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arxiv: 2604.10439 · v2 · submitted 2026-04-12 · 💻 cs.CV

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Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework

Boyang Pan, Chengwei Chen, Chenwei Shao, Danqun Zheng, Langdi Zhong, Nan-Jie Gong, Shuai Li, Xuezhou Li, Yun Bian, Ziheng Guo, Ziqin Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:46 UTC · model grok-4.3

classification 💻 cs.CV
keywords MRI motion artifactdeep learningperceptual lossimage correctionmedical imagingU-Netartifact removalstructural preservation
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The pith

PERCEPT-Net uses a dedicated motion perceptual loss to remove MRI artifacts while preserving anatomical structures on clinical data.

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

The paper develops a deep learning framework called PERCEPT-Net to correct motion artifacts that blur MRI scans when patients move during imaging. Existing methods often fail on real clinical scans because they cannot reliably separate artifacts from actual body structures. PERCEPT-Net adds three parts to a residual U-Net: a multi-scale recovery module for global and fine details, dual attention to focus on relevant features, and a Motion Perceptual Loss trained on paired volumes that combine real and simulated motion. This loss teaches the network generalized artifact patterns so it can suppress them without distorting true anatomy. Tests on prospective clinical data show gains over prior methods in image quality metrics and in radiologist ratings of diagnostic usefulness.

Core claim

PERCEPT-Net is built on a residual U-Net backbone and adds a multi-scale recovery module, dual attention mechanisms, and a Motion Perceptual Loss. The loss is trained on a hybrid dataset of real and simulated paired MRI volumes to learn generalized representations of motion artifacts. On a prospective clinical test set the network suppresses artifacts while keeping anatomical fidelity, outperforming state-of-the-art methods with higher SSIM and PSNR values and with radiologists reporting greater diagnostic confidence in the corrected volumes. Ablation experiments identify the Motion Perceptual Loss as the main source of these gains.

What carries the argument

The Motion Perceptual Loss, an artifact-aware perceptual supervision term that learns generalized representations of MRI motion artifacts to suppress them while maintaining anatomical fidelity.

If this is right

  • The corrected volumes exhibit higher structural consistency and tissue contrast as shown by improved SSIM and PSNR scores.
  • Radiologists assign higher diagnostic confidence scores to the motion-corrected images compared with uncorrected or prior-method outputs.
  • Ablation studies attribute most of the performance gain to the Motion Perceptual Loss rather than the other network components.
  • Hybrid training on real and simulated pairs enables better generalization to new clinical scans than training on simulated data alone.

Where Pith is reading between the lines

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

  • If the perceptual loss captures motion patterns reliably, the same supervision idea could be adapted to correct other MRI artifacts such as those from field inhomogeneity.
  • Wider adoption might allow shorter scan times in pediatric or restless patients by tolerating some motion without repeat acquisitions.
  • The framework's emphasis on perceptual rather than pixel-wise losses points to possible use in other medical image restoration tasks where structure preservation matters.
  • Testing across multiple scanner vendors and field strengths would reveal whether the learned artifact representations remain effective outside the original training distribution.

Load-bearing premise

The Motion Perceptual Loss trained on a hybrid real-and-simulated dataset learns representations that generalize to unseen clinical MRI volumes without distorting true anatomical structures.

What would settle it

Run the trained model on a large prospective clinical dataset where experts have annotated both artifact regions and the underlying true anatomy, then check whether the corrected outputs match the true anatomy more closely than the original scans without introducing new distortions or removals.

read the original abstract

Purpose: Deep learning-based MRI artifact correction methods often demonstrate poor generalization to clinical data. This limitation largely stems from the inability of deep learning models in reliably distinguishing motion artifacts from true anatomical structures, due to insufficient awareness of artifact characteristics. To address this challenge, we proposed PERCEPT-Net, a deep learning framework that enhances structure preserving and suppresses artifact through dedicated perceptual supervision.Method: PERCEPT-Net is built on a residual U-Net backbone and incorporates three auxiliary components. The first multi-scale recovery module is designed to preserve both global anatomical context and fine structural details, while the second dual attention mechanisms further improve performance by prioritizing clinically relevant features. At the core of the framework is the third Motion Perceptual Loss (MPL), an artifact-aware perceptual supervision strategy that learns generalized representations of MRI motion artifacts, enabling the model to effectively suppress them while maintaining anatomical fidelity. The model is trained on a hybrid dataset comprising both real and simulated paired volumes, and its performance is validated on a prospective test set using a combination of quantitative metrics and qualitative assessments by experienced radiologists.Result: PERCEPT-Net outperformed state-of-the-art methods on clinical data. Ablation studies identified the Motion Perceptual Loss as the primary contributor to this performance, yielding significant improvements in structural consistency and tissue contrast, as reflected by higher SSIM and PSNR values. These findings were further corroborated by radiologist evaluations, which demonstrated significantly higher diagnostic confidence in the corrected volumes.

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

3 major / 2 minor

Summary. The manuscript proposes PERCEPT-Net, a residual U-Net based deep learning model for MRI motion artifact removal. The framework includes a multi-scale recovery module, dual attention mechanisms, and a Motion Perceptual Loss (MPL) designed to learn artifact representations from a hybrid real and simulated training dataset. Performance is evaluated on a prospective clinical test set using quantitative metrics (SSIM, PSNR) and qualitative radiologist assessments, with claims of outperformance over state-of-the-art methods and ablation studies highlighting the MPL as the key component for improved structural consistency and tissue contrast.

Significance. If the central claims hold with proper substantiation, this would be a useful contribution to MRI artifact correction by showing that a tailored perceptual loss can improve generalization from hybrid training data to real prospective clinical volumes. The hybrid training approach, prospective validation, and radiologist involvement are positive elements that address common weaknesses in DL-based medical image restoration. The ablation isolating MPL as the dominant factor is a strength if the supporting numbers and controls are provided.

major comments (3)
  1. [Method] Method section: The hybrid training dataset is described only qualitatively as 'comprising both real and simulated paired volumes.' No counts of real versus simulated volumes, motion simulation parameters (e.g., rotation/translation ranges or artifact severity distribution), or confirmation of no patient overlap with the prospective test set are given. These omissions directly affect the load-bearing claim that the MPL learns generalized artifact representations transferable to unseen clinical data without anatomical distortion.
  2. [Results] Results section: The abstract and results state that PERCEPT-Net achieves higher SSIM and PSNR values than SOTA methods and that ablation studies identify MPL as the primary contributor, yet no numerical values, baseline comparisons, standard deviations, error bars, or statistical significance tests are reported. Without these, the quantitative support for outperformance and the ablation conclusion cannot be evaluated.
  3. [Method] Method (Motion Perceptual Loss subsection): Details on the perceptual feature extractor (architecture, pre-training dataset, or artifact-specific fine-tuning) and any explicit controls for structure preservation (e.g., quantitative metrics on motion-free regions or against motion-free ground truth) are absent. This information is required to assess whether the reported ablation gains reflect genuine artifact suppression or potential overfitting to simulated patterns.
minor comments (2)
  1. [Abstract] Abstract: The statement that radiologist evaluations showed 'significantly higher diagnostic confidence' lacks any description of the evaluation protocol, number of readers, scoring scale, or statistical test used.
  2. [Figures/Tables] Figure captions and tables: Ensure all quantitative comparisons include error bars or confidence intervals and that ablation tables explicitly list the metric values for each configuration rather than only qualitative descriptions.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help improve the clarity and rigor of our manuscript on PERCEPT-Net. We address each major comment below and will revise the manuscript accordingly to provide the requested details and quantitative support.

read point-by-point responses

  1. Referee: [Method] Method section: The hybrid training dataset is described only qualitatively as 'comprising both real and simulated paired volumes.' No counts of real versus simulated volumes, motion simulation parameters (e.g., rotation/translation ranges or artifact severity distribution), or confirmation of no patient overlap with the prospective test set are given. These omissions directly affect the load-bearing claim that the MPL learns generalized artifact representations transferable to unseen clinical data without anatomical distortion.

    Authors: We agree these specifics are necessary for reproducibility and to substantiate generalization. In the revised manuscript, we will report the exact counts of real paired volumes (acquired from clinical scans) versus simulated volumes, detail the motion simulation parameters including rotation and translation ranges along with artifact severity distribution, and confirm no patient overlap between the hybrid training set and the prospective test set. These additions will directly support the claim regarding the MPL's transferable artifact representations. revision: yes

  2. Referee: [Results] Results section: The abstract and results state that PERCEPT-Net achieves higher SSIM and PSNR values than SOTA methods and that ablation studies identify MPL as the primary contributor, yet no numerical values, baseline comparisons, standard deviations, error bars, or statistical significance tests are reported. Without these, the quantitative support for outperformance and the ablation conclusion cannot be evaluated.

    Authors: We acknowledge the absence of specific numbers limits evaluation. The revised results section and abstract will include tables reporting mean SSIM and PSNR values for PERCEPT-Net versus all SOTA baselines, with standard deviations, error bars, and statistical significance (e.g., p-values from paired t-tests or Wilcoxon tests). Ablation results will similarly present full quantitative metrics to demonstrate the MPL's primary contribution. revision: yes

  3. Referee: [Method] Method (Motion Perceptual Loss subsection): Details on the perceptual feature extractor (architecture, pre-training dataset, or artifact-specific fine-tuning) and any explicit controls for structure preservation (e.g., quantitative metrics on motion-free regions or against motion-free ground truth) are absent. This information is required to assess whether the reported ablation gains reflect genuine artifact suppression or potential overfitting to simulated patterns.

    Authors: We will expand the Motion Perceptual Loss subsection with the requested details: the feature extractor architecture (a pre-trained VGG network on ImageNet, specifying layers), pre-training dataset, and any MRI-specific fine-tuning. We will also add explicit controls, including quantitative SSIM/PSNR metrics on motion-free regions compared to ground truth, to confirm structure preservation and address potential overfitting concerns. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical DL framework for MRI motion correction

full rationale

The paper describes an empirical deep learning method (residual U-Net with multi-scale recovery, dual attention, and Motion Perceptual Loss) trained on a hybrid real+simulated dataset and evaluated on a prospective clinical test set. Claims of outperformance and ablation results identifying MPL as the main contributor are standard supervised learning outcomes with external validation metrics (SSIM, PSNR, radiologist scores). No derivation chain, first-principles result, fitted parameter renamed as prediction, or self-citation load-bearing step is present; the architecture and loss are defined independently of the reported performance numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied empirical deep-learning study; no mathematical axioms, free parameters beyond standard network weights, or invented physical entities are introduced or required by the central claim.

pith-pipeline@v0.9.0 · 5592 in / 1185 out tokens · 37390 ms · 2026-05-10T15:46:41.473419+00:00 · methodology

discussion (0)

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Reference graph

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