There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

/pdf/modelling-the-physics-in-the-iterative-reconstruction-for-4h1h7qkdgb.pdf

Modelling the physics in the iterative reconstruction for transmission computed tomography

The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accuracy of radiologists. In this paper, we put forward a domain progressive 3D residual convolution network (DP-ResNet) for the LDCT imaging procedure that contains three stages: sinogram domain network (SD-net), filtered back projection (FBP), and image domain network (ID-net). Though both are based on the residual network structure, the SD-net and ID-net provide complementary effect on improving the final LDCT quality. The experimental results with both simulated and real projection data show that this domain progressive deep-learning network achieves significantly improved performance by combing the network processing in the two domains.

Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging

Low-dose X-ray computed tomography (CT) simulation from a high-dose scan is required in optimizing radiation dose to patients. In this paper, we propose a simple low-dose CT simulation strategy in the sinogram domain using the raw data from high-dose scan. Specially, a relationship between the incident fluxes of low- and high-dose scans is first determined according to the repeated projection measurements and analysis. Second, the incident flux level of the simulated low-dose scan is generated by properly scaling the incident flux level of the high-dose scan via the determined relationship in the first step. Third, the low-dose CT transmission data by energy integrating detection is simulated by adding a statistically independent Poisson noise distribution plus a statistically independent Gaussian noise distribution. Finally, a filtered back-projection (FBP) algorithm is implemented to reconstruct the resultant low-dose CT images. The present low-dose simulation strategy is verified on the simulations and real scans by comparing it with the existing low-dose CT simulation tool. Experimental results demonstrated that the present low-dose CT simulation strategy can generate accurate low-dose CT sinogram data from high-dose scans in terms of qualitative and quantitative measurements.

A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan

X-ray computed tomography (CT) is one of the most widely used tools in medical imaging, industrial nondestructive testing, lesion detection, and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid-domain neural network (HDNet) processing for sparse-view CT (SVCT) reconstruction in this study. The HDNet decomposes the SVCT reconstruction problem into two stages and each stage focuses on one mission, which reduces the learning difficulty of the entire network. Experiments based on the simulated and clinical datasets are performed to demonstrate the performance of the proposed method. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method makes a great improvement on artifact suppression, tiny structure restoration, and contrast retention.

Hybrid-Domain Neural Network Processing for Sparse-View CT Reconstruction

Objective 
Evaluation of a new iterative reconstruction algorithm (IMR) for detection/rule-out of pulmonary embolism (PE) in ultra-low dose computed tomography pulmonary angiography (CTPA).


Methods 
Lower dose CT data sets were simulated based on CTPA examinations of 16 patients with pulmonary embolism (PE) with dose levels (DL) of 50%, 25%, 12.5%, 6.3% or 3.1% of the original tube current setting. Original CT data sets and simulated low-dose data sets were reconstructed with three reconstruction algorithms: the standard reconstruction algorithm “filtered back projection” (FBP), the first generation iterative reconstruction algorithm iDose and the next generation iterative reconstruction algorithm “Iterative Model Reconstruction” (IMR). In total, 288 CTPA data sets (16 patients, 6 tube current levels, 3 different algorithms) were evaluated by two blinded radiologists regarding image quality, diagnostic confidence, detectability of PE and contrast-to-noise ratio (CNR).


Results 
iDose and IMR showed better detectability of PE than FBP. With IMR, sensitivity for detection of PE was 100% down to a dose level of 12.5%. iDose and IMR showed superiority to FBP regarding all characteristics of subjective (diagnostic confidence in detection of PE, image quality, image noise, artefacts) and objective image quality. The minimum DL providing acceptable diagnostic performance was 12.5% (= 0.45 mSv) for IMR, 25% (= 0.89 mSv) for iDose and 100% (= 3.57 mSv) for FBP. CNR was significantly (p < 0.001) improved by IMR compared to FBP and iDose at all dose levels.


Conclusion 
By using IMR for detection of PE, dose reduction for CTPA of up to 75% is possible while maintaining full diagnostic confidence. This would result in a mean effective dose of approximately 0.9 mSv for CTPA.

/pdf/ultra-low-dose-ct-pulmonary-angiography-with-iterative-4fnntqfw5c.pdf

Ultra Low Dose CT Pulmonary Angiography with Iterative Reconstruction

Purpose: This paper introduces a new strategy for simulating low-dose computed tomography (CT) scans using real scans of a higher dose as an input. The tool is verified against simulations and real scans and compared to other approaches found in the literature. Methods: The conditional variance identity is used to properly account for the variance of the input high-dose data, and a formula is derived for generating a new Poisson noise realization which has the same mean and variance as the true low-dose data. The authors also derive a formula for the inclusion of real samples of detector noise, properly scaled according to the level of the simulated x-ray signals. Results: The proposed method is shown to match real scans in number of experiments. Noise standard deviation measurements in simulated low-dose reconstructions of a 35 cm water phantom match real scans in a range from 500 to 10 mA with less than 5% error. Mean and variance of individual detector channels are shown to match closely across the detector array. Finally, the visual appearance of noise and streak artifacts is shown to match in real scans even under conditions of photon-starvation (with tube currents as low as 10 and 80 mA). Additionally, the proposed method is shown to be more accurate than previous approaches (1) in achieving the correct mean and variance in reconstructed images from pure-Poisson noise simulations (with no detector noise) under photon-starvation conditions, and (2) in simulating the correct noise level and detector noise artifacts in real low-dose scans. Conclusions: The proposed method can accurately simulate low-dose CT data starting from high-dose data, including effects from photon starvation and detector noise. This is potentially a very useful tool in helping to determine minimum dose requirements for a wide range of clinical protocols and advanced reconstruction algorithms.

A low dose simulation tool for CT systems with energy integrating detectors.

PURPOSE: Evaluation of a new software tool for generation of simulated low-dose computed tomography (CT) images from an original higher dose scan. MATERIALS AND METHODS: Original CT scan data (100 mAs, 80 mAs, 60 mAs, 40 mAs, 20 mAs, 10 mAs; 100 kV) of a swine were acquired (approved by the regional governmental commission for animal protection). Simulations of CT acquisition with a lower dose (simulated 10-80 mAs) were calculated using a low-dose simulation algorithm. The simulations were compared to the originals of the same dose level with regard to density values and image noise. Four radiologists assessed the realistic visual appearance of the simulated images. RESULTS: Image characteristics of simulated low dose scans were similar to the originals. Mean overall discrepancy of image noise and CT values was -1.2% (range -9% to 3.2%) and -0.2% (range -8.2% to 3.2%), respectively, p>0.05. Confidence intervals of discrepancies ranged between 0.9-10.2 HU (noise) and 1.9-13.4 HU (CT values), without significant differences (p>0.05). Subjective observer evaluation of image appearance showed no visually detectable difference. CONCLUSION: Simulated low dose images showed excellent agreement with the originals concerning image noise, CT density values, and subjective assessment of the visual appearance of the simulated images. An authentic low-dose simulation opens up opportunity with regard to staff education, protocol optimization and introduction of new techniques.

/pdf/validation-of-a-low-dose-simulation-technique-for-computed-fpkkbg3j1a.pdf

Validation of a low dose simulation technique for computed tomography images.

Thin-slice images reconstructed from helical multi-slice CT scans typically display artifacts known as windmill artifacts, which arise from not satisfying the Nyquist sampling criteria in the patient longitudinal direction. Since these are essentially aliasing artifacts, they can be reduced or removed by trading off resolution, either globally (by reconstructing thicker slices) or locally (by local smoothing of the strong gradients). The obvious drawback to this approach is the associated loss in resolution. Another approach is to utilize an x-ray tube with the capability to modulate the focal spot in the z-direction, to effectively improve the sampling rate. This work presents a new method for windmill artifact reduction based on total variation minimization in the image domain, which is capable of removing windmill artifacts while at the same time preserving the resolution of anatomic structures within the images. This is a big improvement over previous reconstruction methods that sacrifice resolution, and it provides practically the same benefits as a z-switching x-ray tube with a much simpler impact to the overall CT system.

Method for reducing windmill artifacts in multislice CT images

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Stanislav Žabić

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A low dose simulation tool for CT systems with energy integrating detectors.

Validation of a low dose simulation technique for computed tomography images.

Method for reducing windmill artifacts in multislice CT images