Statistical Image Reconstruction Research Articles

Statistical image reconstruction with beam-hardening compensation for X-ray CT by a calibration step (2DIterBH).

The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters. We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms. The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system. Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions. Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution.

SPLIT: Statistical Positronium Lifetime Image Reconstruction via Time-Thresholding.

Positron emission tomography (PET) is a widely utilized medical imaging modality that uses positron-emitting radiotracers to visualize biochemical processes in a living body. The spatiotemporal distribution of a radiotracer is estimated by detecting the coincidence photon pairs generated through positron annihilations. In human tissue, about 40% of the positrons form positroniums prior to the annihilation. The lifetime of these positroniums is influenced by the microenvironment in the tissue and could provide valuable information for better understanding of disease progression and treatment response. Currently, there are few methods available for reconstructing high-resolution lifetime images in practical applications. This paper presents an efficient statistical image reconstruction method for positronium lifetime imaging (PLI). We also analyze the random triple-coincidence events in PLI and propose a correction method for random events, which is essential for real applications. Both simulation and experimental studies demonstrate that the proposed method can produce lifetime images with high numerical accuracy, low variance, and resolution comparable to that of the activity images generated by a PET scanner with currently available time-of-flight resolution.

Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product

Abstract. The EarthCARE satellite mission's objective is to retrieve profiles of aerosol and cloud physical and optical properties using the combination of cloud-profiling radar (CPR), high-spectral-resolution UV lidar (ATLID) and passive multi-spectral imager (MSI) data. Based on synergistic retrievals using data from these instruments, the 3D atmospheric cloud–aerosol state is estimated and then used to model the top-of-atmosphere (TOA) broadband radiances, which may then be compared to co-incident EarthCARE broadband radiometer (BBR) measurements. A high-spectral-resolution lidar enables the independent retrieval of extinction and backscatter but, being space based, suffers from relatively low signal-to-noise ratio (SNR) levels. The ATLID FeatureMask (A-FM) product provides a feature detection mask for the existence of atmospheric features within the lidar profiles based on a number of (statistical) image reconstruction techniques. Next to this, it also identifies those regions where the lidar beam has been fully attenuated and where the surface backscatter has impacted the measured lidar backscatter signals directly above the surface. From the pixels assigned as clear sky (with no features present above), the clear-sky-averaged profiles for the three ATLID channels, the co-polar Mie channel, the total cross channel and the co-polar Rayleigh channel are created. These feature-free or clear-sky profiles are useful for e.g., assessing the quality of the ATLID Level-1 (L1) attenuated backscatters. An important goal of the A-FM product is to guide smoothing strategies within downstream processors e.g., the ATLID profile retrieval (A-PRO) algorithm which directly follows A-FM within the EarthCARE Level-2 (L2) processing chain. Within the A-PRO algorithm, profiles of extinction, backscatter and linear depolarization ratio are retrieved. However, smoothing of the ATLID L1 attenuated backscatter is necessary since the SNR levels present at the ATLID native resolution are generally not sufficient for meaningful retrievals to be conducted. At the same time, to prevent biased retrievals, any smoothing procedure must respect the cloud–aerosol structure and avoid mixing strong features, e.g., clouds, and weak features, e.g., aerosol regions, together. The A-FM product provides the A-PRO algorithm with important information that is used to guide various smoothing procedures. To enable the processing of the large datasets from observation up to L2 retrievals, each EarthCARE orbit is separated into eight frames, divided at latitudes of 22.5∘ N and 22.5∘ S and 62.5∘ N and 62.5∘ S. As a secondary product, A-FM outputs can be used to conduct a frame-by-frame evaluation of the ATLID L1 cross-talk calibration, where an EarthCARE frame is one-eighth of a full orbit. This evaluation can be performed by comparing the retrieved clear-sky profiles to the expected channel profiles. The A-FM product has been applied to both synthetic data from the EarthCARE end-to-end simulator (ECSIM) and the L1 data from the Aeolus wind lidar mission. Comparisons against the ECSIM model truth indicate that A-FM has a percentage correctness > 90 % and is capable of reliably detecting aerosol and cloud regions within extinctions (> 10−5 m−1).

Statistical Image Reconstruction with Beam-Hardening compensation for X-ray CT by a calibration step (2DIterBH)

Statistical Image Reconstruction with Beam-Hardening compensation for X-ray CT by a calibration step (2DIterBH)

Comparison of supervised-learning approaches for designing a channelized observer for image quality assessment in CT.

The current paradigm for evaluating computed tomography (CT) system performance relies on a task-based approach. As the Hotelling observer (HO) provides an upper bound of observer performances in specific signal detection tasks, the literature advocates HO use for optimization purposes. However, computing the HO requires calculating the inverse of the image covariance matrix, which is often intractable in medical applications. As an alternative, dimensionality reduction has been extensively investigated to extract the task-relevant features from the raw images. This can be achieved by using channels, which yields the channelized-HO (CHO). The channels are only considered efficient when the channelized observer (CO) can approximate its unconstrained counterpart. Previous work has demonstrated that supervised learning-based methods can usually benefit CO design, either for generating efficient channels using partial least squares (PLS) or for replacing the Hotelling detector with machine-learning (ML) methods. Here we investigated the efficiency of a supervised ML-algorithm used to design a CO for predicting the performance of unconstrained HO. The ML-algorithm was applied either (1) in the estimator for dimensionality reduction, or (2) in the detector function. A channelized support vector machine (CSVM) was employed and compared against the CHO in terms of ability to predict HO performances. Both the CSVM and the CHO were estimated with channels derived from the singular value decomposition (SVD) of the system operator, principal component analysis (PCA), and PLS. The huge variety of regularization strategies proposed by CT system vendors for statistical image reconstruction (SIR) make the generalization capability of an observer a key point to consider upfront of implementation in clinical practice. To evaluate the generalization properties of the observers, we adopted a 2-step testing process: (1) achieved with the same regularization strategy (as in the training phase) and (2) performed using different reconstruction properties. We generated simulated- signal-known-exactly/background-known-exactly (SKE/BKE) tasks in which different noise structures were generated using Markov random field (MRF) regularizations using either a Green or a quadratic, function. The CSVM outperformed the CHO for all types of channels and regularization strategies. Furthermore, even though both COs generalized well to images reconstructed with the same regularization strategy as the images considered in the training phase, the CHO failed to generalize to images reconstructed differently whereas the CSVM managed to successfully generalize. Lastly, the proposed CSVM observer used with PCA channels outperformed the CHO with PLS channels while using a smaller training data set. These results argue for introducing the supervised-learning paradigm in the detector function rather than in the operator of the channels when designing a CO to provide an accurate estimate of HO performance. The CSVM with PCA channels proposed here could be used as a surrogate for HO in image quality assessment.

Single-exposure X-ray phase imaging microscopy with a grating interferometer.

The advent of hard X-ray free-electron lasers enables nanoscopic X-ray imaging with sub-picosecond temporal resolution. X-ray grating interferometry offers a phase-sensitive full-field imaging technique where the phase retrieval can be carried out from a single exposure alone. Thus, the method is attractive for imaging applications at X-ray free-electron lasers where intrinsic pulse-to-pulse fluctuations pose a major challenge. In this work, the single-exposure phase imaging capabilities of grating interferometry are characterized by an implementation at the I13-1 beamline of Diamond Light Source (Oxfordshire, UK). For comparison purposes, propagation-based phase contrast imaging was also performed at the same instrument. The characterization is carried out in terms of the quantitativeness and the contrast-to-noise ratio of the phase reconstructions as well as via the achievable spatial resolution. By using a statistical image reconstruction scheme, previous limitations of grating interferometry regarding the spatial resolution can be mitigated as well as the experimental applicability of the technique.

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.

CT Image Reconstruction Using NLMfuzzyCD Regularization Method.

To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. The conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from optimal smoothing as the number of iterations increases. For solving this problem, this paper presents a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means of fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. The proposed model was evaluated on four test cases phantoms. Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compared with the state-of-the-art techniques.

CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning\u2013based reconstruction

ObjectivesThe aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared.MethodsA temperature-regulating water circuit system was developed for the systematic evaluation of the correlation between temperature and Hounsfield units (HU). The measurements were performed on a Revolution CT with gemstone spectral imaging technology (GSI). Complementary measurements were performed without GSI (voltage 120 kVp, current 130–545 mA). The measured object was a tissue equivalent phantom in a temperature range of 18 to 50°C. The evaluation was carried out for VMI at 40 to 140 keV and polychromatic 120-kVp images.ResultsThe regression analysis showed a significant inverse linear dependency between temperature and average HU regardless of ASIR-V and DLIR. VMI show a higher temperature sensitivity compared to polychromatic images. The temperature sensitivities were 1.25 HU/°C (120 kVp) and 1.35 HU/°C (VMI at 140 keV) for fat, 0.38 HU/°C (120 kVp) and 0.47 HU/°C (VMI at 40 keV) for muscle and 1.15 HU/°C (120 kVp) and 3.58 HU/°C (VMI at 50 keV) for bone.ConclusionsDual-energy with VMI enables a higher temperature sensitivity for fat, muscle and bone. The reconstruction with ASIR-V and DLIR has no significant influence on CT-based thermometry, which opens up the potential of drastic dose reductions.Key Points• Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images.• With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images.• The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning–based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.

Efficient Monte-Carlo based system modelling for image reconstruction in preclinical pinhole SPECT

The use of multi-pinhole collimation has enabled ultra-high-resolution imaging of SPECT and PET tracers in small animals. Key for obtaining high-quality images is the use of statistical iterative image reconstruction with accurate energy-dependent photon transport modelling through collimator and detector. This can be incorporated in a system matrix that contains the probabilities that a photon emitted from a certain voxel is detected at a specific detector pixel. Here we introduce a fast Monte-Carlo based (FMC-based) matrix generation method for pinhole imaging that is easy to apply to various radionuclides. The method is based on accelerated point source simulations combined with model-based interpolation to straightforwardly change or combine photon energies of the radionuclide of interest. The proposed method was evaluated for a VECTor PET-SPECT system with (i) a HE-UHR-M collimator and (ii) an EXIRAD-3D 3D autoradiography collimator. Both experimental scans with 99mTc, 111In, and 123I, and simulated scans with 67Ga and 90Y were performed for evaluation. FMC was compared with two currently used approaches, one based on a set of point source measurements with 99mTc (dubbed traditional method), and the other based on an energy-dependent ray-tracing simulation (ray-tracing method). The reconstruction results show better image quality when using FMC-based matrices than when applying the traditional or ray-tracing matrices in various cases. FMC-based matrices generalise better than the traditional matrices when imaging radionuclides with energies deviating too much from the energy used in the calibration and are computationally more efficient for very-high-resolution imaging than the ray-tracing matrices. In addition, FMC has the advantage of easily combining energies in a single matrix which is relevant when imaging radionuclides with multiple photopeak energies (e.g. 67Ga and 111In) or with a continuous energy spectrum (e.g. 90Y). To conclude, FMC is an efficient, accurate, and versatile tool for creating system matrices for ultra-high-resolution pinhole SPECT.

Super-resolved live-cell imaging using random illumination microscopy

SummaryCurrent super-resolution microscopy (SRM) methods suffer from an intrinsic complexity that might curtail their routine use in cell biology. We describe here random illumination microscopy (RIM) for live-cell imaging at super-resolutions matching that of 3D structured illumination microscopy, in a robust fashion. Based on speckled illumination and statistical image reconstruction, easy to implement and user-friendly, RIM is unaffected by optical aberrations on the excitation side, linear to brightness, and compatible with multicolor live-cell imaging over extended periods of time. We illustrate the potential of RIM on diverse biological applications, from the mobility of proliferating cell nuclear antigen (PCNA) in U2OS cells and kinetochore dynamics in mitotic S. pombe cells to the 3D motion of myosin minifilaments deep inside Drosophila tissues. RIM's inherent simplicity and extended biological applicability, particularly for imaging at increased depths, could help make SRM accessible to biology laboratories.

Prior-image-based CT reconstruction using attenuation-mismatched priors

Prior-image-based reconstruction (PIBR) methods are powerful tools for reducing radiation doses and improving the image quality of low-dose computed tomography (CT). Apart from anatomical changes, prior and current images can also have different attenuations because they originated from different scanners or from the same scanner but with different x-ray beam qualities (e.g., kVp settings, beam filters) during data acquisition. In such scenarios, with attenuation-mismatched priors, PIBR is challenging. In this work, we investigate a specific PIBR method, called statistical image reconstruction, using normal-dose image-induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation-mismatched priors and achieve quantitative low-dose CT imaging. We propose two corrective schemes for the original SIR-ndiNLM method, (1) a global histogram-matching approach and (2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validate the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to simulate attenuation mismatches. Meanwhile, we utilize different CT slices to simulate anatomical mismatches or changes between the prior and the current low-dose image. We observe that the original SIR-ndiNLM introduces artifacts to the reconstruction when an attenuation-mismatched prior is used. Furthermore, we find that a larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our two proposed corrective schemes enable SIR-ndiNLM to effectively handle the attenuation mismatch and anatomical changes between the two images and successfully eliminate the artifacts. We demonstrate that the proposed techniques permit SIR-ndiNLM to leverage the attenuation-mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

Reducing motion artifact in sequential-scan dual-energy CT imaging by incorporating deformable registration within joint statistical image reconstruction

Dual-energy computed tomography (DECT) has been widely used to reconstruct basis components. In previous studies, ou DECT algorithm has shown high accuracy in stopping power ratio (SPR) estimation of fixed objects for proton radiotherapy planning. However, patient movement between sequential data acquisitions may lead to severe motion artifacts in the component images. In order to reduce or eliminate the motion artifacts in clinical applications, we combine a deformable registration method with an accurate joint statistical iterative reconstruction algorithm, dual-energy alternating minimization (DEAM). Image registration is a process of geometrically aligning two or more images. We implement a multi-modality symmetric deformable registration method based on Advanced Normalization Tools (ANTs) to automatically align the scans we acquire for the same patient. The precalculated registration mapping and its inverse are then embedded into each iteration of the DEAM algorithm. The performance of warped DEAM is quantitatively assessed. Theoretically, the performance of warped DEAM on moved patients should be comparable to the performance of the original DEAM algorithm on fixed objects. The warped DEAM algorithm reduces motion artifacts while preserving the accuracy of the iterative joint statistical CT reconstruction algorithm, which enables us to reconstruct accurate results from sequentially scanned dual-energy patient data.

Complex diffusion regularisation-based low dose CT image reconstruction

There is a high demand for a framework that can eliminate the Gaussian noise and provide high-quality images in the case of low-dose radiation in computed tomography (CT). This paper presents a new statistical image reconstruction (SIR) algorithm for this purpose. The proposed framework uses a combination of data fidelity and regularisation terms. The data fidelity term is derived using maximum likelihood expectation maximisation (MLEM) algorithm. After that, a complex diffusion (CD) filter is applied as a regularisation term into the proposed framework that minimises the noise while preserving the edges along with the fine structure information in the reconstructed image. The proposed model has been evaluated on both simulated and real standard thorax phantoms. The results are compared with the other standard methods and it is observed that the proposed model has many desirable advantages such as better noise robustness, less computational cost, enhanced denoising effect.

Constructing Large Transmission Matrix of Scattering Sample Based on Super-Resolution Algorithm

A method to construct a large transmission matrix (TM) of highly scattering media based on an algorithm is presented. For imaging through scattering media, measuring a large TM enables image reconstruction with high spatial resolution; however, it is difficult to operate in practice because the time required for the calibration of the TM is proportional to the number of calibrated input modes. Instead of directly achieving calibration using advanced hardware, a large TM is defined and constructed based on a multiframe image super-resolution algorithm, which traditionally aims to recover a high-resolution (HR) image of the original object from several low-resolution images. With sufficient size improvement, the defined large TM is constructed from several naturally measured complementary small TMs, each of them is measured using a subpixel-shifted projector phase mask. The realized large TM, which is constructed through subpixel registration and interpolation operations at the help of a hypothetical HR mask, enables HR image reconstruction from the original object’s speckle signal. The feasibility of the proposed method is proven via optical experiments involving statistical analysis and image reconstruction. The proposed method benefits existing TM-based image reconstruction applications and offers a new perspective on the size of TM measurements and the imaging resolution.

Photo-excited charge carrier imaging by time-resolved pattern illumination phase microscopy

A nanosecond time-resolved imaging technique has been developed for the observation of the photo-excited charge carrier dynamics in photo-devices such as photocatalysts and solar cells. An arbitrary spatial pattern of pump pulse light excites the charge carriers, which are observed by phase-contrast imaging. This patterned excitation is preferable for various statistical image reconstruction techniques based on robust principal component analysis and the least absolute shrinkage and selection operator, which helped the enhancement of the signal-to-noise ratio and the removal of unwanted image components. By using data assimilation with the charge decay model, the lifetime and diffusion coefficients were mapped for the photo-excited electrons in a nano-particulate titanium oxide film and other photo-device materials.

Improved Depictions of the Anterior Choroidal Artery and Thalamoperforating Arteries on 3D-CTA Images Using Model-based Iterative Reconstruction

To evaluate the depictability of intracranial small arteries using high-resolution CTA with model-based iterative reconstruction (MBIR). We retrospectively analyzed 21 patients who underwent brain 3D-CTA. Axial and volume-rendered (VR) images were reconstructed from the 3D-CTA raw data using adaptive statistical image reconstruction (ASIR) and MBIR. As a quantitative assessment, intra-arterial CT values of the ICA and contrast-to-noise ratio were measured to evaluate vessel enhancement. Additionally, CT values and standard deviations (SDs) of CT values and signal to noise ratio in white matter parenchyma were measured to evaluate background noise. As a qualitative assessment, the degree of vessel depictability in the anterior choroidal artery (AchoA) and the perforating branches of thalamoperforating arteries (TPA) on VR images using two different reconstruction algorithms was visually evaluated using a 3-point grading system. The CT value of the ICA [605.27± 89.76 Hounsfield units (HU)] was significantly increased and the SD value (i.e., image noise) of the white matter parenchyma [6.79 ± 0.81(HU)] was decreased on MBIR compared with ASIR [546.76 ± 85.27 (HU)] and [8.04 ± 1.08 HU)] (p <.05 for all). Contrast-to-noise ratio of ICA [84.48 ± 20.17] and signal to noise ratio of white matter [6.18 ± 0.75] with MBIR were significantly higher than ASIR [65.98 ± 13.08] and [5.28 ± 0.78] (p < 0.05 for all). In addition, depictions of the AchoA and TPA on VR images were significantly improved using MBIR compared with ASIR (p < 0.05). MBIR allows depiction of small intracranial arteries such as AchoA and TPA with better visibility than ASIR without increasing the dose of radiation and the amount of contrast agent.

Scaled projected-directions methods with application to transmission tomography

Statistical image reconstruction in X-ray computed tomography yields large-scale regularized linear least-squares problems with nonnegativity bounds, where the memory footprint of the operator is a concern. Discretizing images in cylindrical coordinates results in significant memory savings, and allows parallel operator–vector products without on-the-fly computation of the operator, without necessarily decreasing image quality. However, it deteriorates the conditioning of the operator. We improve the Hessian conditioning by way of a block-circulant scaling operator and we propose a strategy to handle nondiagonal scaling in the context of projected-directions methods for bound-constrained problems. We describe our implementation of the scaling strategy using two algorithms: TRON, a trust-region method with exact second derivatives, and L-BFGS-B, a linesearch method with a limited-memory quasi-Newton Hessian approximation. We compare our approach with one where a change of variable is made in the problem. On two reconstruction problems, our approach converges faster than the change of variable approach, and achieves much tighter accuracy in terms of optimality residual than a first-order method.

Acceleration of Statistical Image Reconstruction in X-ray Phase Tomography

Acceleration of Statistical Image Reconstruction in X-ray Phase Tomography

Estimation of optimized timely system matrix with improved image quality in iterative reconstruction algorithm: A simulation study

The system matrix (SM) being a main part of statistical image reconstruction algorithms establishes relationship between the object and projection space. The aim was to determine it in a short duration time, towards obtaining the best quality of contrast images. In this study, a new analytical method based on Cavalieri's principle as subdividing common regions has been proposed in which the precision of the amounts of estimated areas was improved by increasing the number of divisions (NOD), and consequently the total SM's time was increased. An important issue is the tradeoff between the NODs and computational time. For this purpose, a Monte Carlo simulated Jaszczak phantom study was performed by the Monte Carlo N-Particle transport code version 5 (MCNP5) in which the tomographic images of resolution and contrast phantoms were reconstructed by maximum likelihood expectation maximization (MLEM) algorithm, and the influence of NODs variations was investigated. The results show that the lowest and best quality have been obtained at the NODs of 0 and 8, respectively and in the optimum case, the SM's total time at NOD of 8 was 925 s, which was much lower than those of the conventional Monte Carlo simulations and experimental test.