Intensity correction with a pair of spoiled gradient recalled echo images.
Intensity inhomogeneities in magnetic resonance images (MRI) are a frequently occurring artefact, and result in the same tissue class to have vastly different intensities within an image. These inhomogeneities can be modelled by a slowly varying field, which is also called the bias field. Previous phantom-, image- or sequence based approaches suffer from long scan times, post-processing times or do not sufficiently remove the intensity variations. These intensity variations cause problems for quantitative image analysis algorithms (segmentation, registration) as well as clinicians (e.g. by complicating the visual assessment). This paper presents a novel technique (COIN, correction of intensity inhomogeneities) that uses two calibration images (fast spoiled gradient echo) to map a parameter containing the bias field, which is specific to the patient during a particular exam. This parametric map can then be used to correct any other images acquired during the same exam, regardless of the sequence employed. By using a short repetition time (less than 5 ms) for the calibration scans, the additional scan time is reduced to 60 s (max). The subsequent post-processing time is approximately 60 s per 20 slices. We successfully validate our approach on simulated brain MRI as well as real liver and spinal images. These images were acquired with a number of different coils, sequences and weightings. A comparison of our method with an existing, commercially available algorithm by radiologists shows that COIN is superior.
Noterdaeme
Olivier
O
Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK. olivier@robots.ox.ac.uk
Anderson
Mark
M
Gleeson
Fergus
F
Brady
Sir Michael
SM
D000465:Algorithms; D001853:Bone Marrow / Q000033:anatomy & histology; D001921:Brain / Q000033:anatomy & histology; D002138:Calibration; D002555:Cerebrospinal Fluid; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D009413:Nerve Fibers, Myelinated; D009474:Neurons; D013131:Spine / Q000033:anatomy & histology; D013997:Time Factors