Iterative Reconstruction Methods Research Articles

Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.

No published studies have evaluated the accuracy of volumetric measurement of solid nodules and ground-glass nodules on low-dose or ultra–low-dose chest computed tomography, reconstructed using deep learning–based algorithms. This is an important issue in lung cancer screening. Our study aimed to investigate the accuracy of semiautomatic volume measurement of solid nodules and ground-glass nodules, using two deep learning–based image reconstruction algorithms (Truefidelity and ClariCT.AI), compared with iterative reconstruction (ASiR-V) in low-dose and ultra–low-dose settings. We performed computed tomography scans of solid nodules and ground-glass nodules of different diameters placed in a phantom at four radiation doses (120 kVp/220 mA, 120 kVp/90 mA, 120 kVp/40 mA, and 80 kVp/40 mA). Each scan was reconstructed using Truefidelity, ClariCT.AI, and ASiR-V. The solid nodule and ground-glass nodule volumes were measured semiautomatically. The gold-standard volumes could be calculated using the diameter since all nodule phantoms are perfectly spherical. Subsequently, absolute percentage measurement errors of the measured volumes were calculated. Image noise was also calculated. Across all nodules at all dose settings, the absolute percentage measurement errors of Truefidelity and ClariCT.AI were less than 11%; they were significantly lower with Truefidelity or ClariCT.AI than with ASiR-V (all P<0.05). The absolute percentage measurement errors for the smallest solid nodule (3 mm) reconstructed by Truefidelity or ClariCT.AI at all dose settings were significantly lower than those of this nodule reconstructed by ASiR-V (all P<0.05). Furthermore, the lowest absolute percentage measurement errors for ground-glass nodules were observed with Truefidelity or ClariCT.AI at all dose settings. The absolute percentage measurement errors for ground-glass nodules reconstructed with Truefidelity at ultra–low-dose settings were significantly lower than those of all sizes of ground-glass nodules reconstructed with ASiR-V (all P<0.05). Image noise was lowest with Truefidelity (all P<0.05). In conclusion, the deep learning–based algorithms were more accurate for volume measurements of both solid nodules and ground-glass nodules than ASiR-V at both low-dose and ultra–low-dose settings.

Wide field-of-view line-scanning lensless in-line holographic microscope

In a traditional lensless in-line holographic microscope (LIHM), the field of view (FOV) is generally limited by the size of the imaging sensor, which is usually several millimeters wide. We propose a scanning LIHM system using a line-scan camera to achieve wide field-of-view holographic imaging. In addition, the iterative holographic reconstruction method is applied to obtain the sample image with enhanced contrast and resolution using two hologram measurements, which are acquired by slightly shifting the sample-to-sensor distance. The capability of the proposed scanning LIHM system was demonstrated by experiments with a U.S. Air Force target and a biological sample of leaf vein. The reconstructed sample image had a FOV of 2.87 cm × 4.70 cm (2048 × 3357 pixels), which was much larger than the typical FOV of several square millimeters in a traditional LIHM system.

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

&lt;span&gt;This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.&lt;/span&gt;

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

Cancellation of streak artifacts in radial abdominal imaging using interference null space projection.

In radial abdominal imaging, it has been commonly observed that signal from the arms cause streaks due to system imperfections. We previously introduced a streak removal technique (B-STAR), which is inherently spatially variant and limited to work in image space. In this work, we propose a spatially invariant streak cancellation technique (CACTUS), which can be applied in either image space or k-space and is compatible with iterative reconstructions. Streak sources are typically spatially localized and can be represented using a low-dimensional subspace. CACTUS identifies the streak subspace by leveraging the spatial redundancy of receiver coils and projects the data onto the streak null space to eliminate the streaks. When applied in k-space, CACTUS can be combined with iterative reconstructions. CACTUS was tested in phantoms and in vivo abdominal imaging using a radial turbo spin-echo pulse sequence. In phantoms, CACTUS improved T2 estimation in comparison to previous de-streaking methods. In vivo experiments showed that CACTUS reduced streaks and yielded T2 estimation, in regions affected by streaks, closer to a streak-free reference. Evaluation using a clinical abdominal dataset (n=20) showed that CACTUS is comparable to B-STAR and yields significantly better signal preservation and streak cancellation than coil removal and suppression methods. CACTUS provides superior signal preservation and streak reduction performance compared to coil removal and suppression methods. As a clear advantage over B-STAR, CACTUS can be integrated with iterative reconstruction methods. In abdominal T2 mapping, CACTUS improves the accuracy of parameter estimation in areas affected by streaks.

Calibration-free retrieval of density information from computed tomography data

In many research areas, particularly in materials science, retrieving a 3D distribution of the material density in a sample is of key interest. Although standard lab-based X-ray μCT imaging is capable of retrieving the 3D distribution of local attenuation coefficients, the local density cannot be determined unambiguously without the use of calibration standards due to the polychromaticity of the used X-ray tubes. In this paper, two methods are presented to retrieve this information based on accurate knowledge of the polychromatic properties of source and detector in the used μCT system. The methods use a very different approach, one being a polychromatic pre-processing method and one relying on a novel polychromatic iterative reconstruction method, yet produce equivalent results. They are limited to objects with a homogeneous composition, but are extremely powerful in a broad range of applications in materials characterization where the density distribution is of interest. Moreover, the method relying on iterative reconstruction has the potential to be applied for multi-material objects as well. A number of application examples illustrate the potential of the methods to not only retrieve quantitative estimates of density and sub-voxel porosity, but also to greatly reduce or eliminate the spectral artifacts that often hinder conventional calibration schemes.

A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography.

Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation. In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients. By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm. In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition. The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.

Cycle-consistent learning-based hybrid iterative reconstruction for whole-body PET imaging

Objective. To develop a cycle-consistent learning-based hybrid iterative reconstruction (IR) method that takes only slightly longer than analytic reconstruction, while pursuing the image resolution and tumor quantification achievable by IR for whole-body PET imaging. Approach. We backproject the raw positron emission tomography (PET) data to generate a blurred activity distribution. From the backprojection to the IR label, a reconstruction mapping that approximates the deblurring filters for the point spread function and the physical effects of the PET system is unrolled to a neural network with stacked convolutional layers. By minimizing the cycle-consistent loss, we train the reconstruction and inverse mappings simultaneously. Main results. In phantom study, the proposed method results in an absolute relative error (RE) of the mean activity of 4.0% ± 0.7% in the largest hot sphere, similar to the RE of the full-count IR and significantly smaller than that obtained by CycleGAN postprocessing. Achieving a noise reduction of 48.1% ± 0.5% relative to the low-count IR, the proposed method demonstrates advantages over the low-count IR and CycleGAN in terms of resolution maintenance, contrast recovery, and noise reduction. In patient study, the proposed method obtains a noise reduction of 44.6% ± 8.0% for the lung and the liver, while maintaining the regional mean activity in both simulated lesions and real tumors. The run time of the proposed method is only half that of the conventional IR. Significance. The proposed cycle-consistent learning from the backprojection rather than the raw PET data or an IR result enables improved reconstruction accuracy, reduced memory requirements, and fast implementation speeds for clinical whole-body PET imaging.

ADMM-SVNet: An ADMM-Based Sparse-View CT Reconstruction Network

In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT. In the process of optimizing the iterative model, the approach of directly solving the quadratic penalty function of the objective function can be expected to perform poorly. Compared with the direct solution method, the alternating direction method of multipliers (ADMM) algorithm can avoid the ill-posed problem associated with the quadratic penalty function. However, the regularization items, sparsity transform, and parameters in the traditional ADMM iterative model need to be manually adjusted. In this paper, we propose a data-driven ADMM reconstruction method that can automatically optimize the above terms that are difficult to choose within an iterative framework. The main contribution of this paper is that a modified U-net represents the sparse transformation, and the prior information and related parameters are automatically trained by the network. Based on a comparison with other state-of-the-art reconstruction algorithms, the qualitative and quantitative results show the effectiveness of our method for sparse-view CT image reconstruction. The experimental results show that the proposed method performs well in streak artifact elimination and detail structure preservation. The proposed network can deal with a wide range of noise levels and has exceptional performance in low-dose reconstruction tasks.

Analytical covariance estimation for iterative CT reconstruction methods

Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work. Based on the property of convergent points of cost funtions, the proposed method is composed of three steps, (1) construct a relationship between the covariance of projection and corresponding reconstruction from cost functions at its convergent point, (2) simplify the covariance relationship constructed in (1) by introducing an approximate gradient of penalties, and (3) obtain an analytical covariance estimation according to the simplified relationship in (2). Three approximation methods for step (2) are studied: the linear approximation of the gradient of penalties (LAM), the Taylor apprximation (TAM), and the mixture of LAM and TAM (MAM). TV and qGGMRF penalized weighted least square methods are experimented on. Results from statistical methods are used as reference. Under the condition of unstable 2nd derivative of penalties such as TV, the covariance image estimated by LAM accords to reference well but of smaller values, while the covarianc estimation by TAM is quite off. Under the conditon of relatively stable 2nd derivative of penalties such as qGGMRF, TAM performs well and LAM is again with a negative bias in magnitude. MAM gives a best performance under both conditions by combining LAM and TAM. Results also show that only one noise realization is enough to obtain reasonable covariance estimation analytically, which is important for practical usage. This work suggests the necessity and a new way to estimate the covariance for non-quadratically penalized reconstructions. Currently, the proposed method is computationally expensive for large size reconstructions.Computational efficiency is our future work to focus.

Exact gram filtering and efficient backprojection for iterative CT reconstruction.

Forward and backprojections are the basis of all model-based iterative reconstruction (MBIR) methods. However, computing these accurately is time-consuming. In this paper, we present a method for MBIR in parallel X-ray beam geometry that utilizes a Gram filter to efficiently implement forward andbackprojection. We propose using voxel-basis and modeling its footprint in a box spline framework to calculate the Gram filter exactly and improve the performance of backprojection. In the special case of parallel X-ray beam geometry, the forward and backprojection can be implemented by an estimated Gram filter efficiently if the sinogram signal is bandlimited. In this paper, a specialized sinogram interpolation method is proposed to eliminate the bandlimited prerequisite and thus improve the reconstruction accuracy. We build on this idea by utilizing the continuity of the voxel-basis' footprint, which provides a more accurate sinogram interpolation and further improves the efficiency and quality of backprojection. In addition, the detector blur effect can be efficiently accounted for in our method to better handle realisticscenarios. The proposed method is tested on both phantom and real computed tomography (CT) images under different resolutions, sinogram sampling steps, and noise levels. The proposed method consistently outperforms other state-of-the-art projection models in terms of speed and accuracy for both backprojection andreconstruction. We proposed a iterative reconstruction methodology for 3D parallel-beam X-ray CT reconstruction. Our experimental results demonstrate that the proposed methodology is accurate, fast, and reproducible, and outperforms alternative state-of-the-art projection models on both backprojection and reconstruction resultssignificantly.

Performance evaluation of a micro-CT system for laboratory animal imaging with iterative reconstruction capabilities.

In recent years, there has been a rapid proliferation in micro-computed tomography (micro-CT) systems becoming more available for routine preclinical research, with applications in many areas, including bone, lung, cancer, and cardiac imaging. Micro-CT provides the means to non-invasively acquire detailed anatomical information, but high-resolution imaging comes at the cost of longer scan times and higher doses, which is not desirable given the potential risks related to x-ray radiation. To achieve dose reduction and higher throughputs without compromising image quality, fewer projections can be acquired. This is where iterative reconstruction methods can have the potential to reduce noise since these algorithms can better handle sparse projection data, compared to filtered backprojection PURPOSE: We evaluate the performance characteristics of a compact benchtop micro-CT scanner that provides iterative reconstruction capabilities with GPU-based acceleration. We thereby investigate the potential benefit of iterative reconstruction for dose reduction. Based on a series of phantom experiments, the benchtop micro-CT system was characterized in terms of image uniformity, noise, low contrast detectability, linearity, and spatial resolution. Whole-body images of a plasticized ex vivo mouse phantom were also acquired. Different acquisition protocols (general-purpose versus high-resolution, including low dose scans) and different reconstruction strategies (analytic versus iterative algorithms: FDK, ISRA, ISRA-TV) were compared. Signal uniformity was maintained across the radial and axial field-of-view (no cupping effect) with an average difference in Hounsfield units (HU) between peripheral and central regions below 50. For low contrast detectability, regions with at least ∆HU of 40 to surrounding material could be discriminated (for rods of 2.5mm diameter). A high linear correlation (R2 =0.997) was found between measured CT values and iodine concentrations (0-40mg/ml). Modulation transfer function (MTF) calculations on a wire phantom evaluated a resolution of 10.2 lp/mm at 10% MTF that was consistent with the 8.3% MTF measured on the 50µm bars (10 lp/mm) of a bar-pattern phantom. Noteworthy changes in signal-to-noise and contrast-to-noise values were found for different acquisition and reconstruction protocols. Our results further showed the potential of iterative reconstruction to deliver images with less noise and artefacts. In summary, the micro-CT system that was evaluated in the present work was shown to provide a good combination of performance characteristics between image uniformity, low contrast detectability, and resolution in short scan times. With the iterative reconstruction capabilities of this micro-CT system in mind (ISRA and ISRA-TV), the adoption of such algorithms by GPU-based acceleration enables the integration of noise reduction methods which here demonstrated potential for high-quality imaging at reduced doses.

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies

IntroductionQ.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.MethodsSix [11C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BPND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons.ResultsWhen comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).ConclusionBPND results obtained from quantitative low count brain PET studies using [11C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [18F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method.

To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k=1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%. Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k=1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving<1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability. To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection. Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion. A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRSTMILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRSTMILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRSTSTR showing the closest results (P < 0.001). Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

Evaluation of Noise Properties at Nonuniformity Area and Resolution Properties of CT Image Using Iterative Reconstruction Method

The purpose of this study was to investigate the resolution property and the noise properties at the nonuniformity area in the slice plane of iterative reconstruction (IR)-CT image. CT images of a phantom with nonuniformity areas including multiple contrast (medium, high, and ultra-high contrast) signals were acquired at various scan dose conditions and reconstructed with different iterative intensity levels. The noise properties at the nonuniformity and the uniformity areas were evaluated by measuring normalized noise power spectra (nNPSs) using subtracted images from sequential scanning data under the same scan conditions. We investigated the relationship between the noise properties and the resolution properties evaluated by measuring task transfer function (TTF) using multiple contrast signals before the subtraction. There was a correlation between the nNPS at the nonuniformity area and the TTF because the nNPS values at high spatial frequency were increased with superior TTF (higher dose, mild iterative intensity, and higher contrast level). The differences in the high spatial frequency component of the nNPSs among each task were decreased with the inferior TTF tasks. We conclude that the noise properties in the slice plane at the nonuniformity area and resolution properties of the IR-CT image were correlated similar to image quality properties of the linear imaging system due to the dependence of the nNPSs curve on the TTF value under each task.

Iterative Methods for Signal Reconstruction on Product Graphs with Arbitrary Missing Data

Iterative Methods for Signal Reconstruction on Product Graphs with Arbitrary Missing Data

Spatial-Spectral Extraction for Hyperspectral Anomaly Detection

A novel hyperspectral anomaly detection method is proposed in this letter. This method is motivated by two important observations. First, there are hundreds of bands in a hyperspectral image (HSI), among which redundant bands and some noisy bands could be eliminated to increase the discriminability from the spectral domain. Meanwhile, considering the small amount of the anomalies, they may be neglected in the spatial clustering process, and the anomalies can be highlighted by eliminating the clustered result from the original HSI. In this way, the spatial-spectral extraction is proposed to detect the anomalies in the HSI. First, an iterative optimal neighborhood reconstruction (IONR) method is utilized to select the bands while preserving their divergence. Second, the spatial clustering is achieved by minimizing the intradistance in any given category. Finally, the spatial clustered HSI is eliminated from the band selected HSI and acts as the direct input for the detection process. Experimental results and data analysis have demonstrated the effectiveness of the proposed method.

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

The tropospheric tomography is an ill-posed inversion problem due to the sparsity of Global Navigation Satellite Systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use pre-fixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10&#x0025; and 15&#x0025; when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this study can improve the performance of the iterative methods for GNSS tropospheric tomography.

Dr. Eiichi Tanaka's Achievement in the Research of Image Reconstruction

In memoriam of Dr. Eiichi Tanaka who passed away on August 21, 2021, I review his achievement in the research of tomographic image reconstruction. Tomographic image reconstruction is an important research area which has wide applications including X-ray CT, nuclear medicine imaging such as PET and SPECT, and electron microscopy. Since 1970's, Dr. Tanaka has worked on numerous important topics in tomographic reconstruction fields aiming at using them in image reconstruction for PET and SPECT. Among them, in this paper, I will introduce his research on Filtered BackProjection (FBP) method, analytical attenuation correction in SPECT, image reconstruction in Time-of-Flight PET, image reconstruction for 3-D PET imaging, and iterative image reconstruction method called Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA).