mlsanityData to 7 Oct 2026

Imaging foundation model for universal enhancement of non-ideal measurement CT

arXiv 2410.01591 ↗First posted 2 Oct 2024eess.IV cs.AI cs.CVcited by 6

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Abstract (arXiv)

Non-ideal measurement computed tomography (NICT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degrade image quality, limiting clinical acceptability. Although deep learning methods have been used to enhance NICT images, their reliance on large training datasets and limited generalizability across diverse settings hinder practical use. We propose the multi-scale integrated Transformer AMPlifier (TAMP), the first imaging foundation model for universal NICT enhancement. Pre-trained on 10.8 million physics-driven simulated NICT images, TAMP generalizes effectively across various NICT settings, defect degrees, and body regions. Moreover, a parameter-efficient fine-tuning strategy enables TAMP to adapt to specific clinical scenarios using only few slices. Extensive experiments, including radiologists and real-world validations, demonstrate that TAMP consistently improves image quality and clinical acceptability, underscoring its significant potential to advance CT imaging and broaden NICT applications in clinical practice.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 63 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

18 of 63 references.

References · 18

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6 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learningcited by 4
  2. 2025DeepSparse: A Foundation Model for Sparse-View CBCT Reconstructioncited by 4
  3. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoisingcited by 2
  4. 2025Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentationcited by 0
  5. 2026Foundation Models for Medical Imaging: Status, Challenges, and Directionscited by 0
  6. 2026A deep dictionary network-based foundation model for ultra-low-dose CT denoisingcited by 0