Radial spoke energy for self-navigated motion detection and position-ordered dynamic musculoskeletal MRI

Motion remains a key challenge in MRI, as both involuntary (e.g., head motion) and voluntary (e.g., joint motion) movement can degrade image quality or provide opportunities for dynamic assessment. Existing motion sensing methods, such as external tracking or navigator sequences, often require additional hardware, increase SAR, or demand sequence modification, which limits clinical flexibility. We propose a computationally efficient, self-navigated motion sensing technique based on spoke energy derived from 3D radial k-space data. Using the Fourier Slice and Parseval's theorems, spoke energy captures object-coil alignment and can be computed without altering the sequence. A sliding window summation improves robustness, and a second principal component analysis (2ndPCA) strategy yields a unified motion-sensitive signal. Beyond conventional head motion correction, we demonstrate the novel application of this method in enhancing dynamic 4D MRI of the ankle and knee under a continuous movement protocol. By sorting spokes based on position rather than time, we achieve motion-resolved reconstructions with improved anatomical clarity. This approach enables real-time motion detection and supports broader adoption of motion-aware dynamic MRI.