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arxiv: 2606.21588 · v1 · pith:W66ZQFUSnew · submitted 2026-06-19 · 📡 eess.IV · cs.CV

Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration

Wooseung Kim , Sung-Hong Park This is my paper

Pith reviewed 2026-06-26 12:33 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords susceptibility distortion correctionEPIfMRIunsupervised registrationimage translationinvertible neural networktest-time adaptationcalibration-free
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The pith

SACRED corrects EPI distortions in fMRI without calibration scans by translating BOLD images to T1-weighted contrast for unsupervised registration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes SACRED, a framework that corrects susceptibility-induced geometric distortions in echo-planar imaging for fMRI without requiring additional calibration scans such as field maps. It registers a unidirectional phase-encoding BOLD image to a standard T1-weighted anatomical image by first translating between the two contrasts with an invertible neural network. A modality-independent neighborhood descriptor enforces structural consistency so that a simple mono-contrast similarity loss can train the registration network unsupervised, without any distortion-corrected BOLD ground truth. Test-time adaptation is added to improve results on data from different scanners or populations. This approach would matter if it reliably removes the need for extra scans that are often unavailable in routine fMRI studies.

Core claim

SACRED employs an invertible neural network as the image translation backbone to bridge the contrast gap between BOLD and T1w images while enforcing structural consistency through a modality independent neighborhood descriptor. This design enables the use of a mono-contrast similarity objective to train the registration network in an unsupervised manner without requiring distortion-corrected BOLD images. In addition, we incorporate test-time adaptation (TTA) to further enhance performance on out-of-distribution (OOD) data at inference time. SACRED was evaluated on one in-distribution (ID) dataset and two OOD datasets, and was compared with representative fMRI distortion correction methods.

What carries the argument

Invertible neural network for image translation combined with modality-independent neighborhood descriptor, enabling mono-contrast similarity objective for unsupervised registration across the BOLD-T1w gap.

If this is right

  • SACRED outperforms competing methods on both in-distribution and out-of-distribution datasets.
  • It exhibits robustness to scanner and population shifts partly enabled by test-time adaptation.
  • Distortion correction is possible using only a routinely acquired T1-weighted image and a unidirectional phase-encoding BOLD image.
  • No additional calibration scans are required at acquisition time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same translation-plus-registration pattern could be tested on other contrasts that lack paired ground-truth corrected data.
  • Reducing the dependence on calibration scans may shorten total scan time in clinical fMRI protocols.
  • Further gains might come from combining the current TTA step with additional domain-adaptation losses not explored in the paper.

Load-bearing premise

The invertible neural network plus modality-independent neighborhood descriptor can produce accurate mono-contrast similarity registration across the BOLD-T1w contrast gap without any distortion-corrected BOLD ground truth during training.

What would settle it

On a held-out dataset supplied with field-map ground truth, the positions of anatomical landmarks after SACRED correction do not match the expected undistorted locations more closely than corrections obtained from conventional calibration-based methods.

Figures

Figures reproduced from arXiv: 2606.21588 by Sung-Hong Park, Wooseung Kim.

Figure 1
Figure 1. Figure 1: Overview of the proposed susceptibility distortion correction framework. III. METHODS A. Framework Overview An overview of the proposed SACRED framework is il￾lustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our invertible image translation network is formulated as a mapping f : X → Y, where X denotes the T1w image domain and Y denotes the undistorted BOLD image domain. The mapping is implemented using eight invertible blocks {fi} 7 i=0. Given an input T1w image x, the corresponding pseudo BOLD image y is obtained as y = f7 ◦ f6 ◦ · · · ◦ f1 ◦ f0(x), (1) and the reconstructed T1w image ˆx can be recovered by a… view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of the image registration network (RP E). Given the distorted BOLD and pseudo BOLD images, the final convolutional layer outputs a velocity field, which is converted into a deformation field via an integration layer. D. Loss Functions As shown in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the proposed test-time adaptation. At this stage, only the registration network (RP E) is optimized in an instance-specific manner. IV. EXPERIMENTAL SETUP A. Datasets Three publicly available datasets [37]–[39] from Open￾Neuro [40] were used in this study, as summarized in Table I. These datasets were selected because each includes two BOLD runs acquired with forward and reverse PE. The NIMH-RV… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on the ID NIMH-RV dataset. Columns 1–2 show the ground-truth TOPUP-corrected BOLD image and the original distorted BOLD image, respectively. Columns 3–7 present distortion-corrected BOLD images produced by each method. The first row displays the corrected images, with zoomed views of the anterior frontal region highlighted by orange boxes. Red outlines indicate the boundary of the groun… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results on the OOD SUDMEX-TMS dataset. Columns 1–2 show the ground-truth TOPUP-corrected BOLD image and the original distorted BOLD image, respectively. Columns 3–6 present distortion-corrected BOLD images produced by each method. The first row displays the corrected images, with zoomed views of the anterior frontal region highlighted by orange boxes. Red outlines indicate the boundary of the g… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results on the OOD QTAB dataset. Columns 1–2 show the ground-truth TOPUP-corrected BOLD image and the original distorted BOLD image, respectively. Columns 3–7 present distortion-corrected BOLD images produced by each method. The first row displays the corrected images, with zoomed views of the anterior frontal region highlighted by orange boxes. Red outlines indicate the boundary of the ground-… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of the similarity loss function on test-time adaptation performance. The blue and orange curves indicate the performance trends obtained using the proposed LNCC2 loss for TTA and the L1 loss, which was used for initial training and evaluated here as a TTA objective, respectively. The results were computed by applying a moving average over all volumes from the three datasets. The vertical dashed line… view at source ↗
Figure 9
Figure 9. Figure 9: Ablation study on the image translation network architecture. Generated pseudo BOLD images are shown for each architecture across the ID and OOD datasets. Yellow arrowheads indicate regions where the INN-based architecture better preserves anatomical struc￾tures from the T1w image than the ResNet- and U-Net-based architec￾tures. image translation tasks [52]. For the ResNet- and U-Net-based models, the imag… view at source ↗
read the original abstract

Functional magnetic resonance imaging (fMRI) utilizes echo-planar imaging (EPI) to capture blood-oxygen-level-dependent (BOLD) signals with high temporal resolution. However, EPI is inherently sensitive to magnetic field inhomogeneities, resulting in susceptibility-induced geometric distortions along the phase-encoding (PE) direction. To correct these distortions, conventional approaches rely on additional calibration scans, such as field maps or reverse PE acquisitions, which are not always available in practice. To overcome this limitation, we propose SACRED, a calibration scan-free susceptibility distortion correction framework that corrects geometric distortions via image translation-based registration using only a routinely acquired anatomical T1-weighted (T1w) image and a unidirectional PE BOLD image. SACRED employs an invertible neural network as the image translation backbone to bridge the contrast gap between BOLD and T1w images while enforcing structural consistency through a modality independent neighborhood descriptor. This design enables the use of a mono-contrast similarity objective to train the registration network in an unsupervised manner without requiring distortion-corrected BOLD images. In addition, we incorporate test-time adaptation (TTA) to further enhance performance on out-of-distribution (OOD) data at inference time. SACRED was evaluated on one in-distribution (ID) dataset and two OOD datasets, and was compared with representative fMRI distortion correction methods. The results demonstrate that SACRED significantly outperforms competing methods on both ID and OOD datasets, exhibiting robustness to scanner and population shifts, partly enabled by TTA. The code will be made publicly available upon acceptance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes SACRED, a calibration-scan-free method for correcting susceptibility-induced geometric distortions in unidirectional-PE EPI BOLD images. It employs an invertible neural network to translate between BOLD and T1w contrasts while using a modality-independent neighborhood descriptor (MIND) to enable unsupervised mono-contrast similarity registration; test-time adaptation (TTA) is added to improve robustness on out-of-distribution data. The method is evaluated on one in-distribution and two out-of-distribution datasets and is reported to significantly outperform representative competing fMRI distortion-correction approaches.

Significance. If the central assumption holds—that the learned translation plus MIND descriptor yields a similarity optimum that corresponds to true geometric undistortion rather than mere contrast matching—this approach would address a practical barrier in fMRI preprocessing by removing the requirement for field maps or reverse-PE scans. The explicit use of TTA for scanner/population shifts and the unsupervised training without distortion-corrected BOLD ground truth would be notable strengths for multi-site applicability.

major comments (2)
  1. [Abstract] Abstract: the claim that SACRED 'significantly outperforms competing methods on both ID and OOD datasets' is presented without any quantitative metrics, error bars, dataset sizes, statistical tests, or even the names of the competing methods. Because this performance claim is the primary evidence offered for the method's utility, the absence of these numbers prevents assessment of whether the gains are load-bearing or merely incremental.
  2. [Methods] Methods (image-translation and registration sections): the central modeling assumption—that the invertible-NN translation combined with MIND produces a mono-contrast similarity signal whose optimum aligns with true susceptibility-induced displacements—is not directly tested. No experiment is described that compares the estimated deformation fields against ground-truth distortion maps (field-map or reverse-PE corrected) on held-out data; without such validation it remains possible that the registration is optimizing residual contrast mismatch rather than geometry.
minor comments (1)
  1. [Abstract] Abstract: the acronym SACRED is introduced without expansion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that SACRED 'significantly outperforms competing methods on both ID and OOD datasets' is presented without any quantitative metrics, error bars, dataset sizes, statistical tests, or even the names of the competing methods. Because this performance claim is the primary evidence offered for the method's utility, the absence of these numbers prevents assessment of whether the gains are load-bearing or merely incremental.

    Authors: We agree that the abstract would benefit from quantitative support for the performance claims. In the revised manuscript, we will update the abstract to report specific metrics (e.g., mean displacement errors with standard deviations), dataset sizes (number of subjects and volumes), statistical test results (e.g., p-values), and the names of the competing methods (such as TOPUP and fieldmap-based approaches). This will enable readers to evaluate the magnitude and reliability of the reported improvements. revision: yes

  2. Referee: [Methods] Methods (image-translation and registration sections): the central modeling assumption—that the invertible-NN translation combined with MIND produces a mono-contrast similarity signal whose optimum aligns with true susceptibility-induced displacements—is not directly tested. No experiment is described that compares the estimated deformation fields against ground-truth distortion maps (field-map or reverse-PE corrected) on held-out data; without such validation it remains possible that the registration is optimizing residual contrast mismatch rather than geometry.

    Authors: We acknowledge that a direct comparison of estimated deformation fields to ground-truth distortion maps on held-out data would provide stronger evidence that the similarity optimum corresponds to geometric correction. Our current evaluation relies on indirect validation via improved alignment with T1w images and downstream fMRI task performance on ID and OOD datasets. In the revision, we will add an experiment on available subsets with field maps or reverse-PE data to compare deformation fields against ground truth, thereby addressing this modeling assumption more explicitly. revision: yes

Circularity Check

0 steps flagged

No circularity: standard unsupervised NN registration with external T1w reference

full rationale

The paper describes an image-translation registration method using an invertible neural network and MIND descriptor for unsupervised training on BOLD-T1w pairs. No equations, derivations, or self-citations are presented that reduce any claimed result to its own inputs by construction. Training relies on a mono-contrast similarity objective and an external anatomical T1w image rather than any fitted parameter or self-referential loop. The approach is self-contained against standard ML registration benchmarks and does not invoke uniqueness theorems or prior author work as load-bearing justification.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; full manuscript would be required to audit training losses, network architecture choices, or data assumptions.

pith-pipeline@v0.9.1-grok · 5808 in / 1045 out tokens · 24261 ms · 2026-06-26T12:33:21.135180+00:00 · methodology

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Reference graph

Works this paper leans on

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    We introduce an image translation-based registration framework that leverages an INN and MIND-SSC to bridge the contrast gap between BOLD and T1w images, enabling the use of standard mono-contrast similarity metrics while preserving structural consistency

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    Smoothing Loss:To prevent folding and ensure spatial smoothness in the deformation field, we apply a regularization term. This loss penalizes sudden changes in the deformation field by minimizing theL 2 norm of its spatial gradients∇ϕ: Lsmooth =||∇ϕ|| 2 2.(16)

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