWI和ZOOMit主观定量指标比较
Table 2 Comparison of subjective evaluation indexes between single-shot echo-planar imaging (SS-EPI) DWI and ZOOMit-DWI
| DWI | 解剖细节显示 | 几何变形程度 | 伪影 | 病变清晰度 |
|---|---|---|---|---|
| SS-EPI | 2.40±0.49 | 3.37±0.59 | 3.43±0.50 | 2.51±0.50 |
| ZOOMit | 3.27±0.45 | 2.43±0.50 | 2.67±0.67 | 3.59±0.49 |
| t | 7.838 | 7.397 | 6.662 | 9.258 |
| P | <0.001 | <0.001 | <0.001 | <0.001 |
子宫内膜癌影像学上表现为子宫体积增大,宫腔扩大,内膜不均匀增厚,结合带连续中断不完整,T1WI呈稍高信号,T2WI呈高信号,部分病变层面与子宫肌层界限不清,DWI显示均匀或不均匀高信号,ADC显示低信号(图1,2),增强显著不均匀强化。部分病例盆腔内见少量积液。腹股沟见多个小淋巴结。
图1.
患者,女,47岁,高分化子宫内膜样癌,侵犯浅肌层(IA期)
Figure 1 A 47-year-old female with highly differentiated endometrioid carcinoma invading the superficial myometrium (stage IA) A and B: SS-EPI DWI image and ZOOMit-DWI image. It shows that the endometrium is thickened and the right uterine wall is discontinuous near the bottom of the junction zone, protruding to the broad basal tubercle of the uterine cavity. SS-EPI DWI shows SNR=106.5, CNR=66.9, ZOOMit-DWI shows SNR=538.8, CNR=346.8. C and D: SS-EPI DWI ADC diagram and ZOOMit-DWI ADC diagram. The lesions show low signal intensity and obvious limited diffusion, with ADC of 0.691×10-3 mm2/s, 0.645×10-3 mm2/s, respectively. SS-EPI: Single-shot echo-planar imaging; DWI: Diffusion-weighted imaging; SNR: Signal-to-noise ratio; CNR: Contrast to noise ratio; ADC: Apparent diffusion coefficient.
图2.
患者,女,41岁,高分化子宫内膜样癌,侵犯浅肌层(IA期)
Figure 2 A 41-year-old female with highly differentiated endometrioid carcinoma invading the superficial myometrium (stage IA)
A and B: SS-EPI DWI image and ZOOMit-DWI image. It shows that the thickening of endometrium with nodular changes and DWI is high signal intensity. SS-EPI DWI shows SNR=54.2, CNR=38.3. ZOOMit-DWI shows SNR=588.5, CNR=320. C and D: SS-EPI DWI ADC diagram and ZOOMit-DWI ADC diagram. The lesions show low signal intensity and obvious limited diffusion, with ADC values of 0.626×10-3 mm2/s, 0.685×10-3 mm2/s, respectively. SS-EPI: Single-shot echo-planar imaging; DWI: Diffusion-weighted imaging; SNR: Signal-to-noise ratio; CNR: Contrast to noise ratio; ADC: Apparent diffusion coefficient.
3. 讨 论
MRI具有良好的软组织分辨力,临床上常将其用于各类盆腔疾病的检查。DWI作为MRI检查中的常用序列,已广泛应用于包括子宫内膜癌在内的各类盆腔疾病的MRI检查中。目前,临床上常规DWI为SS-EPI,其优点是成像速度快,对运动不敏感,缺点是解剖细节显示欠清,图像失真较明显,尤其是在高场强下一次射频激发后采用迂回方式进行傅里叶空间(也称K空间)填充时,因此在磁敏感差异明显的组织间容易产生磁敏感伪影[8-9],从而影响子宫内膜小病灶的检出和定性诊断。长可变回波链分段读出(readout segmentation of long variable echo-trains,RESOLVE)DWI采用分段读出平面回波成像序列,使用二维导航回波技术进行相位校正,在读取梯度方向上采用K空间分段平面回波采集,降低了相位编码方向上相位误差的积累,减少了重建图像的几何变形,但是由于分段采样,成像时间长,在盆腔可见膀胱、肠道运动,气体、液体干扰产生的伪影[10-11]。ZOOMit-DWI可以实现目标区域的选择性成像,对目标器官的选择性放大并进行多层采集,进一步减少数据的采集时间,提高图像的空间分辨率[12-13]。
在进行盆腔MRI DWI序列的扫描过程中,由于常规DWI视野比较大,导致扫描过程中图像容易受患者呼吸运动和肠道蠕动的影响而产生运动伪影,并且肠道内的气-液混合体、骨盆骨骼等与周围组织交界处容易引起主磁场不均匀,造成磁敏感伪影及图像变形[14]。DWI在盆腔扫描中有很大的诊断价值,对病变具有非常高的敏感性,尤其是在超高b值时,但是该序列对于磁场和磁敏感不均匀的区域易产生变形和磁敏感伪影,特别是对肠道内气体敏感,肠管扩张时易出现变形和伪影。在扫描范围允许的情况下,适当减低FOV相位,可减少图像变形。通过调整带宽选择最小回波间隙,使TE降到最低,同时编码方向设置为前后,可有效避免金属和肠气干扰。在目前大力推行精准医疗的背景下,常规DWI图像逐渐变得难以满足临床日常的诊断需求。ZOOMit选用了一种全新的射频进行激发,即二维选择性激发,可不被FOV外的信号干扰,但由于扫描范围小,频率编码相对短,减轻了因连续梯度磁场切换累计的相位位移造成的图像变形以及子宫内膜同子宫周围气液交界处磁场不均匀导致的磁敏感伪影,从而得到伪影少且分辨率高的DWI图像[15]。ZOOMit-DWI仅从ROI激发采集信号,可以得到针对ROI的小视野DWI图像,此技术可以减少卷褶伪影和图像失真,提高空间分辨率、提供更多的解剖细节。这种新型DWI扫描技术的运用,在一定程度上弥补了常规DWI的不足。
Seeger等[16]报道ZOOMit-DWI和RESOLVE DWI在葡萄膜黑色素瘤中的应用,认为ZOOMit-DWI能够明显提高图像质量。此外,Thierfelder等[17]研究表明ZOOMit-DWI的SNR、CNR较常规DWI图像均有明显提高,并具有统计学意义,也与本研究结果一致。这是因为ZOOMit-DWI可实现对靶器官的选择性放大成像,减少子宫周围器官如直肠内的气-液混合体的影响而引起的失真及伪影,增加图像的SNR,并可多层采集,弥补了常规DWI图像模糊致病灶易误诊、漏诊的缺点。对于子宫内膜的病变,常规DWI受磁敏感伪影的影响,可能对一些微小病变的诊断存在困难。Wichtmann等[18]报道ZOOMit-DWI可以清晰显示常规DWI上不能显示的小病灶的范围和形态,还可以在减少图像变形和减少伪影的情况下显著提高图像质量。与常规DWI的影像组学特征比较,ZOOMit-DWI的影像组学特征可以更加准确地诊断子宫内膜癌淋巴结转移[19]。主、客观评分也提示ZOOMit-DWI图像能更准确地显示子宫内膜病变的大小、形态及范围,为临床医生的诊断和治疗后的疗效评价提供准确的信息。在本研究中,ZOOMit-DWI图像在解剖细节显示、磁敏感伪影、几何变形及总体诊断上所得的主观评分均高于常规DWI图像。这表明ZOOMit-DWI较常规DWI图像质量有了明显提高,对病变显示也更加清晰,能帮助医生增强诊断信心。
本研究对ZOOMit-DWI与SS-EPI DWI的ADC进行比较,发现差异无统计学意义。这与Mannelli等[20]测量胰腺和Razek等[21]测量前列腺大、小视野ADC的结果一致。这可能是由于ZOOMit-DWI仅仅只是减小了视野,对ADC影响不大所致,与邓保娣[9]等在宫颈癌小视野DWI中的应用结果相符。本研究发现:与SS-EPI DWI的ADC图像比较,ZOOMit-DWI的ADC图像空间分辨率高、解剖细节良好、磁敏感伪影小,更接近被测量组织、器官、病灶的真实ADC,但两者差异无统计学意义。
本研究的局限性:1)样本偏少,可能存在选择偏倚,在下一步的研究中会继续扩大样本量;2)ZOOMit-DWI技术是小视野成像,不能评估远处的淋巴结转移及其他脏器转移情况,需要同其他序列共同评估;3)缺乏对子宫内膜癌不同病理类型的ADC的研究,下一步将继续针对子宫内膜癌的不同病理类型作ADC分析。
综上所述,ZOOMit-DWI的图像质量要显著高于SS-EPI DWI,ZOOMit-DWI与SS-EPI的ADC值差异无统计学意义。ZOOMit-DWI用于子宫内膜癌扫描时,不仅可以减少磁敏感伪影和减少图像变形,还可以提高图像空间分辨率;可使病变轮廓更清晰、对早期微小病变的诊断和子宫肌层浸润的判断更加精准,对子宫内膜癌尤其是早期病变的发现具有一定的临床价值,同时ZOOMit-DWI还可以提供更多的影像组学信息。总之,ZOOMit-DWI可作为子宫内膜病变多参数MRI的重要序列,为临床诊断和治疗提供准确的信息。
基金资助
湖南省自然科学基金(2021JJ30921)。
This work was supported by the National Science Foundation of Hunan Province, China (2021JJ30921).
利益冲突声明
作者声称无任何利益冲突。
作者贡献
汤世雄 磁共振检测,论文撰写;符淳、肖恩华 论文指导;陈红亮 提供临床病例;龙易成 统计分析,论文撰写;卞读军 论文审校。所有作者阅读并同意最终的文本。
原文网址
http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/20230176.pdf
参考文献
- 1. Huvila J, Pors J, Thompson EF, et al. Endometrial carcinoma: molecular subtypes, precursors and the role of pathology in early diagnosis[J]. J Pathol, 2021, 253(4): 355-365. 10.1002/path.5608. [DOI] [PubMed] [Google Scholar]
- 2. Sim KC, Park BJ, Han NY, et al. Efficacy of ZOOMit coronal diffusion-weighted imaging and MR texture analysis for differentiating between benign and malignant distal bile duct strictures[J]. Abdom Radiol (NY), 2020, 45(8): 2418-2429. 10.1007/s00261-020-02625-0. [DOI] [PubMed] [Google Scholar]
- 3. Yıldırım İO, Sağlık S, Çelik H. Conventional and ZOOMit DWI for evaluation of testis in patients with ipsilateral varicocele[J]. Am J Roentgenol, 2017, 208(5): 1045-1050. 10.2214/AJR.16.17292. [DOI] [PubMed] [Google Scholar]
- 4. Tullos H, Dale B, Bidwell G, etal. SU-E-I-67: multi-shot RESOLVE compared to single-shot EPI diffusion- weighted MR imaging acquisition scheme[J]. Med Phys, 2012, 39(6 Part 5): 3640. 10.1118/1.4734783. [DOI] [PubMed] [Google Scholar]
- 5. 东强, 徐青, 郭溪, 等. ZOOMit DWI与常规DWI技术在胃癌评估中的初步比较[J]. 临床放射学杂志, 2021, 40(3): 511-516. 10.13437/j.cnki.jcr.2021.03.022. [DOI] [Google Scholar]; DONG Qiang, XU Qing, GUO Xi, et al. Comparison of ZOOMit DWI and conventional DWI in the evaluation of gastric cancer: apreliminary study[J]. Journal of Clinical Radiology, 2021, 40(3): 511-516. 10.13437/j.cnki.jcr.2021.03.022. [DOI] [Google Scholar]
- 6. Hellms S, Gutberlet M, Peperhove MJ, et al. Applicability of readout-segmented echoplanar diffusion weighted imaging for prostate MRI[J/OL]. Medicine, 2019, 98(29): e16447 [2021-10-08]. 10.1097/MD.0000000000016447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Xie M, Ren Z, Bian D, et al. High resolution diffusion-weighted imaging with readout segmentation of long variable echo-trains for determining myometrial invasion in endometrial carcinoma[J]. Cancer Imaging, 2020, 20(1): 66. 10.1186/s40644-020-00346-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. 但汉丽, 谭钰川, 杨露, 等. MR不同弥散加权序列前列腺图像质量评价研究[J]. 磁共振成像, 2021, 12(3): 54-58. 10.12015/issn.1674-8034.2021.03.012. [DOI] [Google Scholar]; DAN Hanli, TAN Yuchuan, YANG Lu, et al. Image quality assessment on MR images of the prostate acquired in different diffusion weighted sequences[J]. Chinese Journal of Magnetic Resonance Imaging, 2021, 12(3): 54-58. 10.12015/issn.1674-8034.2021.03.012. [DOI] [Google Scholar]
- 9. 邓保娣, 李震, 胡道予, 等. 小视野扩散加权成像在宫颈癌中的临床价值[J]. 磁共振成像, 2020, 11(7): 487-492. 10.12015/issn.1674-8034.2020.07.002. [DOI] [Google Scholar]; DENG Baodi, LI Zhen, HU Daoyu, et al. Clinical value of reduced field-of-view diffusion-weighted imaging in cervical cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2020, 11(7): 487-492. 10.12015/issn.1674-8034.2020.07.002. [DOI] [Google Scholar]
- 10. Klingebiel M, Ullrich T, Quentin M, et al. Advanced diffusion weighted imaging of the prostate: comparison of readout-segmented multi-shot, parallel-transmit and single-shot echo-planar imaging[J]. Eur J Radiol, 2020, 130: 109161. 10.1016/j.ejrad.2020.109161. [DOI] [PubMed] [Google Scholar]
- 11. Donners R. Editorial comment on “diffusion-weighted MRI to assess sacroiliitis: improved image quality and diagnostic performance of readout-segmented echo-planar imaging (EPI) over conventional single-shot EPI”[J]. Am J Roentgenol, 2021, 217(2): 459. 10.2214/AJR.20.24707. [DOI] [PubMed] [Google Scholar]
- 12. Attenberger UI, Tavakoli A, Stocker D, et al. Reduced and standard field-of-view diffusion weighted imaging in patients with rectal cancer at 3 T-Comparison of image quality and apparent diffusion coefficient measurements[J]. Eur J Radiol, 2020, 131: 109257. 10.1016/j.ejrad.2020.109257. [DOI] [PubMed] [Google Scholar]
- 13. Reischauer C, Cancelli T, Malekzadeh S, et al. How to improve image quality of DWI of the prostate—Enema or catheter preparation? [J]. Eur Radiol, 2021, 31(9): 6708-6716. 10.1007/s00330-021-07842-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Luo Y, Mei DD, Gong JS, et al. Multiparametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in endometrial carcinoma[J]. J Magn Reson Imaging, 2020, 52(4): 1257-1262. 10.1002/jmri.27142. [DOI] [PubMed] [Google Scholar]
- 15. Song JC, Lu SS, Zhang J, et al. Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging[J]. Diagn Interv Radiol, 2020, 26(2): 74-81. 10.5152/dir.2019.18366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Seeger A, Batra M, Süsskind D, et al. Assessment of uveal melanomas using advanced diffusion-weighted imaging techniques: value of reduced field of view DWI (“zoomed DWI”) and readout-segmented DWI (RESOLVE)[J]. Acta Radiol, 2019, 60(8): 977-984. 10.1177/0284185118806666. [DOI] [PubMed] [Google Scholar]
- 17. Thierfelder KM, Scherr MK, Notohamiprodjo M, et al. Diffusion-weighted MRI of the prostate: advantages of Zoomed EPI with parallel-transmit-accelerated 2D-selective excitation imaging[J]. Eur Radiol, 2014, 24(12): 3233-3241. 10.1007/s00330-014-3347-y. [DOI] [PubMed] [Google Scholar]
- 18. Wichtmann BD, Zöllner FG, Attenberger UI, et al. Multiparametric MRI in the diagnosis of prostate cancer: physical foundations, limitations, and prospective advances of diffusion-weighted MRI[J]. Rofo, 2021, 193(4): 399-409. 10.1055/a-1276-1773. [DOI] [PubMed] [Google Scholar]
- 19. Kido A, Nishio M. Editorial for “A multiparametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in endometrial carcinoma”[J]. J Magn Reson Imaging, 2020, 52(4): 1263-1264. 10.1002/jmri.27162. [DOI] [PubMed] [Google Scholar]
- 20. Mannelli L, Monti S, Corrias G, et al. Comparison of Navigatortriggering reduced field of view and large field of view diffusion-weighted imaging of the pancreas[J]. J Comput Assist Tomogr, 2019, 43(1): 143-148. 10.1097/RCT.0000000000000778. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Razek AAKA, El-Diasty T, Elhendy A, et al. Prostate imaging reporting and data system (PI-RADS): what the radiologists need to know?[J]. Clin Imaging, 2021, 79: 183-200. 10.1016/j.clinimag.2021.05.026. [DOI] [PubMed] [Google Scholar]


