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On the shape of convolution kernels in MRI reconstruction: Rectangles versus ellipsoids.
Rodrigo A Lobos1, Justin P Haldar1
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Magnetic Resonance in Medicine
|February 25, 2022
Summary
Ellipsoidal kernels in MRI reconstruction offer advantages over rectangular shapes, achieving similar image quality with fewer parameters and reduced computational cost. This study investigates kernel shape impacts across various MRI reconstruction methods.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Image Reconstruction
- Computational Imaging
Background:
- Shift-invariant convolution models are common in MRI reconstruction methods like GRAPPA, SPIRiT, and CNNs.
- Rectangular kernels are standard, but ellipsoidal kernels offer theoretical benefits for spatial resolution and parameter efficiency.
Purpose of the Study:
- To systematically investigate and compare the performance of rectangular versus ellipsoidal kernel shapes in MRI reconstruction.
- To evaluate the impact of kernel shape on image quality, computational efficiency, and model complexity.
Main Methods:
- Empirical analysis using retrospectively undersampled k-space data.
- Testing across seven diverse MRI reconstruction algorithms: GRAPPA, SPIRiT, ESPIRiT, SAKE, LORAKS, AC-LORAKS, and CNN-based methods.
Main Results:
- Both rectangular and ellipsoidal kernels yield comparable image error metrics.
- Ellipsoidal kernels often achieve similar results with reduced computation time, memory usage, and fewer model parameters.
Conclusions:
- Ellipsoidal kernel shapes present potential advantages over traditional rectangular kernels in MRI reconstruction.
- These benefits include improved efficiency and parameter reduction, suggesting broader applicability in various MRI scenarios.
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