Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss
CoRR(2024)
摘要
Long COVID is characterized by persistent symptoms, particularly pulmonary
impairment, which necessitates advanced imaging for accurate diagnosis.
Hyperpolarised Xenon-129 MRI (XeMRI) offers a promising avenue by visualising
lung ventilation, perfusion, as well as gas transfer. Integrating functional
data from XeMRI with structural data from Computed Tomography (CT) is crucial
for comprehensive analysis and effective treatment strategies in long COVID,
requiring precise data alignment from those complementary imaging modalities.
To this end, CT-MRI registration is an essential intermediate step, given the
significant challenges posed by the direct alignment of CT and Xe-MRI.
Therefore, we proposed an end-to-end multimodal deformable image registration
method that achieves superior performance for aligning long-COVID lung CT and
proton density MRI (pMRI) data. Moreover, our method incorporates a novel
Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning
methods for monomodal registration by making them adaptable for multimodal
tasks. The registration results achieve a Dice coefficient score of 0.913,
indicating a substantial improvement over the state-of-the-art multimodal image
registration techniques. Since the XeMRI and pMRI images are acquired in the
same sessions and can be roughly aligned, our results facilitate subsequent
registration between XeMRI and CT, thereby potentially enhancing clinical
decision-making for long COVID management.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要