MedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI

Samuel W. Remedios; Aaron Carass; Jerry L. Prince; Blake E. Dewey

eess.IVOct 29, 2025

MedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI

Authors: Samuel W. Remedios, Aaron Carass, Jerry L. Prince, Blake E. Dewey

Organizations: Image Analysis and Communications Laboratory, Johns Hopkins University · Department of Neurology, Johns Hopkins Hospital

Abstract

This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on $72{,}659$ 1~mm isotropic 3D $T_1$ -weighted MRI human brain image volumes from $38{,}174$ subjects, drawn from a curated corpus of $80{,}675$ volumes from $42{,}506$ subjects spanning $38$ publicly available datasets. These training images were manually inspected to exclude those with poor quality and excessive pathology, and otherwise were minimally processed. The models include six different diffusion training strategies: rectified flow, latent diffusion rectified flow, flow matching, velocity prediction, clean prediction, and noise prediction. Image samples produced by each of these models were compared to each other and against real, ground truth data under downstream segmentation distributions, FID, five inverse problems, and blind human inspection in an observer study. Flow matching was the strongest strategy overall, achieving the best inverse problem solving results at $28.80$ ~dB PSNR and $0.874$ SSIM averaged over the five forward problems, the highest rate of reconstructions judged real by blind human raters at $72.6\%$ , and the closest per-structure match to real segmented anatomy in a permutation test. It was not best everywhere: rectified flow produced the most convincing unconditional samples in the observer study and the best FID, and the latent rectified-flow model achieved the smallest joint distributional distance to real anatomy. No other strategy, however, performed consistently well across all four evaluations. We therefore recommend flow matching as the default MedForj prior, while releasing every strategy so that the choice can be revisited per application. All model weights and corresponding code are publicly available at https://github.com/piksl-research/medforj.

Figures & tables

Dataset Name	Subjects (S)	Volumes (V)	Train S	Train V	Test S	Test V
A4	1742	6903	1594	6320	148	583
ABIDE	781	781	715	715	66	66
ABIDEII	959	1221	878	1112	81	109
ADNI	2587	17965	2368	16505	219	1460
AIBL Ellis et al. (2009)	673	1203	—	—	673	1203
AOMIC-ID1000 Snoek et al. (2021)	928	2768	850	2539	78	229

Table 1 : Distribution of subjects (S) and image volumes (V) by dataset. Note that this is not the total number of data available per dataset, but rather the amount of data after the exclusion described in Sec. 4.1 and Sec. 4.2 .

Figure 1 : A sagittal slice of a representative image used in the inverse problem experiments, with each row corresponding to a different inverse problem. The first column is identical across all rows, showing the ground truth. The second column shows the degraded input for each method. Following columns show the result of each strategy for solving the inverse problem. Yellow boxes indicate a zoomed region, shown in Fig. 2 .

Figure 2 : Zoomed regions corresponding to the yellow boxes in Fig. 1 . Rows and columns follow the same layout: one inverse problem per row, ground truth and degraded input in the first two columns, and one training strategy per remaining column.

PSNR (dB) $\uparrow$
Forward Problem	Input	Noise	Clean	Vel.	Flow	R. Flow	LDM
Super-resolution	21.95	13.47	24.53	24.02	24.77	25.17 ∗	23.09
Inpainting	26.83	20.73	31.64	26.07	32.76 ∗	28.06	25.11
Rician denoising	18.90	25.53	25.68	21.38	25.24	26.97 ∗	17.76
$k$ -space accel.	19.25	16.36	27.91	24.87	28.10 ∗	27.03	22.88
Motion	21.76	21.31	31.97	25.40	33.14 ∗	29.84	25.82

Table 2: Quantitative results (PSNR, SSIM, LPIPS) for the inverse problem solving task over

100

subjects. The final row for each metric shows the average score across all tasks for that metric. “Input” is the input image for each inverse problem, except for super-resolution whose images are linearly interpolated. Other abbreviations: “Vel.” is velocity prediction, “R. Flow” is rectified flow, and “LDM” is the latent diffusion model rectified flow. Bolded numbers indicate the best scoring strategy for that task, and asterisks ∗ indicate statistical significance (

\alpha=0.05

) to a two-sided paired Wilcoxon signed-rank test Wilcoxon (1945) after Holm-Bonferroni Holm (1979) correction.

Figure 3 : Biplanar views of three uncurated samples from each training strategy. These three samples share the exact random seed across all training strategies. All volumes were generated with 256 DDIM steps.

Figure 4 : Biplanar views of synthetic images and their two nearest neighbors. Nearest neighbor is computed as the minimum mean squared error in voxel space.

	Denoising steps (NFE)
Strategy	16	32	64	128	256	512	1000
Noise ( $\epsilon$ ) ‡	131.91	135.34	141.09	144.74	142.12	140.54	143.70
Clean ( $x_{0}$ )	58.07 †	55.17	52.24	54.63	56.76	58.99 †	58.40 †
Velocity	47.23 †	42.41 †	37.43	37.46	35.99	37.19	37.10
Flow	36.74 †	29.95 †	23.44 †	22.54 †	21.50	20.86	20.43
Rectified flow	59.51 †	34.55 †	23.36 †	16.41 †	15.52	14.27	14.38

Table 3: Ablation study to select the number of denoising steps, using FID between

100

samples from each strategy and the entire internal training corpus. Bolded cells give each strategy’s best (lowest) FID. Daggered † cells exceed that strategy’s own best FID by more than

10\%

and so fail the selection rule at that step count. The operating point is the fewest steps at which every strategy is within

10\%

of its own best FID; this is

256

, identically against all three real references. Noise prediction ‡ is reported for completeness but excluded from the rule, as it failed to converge and its FID never approaches that of the other strategies. A stricter

5\%

bound was pre-registered first and returned no step count common to all strategies, so the

10\%

bound reported here is the fallback specified by the same pre-registration rather than a threshold chosen after seeing the data.

Strategy	WASABI ( $\times 10^{4}$ ) $\downarrow$	SD	$\times$ floor
Real vs. real	0.54	0.12	—
Noise ( $\epsilon$ )	32.77	5.14	60.7
Clean ( $x_{0}$ )	6.09	0.82	11.3
Velocity	5.70	0.71	10.6
Flow	4.20	0.78	7.8
Rectified flow	9.99	1.25	18.5

Table 5 : The WASABI score for each training strategy. SD is the standard deviation of the WASABI score.

\times

floor is the row’s distance divided by the real vs. real distance in the first row.

Figure 5 : Raincloud plots showing the distributions of volumes from six regions of interest as computed by SynthSeg. Horizontal lines extend from the real distribution quartiles to aid comparison across strategies.

Mean drift (%) — positive is larger than real
Structure	Noise	Clean	Velocity	Flow	Rectified flow	LDM
Cerebral cortex	$-2.2$	$+11.6$	$+12.4$	$+2.0$	$-0.5$	$-1.9$
Brain stem	$-33.2$	$+15.9$	$+20.3$	$+5.7$	$-0.4$	$+1.1$
Ventricles	$-13.6$	$+28.8$	$+30.8$	$+29.3$	$-21.6$	$-14.9$
Thalamus	$-44.3$	$+6.9$	$+11.1$	$+0.2$	$+0.4$	$+0.1$
Putamen	$-48.9$	$-0.7$	$+8.9$	$+0.1$	$+0.1$	$-4.1$

Table 6 : Per-structure drift of the synthetic SynthSeg volume distributions from real, for all six strategies. Mean drift is

100\times(\bar{x}_{\mathrm{model}}-\bar{x}_{\mathrm{real}})/\bar{x}_{\mathrm{real}}

, so positive means the synthetic structure is larger than real. Cohen’s

d

restates that difference in pooled-SD units so structures of very different absolute size can be compared. SD ratio is the model’s standard deviation divided by the real standard deviation for that structure:

1.0

reproduces the real spread, below

1.0

means the model generates less anatomical volume variation than real, above

1.0

means more.

	Judged real (%, [95% CI])
Strategy	Generative samples	Reconstructions
	Study A	Study B
Real images	79.5 [76.4, 82.3]	—
Noise ( $\epsilon$ )	4.2 [2.0, 8.3]	24.4 [19.0, 30.7]
Clean ( $x_{0}$ )	31.0 [24.2, 38.6]	70.5 [63.7, 76.6]
Velocity	40.6 [33.7, 47.9]	46.2 [38.9, 53.7]

Table 8: Blind observer study. Percentage of lightboxes judged real by human raters who were not told which method produced any image, with Wilson 95% confidence intervals. 2949 reads from 7 independent raters, none of them authors. Raters saw one lightbox at a time and judged it real or generated; an unsure response is counted as not real throughout, so a lightbox counts as passing for real only when a rater affirmatively said so. Generative samples (Study A) are unconditional samples, where a higher rate means the sample passed for real. Reconstructions (Study B) are solutions to the inverse problems; these have no correct real/generated label — a reconstruction of a real subject is neither — so they are reported only as the rate judged real and never as an accuracy. Real images is the study’s ceiling. Bold marks the highest rate within a column.

Panel of Figure 6 — Figure 6 : Parasagittal slices from representative pass and fail images. (a) Images that passed manual quality assurance. (b) Images that failed manual quality assurance. Failure reasons include Pathology ( $\bullet$ ), Limited field-of-view (FOV) ( $\blacksquare$ ), Aggressive defacing ( $\blacktriangle$ ), Motion artifacts ( $\blacksquare$ ), and Noise ( $\blacktriangle$ ).

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