Dynamic small animal lung imaging via a postacquisition respiratory gating technique using micro-cone beam computed tomography

Abstract

Rationale and objectives: Micro computed tomography is an important tool for small animal imaging. On many occasions, it is desirable to image lungs in a live instead of postmortem small animal to perform a pulmonary physiology study. Because the lungs are moving, gating with respect to the ventilatory phase has to be performed to reduce motion artifacts. Precapture ventilation gating may be difficult to achieve in some situations, which motivates us to propose and implement a simple postacquisition gating method.

Materials and methods: Rats were used as the subjects in this study. A sequence of low-dose projection images were acquired at 30 frames per second for each view angle. During each capture sequence the rat undergoes multiple ventilation cycles. Using the sequence of projection images, an automated region of interest algorithm, based on integrated grayscale intensity, tracts the ventilatory phase of the lungs. In the processing of an image sequence, multiple projection images are identified at a particular phase and averaged to improve the signal-to-noise ratio. The resulting averaged projection images from different view angles are input to a Feldkamp cone-beam algorithm reconstruction algorithm to obtain isotropic image volumes.

Results: Reconstructions with reduced movement artifacts are obtained. In the gated reconstruction, registration of the bone is much better, the edge of the lung is clearly defined, and structures within the lung parenchyma are better resolved. Also, different phases of a breathing cycle can be reconstructed from one single tomographic scan by the proposed gating method.

Conclusion: A postacquisition gating method using the phase information encoded in the 2-dimensional cone beam projections is proposed. This method is simple to implement and does not require additional experimental set-up to monitor the respiration. It may find applications in lung tumor detection, dynamic pulmonary physiology studies, and the respiratory systems modeling. Minimal motion artifact data sets improve qualitative and quantitative analysis techniques that are useful in physiologic studies of pulmonary structure and function.