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多模态影像融合神经导航在脑动静脉畸形手术的应用

2019-05-15 吴德权 戴敏超 程超 中国微侵袭神经外科杂志

男6例,女4例;年龄14~62岁。病灶部位:枕叶5例,颞枕3例,额叶2例。首发症状为癫疒间4例,脑出血2例,头痛头晕症状检查发现4例。

1.对象与方法
 
1.1基本资料
 
男6例,女4例;年龄14~62岁。病灶部位:枕叶5例,颞枕3例,额叶2例。首发症状为癫疒间4例,脑出血2例,头痛头晕症状检查发现4例。
 
1.2影像学检查与融合
 
10例病人常规行MRI、MRA扫描及增强扫描,常规MRI较好显示全部病灶。MRA血管成像扫描图像经过三维重建处理后,清晰显示包含畸形血管团、供血动脉、引流静脉在内的全部病灶,其中单支供血动脉1例,2支供血动脉1例,3支供血动脉6例,4支供血动脉2例。神经导航使用StealthStationS7导航系统(美国美敦力公司)。将MRI、MRA影像传到导航工作站,通过软件自动融合,获得畸形血管团立体构造与精确位置,供血动脉来源、数量和位置,引流静脉走向,据此设计手术方案。
 
1.3手术方法
 
全麻后头架固定,开启导航工作站,安装球架,调整红外线机臂,导航棒完成注册。观察融合后影像,分析病灶解剖位置,以及供血动脉和引流静脉,选择最佳手术路径,精确定出皮肤切口和骨瓣位置。术中MRI和MRA模态融合后,MRI可显示脑组织解剖结构,融合后MRA三维血管影像可清晰显示病灶在脑组织的精确解剖部位。使用导航棒实时导航,首先避开邻近重要解剖结构,确定皮质切开位置,然后寻找病灶,尤其是寻找和辨别畸形血管团的供血动脉和引流静脉。在导航棒指引下逐步深入到达病灶,导航指引和显微镜下观察,辨认畸形血管病灶、供血动脉和引流静脉,准确处理相关血管后沿周边将病灶完整切除。
 


图1 多模态融合导航AVM切除。1A脑血管融合后显示病灶在脑组织中的精确解剖部位并重建三维血管图像;1B术中通过融合后血管三维图像导航棒在术野中确认供血动脉
 
2.结果
 
本组10例病人全部成功实施神经导航手术。10例畸形病灶共发现29支供血动脉,其中21支皮质表面血管通过导航成功辨别,在手术开始阶段给予夹闭。8支位于深部的供血动脉主要来自后循环,在手术后期经过导航指引辨认后给予夹闭。10例病人术后复查DSA,显示病灶未有残留。神经系统检查:1例出现视野缺损。全部病例无肢体瘫痪、失语等情况发生。10例术后病理证实均为脑AVM。
 
3.讨论
 
影响AVM手术效果的因素为病变大小、部位深浅、供血动脉来源与数量、及引流到静脉系统的方式等,因此,制定手术计划必须注意以下情况:①手术切口如何设计。②骨窗大小能否充分暴露病灶和满足处理相应供血动脉和引流静脉的需求。③在皮质何处开始切开寻找病灶。④供血动脉的数量以及与病灶的关系。⑤引流静脉何时处理,病灶能不能一次完全切除还是分次切除。要回答好这些问题,归根到底,术者在术前必须对AVM病灶的解剖构建有一个立体认识。
 
目前,将CT和MRI数据源用于神经导航系统已被临床接受,尤其颅内肿瘤切除已经非常成熟,能够精确定位病灶。然而单纯MRI或MRA影像无法对血管病灶进行定位,因此,直视手术往往比较盲目。国内外同行通过DSA、CTA影像进行术中导航,成功切除AVM病灶。本研究团队通过多模态融合技术,将MRI与MRA图像通过神经导航系统叠加融合,采用融合后的图像用于导航计划。术前通过多方位、多角度观察融合后的影像资料,本研究团队对本组AVM病灶的大小、供血动脉来源和数量、引流静脉走向等,做到心中有数。参考多模态影像,设计皮瓣切口和骨窗大小,尽可能满足处理供血动脉和引流静脉的需要。骨瓣成形后,在多模态融合影像导航指引下,切开皮质和解剖脑沟,清除血肿的同时,通过导航定位指引,结合手术显微镜下仔细分离辨认,首先夹闭浅表供血动脉。
 
当镜下遇到较粗的血管、辨认困难时再结合导航影像进行定位辨认,确认无误后夹闭。按照此方法,本研究团队在手术开始阶段成功辨认并处理了21支供血动脉;另有8支供血动脉位于病灶深部,基本来自后循环,待部分血供处理、病灶有所缩小后,在导航定位下辨认夹闭。
 
我们认为多模态融合后的神经导航具有以下优点:①导航可立体精准定位病灶和血管位置,从而更准确设计皮肤切口与骨瓣大小。②术中实时多模态图像动态观察病灶和相关血管位置,可最大限度避免误夹血管,缩短手术时间,减少周围脑组织损伤。③可动态掌握病灶切除程度,避免病灶残留。虽然神经导航还有一些不足之处,如术中发生“脑漂移”会影响导航准确性。但多模态融合神经导航技术可显示畸形病灶及其周围血管的三维立体结构,实现术中实时导航定位,在指导手术入路,避免手术盲目性方面具有重要意义。
 
原始出处:

吴德权,戴敏超,程超,姜成.多模态影像融合神经导航在脑动静脉畸形手术的应用[J].中国微侵袭神经外科杂志,2018(05):226-227.

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    2020-04-29 fusion
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    2019-05-16 licz0427

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