Robust self-navigated body MRI using dense coil arrays.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Imaging Technology
• Biomedical Engineering

Background:

• Free-breathing body MRI requires robust motion estimation for diagnostic image quality.
• Dense coil arrays in MRI offer localized motion information but averaging can obscure dominant motion.
• Existing methods may rely on external devices for motion monitoring, limiting applicability.

Purpose of the Study:

• To develop and validate a novel coil clustering method for automated motion estimation in free-breathing body MRI using dense coil arrays.
• To enable accurate self-navigated abdominal and cardiac MRI without external motion monitoring.
• To compare the proposed method against manual selection and electrocardiography for motion and cardiac triggering accuracy.

Main Methods:

• A coil clustering algorithm was developed to automatically identify dominant motion from dense coil array data.
• The method was applied to free-breathing abdominal MRI and cardiac MRI datasets.
• Performance was evaluated by comparing automated motion estimates with manual selection for respiratory motion and electrocardiography for cardiac triggering.

Main Results:

• The automated motion estimation method demonstrated comparable accuracy to manual selection for respiratory motion in both abdominal and cardiac MRI (correlation coefficients > 0.988).
• Cardiac triggering accuracy using the proposed method was comparable to electrocardiography, with low temporal variability (17.5 ms).
• The coil clustering approach effectively determined dominant motion from dense coil arrays.

Conclusions:

• The proposed coil clustering method provides accurate automated motion estimation for body MRI with dense coil arrays.
• This technique facilitates self-navigated, free-breathing abdominal and cardiac MRI.
• The method eliminates the need for external motion monitoring devices, enhancing clinical utility.