Iterative Image Reconstruction Research Articles

Double Low-Dose Dual-Energy Liver CT in Patients at High-Risk of HCC: A Prospective, Randomized, Single-Center Study.

The aim of this study was to investigate the clinical feasibility of the simultaneous reduction of radiation and contrast doses using spectral computed tomography (CT) in patients at high-risk for hepatocellular carcinoma. Between May 2017 and March 2018, this prospective study recruited participants at risk of hepatocellular carcinoma with body mass indexes less than 30 and randomly assigned them to either the standard-dose group or the double low-dose group, which targeted 30% reductions in both radiation and contrast media (NCT03045445). Lesion conspicuity as a primary endpoint and lesion detection rates were then compared between hybrid iterative reconstruction (iDose) images of standard-dose group and low monoenergetic (50 keV) images of double low-dose group. Qualitative and quantitative image noise and contrast were also compared between the 2 groups. Participants and reviewers were blinded for scan protocols and reconstruction algorithms. Lesion conspicuity was analyzed using generalized estimating equation analysis. Lesion detection was evaluated using weighted jackknife alternative free-response receiver operating characteristic analysis. Sixty-seven participants (male-to-female ratio, 59:8; mean age, 64 ± 9 years) were analyzed. Compared with the standard-dose group (n = 32), significantly lower CTDIvol (8.8 ± 1.7 mGy vs 6.1 ± 0.6 mGy) and contrast media (116.9 ± 15.7 mL vs 83.1 ± 9.9 mL) were utilized in the double low-dose group (n = 35; P < 0.001). Comparative analysis demonstrated that lesion conspicuity was significantly higher on 50 keV images of double low-dose group than on iDose images of standard dose on both arterial (2.62 [95% confidence interval (CI), 2.31-2.93] vs 2.02 [95% CI, 1.73-2.30], respectively, P = 0.004) and portal venous phases (2.39 [95% CI, 2.11-2.67] vs 1.88 [95% CI, 1.67-2.10], respectively, P = 0.005). No differences in lesion detection capability were observed between the 2 groups (figure of merit: 0.63 in standard-dose group; 0.65, double low-dose group; P = 0.52). Fifty kiloelectronvolt images of double low-dose group showed better subjective image noise and contrast than iDose image of standard-dose group on arterial and portal venous phases (P < 0.001 for all). Contrast-to-noise ratio of the aorta and portal vein was also higher in double low-dose group than in standard-dose group (P < 0.001 for all), whereas there was no significant difference of quantitative image noise between the 2 groups on arterial and portal phases (P = 0.4~0.5). Low monoenergetic spectral CT images (50 keV) can provide better focal liver lesion conspicuity than hybrid iterative reconstruction image of standard-dose CT in nonobese patients while using lower radiation and contrast media doses.

The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values.

To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFRML) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFRML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, 'calcified" or "noncalcified" and "≥50% stenosis" or "<50% stenosis', a total of four subgroups by consensus. There were no significant differences of CT-FFRML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P=0.676, 0.414, 0.849, 0.873, respectively) or overall (P=0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFRML≤0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P=0.437). Compared with CT-FFRML value of FBP dataset, the CT-FFRML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFRML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects.

Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT of the brain is to compensate for aberrations and attenuation in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method can be employed that can model the heterogeneous elastic properties of the medium. In this study, an optimization-based image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward-adjoint operator pair based on a finite-difference time-domain discretization of the 3D elastic wave equation is utilized to compute penalized least squares estimates of the initial pressure distribution. Computer-simulation and experimental studies are conducted to investigate the robustness of the reconstruction method to model mismatch and its ability to effectively resolve cortical and superficial brain structures.

View-driven compressed sensing method of CT image reconstruction

In order to significantly reducing the X-ray dose of CT examination, this study focuses on reconstructing the tomographic images from sparse projection data with sufficient accuracy. Aiming at two-dimensional (2D) image reconstruction of fan-beam projection under circular scanning, this paper proposes the view-driven system model and designs a CT iterative image reconstruction method by combining the iterative algorithm and the compressed sensing theory. The simulation results show that the algorithm has high numerical accuracy, low computational complexity, less memory overhead, and strong engineering practicability under the condition of ultra-sparse projection data (no more than 20 view angles for fan-beam scanning in the range of [0,2π)).

Effect of Iterative Reconstruction Algorithm Associated with Low Contrast Detectability Performance from CT Pulmonary Angiography Examinations

Iterative reconstruction (IR) in Computed Tomography (CT) scan examinations are known for noise reduction to achieve a higher level of diagnostic. This study aims to evaluate the radiation dose and image quality performance in different iterative reconstruction levels on CT Pulmonary Angiography (CTPA) examination and to establish a figure of merit (FOM) between both parameters. Thirty adult patients who underwent CTPA examinations were retrospectively included in the present study. Scanning acquisition protocol and radiation dose information, together with patient habitus information, was collected and recorded. The vessel attenuation was measured in five consecutive areas surrounding the pulmonary artery for each iterative reconstruction image. Results of radiation dose were presented in terms of Computed Tomography Dose Index (CTDIvol) obtained from CT-console and SSDE that was calculated by the AAPM report 204 recommendation method. Meanwhile, image quality was estimated by contrast-to-noise ratio (CNR) on average from five consecutive areas of CNR. The mean values of CTDIvol and SSDE were 13.79 ± 7.72 mGy and 17.25 ± 8.92 mGy, respectively. The amount of CNR was significantly different within the iterative reconstruction level (p <0.001). There was a significant difference in the figure of merit for both CNR2/SSDE and CNR2/CTDIvol attained in different iterative reconstruction levels, (p <0.001) and (p <0.001), respectively. In conclusion, we successfully evaluate the value of radiation dose and image quality performance and establish a figure of merit for both parameters to aid further verification of scanning protocols by radiology staff.

Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling

PurposeTo accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.MethodsSelf-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency, which enables sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects. The data were retrospectively undersampled, and reconstructed using SPIRiT, l1-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate reconstruction performance.ResultssRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and l1-SPIRiT, especially at high acceleration rates in targeted coronary MRI. Quantitative analysis shows that sRAKI outperforms these techniques in terms of normalized mean-squared-error (~44% and ~21% over SPIRiT and -SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and l1-SPIRiT at rate 5). Whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and l1-SPIRiT, respectively.ConclusionsRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over l1 regularization techniques.

Real-time MRI for dynamic assessment of gastroesophageal reflux disease: Comparison to pH-metry and impedance

PurposeTo evaluate the diagnostic potential of real-time MRI for dynamic assessment of gastroesophageal reflux in patients with GERD (gastroesophageal reflux disease)-like symptoms compared to pH-metry and impedance. MethodsPatients who underwent real-time MRI and pH-metry between 2015–2018 were included in this retrospective study. Real-time MRI at 3 T was achieved by undersampled radial FLASH acquisitions with iterative image reconstruction by NLINV. Real-time MRI visualized transit of pineapple juice through the gastroesophageal junction and during Valsalva maneuver. MRI results were compared to 24 h pH-metry to assess acidic reflux (following Lyon Consensus guidelines) and to impedance to assess non-acidic reflux. A standard 2 × 2 table was chosen to calculate diagnostic performance. Results91/93 eligible patients fulfilled inclusion criteria (male n = 49; female n = 42; median age 55 y). All MRI studies were successfully completed without adverse events at a mean examination time of 15 min. On real-time MRI, reflux was evident in 60 patients (66 %). pH-metry revealed reflux in 41 patients (45 %), and impedance in 54 patients (59 %). Compared to pH-metry and impedance, real-time MRI sensitivity was 0.78 (95 % CI: 0.66-0.87), specificity 0.67 (95 % CI: 0.45-0.84) and PPV 0.87 (95 % CI: 0.75-0.94). ConclusionReal-time MRI is an imaging method for assessment of gastroesophageal reflux in patients with GERD-like symptoms. Considering its high positive predictive value, real-time MRI can accurately identify patients in which further invasive testing with pH-metry and impedance might be considered.

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. MATERIALS AND METHODS. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest (n = 22; 16 women, six men) and abdominopelvic (n = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. RESULTS. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (p < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (p = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (p < 0.0001). CONCLUSION. At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.

A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images

The circular‐edge technique using a low‐contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular‐edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTFcorrected) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTFcorrected with the actual images obtained with the scanner. The calculated image using MTFcorrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular‐edge technique with image averaging, and suggest a method to correct it.

Association of Low-Dose Whole-Body Computed Tomography With Missed Injury Diagnoses and Radiation Exposure in Patients With Blunt Multiple Trauma

Initial whole-body computed tomography (WBCT) for screening patients with suspected blunt multiple trauma remains controversial and a source of excess radiation exposure. To determine whether low-dose WBCT scanning using an iterative reconstruction algorithm does not increase the rate of missed injury diagnoses at the point of care compared with standard-dose WBCT with the benefit of less radiation exposure. This quasi-experimental, prospective time-series cohort study recruited 1074 consecutive patients admitted for suspected blunt multiple trauma to an academic metropolitan trauma center in Germany from September 3, 2014, through July 26, 2015, for the standard-dose protocol, and from August 7, 2015, through August 20, 2016, for the low-dose protocol. Five hundred sixty-five patients with suspected blunt multiple trauma prospectively received standard-dose WBCT, followed by 509 patients who underwent low-dose WBCT. Confounding was controlled by segmented regression analysis and a secondary multivariate logistic regression model. Data were analyzed from January 16, 2017, through October 14, 2019. Standard- or low-dose WBCT. The primary outcome was the incidence of missed injury diagnoses at the point of care, using a synopsis of clinical, surgical, and radiological findings as an independent reference test. The secondary outcome was radiation exposure with either imaging strategy. Of 1074 eligible patients, 971 (mean [SD] age, 52.7 [19.5] years; 649 men [66.8%]) completed the study. A total of 114 patients (11.7%) had multiple trauma, as defined by an Injury Severity Score of 16 or greater. The proportion of patients with any missed injury diagnosis at the point of care was 109 of 468 (23.3%) in the standard-dose and 107 of 503 (21.3%) in the low-dose WBCT groups (risk difference, -2.0% [95% CI, -7.3% to 3.2%]; unadjusted odds ratio, 0.89 [95% CI, 0.66-1.20]; P = .45). Adjustments for autocorrelation and multiple confounding variables did not alter the results. Radiation exposure, measured by the volume computed tomography dose index, was lowered from a median of 11.7 (interquartile range, 11.7-17.6) mGy in the standard-dose WBCT group to 5.9 (interquartile range, 5.9-8.8) mGy in the low-dose WBCT group (P < .001). Low-dose WBCT using iterative image reconstruction does not appear to increase the risk of missed injury diagnoses at the point of care compared with standard-dose protocols while almost halving the exposure to diagnostic radiation.

Bootstrap-Optimised Regularised Image Reconstruction for Emission Tomography.

In emission tomography, iterative image reconstruction from noisy measured data usually results in noisy images, and so regularisation is often used to compensate for noise. However, in practice, an appropriate, automatic and precise specification of the strength of regularisation for image reconstruction from a given noisy measured dataset remains unresolved. Existing approaches are either empirical approximations with no guarantee of generalisation, or else are computationally intensive cross-validation methods requiring full reconstructions for a limited set of preselected regularisation strengths. In contrast, we propose a novel methodology embedded within iterative image reconstruction, using one or more bootstrapped replicates of the measured data for precise optimisation of the regularisation. The approach uses a conventional unregularised iterative update of a current image estimate from the noisy measured data, and then also uses the bootstrap replicate to obtain a bootstrap update of the current image estimate. The method then seeks the regularisation hyperparameters which, when applied to the bootstrap update of the image, lead to a best fit of the regularised bootstrap update to the conventional measured data update. This corresponds to estimating the degree of regularisation needed in order to map the noisy update to a model of the mean of an ensemble of noisy updates. For a given regularised objective function (e.g. penalised likelihood), no hyperparameter selection or tuning is required. The method is demonstrated for positron emission tomography (PET) data at different noise levels, and delivers near-optimal reconstructions (in terms of reconstruction error) without any knowledge of the ground truth, nor any form of training data.

Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

One of the latest trends in Computed Tomography (CT) is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artifacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative algorithms to enhance image quality. Most approaches are built around two main operators, backprojection and projection, which are computationally expensive.In this work, we present an implementation of those operators for iterative reconstruction methods exploiting the Big Data paradigm. We define an architecture based on Apache Spark that supports both Graphical Processing Units (GPU) and CPU-based architectures. The aforementioned are parallelized using a partitioning scheme based on the division of the volume and irregular data structures in order to reduce the cost of communication and computation of the final images. Our solution accelerates the execution of the two most computational expensive components with Apache Spark, improving the programming experience of new iterative reconstruction algorithms and the maintainability of the source code increasing the level of abstraction for non-experienced high performance programmers. Through an experimental evaluation, we show that we can obtain results up to 10× faster for projection and 21× faster for backprojection when using a GPU-based cluster compared to a traditional multi-core version. Although a linear speed up was not reached, the proposed approach can be a good alternative for porting previous medical image reconstruction applications already implemented in C/C++ or even with CUDA or OpenCL programming models. Our solution enables the automatic detection of the GPU devices and execution on CPU and GPU tasks at the same time under the same system, using all the available resources.

Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator

To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images.Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp–Davis–Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values.The results after VM-CBCT production showed good material decomposition and consistent HUVM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0% ± 1.8%.This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.

Quantitative SPECT/CT-Technique and Clinical Applications.

The continuous development of SPECT over the past 50years has led to improved image quality and increased diagnostic confidence. The most influential developments include the realization of hybrid SPECT/CT devices, as well as the implementation of attenuation correction and iterative image reconstruction techniques. These developments have led to a preference for SPECT/CT devices over SPECT-only systems and to the widespread adoption of the former, strengthening the role of SPECT/CT as the workhorse of Nuclear Medicine imaging. New trends in the ongoing development of SPECT/CT are diverse. For example, whole-body SPECT/CT images, consisting of acquisitions from multiple consecutive bed positions in the manner of PET/CT, are increasingly performed. Additionally, in recent years, some interesting approaches in detector technology have found their way into commercial products. For example, some SPECT cameras dedicated to specific organs employ semiconductor detectors made of cadmium telluride or cadmium zinc telluride, which have been shown to increase the obtainable image quality by offering a higher sensitivity and energy resolution. However, the advent of quantitative SPECT/CT which, like PET, can quantify the amount of tracer in terms of Bq/mL or as a standardized uptake value could be regarded as most important development. It is a major innovation that will lead to increased diagnostic accuracy and confidence, especially in longitudinal studies and in the monitoring of treatment response. The current work comprises two main aspects. At first, physical and technical fundamentals of SPECT image formation are described and necessary prerequisites of quantitative SPECT/CT are reviewed. Additionally, the typically achievable quantitative accuracy based on reports from the literature is given. Second, an extensive list of studies reporting on clinical applications of quantitative SPECT/CT is provided and reviewed.

Computationally Efficient System Matrix Calculation Techniques in Computed Tomography Iterative Reconstruction.

Background:Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs.Methods:The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM).Results:The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM.Conclusion:We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.

IMPEDANCE IMAGE RECONSTRUCTION WITH ARTIFICIAL NEURAL NETWORK IN ELECTRICAL IMPEDANCE TOMOGRAPHY

Electrical impedance tomography views the electrical properties of the objects by injecting current with surface electrodes and measuring voltages. Then using a reconstructing algorithm, from the measured voltage-current values, conductivity distribution of the object calculated. Finding internal conductivity from surface voltage-current measurements is a reverse and ill-posed problem. Therefore, high error sensitivity, and making approximations in conceiving complex computations cause to limited spatial resolution. The classic iterative image reconstruction algorithms have reconstruction errors. Accordingly, Electrical impedance tomography images suffer low accuracy. It is necessary to evaluate the collected data from the object surface with a new approach. In this paper, the forward problem solved with the finite element method to reconstruct the conductivity distribution inside the object, the reverse problem solved by the neural network approach. Image reconstruction speed, conceptual simplicity, and ease of implementation maintained by this approach.

A New Iterative Algorithm Based on Correction of Sensitivity Matrix for Electrical Resistance Tomography

Electrical resistance tomography (ERT) is used to reconstruct the resistance/conductivity distribution. Usually, a uniform distribution is assumed as the initial condition to obtain a generic sensitivity matrix, which may be very different from a theoretical sensitivity matrix, resulting in a large error. The aim of this study is to analyse the difference between a generalized sensitivity matrix and a theoretical sensitivity matrix and to improve image reconstruction. The effect of the generic sensitivity matrix and theoretical sensitivity matrix on image reconstruction is analyzed. The error caused by the use of the generic sensitivity matrix is estimated. To reduce the error, an improved iterative image reconstruction algorithm is proposed, which is based on calculation of the error between the generic sensitivity matrix and the theoretical sensitivity matrix, and a correction coefficient with a penalty. During the iterative process, the resistivity distribution and sensitivity matrix are alternatively corrected. Simulation and experimental results show that the proposed algorithm can improve the quality of images, e.g., of two-phase distributions.

Синхронный алгоритм параллельной реконструкции трехмерных изображений на основе системы обмена сообщениями MPI

Одни из наиболее важных вычислительных задач разработка и адаптация итерационных методов для решения сверхбольших разреженных систем алгебраических уравнений. К таким вычислительным задачам приводит задача итерационной параллельной реконструкции трехмерных изображений промышленных изделий. Важно, что итерационные методы решения вычислительных задач большой размерности реализуются на параллельных структурах намного эффективнее, чем прямые методы их решения. В этой работе описан синхронный параллельный алгоритм, основанный на использовании системы MPI для решения задачи реконструкции трехмерных изображений промышленных изделий. Purpose. Currently, one of the most important tasks is the development and adaptation of iterative methods for solving ultra-large sparse systems of algebraic equations. Such computational problems are caused by the iterative parallel reconstruction of three-dimensional images of industrial products. It is important that iterative methods for solving computational problems of large size are implemented on parallel structures much more efficiently than direct methods for solving them. This paper describes a synchronous parallel algorithm based on the MPI system for solving the problem of reconstruction of three-dimensional images of industrial products. Methodology. It is important that iterative methods for solving computational problems of larger dimensionality on parallel structures are implemented more efficiently than the direct ones. Most algorithms based on direct solving methods have a significant hereditary sequential structure and require a large number of processors interactions which cannot be executed in parallel mode. Iterative methods, for the most part, require a significantly smaller number of interactions of this type and are relatively easily mapped onto parallel computational structures. Equally important, in most cases parallel implementations of classical iterative methods are more effective in terms of computational speed. The parallel execution of the algorithm is based on the distribution of the process in some way between various groups of processors. Depending on the interaction method between local processors, two different types of parallel iterative algorithms execution are distinguished: synchronous and asynchronous. In the former case, it is assumed that the processors complete the calculations and exchange all the necessary results before the start of a new iteration. The main disadvantage of synchronous parallel algorithms is that they require synchronization of iterations. This is a very difficult task, especially with large number of processors. In addition, the overall calculation speed is limited by the speed of the slowest processor. At the same time, faster processors spend most of their time in the waiting mode. But, nevertheless, the implementation of these parallel algorithms can be effectively achieved using the MPI standard. Findings. Synchronous parallel computing algorithms and program codes for threedimensional tomographic reconstruction in a conical beam were developed. Program code debugging and numerical calculations were performed on a hybrid cluster based on the OpenPOWER architecture using the MPI system. For designing a parallel threedimensional tomographic reconstruction, a voxel form of parallelism was used. Originality. Implemented parallel iterative technology of three-dimensional images reconstruction has undeniable advantages over traditional sequential iterative tomographic reconstruction. It allows reducing the time of tomographic reconstruction as many as tens of times, provides the ability to reconstruct products with sizes of 5123 to 10243 voxels with simultaneous storage of the submatrix node of the projection matrix in RAM, which eliminates the need for recalculation of matrix coefficients for each new iteration.

Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality.

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m2 were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

Iterative parallel reconstruction of images on OPEN- POWER cluster

Now one of the most important tasks is development and adaptation of iterative methods for the solution of the big rarefied systems of the algebraic equations. The problem of iterative parallel reconstruction of three-dimensional images of industrial facilities leads to such computing tasks. The fact that iterative methods of the solution of computing problems of big dimension are implemented on parallel structures much more effectively is important than direct methods of their decision. In this work the synchronous parallel algorithm is described, based on use of the MPI system for the solution of a problem of reconstruction of three-dimensional images of industrial facilities.