arXiv:2603.05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack 3D anatomical continuity. We present a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models (DDPM). Our approach utilizes multi-channel conditioning to incorporate longitudinal context from distinct visits (t_1, t_2) and extends Regio
The increasing sophistication of deep generative models, particularly Denoising Diffusion Probabilistic Models (DDPMs), is enabling more accurate and complex medical image synthesis and analysis applications.
Accurate longitudinal analysis of medical scans is crucial for tracking disease progression and treatment efficacy, and AI advancements in this area directly impact healthcare precision and efficiency.
This advancement promises to improve the reliability of automated neuroimaging pipelines by addressing the challenge of evolving lesions, leading to more consistent and robust clinical assessments.
- · AI healthcare tech companies
- · Medical imaging diagnostics
- · Neurology research
- · Patients with neurological conditions
- · Manual image analysis workflows
- · Clinical research relying on less accurate longitudinal data
Improved diagnostic accuracy and personalized treatment plans for brain-related conditions.
Accelerated development of new therapies and insights into neurological diseases due to better data analysis.
Potential for early detection of subtle changes, leading to preventative interventions and chronic disease management.
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Read at arXiv cs.AI