MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Advancements in AI for medical imaging offer significant potential. However,
their applications are constrained by the limited availability of data and the
reluctance of medical centers to share it due to patient privacy concerns.
Generative models present a promising solution by creating synthetic data as a
substitute for real patient data. However, medical images are typically
high-dimensional, and current state-of-the-art methods are often impractical
for computational resource-constrained healthcare environments. These models
rely on data sub-sampling, raising doubts about their feasibility and
real-world applicability. Furthermore, many of these models are evaluated on
quantitative metrics that alone can be misleading in assessing the image
quality and clinical meaningfulness of the generated images. To address this,
we introduce MedLoRD, a generative diffusion model designed for computational
resource-constrained environments. MedLoRD is capable of generating
high-dimensional medical volumes with resolutions up to
512$\times$512$\times$256, utilizing GPUs with only 24GB VRAM, which are
commonly found in standard desktop workstations. MedLoRD is evaluated across
multiple modalities, including Coronary Computed Tomography Angiography and
Lung Computed Tomography datasets. Extensive evaluations through radiological
evaluation, relative regional volume analysis, adherence to conditional masks,
and downstream tasks show that MedLoRD generates high-fidelity images closely
adhering to segmentation mask conditions, surpassing the capabilities of
current state-of-the-art generative models for medical image synthesis in
computational resource-constrained environments.
