丘腦底核磁共振成像技術(shù)研究進展

孔維詩， 路明寬， 陽青松， 陳智昌， 仇一青， 吳 曦*

1.海軍軍醫(yī)大學(xué)學(xué)員旅，上海 200433 2.海軍軍醫(yī)大學(xué)附屬長海醫(yī)院影像科， 上海 200433 3.海軍軍醫(yī)大學(xué)海醫(yī)系，上海 200433 4.海軍軍醫(yī)大學(xué)附屬長海醫(yī)院神經(jīng)外科，上海 200433

丘腦底核磁共振成像技術(shù)研究進展

孔維詩1△， 路明寬1△， 陽青松2， 陳智昌3， 仇一青4， 吳 曦4*

1.海軍軍醫(yī)大學(xué)學(xué)員旅，上海 200433 2.海軍軍醫(yī)大學(xué)附屬長海醫(yī)院影像科， 上海 200433 3.海軍軍醫(yī)大學(xué)海醫(yī)系，上海 200433 4.海軍軍醫(yī)大學(xué)附屬長海醫(yī)院神經(jīng)外科，上海 200433

丘腦底核(subthalamic nucleus, STN)是我國帕金森病患者接受腦深部電刺激(deep brain stimulation, DBS)治療的主要核團。磁共振(MRI)影像個體化、精確植入電極至STN的感覺運動部要求MRI成像對STN邊界清晰劃分，并確保圖像保真。目前使用的MRI序列可分為3大類：基于自旋回波序列的T2加權(quán)成像、反轉(zhuǎn)恢復(fù)序列、擴散張量成像、各向異性分數(shù)；基于磁化轉(zhuǎn)移技術(shù)的磁敏感加權(quán)成像、自由衰減的T2*成像等；基于磁敏感圖像重建技術(shù)的定量磁敏感圖譜。其中，定量磁敏感圖譜的STN與周邊的信噪比最優(yōu)、邊界最清晰，T2*技術(shù)次之；T2加權(quán)成像在患者戴框架時幾何精度較高，適合戴框架直接定位使用。

丘腦底核; 磁共振成像; 腦深部電刺激; 帕金森病

丘腦底核(subthalamic nucleus, STN)是我國帕金森病患者接受腦深部電刺激(deep brain stimulation, DBS)采用最多的核團[1]。以往對STN的定位是利用腦解剖結(jié)構(gòu)(前聯(lián)合與后聯(lián)合)的經(jīng)驗性定位。隨著MRI的發(fā)展，其逐漸被應(yīng)用于STN定位，包括戴框架的直接定位和與CT影像融合定位。為了能夠個體化、精確地將電極植入至STN的感覺運動部(背外側(cè)區(qū)域)，要求在MRI圖像上清晰劃分核團邊界并保證圖像的最小幾何失真，但目前效果欠理想[2]，原因如下：(1)STN體積較小，呈傾斜的凸透鏡形；(2)低場強的MRI下STN邊界不夠清晰，STN和黑質(zhì)(substantia nigra, SN)、未定帶(zona incerta, ZI)之間邊界不清晰[3]；(3)傳統(tǒng)的立體定向頭部框架和指示器較大，佩戴后無法使用高質(zhì)量的多通道MRI頭部線圈，影響了MRI下直接定位的圖像質(zhì)量。因此，國內(nèi)大部分臨床中心對患者采用術(shù)前MRI與戴頭架時CT的融合定位，有時輔以術(shù)中電生理監(jiān)測以糾正電極植入誤差[1]。但是，如果術(shù)前MRI圖像存在失真和圖像辨識不清，也會影響融合后的靶點計算結(jié)果。因此，需要對不同MRI序列對STN的顯影效果進行研究。

目前采用的MRI序列可以分為3大類：基于自旋回波序列的T2加權(quán)成像(T2-weighted imaging, T2WI)反轉(zhuǎn)恢復(fù)序列(inversion recovery, IR)、擴散張量成像(diffusion tensor imaging, DTI)；基于磁化轉(zhuǎn)移技術(shù)的磁敏感加權(quán)成像(susceptibility weighted imaging, SWI)、自由衰減的T2*成像(T2-weighted magnitude imaging,T2*WI)、 磁敏感加權(quán)相位成像(susceptibility-weighted phase imaging, SWPI)；基于圖像重建技術(shù)的定量磁敏感圖譜(quantitative susceptibility mapping, QSM)。本文對此進行總結(jié)，以期為神經(jīng)外科醫(yī)師進一步了解目前臨床應(yīng)用技術(shù)條件下掃描參數(shù)的優(yōu)缺點并做出合理選擇提供參考。

1 基于自旋回波技術(shù)的序列

1.1 T2WI序列 T2WI是STN MRI研究中涉及最多的序列。在此序列下，STN較周圍組織顯示出更低的信號[4]。但在區(qū)分STN與其周圍組織交界時，容易出現(xiàn)圖像失真且分辨不清的情況，尤其是在低場強(1.5 T)下更加明顯。根據(jù)T2WI對STN直接定位雖然比經(jīng)驗性解剖定位更準(zhǔn)確[2]，但是圖像失真和分辨率較低可能使定位出現(xiàn)偏差[5-6]。當(dāng)患者佩戴立體定位框架行T2WI掃描時，失真程度可能更大[7]。術(shù)中實時MRI雖然也使用的是低場強的T2WI，但是電極植入時，可以通過多次掃描獲得電極植入過程的動態(tài)位置，從而調(diào)整電極圖像與STN的相對關(guān)系，減少圖像失真引起的誤差以及腦脊液減少造成的腦移位[8-9]。

更大的MRI場強允許增加T1重復(fù)時間(repetition time, TR)并減小T2WI的TR，這可以改善圖像分辨率和(或)縮短成像時間[10]，從而提高信噪比(signal-to-noise ratio, SNR)[11]。因此，3.0 T下T2WI可更好地顯示STN在矢狀位、冠狀位、水平位的邊界[12]。雖然7.0 T和9.4 T較3.0 T更能提高SNR[13-14]，但是更大的場強下，影像的幾何失真可能更嚴(yán)重，甚至抵消SNR改善帶來的獲益。雖然有研究[15]顯示，9.4 T下STN影像邊界與病理的組織學(xué)邊界吻合，但是該研究樣本量太少，結(jié)論仍需要進一步評估。

計算機可自動輔助界定STN邊界并重建核團圖像[16]，但仍需以影像學(xué)數(shù)據(jù)為基礎(chǔ)。研究[17]用快速自旋回波 (fast spin echo, FSE)T2WI重建STN ，術(shù)中電生理檢查顯示電極位置良好，但是該研究沒有測量電生理邊界與影像學(xué)邊界的相關(guān)性。因此，基于FSE-T2WI的STN計算機重建圖像的準(zhǔn)確性仍需要評估。

1.2 DTI序列 DTI常用于白質(zhì)纖維束的檢查。在DBS術(shù)中，其FA參數(shù)可以幫助醫(yī)師識別STN周圍的傳導(dǎo)束[18]。通過計算電極觸點與白質(zhì)纖維的距離[19]，調(diào)整電極植入軌跡，從而盡可能避免激活鄰近白質(zhì)[20]。而目前在常規(guī)手術(shù)中，對于局麻清醒或術(shù)中喚醒的患者仔細進行宏電極刺激測試，即可以避免電刺激不良反應(yīng)的發(fā)生。因此，使用DTI的獲益有待進一步評估。

1.3 IR序列 IR序列應(yīng)用范圍較廣，其通過選擇性地抑制組織中的特定成分增強SNR，常用于STN定位。IR常用序列包括液體衰減反轉(zhuǎn)恢復(fù)序列(fluid attenuated inversion recovery, FLAIR)、短反轉(zhuǎn)恢復(fù)時間的反轉(zhuǎn)恢復(fù)序列(short tau inversion recovery, STIR)、相位敏感反轉(zhuǎn)恢復(fù)序列(phase-sensitive inversion recovery, PSIR)和快速灰質(zhì)采集T1反轉(zhuǎn)恢復(fù)序列(fast gray matter acquisition T1inversion recovery, FGATIR)等。

FLAIR顯示STN的優(yōu)點是使其邊界更清晰、掃描時間快，但是STN與SN邊界仍難以分清。有研究[21]顯示，3D FLAIR較2D 快速場回波(fast-field echo， FFE)-T2*WI、快速自旋回波序列(turbo spin-echo, TSE)-T2WI有更好的對比信噪比(contrast-to noise ratio, CNR)，可以更好地辨識STN的邊界。

PSIR顯示STN時有良好的SNR和CNR[22]，經(jīng)過Leksell G立體定位框架驗證，僅有小于1%的幾何失真[23]。4 000 ms的TR和200 ms的反轉(zhuǎn)時間(TI)可提高STN顯像效果[23]。但與T2WI、T2*WI、SWI比較，PSIR顯示STN的CNR明顯較差[24]。盡管如此，由于SWI和T2*WI缺乏幾何精確度相關(guān)的數(shù)據(jù)支持，習(xí)慣用MRI直接定位而不是CT/MRI融合技術(shù)的醫(yī)師更傾向于用PSIR[25]。

T1STIR序列與FSE-T2WI結(jié)合有助于識別STN的所有邊界[26]。STIR在STN與SN邊界有更高的CNR，而FSE-T2WI對STN與內(nèi)囊、ZI的外側(cè)界和上界的區(qū)分更加清晰。然而，上述研究是基于健康人群開展的，且STN隨著患者年齡增加在STIR上可能逐漸難以辨識[27]，因此上述結(jié)論還需要進一步的證據(jù)支持。

與T1WI和T2WI FLAIR序列相比，F(xiàn)GATIR顯示更優(yōu)的CNR[28]，但是仍需要進一步研究驗證其幾何精確性。

2 基于磁敏感相關(guān)序列

SWI以T2*三維、速度補償梯度回波序列作為基礎(chǔ)[29]，根據(jù)不同組織間的磁敏感性差異使圖像對比增強，可同時獲得磁距圖像和相位圖像(SWPI)。T2*WI不是一個序列而是一類序列，大多為梯度回波序列(gradient recalled echo, GRE)，包括快速小角度激發(fā)(fast low-angle shot, FLASH)重聚焦梯度回波序列GRE(post-excitation refocused GRE)等。隨著患者年齡增長，尤其是發(fā)生神經(jīng)退行性疾病時，STN的鐵含量增加[4,30]。T2*WI利用組織之間的磁化率差異，可以實現(xiàn)STN與周圍結(jié)構(gòu)之間的良好對比，因此較傳統(tǒng)T2WI對暗邊界的顯示更明顯。

T2*FLASH因為掃描時間短、圖像SNR高，被廣泛使用和研究。3.0 T和7.0 T下，T2*FLASH較SWI能更好地顯示冠狀位時STN與ZI、SN的分界[31-32]。T1-T2*WI融合也可更清晰地顯示STN邊界[33]。但是上述研究沒有測試幾何精度，因此對STN定位情況還需要進一步研究。

SWPI不依賴于T1和T2弛豫參數(shù)，并且較少受到場強不均勻的影響，這是其相對于各種T2*WI的主要優(yōu)勢[34]。但是，SWPI圖像在大多數(shù)情況下無法與CT圖像良好融合，所以其應(yīng)用受限。

SWI是相位和幅值圖像的組合，其對出血、鈣化、鐵沉積和含有脫氧血紅蛋白的慢速靜脈血敏感[35]，具有從SN和ZI中顯示STN邊界的能力[36]，其描繪的邊界與電生理記錄描記的邊界高度吻合[37]。SWI即使在低場強下仍較T2WI有更好的SNR[24,38]。此外，SWI可顯示深部腦靜脈及跨實質(zhì)血管，有助于術(shù)前制定穿刺軌跡以避開這些血管[39-40]。越來越多的中心選擇SWI作為CT/MRI融合定位的序列。

磁敏感相關(guān)序列也有一些缺點，包括信號損失、失真和局部場強不均勻[35]。特別是在高場強下，非局部磁敏性效應(yīng)(造成暈狀偽影)可能導(dǎo)致STN的邊緣模糊[41]。由于STN傾斜于3個平面中且結(jié)構(gòu)較小，這些暈狀偽影需要在精確定位STN之前進行量化和校正。

3 基于圖像后處理技術(shù)

QSM是可以在SWI序列的GRE相位圖像上使用的圖像后處理技術(shù)，可解決暈狀偽影問題[41]。QSM成像的灰度與腦組織中鐵濃度的線性關(guān)系較SWI有更多的階梯梯度[41]，對組織鐵濃度的估算更準(zhǔn)確，因此可以更好地辨識STN與周圍富含鐵的結(jié)構(gòu)邊界[35,41-44]。QSM在STN定位中的應(yīng)用可能有很好的前景。

4 總結(jié)及展望

清晰度高、對比度高、無幾何失真的STN及其周邊結(jié)構(gòu)的MRI圖像對于功能神經(jīng)外科醫(yī)師對STN解剖定位來說至關(guān)重要。隨著技術(shù)進步以及多種基于磁敏感成像的新序列出現(xiàn)，MRI對STN定位的誤差越來越小。然而，目前使用的序列仍然存在兩個主要問題。(1)幾何失真：使用MRI和CT融合技術(shù)可以減少MRI技術(shù)中的幾何誤差，但是融合過程可能增加了幾何誤差[45]。雖然有失真校正算法，但直接生成無失真的圖像顯然更理想。(2)圖像質(zhì)量差：雖然可通過在全麻下延長掃描采集時間改善CNR，但順磁性頭架的頭部線圈的限制仍未改善。

對上述序列下STN的成像效果的比較研究顯示：目前在基于自旋回波技術(shù)的序列中，F(xiàn)SE-T2WI的成像效果最佳[24]；而基于磁敏感的SWI、T2*WI明顯優(yōu)于FSE-T2WI[31]?；诖琶舾械男蛄兄校琒WI比T2*FLAIR有更好的CNR[31]；SWPI對STN的SNR是SWI的2倍、T2*WI的3.5倍[36]，僅次于QSM[38]。QSM的CNR優(yōu)于T2、FSE-T2WI、T2*WI、SWPI和SWI[38]，可清晰顯示STN在ZI上方和SN下方的部分，且能減少由GRE序列產(chǎn)生的暈狀偽影。因此，基于磁敏感的序列和影像重建序列在STN顯像中可能替代T2WI。

[ 1 ] 張建國. 功能神經(jīng)外科發(fā)展十年[J]. 中國現(xiàn)代神經(jīng)疾病雜志,2010,10(1):117-122.

[ 2 ] ASHKAN K, BLOMSTEDT P, ZRINZO L, et al. Variability of the subthalamic nucleus: the case for direct MRI guided targeting[J]. Br J Neurosurg, 2007, 21(2):197-200.

[ 3 ] PATIL P G, CONRAD E C, ALDRIDGE J W, et al. The anatomical and electrophysiological subthalamic nucleus visualized by 3-T magnetic resonance imaging[J]. Neurosurgery, 2017, 71(6):1089-1095.

[ 4 ] DORMONT D, RICCIARDI K G, TANDé D, et al. Is the subthalamic nucleus hypointense on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data[J]. AJNR Am J Neuroradiol, 2004, 25(9):1516-1523.

[ 5 ] ZONENSHAYN M, REZAI A R, MOGILNER A Y, et al. Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting[J]. Neurosurgery, 2000, 47(2):282-292.

[ 6 ] ANDRADE-SOUZA Y M, SCHWALB J M, HAMANI C, et al. Comparison of three methods of targeting the subthalamic nucleus for chronic stimulation in Parkinson’s disease[J]. Neurosurgery, 2005, 56 (2 Suppl):360-368.

[ 7 ] SIMON S L, DOUGLAS P, BALTUCH G H, et al. Error analysis of MRI and Leksell stereotactic frame target localization in deep brain stimulation surgery[J]. Stereotact Funct Neurosurg, 2005, 83(1):1-5.

[ 8 ] CHABARDES S, ISNARD S, CASTRIOTO A, et al. Surgical implantation of STN-DBS leads using intraoperative MRI guidance: technique, accuracy, and clinical benefit at 1-year follow-up[J]. Acta Neurochir (Wien), 2015, 157(4):729-737.

[ 9 ] CUI Z, PAN L, SONG H, et al. Intraoperative MRI for optimizing electrode placement for deep brain stimulation of the subthalamic nucleus in Parkinson disease[J].J Neurosurg, 2016, 124(1):62-69.

[10] LU H, NAGAE-POETSCHER L M, GOLAY X, et al. Routine clinical brain MRI sequences for use at 3.0 Tesla[J]. J Magn Reson Imaging, 2005, 22(1):13-22.

[11] CHENG C H, HUANG H M, LIN H L, et al. 1.5T versus 3T MRI for targeting subthalamic nucleus for deep brain stimulation[J].Br J Neurosurg, 2014, 28(4):467-470.

[12] MALLET L, SCHüPBACH M, N’DIAYE K, et al. Stimulation of subterritories of the subthalamic nucleus reveals its role in the integration of the emotional and motor aspects of behavior[J]. Proc Natl Acad Sci U S A, 2007, 104(25):10661-10666.

[13] MASSEY L A, MIRANDA M A, ZRINZO L, et al. High resolution MR anatomy of the subthalamic nucleus: imaging at 9.4 T with histological validation[J].Neuroimage, 2012,59(3):2035-2044.

[14] ABOSCH A, YACOUB E, UGURBIL K, et al. An assessment of current brain targets for deep brain stimulation surgery with susceptibility-weighted imaging at 7 tesla[J]. Neurosurgery, 2010, 67(6):1745-1756.

[15] AL-HELLI O, THOMAS D L, MASSEY L, et al. Brain stimulation of the subthalamic nucleus: histological verification and 9.4-T MRI correlation[J]. Acta Neurochir (Wien), 2015, 157(12):2143-2147.

[16] XIAO Y, JANNIN P, D’ALBIS T, et al. Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson’s disease patients using automatic segmentation and PCA-based analysis[J]. Hum Brain Mapp, 2014, 35(9):4330-4344.

[17] LI B, JIANG C, LI L, et al. Automated segmentation and reconstruction of the subthalamic nucleus in Parkinson’s disease patients[J]. Neuromodulation, 2016, 19(1):13-19.

[18] BARKHOUDARIAN G, KLOCHKOV T, SEDRAK M, et al. A role of diffusion tensor imaging in movement disorder surgery[J]. Acta Neurochir (Wien), 2010, 152(12):2089-2095.

[19] SAID N, ELIAS W J, RAGHAVAN P, et al. Correlation of diffusion tensor tractography and intraoperative macrostimulation during deep brain stimulation for Parkinson disease[J]. J Neurosurg, 2014, 121(4):929-935.

[20] SHIELDS D C, GORGULHO A, BEHNKE E, et al. Contralateral conjugate eye deviation during deep brain stimulation of the subthalamic nucleus[J]. J Neurosurg, 2007, 107(1):37-42.

[21] HEO Y J, KIM S J, KIM H S, et al. Three-dimensional fluid-attenuated inversion recovery sequence for visualisation of subthalamic nucleus for deep brain stimulation in Parkinson’s disease[J]. Neuroradiology, 2015, 57(9):929-935.

[22] STARR P A, VITEK J L, DELONG M, et al. Magnetic resonance imaging-based stereotactic localization of the globus pallidus and subthalamic nucleus[J]. Neurosurgery, 1999, 44(2):303-313,discussion 313-314.

[23] ISHIMORI T, NAKANO S, MORI Y, et al. Preoperative identification of subthalamic nucleus for deep brain stimulation using three-dimensional phase sensitive inversion recovery technique[J]. Magn Reson Med Sci, 2007, 6(4):225-229.

[24] O’GORMAN R L, SHMUELI K, ASHKAN K, et al. Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus[J]. Eur Radiol, 2011, 21(1):130-136.

[25] THANI N B, BALA A, LIND C R. Accuracy of magnetic resonance imaging-directed frame-based stereotaxis[J]. Neurosurgery, 2012, 70 (1 Suppl Operative):114-123.

[26] KITAJIMA M, KOROGI Y, KAKEDA S, et al. Human subthalamic nucleus: evaluation with high-resolution MR imaging at 3.0 T[J]. Neuroradiology, 2008, 50(8):675-681.

[27] SARKAR S N, SARKAR P R, PAPAVASSILIOU E. Subthalamic nuclear tissue contrast in inversion recovery MRI decreases with age in medically refractory Parkinson’s disease[J]. J Neuroimaging, 2015, 25(2):303-306.

[28] SUDHYADHOM A, HAQ I U, FOOTE K D, et al. A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: the Fast Gray Matter Acquisition T1Inversion Recovery (FGATIR)[J]. Neuroimage, 2009, 47 Suppl 2:T44-T52.

[29] HAACKE E M, XU Y, CHENG Y C, et al. Susceptibility weighted imaging (SWI) [J]. Magn Reson Med, 2004, 52(3):612-618.

[30] BRASS S D, CHEN N K, MULKERN R V, et al. Magnetic resonance imaging of iron deposition in neurological disorders[J]. Top Magn Reson Imaging, 2006, 17(1):31-40.

[31] KERL H U, GERIGK L, PECHLIVANIS I, et al. The subthalamic nucleus at 3.0 Tesla: choice of optimal sequence and orientation for deep brain stimulation using a standard installation protocol: clinical article[J]. J Neurosurg, 2012, 117(6):1155-1165.

[32] KERL H U, GERIGK L, PECHLIVANIS I, et al. The subthalamic nucleus at 7.0 Tesla: evaluation of sequence and orientation for deep-brain stimulation[J]. Acta Neurochir (Wien), 2012, 154(11):2051-2062.

[33] XIAO Y, FONOV V, BéRIAULT S, et al. Multi-contrast unbiased MRI atlas of a Parkinson’s disease population[J]. Int J Comput Assist Radiol Surg, 2015, 10(3):329-341.

[34] SHMUELI K, DE ZWART J A, van GELDEREN P, et al. Magnetic susceptibility mapping of brain tissueinvivousing MRI phase data[J]. Magn Reson Med, 2009, 62(6):1510-1522.

[35] GASPAROTTI R, PINELLI L, LISERRE R. New MR sequences in daily practice: susceptibility weighted imaging. A pictorial essay[J]. Insights Imaging, 2011, 2(3):335-347.

[36] VERTINSKY A T, COENEN V A, LANG D J, et al. Localization of the subthalamic nucleus: optimization with susceptibility-weighted phase MR imaging[J]. AJNR Am J Neuroradiol, 2009, 30(9):1717-1724.

[37] MCEVOY J, UGHRATDAR I, SCHWARZ S, et al. Electrophysiological validation of STN-SNR boundary depicted by susceptibility-weighted MRI[J].Acta Neurochir (Wien), 2015, 157(12):2129-2134.

[38] LIU T, ESKREIS-WINKLER S, SCHWEITZER A D, et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping[J].Radiology, 2013, 269(1):216-223.

[39] RAUSCHER A, SEDLACIK J, BARTH M, et al. Nonnvasive assessment of vascular architecture and function during modulated blood oxygenation using susceptibility weighted magnetic resonance imaging[J]. Magn Reson Med, 2005, 54(1):87-95.

[40] BEN-HAIM S, ASAAD W F, GALE J T, et al. Risk factors for hemorrhage during microelectrode-guided deep brain stimulation and the introduction of an improved microelectrode design[J]. Neurosurgery, 2009, 64(4):754-763.

[41] LI J, CHANG S, LIU T, WANG Q, et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping[J]. Magn Reson Med, 2012, 68(5):1563-1569.

[42] LANGKAMMER C, SCHWESER F, KREBS N, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study[J]. Neuroimage, 2012, 62(3):1593-1599.

[43] DE ROCHEFORT L, LIU T, KRESSLER B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging[J]. Magn Reson Med, 2010, 63(1):194-206.

[45] THANI N B, BALA A, SWANN G B, et al. Accuracy of postoperative computed tomography and magnetic resonance image fusion for assessing deep brain stimulation electrodes[J]. Neurosurgery, 2011, 69(1):207-214.

Recent progresses of magnetic resonance imaging of subthalamic nucleus

KONG Wei-shi1△, LU Ming-kuan1△, YANG Qing-song2, CHEN Zhi-chang3, QIU Yi-qing4, WU Xi4*

1. Cadet Brigade, Navy Military Medical University, Shanghai 200433, China 2. Department of Medical Imaging, Changhai Hospital, Navy Military Medical University, Shanghai 200433, China 3. Department of Navy Medicine, Navy Military Medical University, Shanghai 200433, China 4. Department of Neurosurgery, Changhai Hospital, Navy Military Medical University, Shanghai 200433, China

Subthalamic nucleus (STN) is the main target nucleus for deep brain stimulation (DBS) treatment in patients with Parkinson disease. To implant the electrode on the sensorimotor part of STN individually and accurately, the boundary of STN is required to be clarified clearly on the magnetic resonance imaging (MRI) without geometric distortion. At present, there are three categories of MRI sequences: spin echo sequence including T2-weighted imaging (T2WI), inversion recovery (IR), diffusion tensor imaging (DTI), and fractional anisotropy (FA); magnetization transfer technique including magnetic susceptibility weighted imaging (SWI) and T2-weighted magnitude imaging (T2*WI); image reconstruction technique such as quantitative susceptibility mapping (QSM). It is found that QSM can provide optimal signal-noise ratio to identify the boundary of STN, T2*technique comes second. T2WI has high geometric accuracy when the patients wear frame, which is appropriate for direct DBS implantation on STN with frame.

subthalamic nucleus; magnetic resonance imaging; deep brain stimulation; Parkinson disease

2017-06-12接受日期2017-09-18

國家重點研發(fā)計劃“數(shù)字診療裝備研發(fā)”試點專項課題(2016YFC0105900). Supported by “Digital Equipment of Diagnosis and Treatment” Special Program of National Key Research and Development Plan (2016YFC0105900).

孔維詩, 海軍軍醫(yī)大學(xué)2014級本科學(xué)員. E-mail：2941106756@qq.com 路明寬, 海軍軍醫(yī)大學(xué)2013級本科學(xué)員. E-mail：630071751@qq.com

△共同第一作者(Co-first authors).

*通信作者(Corresponding author). Tel: 021-31161789, E-mail：wuxi_smmu@sina.com

10.12025/j.issn.1008-6358.2017.20170502

R 742

A

[本文編輯] 姬靜芳