An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI

By Prof. Dr. Stefanie Remmele, Tobias Schaeffter, T. Koehler, H. Eggers, Olaf Dössel. IEEE Transactions on Medical Imaging, 2007

718 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8f29b9c79c982fa979f1865d370b23a3d7402ac7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Daniel K. Sodickson
2. Ricardo Otazo
3. Michael S. Hansen
4. E.J. Candes
5. Thomas Sangild Sørensen
6. Richard Baraniuk
7. Justin K. Romberg
8. Martin J. Wainwright

In Computer Vision and Pattern Recognition

1. Daniel K. Sodickson
2. Ricardo Otazo
3. Michael S. Hansen
4. E.J. Candes
5. Thomas Sangild Sørensen
6. David L. Donoho
7. Justin K. Romberg
8. Richard Baraniuk

In Radiology, Nuclear Medicine and Imaging

1. Daniel K. Sodickson
2. Ricardo Otazo
3. Michael S. Hansen
4. E.J. Candes
5. Thomas Sangild Sørensen
6. David L. Donoho
7. Richard Baraniuk
8. Justin K. Romberg

Citations in the corpus

• 4D flow MRI (2012, methodology): “In combination with PC-VIPR and related Golden angle radial imaging techniques (71), these self-gating Figure 4.”
• Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI (2013, methodology): “When acquiring radial data according to the golden-angle ordering scheme (28), where the angle of the radial lines is increased continuously by 111.”
• Gadgetron: An open source framework for medical image reconstruction (2013, methodology): “This can be achieved with a rotating radial acquisition scheme or with the golden ratio radial acquisition scheme (24), which was used in this case.”
• XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing (2015, methodology): “25 angular increment between consecutive spokes (32), is used for data acquisition, because the repeated sampling of the k-space center enables extraction of motion-state signals (16,29), and it allows the possibility of arbitrary data sorting with approximately uniform k-space coverage in each moti”
• Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components (2014, background): “Golden-angle radial sampling (34) is well-suited for compressed sensing due to the presence of significant spatial and temporal incoherence given by the different k-space trajectory used to acquire each spoke.”