Presentation for unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization (MEDIA, 2022)

1. Unsupervised Resolution-Agnostic
Quantitative Susceptibility Mapping
using Adaptive Instance Normalization
Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, Won-Jin Moon,
and Jong Chul Ye

2. 1. Introduction
• Quantitative susceptibility mapping (QSM)
• Measures magnetic susceptibility values from MRI phase images.
• Sensitive to iron deposition, biomarkers for neurological disorders
Magnitude Phase QSM

3. 1. Introduction
• Dipole inversion
• 𝑏: phase, 𝑑: dipole kernel, 𝜒: QSM
Spatial Domain Fourier Domain
𝑏 𝑟 = 𝑑 𝑟 ∗ 𝜒 𝑟
𝑑 𝑘 =
1
3
−
𝑘𝑧
2
𝑘
2
𝑏 𝑘 = 𝑑 𝑘 𝜒 𝑘
𝑑 𝑟 =
1
4𝜋
3 cos2
𝜃 − 1
𝑟 3
Relationship
Dipole kernel
Convolution Element-wise product
Conical surfaces ( 𝑘
2
= 3𝑘𝑧
2
): zero
𝜒 𝑘 =
𝑏 𝑘
𝑑 𝑘
Ill-posed

4. 2. Related Works
• COSMOS
• Multiple orientation data  compensate missing values
• Gold standard algorithm
• Long acquisition time
Orientation 1 Orientation 2 Orientation 3 COSMOS

5. 2. Related Works
• Conventional algorithms
• Filling conical surface, iterative algorithms
• Streaking artifact
• High computational complexity
• Deep learning algorithms
• Improved performance, fast reconstruction
• Supervised learning  Ground truth
• Training data ≠ test data  Performance↓

6. 3. Methods
• Unsupervised QSM reconstruction

7. 3. Methods
• Training & test data
• Resolution of training data ≠ resolution of test data
• Resolution-agnostic QSM reconstruction method is required
Training Test

8. 3. Methods
• AdaIN-switchable CycleGAN
• 𝐹: adaptive instance normalization (AdaIN) code generator
• Changing AdaIN code  single generator learn the bidirectional image-to-image translation

9. 3. Methods
• Unsupervised resolution-agnostic QSM reconstruction

10. 3. Methods
• QSM generator & AdaIN code generator

11. 3. Methods
• Training & test data
• Resizing by interpolation  synthetic multi-resolution phase data
• In-plane: 0.424~1.28, Axial: 0.427~2.13
• Resolution range of the resized training data covers the resolution of the test data
Training Test

12. 3. Methods
• Comparison methods
• Conventional algorithms: TKD, MEDI, iLSQR
• Fidelity imposed network edit (FINE)
- Supervised learning  fine tuning with test data using unsupervised fidelity loss
• Meta-QSM
- Resolution-arbitrary network for QSM reconstruction
- Weight-predict convolutional layers

13. 4. Experimental Results
• Effects of AdaIN transform / unsupervised learning
• AdaIN  IN
• Artifacts (yellow arrows)
• Unsupervised  supervised
• Cannot reconstruct detailed structures
• Supervised learning is more sensitive to lack
of training data than unsupervised learning

14. 4. Experimental Results
• Cornell data

15. 4. Experimental Results
• KAIST data
SN/RN: substantia nigra/red nucleus, PN/GP: putamen/globus pallidus, CN: caudate nucleus

16. 4. Experimental Results
• KAIST data

17. 4. Experimental Results
• KUH data

18. 4. Experimental Results
• Clinical evaluation

19. 5. Discussion & Conclusion
• Conventional methods
• Noticeable noise, artifacts (e.g. streaking artifact, digitized artifact)
• Time consuming
• Deep learning algorithms
• Under/overestimation
• Blurry output
• Fail to restore susceptibility maps of some resolution data
• Supervised learning  sensitive to lack of training data
• Proposed method
- Reconstruct QSM of various resolutions with less artifacts and noise
- Unsupervised learning  does not suffer from problems of the supervised learning methods