Literature DB >> 22437568

Presurgical planning for arteriovenous malformations using multidetector row CT.

Takeshi Mikami1, Tohru Hirano, Toshiya Sugino, Kei Miyata, Satoshi Iihoshi, Masahiko Wanibuchi, Nobuhiro Mikuni.   

Abstract

Microsurgical resection remains an important treatment for cerebral arteriovenous malformations (AVMs). We developed an accurate method for planning AVM resections using multidetector row CT (MDCT). Between January 2007 and January 2011, 21 consecutive patients with AVMs were enrolled in this study. Sixteen patients were symptomatic, and of these, 15 had a hemorrhagic onset. Preoperative CT angiography (CTA) was performed using an MDCT scanner (GE Lightspeed VCT; GE Healthcare, Milwaukee, WI, USA). In total, 1 to 1.2 mL/kg of iopamidol, a low-osmolar iodinated contrast material, was administered intravenously with the bolus tracking method. In all cases, three-dimensional CTA imaging demonstrated critical arterial feeders and their specific geometric associations with the nidus. Accurate visualization of the architecture of AVMs and surgical trajectory was possible with the volume rendering method, especially when using transparency imaging. Additionally, employing feeder extraction imaging with segmentation post-processing, clear preoperative identification of the feeding arteries around the surrounding structures allowed for not only intraoperative orientation but also planning for presurgical embolization. Moreover, the precise anatomical structures of the brain surface were obtained using fusion imaging with MRI. While it is difficult to scientifically assign a value to a technology, we sought to objectively assess the utility of the currently available CTA. The significant benefits of this modality for presurgical planning include enhanced surgeon confidence and optimization of the sequence of surgical procedures.

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Year:  2012        PMID: 22437568     DOI: 10.1007/s10143-012-0383-4

Source DB:  PubMed          Journal:  Neurosurg Rev        ISSN: 0344-5607            Impact factor:   3.042


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