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Keywords:
Image registration, Diagnostic procedure, Ultrasound-Colour Doppler, Ultrasound, MR, Neuroradiology brain, Head and neck
Authors:
S. J. Schreiber1, M. Laganà2, S. de Beni3, S. D'Onofrio3, V. Kolev4, L. Forzoni3; 1Berlin/DE, 2Milan/IT, 3Genoa/IT, 4Darmstad/DE
DOI:
10.1594/ecr2015/C-0828
Aims and objectives
Ultrasound (US) fusion imaging is an emerging imaging technique which permits real-time US with parallel imaging of a pre-acquired second imaging dataset,
e.g.
a Computed Tomography (CT),
Magnetic Resonance Image (MRI),
and/or PET/CT [1,2].
In recent years,
this technique has become increasingly used in both diagnosis and image-guided interventional procedures.
The main focus of interest so far has been on abdominal US,
e.g.
the liver and the kidneys [3-11].
More recently,
fusion imaging gained interest in diagnostic Neurosonology [12] and Neurosurgery applications [13,
14].
An important precondition for a stepwise distribution of the technique from specialized centers into widespread everyday practice is the ease of its use,
i.e.
the fact to have a user friendly system interface and workflow pathways which simplify the complexity of the procedure.
To achieve this goal,
a simple,
fast and reliable matching algorithm is required,
which subsequently secures a precise real-time fusion between US and the second imaging modality.
Current registration procedures usually comprise a manual definition of matching points within the pre-acquired MR or CT imaging volume dataset and the patient or the patient’s US image.
The second precondition,
to keep the matching throughout the examination despite spontaneous movements of the patient,
has recently been solved by the introduction of a Motion Control Sensor [15-17].
This sensor,
which is attached to the patient,
instantly and continuously corrects any voluntary or involuntary movement of the patient,
avoiding therefore any loss of matching after the registration procedure.
The present work is a feasibility study and a performance analysis of a newly developed automatic registration algorithm for MRI and US real-time fusion imaging using in vivo registration for the transcranial color-coded duplex US application.