Radiotherapy and Oncology · 2018 · 17 citations · 34 references
MedicineOncologyRadiologyBiomedical ImagingDiagnostic ImagingDiagnostic RadiologyImage-guided RadiotherapyImage-guided TherapyMagnetic Resonance Image ReconstructionMagnetic Resonance ImagingMedical ImagingRadiation TherapyMagnetic Resonance-guided RadiotherapySuper-resolutionT2-weighted Imaging
BACKGROUND AND PURPOSE: The superior soft-tissue contrast of 4D-T2w MRI motivates its use for delineation in radiotherapy treatment planning. We address current limitations of slice-selective implementations, including thick slices and artefacts originating from data incompleteness and variable breathing. MATERIALS AND METHODS: ). The method employed image-derived respiratory surrogates, deformable image registration and super-resolution reconstruction. Volunteer imaging and a respiratory motion phantom were used for validation. The minimum number of dynamic acquisitions needed to calculate a representative midposition image was investigated by retrospectively subsampling the data (10-30 dynamic acquisitions). RESULTS: , 8 respiratory phases) did not suffer from data incompleteness and exhibited reduced stitching artefacts compared to sorted multi-slice MRI. Experiments using a respiratory motion phantom and colour-intensity projection images demonstrated a minor underestimation of the motion range. Midposition diaphragm differences in retrospectively subsampled acquisitions were <1.1 mm compared to the full dataset. 10 dynamic acquisitions were found sufficient to generate midposition MRI. CONCLUSIONS: A motion-modelling and super-resolution method was developed to calculate high quality 4D/midposition T2w MRI from orthogonal 2D-T2w MRI.
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Improving resolution by image registration
Michal Irani · CVGIP Graphical Models and Image Processing · 1991 · 1.9K citations
Diffeomorphic demons: Efficient non-parametric image registration
Tom Vercauteren, Xavier Pennec, Aymeric Perchant et al. · NeuroImage · 2008 · 1.6K citations · Full text