Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with different computational roles and different compatibility with the trained network. We study these effects across 120 convolutional U-Net and vision transformer models using controlled edits evaluated before retraining. In U-Net, preserving high-resolution computation improves reconstruction at equal deletion counts across all tested sparsity levels. Equal-operation controls reveal additional sensitivity of the first convolution, which operation count alone cannot explain. In transformers, retained pretrained weight energy correlates with quality within sparsity levels, yet the same masks reverse their reconstruction ranking when the surrounding trained weights change. At 90% sparsity, matching intermediate output scales largely removes a high-energy-mask penalty while retaining an advantage for the network's original mask. Thus sparsity alone does not characterize reconstruction quality: architecture-specific descriptors are informative, but mask quality can still depend on the surrounding trained network.
Figures & tables
Model
Descriptor
fastMRI
M4Raw
U-Net
Active count
−.17 to .39
−.34 to .19
Operations
.54 to .72
.14 to .69
ViT
Energy
.81 to .97
.77 to .96
Table 1: Within-sparsity Pearson correlations with PSNR. Entries span five levels, with 12 trained models per level. ViT energy uses Eq. 2 . These are descriptive associations.
Sparsity
F
F+J
M
M+J
50%
3.49-8.72
1.28-4.34
1.28-4.22
.81-3.01
60%
2.30-9.20
.86-5.17
.64-4.44
.35-3.45
70%
1.52-8.07
.36-4.41
.85-4.21
.43-3.01
80%
2.11-7.66
.06-4.22
.95-3.74
.53-2.89
90%
2.35-7.89
.79-4.48
.87-4.33
.48-3.27
Table 2: U-Net PSNR difference H minus L (dB). H deletes at low resolution first, L at high resolution first, with equal counts. Each range spans 12 models after averaging five deletion seeds. F denotes fastMRI, M denotes M4Raw, and J denotes sampling jitter. All 240 means are positive.
Figure 1: M4Raw reconstruction from a SNIP U-Net at 70% sparsity, using the first deletion seed. H preserves high-resolution computation, while L deletes it first. The slice was selected before visual inspection as the example closest to the median H-minus-L PSNR difference. Labels give PSNR in dB. Image intensities and absolute errors relative to the target use shared ranges [0,1] and [0,.2] .
Figure 2: ViT mask exchange on M4Raw, with 144 combinations per sparsity level. Rows select trained context and columns select masks in decreasing retained energy. S denotes SNIP, R random, P PUN-IT, and G RigL. Color shows PSNR relative to each context’s median across 12 masks, clipped at ±6 dB. The line separates the three highest-energy masks. High-energy masks benefit SNIP contexts, while their preference reverses in random contexts at 90% sparsity. All MLP values are reset to the shared pretrained reference.
Model family
First minus
Encoder minus
adjacent
decoder
PUN-IT
−1.43
−.05 to .08
RigL
−1.09
−.04 to .01
SNIP
−2.87 to −1.35
−.18 to .14
Random
−.13 to −.05
−.34 to .09
Table 3: Equal-count, equal-operation U-Net deletions on M4Raw at 70% sparsity. Entries are scale-invariant PSNR differences (dB). First minus adjacent compares the first convolution with its neighbor. Encoder minus decoder spans four matched resolutions. Negative means the first-named location is more damaging. Ranges span masks and, in the last column, resolutions.
Trained context
Difference (dB)
Interval
SNIP
+4.243
[4.034,4.458]
Random
−.177
[−.566,.235]
PUN-IT
+.513
[−.034,1.098]
RigL
−.474
[−.847,−.061]
Table 4: SNIP-mask minus RigL-mask PSNR on M4Raw at 70% ViT sparsity. All masks use shared pretrained MLP values and 94.935 GFLOPs. Brackets give group-bootstrap intervals adjusted within 16 comparisons. Positive favors SNIP.
Figure 3: Controlled retained-energy test on M4Raw. Masks keep equal counts and have zero overlap with the context’s original mask (Eq. 3 ). Each line evaluates low-energy, random and high-energy selections in one of two randomly pruned contexts. Fitted energy-quality slopes are positive at 60 to 80% sparsity and negative at 90%. Each line contains only three masks, and the middle-energy mask is best in one 90% context.
Original-mask advantage
50%
70%
90%
Before retraining
1.73
2.45
4.64
After one epoch
.95
1.35
1.85
Table 5: ViT controls on M4Raw (dB). Top: original-mask advantage over the 11 foreign masks, averaged over 12 contexts per level, before and after one training epoch. Bottom: the 90% scale-control comparison. Original-mask lead uses the same contrast. High-energy effect is the reported high-energy-minus-count-matched-random mask contrast. Scale factors use 32 unlabeled slices.