Browsing by Subject "Micro-CT"

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

X-ray computed tomography (CT) is a powerful multi-dimensional (3D + time or energy) imaging modality lauded for its high temporal and spatial resolution. There have been considerable efforts to expand the modality from an anatomical to a functional one, systems capable of measuring metrics such as cardiac and lung function, receptor expression, or metabolic rate in addition to anatomical morphology. This can be accomplished through temporal and spectral imaging CT imaging, where energy dependent material attenuation allows for quantitative separation. These methods are combined synergistically with novel contrast agents, such as targeted nanoparticle-based contrast agents designed to accumulate in cancerous growths, marking tumor boundaries, and provide therapeutic benefits. Here we set forth work furthering the preclinical imaging capabilities of micro-CT, including the design and validation of new micro-CT imaging systems capable of functional imaging in small animal models. These systems are used for longitudinal in vivo imaging and ex vivo validation studies, including temporally resolved cardiac and respiratory imaging and cancer imaging studies. Additionally, image processing tools are presented for addressing artifacts, improving image quality, and image analysis for the studies performed in a preclinical setting. These tools are based on both conventional image processing and powerful new artificial intelligence methods. They involve correction of spatial and spectral image artifacts, segmentation and detection of tumors, and radiomic analysis of imaging data, and have shown use in automating high-throughput imaging. Our imaging results illustrate that functional micro-CT can further the utility of preclinical imaging. These systems and tool will benefit nanotechnology and cancer research, providing a test bed in which to test and optimize novel imaging methods. The developed micro-CT systems can serve to further research in diagnostic and therapeutic, or theranostics, applications for personalized medicine.