Reconstruction strategies
All strategies implement the same ReconStrategy trait, take a
calibrated k-space tensor [ch, kz, ky, kx], and return image-
domain volumes. 3D acquisitions with ky-only undersampling
decouple along kz via a 1-D IFFT and then run independent
per-slice unfolds, so parallel imaging is supported for both 2D
multi-slice and 3D Cartesian data.
ifft-rss
Centred IFFT plus root-sum-of-squares coil combination. Fastest strategy; produces magnitude images only. Recommended baseline.
grappa
k-space GRAPPA (Griswold 2002). ACS lines are extracted automatically; a convolution kernel is fit via ridge regression and applied to fill the missing ky lines.
| Flag | Default | Description |
|---|---|---|
--grappa-kernel-ky <k> | 4 | Kernel size along ky |
--grappa-kernel-kx <k> | 5 | Kernel size along kx |
--grappa-ridge <lam> | 1e-3 | Tikhonov ridge for kernel fit |
sense
Image-domain SENSE unfold (Pruessmann 1999) with ridge stabilisation. Sensitivity maps are estimated via either Walsh (eigenvector method, Walsh 2000) or ESPIRiT (auto-calibrating, Uecker 2014).
| Flag | Default | Description |
|---|---|---|
--sense-maps walsh|espirit | walsh | Coil sensitivity estimation |
--sense-walsh-window <w> | 7 | Walsh smoothing window |
--sense-walsh-iters <n> | 3 | Walsh power-method iterations |
--espirit-kernel <k> | 6 | ESPIRiT kernel size |
--espirit-threshold <f> | 0.02 | ESPIRiT singular value threshold |
--espirit-iters <n> | 50 | ESPIRiT power iterations |
--sense-ridge <lam> | 1e-4 | Tikhonov ridge for unfold |
--sense-gfactor | off | Compute g-factor map |
--write-gfactor | off | Also emit g-factor PNGs |
cs
L1-wavelet compressed sensing via FISTA (Lustig 2007; Beck & Teboulle 2009). Slower than GRAPPA/SENSE but better at high acceleration with random ky undersampling.
| Flag | Default | Description |
|---|---|---|
--cs-iters <n> | 80 | FISTA iterations |
--cs-lambda <lam> | 1e-3 | L1 regularisation strength |
--cs-wavelet-levels <n> | 4 | Wavelet decomposition levels |