mlsanityData to 14 Oct 2026

Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions

arXiv 2507.00670 ↗First posted 1 Jul 2025eess.IV cs.CVcited by 2

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Abstract (arXiv)

In recent years, accelerated MRI reconstruction based on deep learning has led to significant improvements in image quality with impressive results for high acceleration factors. However, from a clinical perspective image quality is only secondary; much more important is that all clinically relevant information is preserved in the reconstruction from heavily undersampled data. In this paper, we show that existing techniques, even when considering resampling for diffusion-based reconstruction, can fail to reconstruct small and rare pathologies, thus leading to potentially wrong diagnosis decisions (false negatives). To uncover the potentially missing clinical information we propose ``Semantically Diverse Reconstructions'' (SDR), a method which, given an original reconstruction, generates novel reconstructions with enhanced semantic variability while all of them are fully consistent with the measured data. To evaluate SDR automatically we train an object detector on the fastMRI+ dataset. We show that SDR significantly reduces the chance of false-negative diagnoses (higher recall) and improves mean average precision compared to the original reconstructions. The code is available on https://github.com/NikolasMorshuis/SDR

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

20 of 27 references.

References · 20

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2018Robustness May Be at Odds with Accuracycited by 880
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  6. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
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  8. 2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Modelscited by 120
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  10. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 65
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  12. 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 44
8 more
  1. 2024Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problemscited by 21
  2. 2023CLIPAG: Towards Generator-Free Text-to-Image Generationcited by 12
  3. 2023From Posterior Sampling to Meaningful Diversity in Image Restorationcited by 12
  4. 2022Adversarial Robustness of MR Image Reconstruction under Realistic Perturbationscited by 11
  5. 2025Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metricscited by 10
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  8. 2024Segmentation-guided MRI reconstruction for meaningfully diverse reconstructionscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRIcited by 1
  2. 2026Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstructioncited by 0