C-arm flat detector computed tomography: the technique and its applications in interventional neuro-radiology
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Flat detector computed tomography (FDCT) is an imaging tool that generates three-dimensional (3-D) volumes from data obtained during C-arm rotation using CT-like reconstruction algorithms. The technique is relatively new and, at current levels of performance, lags behind conventional CT in terms of image quality. However, the advantage of its availability in the interventional room has prompted neuro-radiologists to identify clinical settings where its role is uniquely beneficial.
We performed a search of the online literature databases to identify studies reporting experience with FDCT in interventional neuro-radiology. The studies were systematically reviewed and their findings grouped according to specific clinical situation addressed.
FDCT images allow detection of procedural complications, evaluation of low-radiopacity stents and assessment of endosaccular coil packing in intra-cranial aneurysms. Additional roles are 3-D angiography that provides an accurate depiction of vessel morphology with low concentrations of radiographic contrast media and a potential for perfusion imaging due to its dynamic scanning capability. A single scan combining soft tissue and angiographic examinations reduces radiation dose and examination time. Ongoing developments in flat detector technology and reconstruction algorithms are expected to further enhance its performance and increase this range of applications.
FDCT images provide useful information in neuro-interventional setting. If current research confirms its potential for assessing cerebral haemodynamics by perfusion scanning, the combination would redefine it as an invaluable tool for interventional neuro-radiology procedures. This facility and its existing capabilities of parenchymal and angiographic imaging would also extend its use to the triage of acute stroke patients.
KeywordsFDCT Parenchymal images Procedural complications Angiography Perfusion imaging
We thank Siemens for providing the hardware and software support.
Conflict of interest statement
MK is funded by The Rhodes Trust. JVB is supported by the Oxford Biomedical Research Centre.