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 mm2, a thickness of 3 mm, and four energy thresholds. Due to the PCD’s limited size (8 × 8 mm2), 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-μm voxels) and high-resolution energy-integrating detector data (82-μm 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.
Dual energy (DE) micro-CT shows great potential to provide accurate tissue composition by utilizing the energy dependence of x-ray attenuation in different materials. This is especially well-suited for pre-clinical imaging using nanoparticle-based contrast agents in situations where quantitative material decomposition helps probe processes which are otherwise limited by poor soft tissue contrast. We have previously proposed optimal in vivo DE micro-CT methods for imaging using iodinated and gold nanoparticles. However, in vivo studies are limited in spatial resolution due to constraints in sampling time and radiation dose. Ex vivo dual energy imaging can provide much higher resolution and can serve as a validation of in vivo studies. Our study proposes multiscale in vivo and ex vivo DE micro-CT of the same subjects using two in-house developed micro-CT systems. We use a dual source micro-CT system to scan a mouse that has been injected with both iodinated and gold nanoparticles for in vivo DE scanning at 63 micron resolution. The same mouse is then scanned ex vivo with DE on a separate single source micro-CT system at a spatial resolution of 22 microns. We perform reconstructions using filtered back projection followed by noise reduction via joint bilateral filtration. A dynamic flat field correction method has been applied on the ex vivo micro-CT data to correct for image artifacts. A DE post-reconstruction decomposition is used to create iodine and gold material maps which are used to measure accumulation of contrast agent within the body. We evaluate challenges associated with each imaging methodology. Our results compare image quality and material maps. Overall, our methods represent a substantial tool for multiscale DE micro-CT imaging using wellcharacterized contrast agents and serving various applications in biological research.
Spectral CT using photon counting x-ray detectors (PCXDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCXDs are especially suited for imaging Kedge contrast agents, revealing the spatial distribution of select imaging probes through quantitative material decomposition. To further advance the field, there is a clear and continuing need to develop PCXD hardware and software as part of a new generation of spectral CT imaging systems. Our group specializes in the development of preclinical microCT systems and of novel imaging probes based on K-edge materials. Toward this goal, we have now developed a prototype spectral micro-CT system with a PCXD produced by DxRay. This CZT-based PCXD has 16x16 pixels, each with a size of 0.5 x 0.5 mm, a thickness of 3 mm, and 4 configurable energy thresholds. The detector is thus only 8 mm x 8 mm in size. Due to the limited size of this detector tile, we have implemented a translate-rotate micro-CT system (i.e. a 2nd generation scanner). In this paper we summarize considerable efforts which went into compensating for dead pixels and for pixels with non-linear responses to prevent artifacts in the CT reconstruction results. We also present spectral response measurements for the detector and the results of both phantom and animal experiments with iodine- and gold-based contrast agents. The results confirm our ability to sample and reconstruct tomographic images, but also show that the PCXD prototype has limitations in imaging iodine.
Advances in CT hardware have propelled the development of novel CT contrast agents. Combined with the spectral capabilities of X-ray CT, molecular imaging is possible using multiple heavy-metal contrast agents. Nanoparticle platforms make particularly attractive agents because of (1) their ability to carry a large payload of imaging moieties, and (2) their ease of surface modification to facilitate molecular targeting. While several novel imaging moieties based on high atomic number elements are being explored, iodine (I) and gadolinium (Gd) are particularly attractive because they are already in clinical use. In this work, we investigate the feasibility for in vivo discrimination of iodine and gadolinium nanoparticles using dual energy micro-CT. Phantom experiments were performed to measure the CT enhancement for I and Gd over a range of voltages from 40 to 80 kVp using a dual-source micro-CT system with energy integrating detectors having cesium iodide scintillators. The two voltages that provide maximum discrimination between I and Gd were determined to be 50 kVp with Cu filtration and 40 kVp without any filtration. Serial dilutions of I and Gd agents were imaged to determine detection sensitivity using the optimal acquisition parameters. Next, an in vivo longitudinal small animal study was performed using Liposomal I (Lip-I) and Liposomal Gd (Lip-Gd) nanoparticles. The mouse was intravenously administered Lip-Gd and imaged within 1 h post-contrast to visualize Gd in the vascular compartment. The animal was reimaged at 72 h post-contrast with dual-energy micro-CT at 40 kVp and 50 kVp to visualize the accumulation of Lip-Gd in the liver and spleen. Immediately thereafter, the animal was intravenously administered Lip-I and re-imaged. The dual energy sets were used to estimate the concentrations of Gd and I via a two-material decomposition with a non-negativity constraint. The phantom results indicated that the relative contrast enhancement per mg/ml of I to Gd was 0.85 at 40 kVp and 1.79 at 50 kVp. According to the Rose criterion (CNR<5), the detectability limits were 2.67 mg/ml for I and 2.46 mg/ml for Gd. The concentration maps confirmed the expected biodistribution, with Gd concentrated in the spleen and with I in the vasculature of the kidney, liver, and spleen. Iterative reconstruction provided higher sensitivity to detect relatively low concentrations of gadolinium. In conclusion, dual energy micro-CT can be used to discriminate and simultaneously image probes containing I and Gd.
Spectral CT can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. We have introduced image reconstruction and material decomposition algorithms for multi-energy CT data acquired either with energy integrating detectors (EID) or photon counting detectors (PCD); however, material decomposition is an ill-posed problem due to the potential overlap of spectral measurements and to noise. Recently, convolutional neural networks (CNN) have generated excitement in the field of machine learning and computer vision. The goal of this work is to develop CNN-based methods for material decomposition in spectral CT. The CNN for decomposition had a U-net structure and was trained with either five-energy PCD-CT or DE-CT. As targets for training, we used simulated phantoms constructed from random combinations of water and contrast agents (iodine, barium, and calcium for five-energy PCD-CT; iodine and gold for DE EID-based CT). The experimentally measured sensitivity matrix values for iodine, barium, and calcium or iodine and gold were used to recreate the CT images corresponding to both PCD and DE-CT cases. These CT images were used to train CNNs to generate material maps at each pixel location. After training, we tested the CNNs by applying them to experimentally acquired DE-EID and PCD-based micro-CT data in mice. The predicted material maps were compared to the absolute truth in simulations and to sensitivity-based decompositions for the in vivo mouse data. The CNN-based decomposition provided higher accuracy and lower noise. In conclusion, our U-net performed a more robust spectral micro-CT decomposition because it inherently better exploits spatial and spectral correlations.