Local shape adaptation for curved slice selection.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Radiofrequency (RF) Pulse Design
• Spatial Encoding

Background:

• Nonlinear spatial encoding magnetic fields enable excitation and local encoding of curved MRI slices.
• The inherent nonlinearity of these fields causes variations in slice thickness.
• A framework for nonlinear receive encoding is extended to multidimensional excitation for pattern determination.

Purpose of the Study:

• To combine nonlinear spatial encoding with multidimensional RF excitation for local adaptation of curved slice shape.
• To achieve constant slice thickness in curved slice imaging.
• To explore modifications of slice shape for enhanced anatomical adaptation.

Main Methods:

• Utilizing a framework for nonlinear encoding and multidimensional excitation.
• Acquiring cross-sections of curved slices in phantoms with adapted thickness and curvature.
• Analyzing voxel shapes to determine the technique's applicability.
• Applying slice-thickness adaptation to in vivo curved slice imaging.

Main Results:

• Demonstrated feasibility of local slice thickness adaptation in both phantom and in vivo studies.
• Achieved local adaptation of slice curvature.
• Identified limitations including prolonged pulse duration and voxel shape distortion.

Conclusions:

• Multidimensional excitation facilitates curved slice imaging with uniform thickness.
• The technique offers potential for further slice shape modifications to better match anatomy.
• This approach enhances the adaptability of MRI to complex anatomical structures.