Diffusion Deformable Model for 4D Temporal Medical Image Generation

نویسندگان

چکیده

Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural have been extensively studied, approaches image generation such as 4D cardiac data limited. In this work, we present a novel deep learning model that generates intermediate volumes between source and target volumes. Specifically, propose diffusion deformable (DDM) by adapting the denoising probabilistic has recently widely investigated realistic generation. Our proposed DDM is composed of deformation modules so can learn spatial provide latent code generating frames along geodesic path. Once our trained, estimated from module simply interpolated fed into module, which enables generate continuous trajectory while preserving topology image. We demonstrate method MR diastolic systolic phases each subject. Compared existing methods, achieves high performance on

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-16431-6_51