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

2025-01-23

AI Summary

Low-dose acquisition, inter-scanner variability, motion artifacts, and metal interference severely degrade portal venous phase (PVP) images in multiphase CT, impeding reliable pancreatic identification. Method: We propose the first multi-phase CT collaborative reconstruction framework specifically designed for single-phase image quality enhancement—leveraging non-contrast, arterial, and low-quality PVP CT volumes to train a 3D progressive fusion and non-local (PFNL) neural network that jointly learns a mapping from degraded multiphase inputs to high-fidelity PVP output. Contribution/Results: This work is the first to empirically demonstrate that synergistic multiphase information enhances downstream clinical tasks: in pancreatic cancer tracking, pancreatic segmentation Dice score improves by 3.0% over baseline low-quality PVP images, substantially boosting clinical utility. The method establishes a new paradigm for leveraging complementary temporal-phase information to overcome single-phase imaging limitations.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Portal Vein
• CT Image Enhancement
• Pancreas Visualization

Innovation

• Multi-phase CT
• 3D Network (PFNL)
• Pancreas Segmentation Accuracy