Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging.
Authors
Affiliations (4)
Affiliations (4)
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Box 3302, Durham, NC 27710, USA, Durham, North Carolina, 27710-1000, UNITED STATES.
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Box 3302, Durham, NC 27710, USA, Durham, North Carolina, 277075164, UNITED STATES.
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Box 3302, Durham, NC 27710, USA, Durham, North Carolina, 27710, UNITED STATES.
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Box 3302, Durham, North Carolina, 27710-1000, UNITED STATES.
Abstract
In this work, we introduce a benchtop, turn-table photon-counting (PC) micro-CT scanner and highlight its application for dynamic small animal perfusion imaging.
Approach: Built on recently published hardware, the system now features a CdTe-based photon-counting detector (PCD). We validated its static spectral PC micro-CT imaging using conventional phantoms and assessed dynamic performance with a custom flow-configurable dual-compartment perfusion phantom. The phantom was scanned under varied flow conditions during injections of a low molecular weight iodinated contrast agent. In vivo mouse studies with identical injection settings demonstrated potential applications. A pretrained denoising CNN processed large multi-energy, temporal datasets (20 timepoints × 4 energies × 3 spatial dimensions), reconstructed via weighted filtered back projection. A separate CNN, trained on simulated data, performed gamma variate-based 2D perfusion mapping, evaluated qualitatively in phantom and in vivo tests.
Main Results: Full five-dimensional reconstructions were denoised using a CNN in ~3% of the time of iterative reconstruction, reducing noise in water at the highest energy threshold from 1206 HU to 86 HU. Decomposed iodine maps, which improved contrast to noise ratio from 16.4 (in the lowest energy CT images) to 29.4 (in the iodine maps), were used for perfusion analysis. The perfusion CNN outperformed pixelwise gamma variate fitting by ~33%, with a test set error of 0.04 vs. 0.06 in blood flow index (BFI) maps, and quantified linear BFI changes in the phantom with a coefficient of determination of 0.98.
Significance: This work underscores the PC micro-CT scanner's utility for high-throughput small animal perfusion imaging, leveraging spectral PC micro-CT and iodine decomposition. It provides a versatile platform for preclinical vascular research and advanced, time-resolved studies of disease models and therapeutic interventions.