MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis (2503.13211v1)

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.

Overview of MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis

The paper "MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis" by Seyfarth et al. presents a novel approach for generating synthetic high-resolution 3D medical images using a diffusion model optimized for environments with limited computational resources. The authors introduce MedLoRD, a generative AI framework that aims to address the inherent challenges in medical image synthesis, particularly focusing on computational efficiency and the generation of clinically useful images.

Key Contributions

The primary innovation of MedLoRD is its capability to generate volumetric medical images at high resolutions (up to $512 \times 512 \times 256$) using GPUs with only 24GB of VRAM. This is made feasible by reconstructing images in a latent space and employing a latent diffusion model that integrates a Vector Quantised Variational Autoencoder (VQ-VAE) with a 3D U-Net for image denoising.

MedLoRD's capability to generate realistic and diagnostically useful imagery is evaluated extensively across modalities, including Coronary Computed Tomography Angiography (CCTA) and Lung Computed Tomography (CT) datasets. It exhibits superior performance compared to existing state-of-the-art models, such as MAISI, VQ-Trans, and HA-GAN, particularly in settings where computational resources are constrained.

Methodological Approaches

The authors utilize VQ-VAE GANs at the core of their approach for encoding medical images into a compressed latent space. This encoding is combined with a U-Net based model for denoising, enhanced through 3D convolutional operations, streamlining the process for three-dimensional volume synthesis.

MedLoRD introduces an efficient ControlNet variant with a reduced size for conditional generation, enabling the model to incorporate specific anatomical conditions. This flexibility is achieved with notable memory efficiency, aligning with the computational limitations in many healthcare settings.

Moreover, the paper evaluates the synthetic images using an array of metrics, including Fréchet Inception Distance (FID), Regional Volume Ratio (RVR), and radiological assessment, allowing a comprehensive evaluation of the model's output beyond traditional quantitative measures. The paper criticizes the reliance on FID alone for medical image evaluation, proposing a multi-faceted approach including radiological validation as a more reliable measure.

Results and Implications

MedLoRD demonstrates the ability to generate synthetic images that closely resemble real medical images, as shown through radiological evaluations where 6 out of 10 samples from the CCTA dataset were rated as indistinguishable from real ones. Furthermore, the image synthesis achieved by MedLoRD is validated by maintaining high DICE scores which affirm the model's capability to preserve anatomical fidelity.

The results underscore MedLoRD's practicality for real-world medical scenarios, bridging the gap between high-quality image synthesis and limited available computational infrastructure in typical clinical environments. This has significant implications for data sharing and augmentation in medical imaging, potentially easing concerns surrounding patient privacy and data scarcity.

Future Directions

The paper opens several avenues for future research. One aspect is the potential to integrate more robust conditional settings and address the nuances of different modalities with improved accuracy. The authors also highlight the prospect of refining evaluation criteria to better capture the diagnostic utility and avoid reliance on single-metric evaluations. Incorporating techniques to mitigate data memorization risks in synthetic data sharing also represents a promising research trajectory.

Overall, MedLoRD provides a meaningful advance in the domain of generative models for medical imaging, illustrating an effective balance between computational constraints and clinical applicability. This work offers a basis for further exploration of diffusion models in healthcare, heralding more efficient synthetic data generation with practical clinical relevance.