An unsupervised approach for projection binning to reduce motion artifacts in free-breathing animal models

Dynamic imaging holds great potential in the diagnosis and comprehensive evaluation of different diseases by capturing mechanical and dynamic characteristics of moving organs. Nonetheless, motion artifacts notably impair image quality, hindering accurate and localized analysis-particularly in free-breathing scenarios. In preclinical studies, traditional methods often necessitate artificial breathing control or use invasive techniques that do not permit functional lung assessment under normal physiological conditions, potentially biasing results and restricting longitudinal studies.

This study aimed to mitigate motion artifacts and preserve temporal information, thus enhancing the spatiotemporal resolution of dynamic micro-CT images in free-breathing animals. We sought to combine the benefits of standard amplitude and phase binning within an unsupervised learning approach, without the need for iterative methods, prior knowledge, or alteration of the reconstruction process. Our approach facilitates accurate imaging of free-breathing animals under various protocols, without requiring artificial breathing control or invasive interventions, through a straightforward, immediately applicable retrospective analysis method.

A novel periodic line-constrained K-means clustering technique was developed as an unsupervised method for projection/interleave binning. To validate this technique on preclinical micro-CT images, a syringe-spring system was engineered to simulate respiratory motion. Imaging was performed on this moving phantom with various breathing rates and inhale-to-exhale (I/E) ratios, as well as in free-breathing rats and rabbits. Additionally, we detail a method for extracting the breathing signal directly from the x-ray projection images and introduce a systematic approach for data imputation in limited-angle scenarios. We also established a metric for quantifying motion artifacts in our 4DCT images.

The clustering method effectively integrated the benefits of both amplitude and phase binning, leading to a marked reduction in motion artifacts across all tests. Notably, our method yielded enhanced image clarity and improved accuracy in capturing dynamic lung volumes, evidenced by sharper diaphragm edges, better visibility of blood vessels, and diminished blurring and motion artifacts. Quantitative analysis using linear regression of diaphragm speed versus blur measure showed a near-zero slope for both rats and rabbits, indicating a substantial decrease in motion artifact presence compared to traditional binning methods.

The periodic line-constrained K-means clustering method provides a robust solution for enhancing the quality of dynamic micro-CT imaging in preclinical studies. By reducing motion artifacts and improving image resolution, this approach enables more precise evaluations of lung function under physiologically relevant conditions. Future work will explore the application of this method to various respiratory disease models and assess its potential for broader clinical use in dynamic imaging of other organs.