Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography.
Matthew D HolbrookDarin P ClarkCristian T BadeaPublished in: Journal of medical imaging (Bellingham, Wash.) (2018)
Spectral computed tomography (CT) using photon counting detectors (PCDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCDs are especially suited for K-edge imaging, revealing the spatial distribution of select imaging probes through quantitative material decomposition. We report on a prototype spectral micro-CT system with a CZT-based PCD (DxRay, Inc.) that has 16 × 16 pixels of 0.5 × 0.5 mm 2 , a thickness of 3 mm, and four energy thresholds. Due to the PCD's limited size ( 8 × 8 mm 2 ), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ( ∼ 4.5 cm ). Projection corrections were implemented to minimize artifacts associated with dead pixels and projection stitching. A sophisticated iterative algorithm was used to reconstruct both phantom and ex vivo mouse data. To achieve preclinically relevant spatial resolution, we trained a convolutional neural network to perform pan-sharpening between low-resolution PCD data ( 247 - μ m voxels) and high-resolution energy-integrating detector data ( 82 - μ m voxels), recovering a high-resolution estimate of the spectral contrast suitable for material decomposition. Long-term, preclinical spectral CT systems such as ours could serve in the developing field of theranostics (therapy and diagnostics) for cancer research.
Keyphrases
- image quality
- dual energy
- high resolution
- computed tomography
- convolutional neural network
- contrast enhanced
- optical coherence tomography
- electronic health record
- positron emission tomography
- mass spectrometry
- deep learning
- big data
- magnetic resonance imaging
- cell therapy
- single molecule
- machine learning
- high speed
- small molecule
- tandem mass spectrometry
- squamous cell carcinoma
- artificial intelligence
- photodynamic therapy
- high intensity