Electrodes Recognition Ability Clinical Trial
Official title:
Efficient Automated Localization of ECoG Electrodes in CT Images Via Shape Analysis
People with drug epilepsy (PwE) refractory to anti-seizure medications may be evaluated for
surgery. In several cases non invasive presurgical work-up is not sufficient for localization
of the Epileptogenic Zone and its correct delineation requires intracranial investigations by
means of intraparenchymal or subdural electrodes.The methodological approach with subdural
electrodes allows to obtain electrocorticography (ECoG) covering large cortical regions and
to map eloquent areas.
To delineate the seizure onset zone it is mandatory to precisely localize the electrode
position on the cortical surface. Electrodes are usually recognized by processing patients'
computed tomography (CT) images using simple image processing (e.g. thresholding) that
isolates metal objects. However, also wires, stitches, clips and other metal objects are
actually recognized and need to be removed by manual intervention. A new automated method,
based on shape analysis, will be retrospectively tested in a group of subjects with
refractory focal epilepsy previously investigated with subdural electrodes for diagnostic
purposes to provide advanced ECoG subdural electrodes recognition. A total of 24 CT scans
with a large number (> 1700) of round platinum electrodes arrays will be recruited for
testing.
For people with epilepsy (PwE) refractory to anti-seizure medication sometimes the
non-invasive presurgical evaluation using ElectroEncephaloGram (EEG) recorded directly from
the scalp is not sufficient to delineate the epileptogenic zone and to identificate the
eloquent cortex. In these cases, an invasive approach using intracranial
electroencephalography (iEEG) is needed Subdural electrodes are used frequently in the
presurgical evaluation of patients who are candidates for epilepsy surgery. Electrodes placed
directly on the surface of the cortex provide a signal with a much higher resolution than
that provided from scalp electrodes, and have a much clear view of small loci of activity
which is difficult to see on the scalp.
Subdural electrodes allow not only the localization of abnormal epileptic tissue but also the
localization of adjacent normal functions. Therefore, the precise anatomical localization of
the electrodes on the patient's brain plays a key role in the definition of the epileptogenic
zone or in the mapping of eloquent cortex.
From a clinical point of view, the accurate localization of the anatomical boundaries of the
epileptogenic zone allows to exclude eloquent areas, avoid deficits to patient and minimize
brain volume resection.
The localization of these electrodes is generally obtained by matching the locations of the
electrodes with the brain anatomy of the patient. Commonly, a pre-implant magnetic resonance
image (MRI) is co-registered to a post-implant computed tomography scan (CT) because MRI
offers higher brain tissue contrast, while CT supports electrodes localization , even if CT
images are affected by metal artifacts.
Various dedicated software tools that support pre-surgical evaluation are currently available
as Matlab-based packages or open source softwares, also with graphical user interfaces. They
mainly provide MRI-CT co-registration and offer only basic features for recognition of ECoG
electrodes from CT scans. Most dedicated softwares segment the electrodes via simple image
thresholding and allow manual interaction to correct the data. Manual methods are very time
consuming,user-dependent and prone to inaccuracy. On the other hand, the mere CT image
thresholding method is not able to recognize all the electrodes and to completely exclude
other metallic objects, such as wires, tooth filings, intracranial clips, splinters,
stitches, hearing aids or intracranial stents. Hence, manual intervention is often required
to adjust the data. For example, the ALICE tool considers the volume of segmented clusters to
identify the electrodes, but turned out to be unable to exclude other objects with comparable
volumes (e.g. wire clusters).
The aim of this project is to develop a novel, robust, automated method to recognize ECoG
electrodes in CT volumes. It consists of metal artifacts removal from CT volumes,
identification of groups/arrays of metal objects within the skull and shape analysis of
detected objects to achieve ECoG electrodes localization.The proposed approach could be
easily integrated in existing tools.
;