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Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation

arXiv 2501.14013 ↗First posted 23 Jan 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low radiation doses, different scanners, and are frequently affected by motion and metal artifacts. Prior approaches have targeted the quality improvement of one specific CT phase (e.g., non-contrast CT). In this work, we hypothesized that leveraging multiple CT phases for the quality enhancement of one phase may prove advantageous for downstream tasks, such as segmentation. A 3D progressive fusion and non-local (PFNL) network was developed. It was trained with three degraded (low-quality) phases (non-contrast, arterial, and portal venous) to enhance the quality of the portal venous phase. Then, the effect of scan quality enhancement was evaluated using a proxy task of pancreas segmentation, which is useful for tracking pancreatic cancer. The proposed approach improved the pancreas segmentation by 3% over the corresponding low-quality CT scan. To the best of our knowledge, we are the first to harness multiphase CT for scan quality enhancement and improved pancreas segmentation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 23 Jan 2025.

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10 of 28 references.

References · 10

  1. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
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  8. 2016Low-dose CT denoising with convolutional neural networkcited by 18
  9. 2022Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Samplingcited by 10
  10. 2024Region Attention Transformer for Medical Image Restorationcited by 5

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