Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction

Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó, Natascha Niessen, Maik Dannecker, Sevgi Gokce Kafali, Sila Kurugol, Julia A. Schnabel, Daniel Rueckert

TL;DR

Experiments show that Gabor primitives consistently outperform compressed sensing, Gaussian primitives, and hash-grid INR baselines, while providing a compact, continuous-resolution representation with physically meaningful parameters.

Abstract

Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific reconstruction without large training datasets, but encode content implicitly in network weights without physically interpretable parameters. Gaussian primitives provide an explicit and geometrically interpretable alternative, but their spectra are confined near the k-space origin, limiting high-frequency representation. We propose Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential to place its spectral support at an arbitrary k-space location, enabling efficient representation of both smooth structures and sharp boundaries. To exploit spatiotemporal redundancy in cardiac cine, we decompose per-primitive temporal variation into a low-rank geometry basis capturing cardiac motion and a signal-intensity basis modeling contrast changes. Experiments on cardiac cine data with Cartesian and radial trajectories show that Gabor primitives consistently outperform compressed sensing, Gaussian primitives, and hash-grid INR baselines, while providing a compact, continuous-resolution representation with physically meaningful parameters.

Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction

TL;DR

Experiments show that Gabor primitives consistently outperform compressed sensing, Gaussian primitives, and hash-grid INR baselines, while providing a compact, continuous-resolution representation with physically meaningful parameters.

Abstract

Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific reconstruction without large training datasets, but encode content implicitly in network weights without physically interpretable parameters. Gaussian primitives provide an explicit and geometrically interpretable alternative, but their spectra are confined near the k-space origin, limiting high-frequency representation. We propose Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential to place its spectral support at an arbitrary k-space location, enabling efficient representation of both smooth structures and sharp boundaries. To exploit spatiotemporal redundancy in cardiac cine, we decompose per-primitive temporal variation into a low-rank geometry basis capturing cardiac motion and a signal-intensity basis modeling contrast changes. Experiments on cardiac cine data with Cartesian and radial trajectories show that Gabor primitives consistently outperform compressed sensing, Gaussian primitives, and hash-grid INR baselines, while providing a compact, continuous-resolution representation with physically meaningful parameters.
Paper Structure (17 sections, 7 equations, 3 figures, 1 table)

This paper contains 17 sections, 7 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Gabor Primitive Basis
  4. Spatiotemporal Forward Model
  5. Geometry Basis.
  6. Contrast Basis.
  7. Multi-Coil Forward Model.
  8. Training and Inference
  9. Experiments
  10. Datasets.
  11. Baselines.
  12. Implementation Details.
  13. Results
  14. Quantitative Comparison.
  15. Qualitative Comparison.
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Left: Gaussian vs. Gabor primitives in image space and k-space. A Gaussian's spectral support is fixed at the k-space origin; a Gabor primitive shifts it to $\vec{\xi}_i$ via complex-exponential modulation, enabling more efficient frequency coverage. Right: Cardiac cine image is modeled as a mixture of time-varying Gabor primitives. Geometry parameters ($\vec{\mu}$, $\vec{s}$, $\theta$, $\vec{\xi}$) and complex weights $w$ are generated from low-rank geometry (blue) and intensity (orange) bases. Primitives are rasterized, passed through a multi-coil forward model, and fitted to acquired k-space data end-to-end.
  • Figure 2: Reconstruction comparison on Cartesian (top) and radial (bottom) cardiac cine. Each block shows a representative frame with metrics, zoomed ROI with error map, and $y$--$t$ profile with temporal error.
  • Figure 3: Unique capabilities of Gabor primitives. (a)k-space primitive distribution: each dot is a primitive's center frequency $\vec{\xi}_n$ with $3\sigma$ support ellipses. (b) Frequency-band PSNR (low, mid, high) on the radial dataset, averaged over all subjects. (c) Frequency decomposition: primitives partitioned by $|\vec{\xi}_n|$ into low- ($<\!\tfrac{1}{4}$) and high-frequency ($\geq\!\tfrac{1}{4}$) groups and their sum. (d)$4\times$ super-resolution on the marked ROI; bicubic upsampling of the reference shown for comparison.