RadRotator: 3D Rotation of Radiographs with Diffusion Models (2404.13000v1)

Abstract: Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few previous studies attempted to convert two or more input radiographs into computed tomography (CT) volumes. Following their effort, we introduce a diffusion model-based technology that can rotate the anatomical content of any input radiograph in 3D space, potentially enabling the visualization of the entire anatomical content of the radiograph from any viewpoint in 3D. Similar to previous studies, we used CT volumes to create Digitally Reconstructed Radiographs (DRRs) as the training data for our model. However, we addressed two significant limitations encountered in previous studies: 1. We utilized conditional diffusion models with classifier-free guidance instead of Generative Adversarial Networks (GANs) to achieve higher mode coverage and improved output image quality, with the only trade-off being slower inference time, which is often less critical in medical applications; and 2. We demonstrated that the unreliable output of style transfer deep learning (DL) models, such as Cycle-GAN, to transfer the style of actual radiographs to DRRs could be replaced with a simple yet effective training transformation that randomly changes the pixel intensity histograms of the input and ground-truth imaging data during training. This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference.

• The paper demonstrates that applying conditional DDPMs generates high-quality 3D rotations from single-view radiographs.
• It introduces a novel RandHistogramShift technique to harmonize training data and align generated images with real radiographic characteristics.
• The model achieves anatomical accuracy in rotations up to ±15 degrees, offering potential improvements for non-invasive diagnostic imaging.

Analyzing the RadRotator: 3D Rotation of Radiographs Using Diffusion Models

Overview of Methodologies and Innovations

RadRotator introduces a novel application of Denoising Diffusion Probabilistic Models (DDPMs) to rotate single-view radiographs into three-dimensional views. This approach leverages conditional DDPMs to generate digitized articulated representations from any angle, enhancing radiographs' analytical value without demanding multiple or adjusted patient exposures. The key innovations presented in the paper include:

• Utilization of DDPMs: The framework employs conditional DDPMs for image generation, which notably outperforms traditional Generative Adversarial Networks (GANs) in image quality and diversity.
• Training Data Transformation: The introduced RandHistogramShift transformation method effectively eliminates the need for separate style-transfer models by randomizing pixel intensity histograms during training, which aligns generated outputs closely with real imaging characteristics.

Technical Specifications and Implementation

The RadRotator model draws from a substantial dataset of CT scans converted into Digitally Reconstructed Radiographs (DRRs) using the DeepDRR technique, simulating traditional radiography but with controlled experimental variability. Here are details regarding process and implementation:

1. Data Collection and Preparation
   • DRRs were synthesized from 9,044 non-contrast-enhanced CT scans using material decomposition and advanced image processing techniques to simulate x-ray passage through tissue.
   • The final dataset consisted of over 2.5 million DRR pairs stratified and divided across training, validation, and testing sets, emphasizing diverse clinical scenarios.
2. Model Architecture and Training
   • The DDPM was trained with a target to reverse the noise application process, using a U-Net-like architecture enhanced with residual blocks and attention mechanisms.
   • Training involved typical data augmentation practices and a novel RandHistogramShift technique to harmonize the style variance between DRRs and conventional radiographs.

Findings and Model Evaluations

Initial qualitative assessments demonstrated that the RadRotator could reliably rotate radiographs in the x, y, and z planes up to ±15 degrees with substantial anatomical accuracy and visual consistency. The model generates these rotated views by either predicting single rotations or creating a series of rotational frames simulating continuous movement, potentially supporting dynamic surgical planning and diagnostics.

However, the research acknowledges limitations in extrapolation beyond the trained rotational degrees and suggests future work with expanded DRR datasets to enhance rotational range. The current findings, while promising, underscore the need for systematic quantitative evaluations and possibly clinical trials to validate practical usability and effectiveness.

Future Prospects and Considerations

The introduction of RadRotator paves the way for substantial future developments in medical imaging:

• Enhanced Diagnostic Tools: With further validation, this model could significantly enhance diagnostic capabilities by providing 3D perspectives from single 2D radiographs, reducing the need for more costly or invasive imaging techniques.
• Integration into Clinical Workflows: Seamless integration into existing medical imaging platforms could democratize advanced imaging capabilities, making them more accessible to various healthcare settings.
• Further Research in Generative Models: The application of DDPMs in medical imaging invites more extensive research, potentially expanding into other imaging modalities or diagnostic parameters.

The RadRotator project represents a significant step forward in applying advanced AI techniques to practical problems in medical imaging, highlighting both the potential and the challenges of these technologies in clinical and diagnostic applications.