Photon-counting detector (PCD) bring multiple advantages, including higher contrast, lower noise, and improved spatial resolution compared to the conventional energy-integrating detector (EID) scanners. We investigated the image quality performance of a prototype CdZnTe-based photon-counting detector (PCD) CT scanner in this phantom study. We performed a phantom study 3D-printed inserts which mimicked coronary artery plaques along with calibrated concentrations of iodine, water, soft plaque (fat), and hard plaque (calcium). The phantom was scanned with similar settings on a CdZnTe-based PCD-CT system and a comparable state-of-the-art EID-CT system. Image noise, CT number stability, and CNR were measured in matched circular regions of interest. PCD-CT demonstrated ~50% lower noise compared to EID-CT across all x-ray exposures. Both systems showed a CT number deviation due to noise in the ±2 HU range. CNR across iodine, soft and hard plaques, and water showed improvement in the 201%-332% range for PCD-CT over EID-CT. Lastly, in a noise-matched setting PCD-CT can achieve similar image quality as EID-CT at 25% of the radiation dose.
Purpose: to investigate image quality of the ultra-high-resolution (UHR) mode of a dual-source photon-counting CT scanner in visualizing mixed (soft and hard) coronary artery plaques. Materials and methods: We scanned a custom-made phantom with 10 mixed plaques of various sizes and compositions. Each scan was repeated three times. Images were reconstructed with FBP, and model-based quantum iterative reconstruction (QIR). Image quality was investigated by measuring mean CT numbers, noise standard deviation (SD), and by line profiles.
Results: UHR mode provided sharper difference between soft and hard plaques, and the lumen by reducing blooming artifacts. Furthermore, it improved the true CT number of the values by reducing partial volume However, SD of noise increases by a factor of ~8 in FBP reconstructions at thinnest slice thickness (0.2 mm). Quantum iterative reconstruction algorithm reduced image noise x4 of the SR FBP without any apparent loss of spatial resolution.
Conclusion: UHR PCCT improves plaque characterization through improved spatial resolution which results in lower blooming artifacts and partial volume effects. The increase in image noise can be mitigated by using model-based iterative reconstruction algorithms without any loss of spatial resolution. Depending on the imaging task, further noise reduction can be achieved by reconstructing thicker slices. A more detailed investigation with noise power spectrum analysis and observer model studies is warranted.
Patient specific dose estimation is traditionally calculated though Monte-Carlo methods but not performed during CT image acquisition due to long computation times. We propose the implementation of a NN to perform patient specific dose estimation that can be performed alongside the CT acquisition due to the reduction in computation time. This is achieved by first performing MC simulations and then training the NN to replicate these predictions. The NN shows promise with a high degree of total cranial dose accuracy between the predictions and ground truth with a standard deviation of less than ±0.5 mGy.
We compared CT x-ray beam-hardening artifacts in a hybrid scanner with energy-integrating detectors (EID) versus photon-counting detectors (PCD) subsystems. EID-CT images had less beam hardening artifacts compared to PCD-CT images for x-ray tube voltages 120 kVp and higher. We further demonstrated that the inherent spectral information of PCDs can be used to effectively eliminate beam-hardening artifacts.
Background: With new advances in CT energy-integrating (EID) and photon-counting (PCD) detector technology, the spatial resolution of CT has significantly improved from 0.50 mm to less than 0.25 mm isotropic. Our goal was to explore the image quality improvements that are associated with UHR CT, specifically in imaging and characterization of atherosclerotic plaques. Methods: We imaged seven excised human hearts, with a known history of atherosclerosis, on an EID-CT scanner equipped with a UHR comb. The scans were performed at 120 kVp and 211 mAs, with 1-sec gantry rotation time. The images were reconstructed twice; first with a standard resolution (SR) reconstruction kernel (Uq36u) at 0.5x0.5x3.0 mm3 voxel size, and second with a UHR kernel (Uq77u) at 0.2x0.2x1.5 mm3. We measured calcium volume, Agatston score, and number of lesions with dense calcification (HU>1000). We propose a multi-resolution visualization scheme in which smooth soft tissue features are derived from the SR image, and the sharp hard plaque features blended from the UHR images. Results: We detected a total of 105 lesions in the seven hearts. The UHR images had significantly reduced blooming artifacts which leads to higher average HU values and smaller lesion volumes. The calcium volume for UHR was 67% of the SR volumes (r2= 0.81). Agatston scores were also systematically lower in UHR compared to SR 41% (r2= 0.81). In addition, we found 102 lesions with dense calcification compared to 4 in SR. Conclusion: UHR-CT can significantly reduce blooming artifacts and partial volume effects in atherosclerotic plaque imaging.
Virtual mono-energetic images (VMI) are derived from dual- and multi-energy CT acquisitions to visualize attenuation tomograms of an object at a specific x-ray energy. Conventional VMI calculation does not involve k-edge imaging for heavier contrast materials, such as gadolinium and bismuth. To achieve results closer to real-world values, we have developed a VMI calculation and visualization tool that includes k-edge imaging. The algorithm is tested on multi-material decomposition of iodine and gadolinium in a virtual phantom. Attenuation values are calculated for varying concentration of iodine and gadolinium in the simulated phantom. A visualization toolkit is developed to perform VMI calculation and displays output images. Attenuation changes based on incident x-ray energies can be tested and observed, and a characteristic jump in attenuation can be observed in VMI at the 50kV energy (k-edge of gadolinium) in vials containing Gd.
Motion of the coronary arteries during the cardiac cycle can distort the reconstructed CT image and negatively affect the evaluation of calcified plaques. These movements are manifested as motion artifacts. These artifacts and their corresponding stationary calcifications were used to train a Deep Convolutional Neural Network (DCNN). We used reported ranges of motions for coronary arteries to create a computer moving phantom of calcified plaques. We created a computer model of a CT scanner and created CT projections and reconstructions of stationary and moving plaques. CT images with artifacts and stationary images were used as input and targets of the DCNN, respectively. To control the progression of the DCNN, transfer learning was implemented to slowly introduce increasingly complicated images. The results of the regression plots generated before and after from a representative data set show a slope of 1.85 (r2=0.72) vs 1.08 (r2=0.90) before the network recovery and after DCNN, respectively. DCNNs demonstrate a promising approach to the complicated problem of CT motion correction in computer simulations. Further evaluation with actual motion artifacts is needed.
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