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.
Semiconductor-based photon counting detectors (PCDs) measure each incident photon energy through direct conversion process. Comparing to the conventional scintillator-based energy-integrating detectors (EIDs), it can be made with smaller pixel sizes and hence improve the system spatial resolution. The actual performance of the PCDs in CT system is compromised from ideal mainly due to two factors: charge sharing effect and pulse pileup. In particular, the charge sharing effect introduces signal crosstalk between neighboring pixels and degrade the detection spatial resolution. In this study, we derive a rigorous charge sharing detection formalism based on a comprehensive detector response model. The simulated results are compared with a pixel-to-pixel covariance measurement from a CZT-based prototype photon counting system. The results suggests that while a large fraction of detected events is affected by charge sharing, the actual measured crosstalk between neighboring pixels is greatly suppressed by a proper detection threshold in the counting mode. To understand the practical impact in image quality and optimize system design, a simplified crosstalk model based on the estimated charge sharing event rate is integrated into a system level simulation framework with realistic system specifications. The simulated image MTFs are measured for systems with four different pixel sizes. Results indicate that with realistic charge sharing effect, in the pixel size range that we tested, the image MTF steadily increases as the detector pixel size decreases.
Low-contrast lesions are difficult to detect in noisy low-dose CT images. Improving CT image quality for this detection task has the potential to improve diagnostic accuracy and patient outcomes. In this work, we use tunable neural networks for CT image restoration with a hyperparameter to control the variance/bias tradeoff. We use clinical images from a super-high-resolution normal-dose CT scan to synthesize low-contrast low-dose CT images for supervised training of deep learning CT reconstruction models. Those models are trained using with multiple noise realizations so that variance and bias can be penalized separately. We use a training loss function with one hyperparameter called the denoising level, which controls the variance/bias tradeoff. Finally, we evaluate the CT image quality to find the optimal denoising level for low-contrast lesion detectability. We evaluate performance using a shallow neural network model classification model to represent a suboptimal image observer. Our results indicate that the optimal networks for low-contrast lesion detectability are those that prioritize bias reduction rather than mean-squared error, which demonstrates the potential clinical benefit of our proposed tunable neural networks.
Dual-energy CT (DECT) has become increasingly popular in practice due to its unique capability of material differentiation. One typical implementation of DECT is to use fast kV switching acquisition technique, which rapidly alternates the X-ray tube voltage between two predetermined kVs in a frequent manner. However, usage of such technique may be limited in practice, as it typically requires sophisticated hardware of high cost and lacks of dose efficiency due to difficulty in tube current modulation. One possible solution is to reduce the frequency of voltage switching during acquisition. However, this alternative approach may potentially compromise the image quality, as it results in sparse measurements for both kVs. In this paper, we proposed a cascaded deep-learning reconstruction framework for sparse-view kV-switching DECT, where two deep convolutional neural networks were employed in the reconstruction, one completing the missing views in the sinogram space and the other improving image quality in the image space. We demonstrated the feasibility of proposed method using sparse-view kV-switching data simulated from rotate-rotate DECT scans with phantom and clinical data. Experimental results show that the proposed method on sparse-view kV-switching data achieve comparable image quality and quantitative accuracy as compared to traditional method on fully-sampled rotate-rotate data
Due to the wide variability of intra-patient respiratory motion patterns, traditional short-duration cine CT used in respiratory gated PET/CT may be insufficient to match the PET scan data, resulting in suboptimal attenuation correction that eventually compromises the PET quantitative accuracy. Thus, extending the duration of cine CT can be beneficial to address this data mismatch issue. In this work, we propose to use a long-duration cine CT for respiratory gated PET/CT, whose cine acquisition time is ten times longer than a traditional short-duration cine CT. We compare the proposed long-duration cine CT with the traditional short-duration cine CT through numerous phantom simulations with 11 respiratory traces measured during patient PET/CT scans. Experimental results show that, the long-duration cine CT reduces the motion mismatch between PET and CT by 41% and improves the overall reconstruction accuracy by 42% on average, as compared to the traditional short-duration cine CT. The long-duration cine CT also reduces artifacts in PET images caused by misalignment and mismatch between adjacent slices in phase-gated CT images. The improvement in motion matching between PET and CT by extending the cine duration depends on the patient, with potentially greater benefits for patients with irregular breathing patterns or larger diaphragm movements.
Low dose CT imaging is typically constrained to be diagnostic. However, there are applications for even lowerdose CT imaging, including image registration across multi-frame CT images and attenuation correction for PET/CT imaging. We define this as the ultra-low-dose (ULD) CT regime where the exposure level is a factor of 10 lower than current low-dose CT technique levels. In the ULD regime it is possible to use statistically-principled image reconstruction methods that make full use of the raw data information. Since most statistical based iterative reconstruction methods are based on the assumption of that post-log noise distribution is close to Poisson or Gaussian, our goal is to understand the statistical distribution of ULD CT data with different non-positivity correction methods, and to understand when iterative reconstruction methods may be effective in producing images that are useful for image registration or attenuation correction in PET/CT imaging. We first used phantom measurement and calibrated simulation to reveal how the noise distribution deviate from normal assumption under the ULD CT flux environment. In summary, our results indicate that there are three general regimes: (1) Diagnostic CT, where post-log data are well modeled by normal distribution. (2) Lowdose CT, where normal distribution remains a reasonable approximation and statistically-principled (post-log) methods that assume a normal distribution have an advantage. (3) An ULD regime that is photon-starved and the quadratic approximation is no longer effective. For instance, a total integral density of 4.8 (ideal pi for ~24 cm of water) for 120kVp, 0.5mAs of radiation source is the maximum pi value where a definitive maximum likelihood value could be found. This leads to fundamental limits in the estimation of ULD CT data when using a standard data processing stream
Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.
Model- based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially dras tically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refine ment of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues.
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