Iterative CT Reconstruction Research Articles

Iterative spectral CT reconstruction based on low rank and average-image-incorporated BM3D

In a photon counting detector integrated spectral CT scanner, the received photons are counted in several energy channels to generate the corresponding projections. Since the projection in each energy channel is generated using part of the received photons, the reconstructed channel image suffers from severe noise. Therefore, image reconstruction in spectral CT is considered to be a big challenge. Because the inter-channel images are all from the same object but in different energy bins, there exists a strong correlation among these images. Moreover, it is suggested that there are similarities among various patches of CT images in the spatial domain. In this work, we propose average-image-incorporated block-matching and 3D (aiiBM3D) filtering along with low rank regularization for iterative spectral CT reconstruction. The aiiBM3D method is based on filtered 3D data arrays formed by similar 2D blocks using the mapped version of the average image obtained from linear regression. The reconstruction procedure consists of two main steps. First, the alternating direction method of multipliers is employed to solve the problem with low rank regularization where the goal is to exploit the correlation in inter-channel images. Second, our proposed BM3D-based algorithm is applied to all the channel images to make use of the redundant information in the spatial domain and inter-channel. The two steps repeat until the stopping criteria are satisfied. The proposed method is validated on numerically simulated and preclinical datasets. Our results confirm its high performance in terms of signal to noise ratio and structural preservation.

OA160] A simple method for low contrast detectability, image quality and dose optimization with CT iterative reconstruction algorithms and model observers

Purpose The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Methods Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four alternative forced-choice test) and model observers were performed across the various images. Results Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. Conclusions IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low contrast detection tasks and in dose optimization.

Iterative reconstructions in multiphasic CT imaging of the liver: qualitative and task-based analyses of image quality

To evaluate the clinical benefits on image quality (IQ) of adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in multiphasic liver CT compared to filtered back-projection (FBP), in patients and on phantoms using a novel task-based metric. Image data of 65 patients who underwent a routine multiphasic liver CT during a 1-month period were reconstructed with FBP, ASIR50, ASIR80, and MBIR. IQ was assessed qualitatively by ranking the most distal hepatic artery (HA) and portal vein (PV) visible; and quantitatively by measuring contrast-to-noise ratio (CNR) of the liver parenchyma, HA and PV. IQ was compared between each reconstruction and correlated to CNR and detectability index (d') measurements computed on phantoms scanned with the same CT protocol as for patients. HA and PV were seen more distally on MBIR and ASIR80 compared to FBP (p≤0.001). The CNR correlated weakly between patient and phantom (r=0.76 and 0.80 for HA and PV, respectively), whereas d' correlated strongly with the division order of HA and PV (r=0.96 and 0.95, respectively). MBIR and ASIR significantly improve the IQ of multiphasic liver CT, especially through better distal detection of HA and PV, in agreement with the adapted task-based metric d' estimated on phantoms.

Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning.

A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative importance among them. It is of critical importance to adjust these parameters, as quality of the solution depends on their values. Tuning parameters are a relatively straight forward task for a human, as one can intuitively determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but also becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative computed tomography (CT) reconstruction with a pixel-wise total-variation regularization term. We set up a parameter-tuning policy network (PTPN), which maps a CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN, reconstructed CT images attain quality similar or better than those reconstructed with manually tuned parameters.

Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization

Myocardial perfusion computed tomography (MPCT) imaging is commonly used to detect myocardial ischemia quantitatively. A limitation in MPCT is that an additional radiation dose is required compared to unenhanced CT due to its repeated dynamic data acquisition. Meanwhile, noise and streak artifacts in low-dose cases are the main factors that degrade the accuracy of quantifying myocardial ischemia and hamper the diagnostic utility of the filtered backprojection reconstructed MPCT images. Moreover, it is noted that the MPCT images are composed of a series of 2/3D images, which can be naturally regarded as a 3/4-order tensor, and the MPCT images are globally correlated along time and are sparse across space. To obtain higher fidelity ischemia from low-dose MPCT acquisitions quantitatively, we propose a robust statistical iterative MPCT image reconstruction algorithm by incorporating tensor total generalized variation (TTGV) regularization into a penalized weighted least-squares framework. Specifically, the TTGV regularization fuses the spatial correlation of the myocardial structure and the temporal continuation of the contrast agent intake during the perfusion. Then, an efficient iterative strategy is developed for the objective function optimization. Comprehensive evaluations have been conducted on a digital XCAT phantom and a preclinical porcine dataset regarding the accuracy of the reconstructed MPCT images, the quantitative differentiation of ischemia and the algorithm’s robustness and efficiency.

Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes

This work presents a framework to exploit the synergy between Digital Volume Correlation (DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-µCT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360° rotation.

Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study

PurposeTo investigate how various generations of iterative reconstruction (IR) algorithms impact low-contrast detectability (LCD) in abdominal computed tomography (CT) for different patient effective diameters, using a quantitative task-based approach. MethodsInvestigations were performed using an anthropomorphic abdominal phantom with two optional additional rings to simulate varying patient effective diameters (25, 30, and 35 cm), and containing multiple spherical targets (5, 6, and 8 mm in diameter) with a 20-HU contrast difference. The phantom was scanned using routine abdominal protocols (CTDIvol, 5.9–16 mGy) on four CT systems from two manufacturers. Images were reconstructed using both filtered back-projection (FBP) and various IR algorithms: ASiR 50%, SAFIRE 3 (both statistical IRs), ASiR-V 50%, ADMIRE 3 (both partial model-based IRs), or Veo (full model-based IR). Section thickness/interval was 2/1 mm or 2.5/1.25 mm, except 0.625/0.625 mm for Veo. We assessed LCD using a channelized Hotelling observer with 10 dense differences of Gaussian channels, with the area under the receiver operating characteristic curve (AUC) as a figure of merit. ResultsFor the smallest phantom (25-cm diameter) and smallest lesion size (5-mm diameter), AUC for FBP and the various IR algorithms did not significantly differ for any of the tested CT systems. For the largest phantom (35-cm diameter), Veo yielded the highest AUC improvement (8.5%). Statistical and partial model-based IR algorithms did not significantly improve LCD. ConclusionIn abdominal CT, switching from FBP to IR algorithms offers limited possibilities for achieving significant dose reductions while ensuring a constant objective LCD.

Distributed Framework for Fast Iterative CT Reconstruction from View-subsets

Distributed Framework for Fast Iterative CT Reconstruction from View-subsets

Evaluation of an Analytic Reconstruction Method as a Platform for Spectral Cone-beam CT.

With the fast development of photon counting detection techniques, spectral CT with a photon counting detector has attracted considerable attention by increasing energy-resolution to identify and discriminate materials. The conventional analytic reconstruction algorithms can be directly applied to reconstruct spectral CT images for each spectrum or energy bin. However, a comprehensive evaluation of analytic reconstruction algorithms for spectral CT has not been reported yet. This motivates us to evaluate the analytic spiral cone-beam CT algorithms and to provide a fair comparison platform for the state-of-the-art iterative spectral CT reconstruction algorithms. Considering the fact that a narrow energy bin has high noise which degrades the imaging quality of spectral CT, an adaptive maximum a posterior (MAP) projection restoration algorithm is first used to reduce the noise, and then a 2D/3D weighted spiral Feldkamp-Davis-Kress (FDK) algorithm is implemented to reconstruct the spectral CT images at different energy bins. Finally, the principle component analysis (PCA) is employed to render the spectral reconstruction results into a color space. Our numerical results show that the analytic reconstruction approach is fast and it can provide high spatial resolution, high contrast resolution and high signal-noise-ratio (SNR) under higher helical pitches. This makes it possible to serve as a platform to evaluate the state-of-the-art iterative spectral CT algorithms.

Advanced CT acquisition protocol with a third-generation dual-source CT scanner and iterative reconstruction technique for comprehensive prosthetic heart valve assessment

ObjectivesMultidetector CT (MDCT) is a valuable tool for functional prosthetic heart valve (PHV) assessment. However, radiation exposure remains a concern. We assessed a novel CT-acquisition protocol for comprehensive PHV evaluation at limited dose.MethodsPatients with a PHV were scanned using a third-generation dual-source CT scanner (DSCT) and iterative reconstruction technique (IR). Three acquisitions were obtained: a non-enhanced scan; a contrast-enhanced, ECG-triggered, arterial CT angiography (CTA) scan with reconstructions at each 5 % of the R-R interval; and a delayed high-pitch CTA of the entire chest. Image quality was scored on a five-point scale. Radiation dose was obtained from the reported CT dose index (CTDI) and dose length product (DLP).ResultsWe analysed 43 CT examinations. Mean image quality score was 4.1±1.4, 4.7±0.5 and 4.2±0.6 for the non-contrast-enhanced, arterial and delayed acquisitions, respectively, with a total mean image quality of 4.3±0.7. Mean image quality for leaflet motion was 3.9±1.4. Mean DLP was 28.2±17.1, 457.3±168.6 and 68.5±47.2 mGy.cm for the non-contrast-enhanced (n=40), arterial (n=43) and delayed acquisition (n=43), respectively. The mean total DLP was 569±208 mGy.cm and mean total radiation dose was 8.3±3.0 mSv (n=43).ConclusionComprehensive assessment of PHVs is possible using DSCT and IR at moderate radiation dose.Key points• Prosthetic heart valve dysfunction is a potentially life-threatening condition.• Dual-source CT can adequately assess valve leaflet motion and anatomy.• We assessed a comprehensive protocol with three acquisitions for PHV evaluation.• This protocol is associated with good image quality and limited dose.

Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT.

To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm. 30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists. For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p < 0.05). In terms of subjective image evaluation, all ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p < 0.05), and 60% ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher. In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non-enhanced chest CT images reconstructed with 60% ASIR-V have the highest image quality. Advances in knowledge: This is the first clinical study to evaluate the clinical value of ASIR-V in the same patients using the same CT scanner in the non-enhanced chest CT scans. It suggests that ASIR-V provides the better image quality and higher diagnostic confidence in comparison with ASIR algorithm.

Technical Note: Evaluation of an iterative reconstruction algorithm for optical CT radiation dosimetry.

Iterative CT reconstruction algorithms are gaining popularity as GPU-based computation becomes more accessible. These algorithms are desirable in x-ray CT for their ability to achieve similar image quality at a fraction of the dose required for standard filtered backprojection reconstructions. In optical CT dosimetry, the noise reduction capability of such algorithms is similarly desirable because noise has a detrimental effect on the precision of dosimetric analysis, and can create misleading test results. In this study, we evaluate an iterative CT reconstruction algorithm for gel dosimetry, with special attention to the challenging dosimetry of small fields. An existing ordered subsets convex algorithm using total variation minimization regularization (OSC-TV) was implemented. Three datasets, which represent the extreme cases of gel dosimetry, were examined: a large, 15cm diameter uniform phantom, a 1.35cm diameter finger phantom, and a 15cm gel dosimeter irradiated with 3×3, 2×2, 1×1, and 0.6×0.6cm fields. These were scanned on an in-house scanning laser system, and reconstructed with both filtered backprojection and OSC-TV with a range of regularization constants. The contrast to artifact+noise ratio (CANR) and penumbra width measurements (80% to 20% and 95% to 5% distances) were used to compare reconstructions. Our results showed that OSC-TV can achieve 3-5× improvement in contrast to artifact+noise ratio compared to filtered backprojection, while preserving the shape of steep dose gradients. For very small objects (≤0.6×0.6cm fields in a 16×16cm field of view), the mean value in the center of the object can be suppressed if the regularization constant is improperly set, which must be avoided. Overall, the results indicate that OSC-TV is a suitable reconstruction algorithm for gel dosimetry, provided care is taken in setting the regularization parameter when reconstructing objects that are small compared to the scanner field of view.

Shading correction assisted iterative cone-beam CT reconstruction

Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under-sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non-ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low-dose CBCT reconstruction and propose a clinically acceptable and stable three-dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low-pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage-thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan© 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low-dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low-dose CBCT imaging in the clinic.

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers

BackgroundThe aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment.MethodsImages of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images.ResultsCompared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result.ConclusionIR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

Value of 100 kVp scan with sinogram-affirmed iterative reconstruction algorithm on a single-source CT system during whole-body CT for radiation and contrast medium dose reduction: an intra-individual feasibility study

To perform an intra-individual investigation of the usefulness of a contrast medium (CM) and radiation dose-reduction protocol using single-source computed tomography (CT) combined with 100 kVp and sinogram-affirmed iterative reconstruction (SAFIRE) for whole-body CT (WBCT; chest-abdomen-pelvis CT) in oncology patients. Forty-three oncology patients who had undergone WBCT under both 120 and 100 kVp protocols at different time points (mean interscan intervals: 98 days) were included retrospectively. The CM doses for the 120 and 100 kVp protocols were 600 and 480 mg iodine/kg, respectively; 120 kVp images were reconstructed with filtered back-projection (FBP), whereas 100 kVp images were reconstructed with FBP (100 kVp-F) and the SAFIRE (100 kVp-S). The size-specific dose estimate (SSDE), iodine load and image quality of each protocol were compared. The SSDE and iodine load of 100 kVp protocol were 34% and 21%, respectively, lower than of 120 kVp protocol (SSDE: 10.6±1.1 versus 16.1±1.8 mGy; iodine load: 24.8±4versus 31.5±5.5 g iodine, p<0.01). Contrast enhancement, objective image noise, contrast-to-noise-ratio, and visual score of 100 kVp-S were similar to or better than of 120 kVp protocol. Compared with the 120 kVp protocol, the combined use of 100 kVp and SAFIRE in WBCT for oncology assessment with an SSCT facilitated substantial reduction in the CM and radiation dose while maintaining image quality.

Low-dose CT reconstruction using spatially encoded nonlocal penalty.

Computed tomography (CT) is one of the most used imaging modalities for imaging both symptomatic and asymptomatic patients. However, because of the high demand for lower radiation dose during CT scans, the reconstructed image can suffer from noise and artifacts due to the trade-off between the image quality and the radiation dose. The purpose of this paper is to improve the image quality of quarter dose images and to select the best hyperparameters using the regular dose image as ground truth. We first generated the axially stacked two-dimensional sinograms from the multislice raw projections with flying focal spots using a single slice rebinning method, which is an axially approximate method to provide simple implementation and efficient memory usage. To improve the image quality, a cost function containing the Poisson log-likelihood and spatially encoded nonlocal penalty is proposed. Specifically, an ordered subsets separable quadratic surrogates (OS-SQS) method for the log-likelihood is exploited and the patch-based similarity constraint with a spatially variant factor is developed to reduce the noise significantly while preserving features. Furthermore, we applied the Nesterov's momentum method for acceleration and the diminishing number of subsets strategy for noise consistency. Fast nonlocal weight calculation is also utilized to reduce the computational cost. Datasets given by the Low Dose CT Grand Challenge were used for the validation, exploiting the training datasets with the regular and quarter dose data. The most important step in this paper was to fine-tune the hyperparameters to provide the best image for diagnosis. Using the regular dose filtered back-projection (FBP) image as ground truth, we could carefully select the hyperparameters by conducting a bias and standard deviation study, and we obtained the best images in a fixed number of iterations. We demonstrated that the proposed method with well selected hyperparameters improved the image quality using quarter dose data. The quarter dose proposed method was compared with the regular dose FBP, quarter dose FBP, and quarter dose l1 -based 3-D TV method. We confirmed that the quarter dose proposed image was comparable to the regular dose FBP image and was better than images using other quarter dose methods. The reconstructed test images of the accreditation (ACR) CT phantom and 20 patients data were evaluated by radiologists at the Mayo clinic, and this method was awarded first place in the Low Dose CT Grand Challenge. We proposed the iterative CT reconstruction method using a spatially encoded nonlocal penalty and ordered subsets separable quadratic surrogates with the Nesterov's momentum and diminishing number of subsets. The results demonstrated that the proposed method with fine-tuned hyperparameters can significantly improve the image quality and provide accurate diagnostic features at quarter dose. The performance of the proposed method should be further improved for small lesions, and a more thorough evaluation using additional clinical data is required in the future.

Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image. The recent development of artificial neural networks and machine learning enabled learning of more complex features of image, which has the potential to improve reconstruction quality. In this letter, K-sparse auto encoder was used for unsupervised feature learning. A manifold was learned from normal-dose images and the distance between the reconstructed image and the manifold was minimized along with data fidelity during reconstruction. Experiments on 2016 Low-dose CT Grand Challenge were used for the method verification, and results demonstrated the noise reduction and detail preservation abilities of the proposed method.

A New Voxelization Strategy in Compressed-Sensing (CS)-Based Iterative CT Reconstruction for Reducing Computational Cost: Simulation and Experimental Studies

In recent computed tomography (CT) reconstruction, iterative methods have been often used owing to the potential to provide three-dimensional (3D) images of superior reconstruction quality to common filtered-backprojection (FBP)-based methods. However, these methods require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, to overcome these difficulties, we propose a new cone-beam CT reconstruction with a dual-resolution voxelization strategy for a small region-of-interest (ROI) reconstruction in which the voxels outside the target ROI are binned by, for example, 2 × 2 × 2, 4 × 4 × 4, 8 × 8 × 8, etc., while the other voxels remain unbinned. We considered a compressed-sensing (CS)-based algorithm with a dual regularization strategy, rather than common FBP-based methods, for more accurate CT reconstruction. We implemented the proposed CS-based algorithm and performed a systematic simulation and experiment. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative CT reconstruction, keeping the reconstruction quality inside the target ROI not much degraded.

Iterative reconstruction in single-source dual-energy CT angiography: feasibility of low and ultra-low volume contrast medium protocols.

To evaluate the feasibility of using contrast medium (CM) of low and ultra-low volumes and injection rates in aortic CT angiography (CTA) through the joint application of single-source dual-energy CT (ssDECT) and adaptive statistical iterative reconstruction (ASIR). 120 patients with known or suspected aortic dissection underwent aortic CTA and were equally divided into 3 groups. Conventional 120-kVp scan with a CM volume of 70 ml and an injection rate of 5 ml s-1 was performed on Group A. Groups B and C underwent ssDECT scan with CM volumes of 0.6 and 0.4 ml kg-1, respectively. 40% and 50% ASIR algorithms were applied for Groups B and C, respectively. A five-point grading scheme was utilized to subjectively evaluate the image quality, and the CT value and contrast-to-noise ratio were recorded as objective measures. The radiation dose was also evaluated. Groups B and C had equivalent subjective scores and CT values as Group A, whereas they had higher or equivalent contrast-to-noise ratios. Group B had 40.1% and 30% reductions on CM volume and injection rate, respectively, than Group A. Group C further resulted in 19.2% and 22% lesser CM volume and injection rate than Group B. The average effective radiation doses for the study groups were 22.5-24.5% lower than the control group. With the aid of ASIR and ssDECT for aortic CTA, it is feasible to adopt low and ultra-low CM volumes and injection rates while obtaining good quality images. Advances in knowledge: Low and ultra-low CM volumes and injection rates are feasible in CTA through the joint application of ssDECT and ASIR.

Pixel-wise estimation of noise statistics on iterative CT reconstruction from a single scan.

As iterative CT reconstruction continues to advance, the spatial distribution of noise standard deviation (STD) and accurate noise power spectrum (NPS) on the reconstructed CT images become important for method evaluation as well as optimization of algorithm parameters. Using a single CT scan, we propose a practical method for pixel-wise calculation of noise statistics on an iteratively reconstructed CT image, which enables accurate calculation of noise STD for each pixel and NPS. We first derive the noise propagation from measured projections to an iteratively reconstructed CT image provided that the projection noise is known. We then show that the model of noise propagation remains approximately unchanged for extra simulated noise added on the measured projections. To compute the noise STD map and the NPS map on an iteratively reconstructed CT image from a single scan, we first iteratively reconstruct the CT image from the measured projections using an existing reconstruction algorithm. The same measured projections are added by different sets (a total of 32 sets in our implementation) of projection noise simulated from an estimated projection noise model, and are then used to iteratively reconstruct different CT images. The calculations of the noise STD map and the NPS map are finally performed on the entire stack of these different reconstruction images. We evaluate our method on an anthropomorphic head phantom, and demonstrate the clinical utility on a set of head and neck patient CT data, using two iterative CT reconstruction algorithms: the penalized weighted least-square (PWLS) algorithm and the total-variation (TV) regularization. In the head phantom case, repeated scans are acquired to generate the ground truths of noise STD and NPS maps. Using only one single scan, the proposed method accurately calculates the noise STD maps with a root-mean-square error (RMSE) of less than 5HU. In the NPS map estimation, we compare the result of our proposed method with that of the conventional method which calculates the NPS maps on a uniform region of interest on one CT image. Our method outperforms the conventional method on the NPS map estimation with RMSE reduced by 92%. The implementation of the proposed method on the patient data successfully provides the noise STD values around complex structures and a high-quality NPS map. The proposed method accurately calculates noise STD for each pixel and NPS on an iteratively reconstructed CT image, with no requirement of repeated CT scans. It provides a detailed evaluation of imaging performance of different iterative reconstruction methods on the same CT dataset.