Real-time Volumetric Imaging for Minimally-Invasive Thoracic Interventions: A Deformable Phantom Study

Objectives

This study aimed to evaluate the ability of VoxelMap, a deep learning framework for 3D respiratory motion estimation, to generate volumetric images in real-time from 2D projections acquired for two deformable lung phantoms on a clinical interventional C-arm angiography system.

Materials and Methods

VoxelMap was evaluated using the PixelPrint ^4D and Casper deformable physical thoracic respiratory phantoms. Four-dimensional cone-beam CT (4D CBCT) images acquired on a clinical fixed-room C-arm angiography system were used for network training and volumetric ground truth. Prediction accuracy was evaluated on withheld respiratory phases using structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Model inference time was measured to assess suitability for continuous real-time processing. Deep ensembles were also used to assess model uncertainty. Correlation between ensemble uncertainty and prediction error was evaluated for both phantoms.

Results

VoxelMap achieved high prediction accuracy across withheld respiratory phases in both physical phantoms. SSIM values exceeded 0.92 in all experiments, with a higher performance observed for the Casper phantom (0.969-0.973) than for the PixelPrint ^4D phantom (0.927-0.943). Model inference times were less than 15 ms for both phantoms which is compatible with real-time processing. Ensemble uncertainty was positively correlated with prediction error in both phantoms, indicating that higher uncertainty was associated with less accurate volumetric predictions.

Conclusions

Our study demonstrated that VoxelMap generates accurate volumetric deformations of respiratory anatomy from projections acquired on a clinical interventional C-arm system, with computational inference times compatible with continuous processing. By deriving volumetric information directly from two-dimensional fluoroscopic projections, VoxelMap has the potential to augment continuous fluoroscopic guidance with continuously updated volumetric information. Ensemble uncertainty may provide a potentially useful indicator of prediction reliability. Further studies using clinically representative training data and patient studies is required to establish clinical utility.

Our code is available at https://github.com/Image-X-Institute/Voxelmap .