Standard Reconstruction Algorithms Research Articles

Optical breast atlas as a testbed for image reconstruction in optical mammography

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Tuneable in-situ nanoCT workflow using FIB/SEM

Inspired by the standard computed tomography, a new method of 3D X-ray imaging embedded in FIB-SEM microscope is proposed. The unique combination of TEM-like specimen stage enabling in lens STEM detection (referred to as CompuStage), nanomanipulator (referred to as EasyLift) facilitating in-situ sample transfer from bulk sample to TEM-like stage and pixelated in-situ Timepix X-ray detector in Helios G4 FX FIB-SEM system offers an unprecedented workflow. Motivated by common circular CT scan known from microCT world, the object under study is placed on CompuStage rod which enables two possible rotation (in TEM/SEM terminology called tilt) movements – α-tilt – rotation of the CompuStage rod around its axis, and β-tilt – rotation around axis perpendicular to CompuStage rod. β-tilt rotation enables a circular movement of the sample while α-tilt sets the correct position of sample with respect to target and detector. Thin metal lamella of suitable material welded to EasyLift manipulator needle is used as an X-ray target. The final target-sample geometry – position, distance – can be fine-tuned using position control of CompuStage and EasyLift and in-situ monitored by SEM. Both sample and target can also be easily prepared in-situ. Radiographs are recorded by Timepix detector with inherent noise-free operation and energy filtration. For the 3D reconstruction standard microCT reconstruction algorithm is used with the procedure adjusted for the format and quality of nanoCT images.The experiments were carried out on Helios G4 FX DualBeam using titanium and tungsten targets and various semiconductor samples. The ultimate resolution of the proposed method in orders of tens of nanometers was achieved both by the possibility of close target to sample positioning and of adjustment of primary beam energy down to low energies reducing the interaction volume in the target. Since the lower energy radiation is well suited for life-science, the method was also tested on several bio-samples using silver target. The silver target, thanks to its massive low energy Lα line, allowed to distinguish subtle structures in the resin embedded stained mouse brain and also to observe and reconstruct canaliculi in the mouse bone (earlier reported by Dierolf et al. 2010, Nature 467 436).

DIR-DBTnet: Deep iterative reconstruction network for three-dimensional digital breast tomosynthesis imaging.

The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth. In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

Reconstruction of atomic resonances with attosecond streaking.

Recent development of spectroscopic techniques based on attosecond radiation has given the community the right tools to study the timing of the photoelectron process. In this work we investigate the effect of Fano resonances in attosecond streaking spectrograms and the application of standard phase-reconstruction algorithms. We show that while the existence of the infrared coupling (ac-Stark shift) hinders the applicability of FROG-like methods, under certain conditions it is still possible to use standard reconstruction algorithms to retrieve the photoemission delay of the bare resonance. Finally, we propose two strategies to study the strength of IR coupling using the attosecond streaking technique.

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography.

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

Accurate Measurement of Agatston Score Using kVp-Independent Reconstruction Algorithm for Ultra-High-Pitch Sn150 kVp CT.

ObjectiveTo investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose.Materials and MethodsThis prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared.ResultsNo significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0–232.28) and C (median, 60.25; IQR, 0–195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (−7.82) and with the limits of agreement from −65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0–120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode.ConclusionThe Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks.

Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp–Davis–Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.

CT Phantom Evaluation of 67,392 American College of Radiology Accreditation Examinations: Implications for Opportunistic Screening of Osteoporosis Using CT.

OBJECTIVE. The purpose of this study was to investigate whether systematic bias in attenuation measurements occurs among CT scanners made by four major manufacturers and the relevance of this bias regarding opportunistic screening for osteoporosis. MATERIALS AND METHODS. Data on attenuation measurement accuracy were acquired using the American College of Radiology (ACR) accreditation phantom and were evaluated in a blinded fashion for four CT manufacturers (8500 accreditation submissions for manufacturer A; 18,575 for manufacturer B; 8278 for manufacturer C; and 32,039 for manufacturer D). The attenuation value for water, acrylic (surrogate for trabecular bone), and Teflon (surrogate for cortical bone; Chemours) materials for an adult abdominal CT technique (120 kV, 240 mA, standard reconstruction algorithm) was used in the analysis. Differences in attenuation value across all manufacturers were assessed using the Kruskal-Wallis test followed by a post hoc test for pairwise comparisons. RESULTS. The mean attenuation value for water ranged from -0.3 to 2.7 HU, with highly significant differences among all manufacturers (p < 0.001). For the trabecular bone surrogate, differences in attenuation values across all manufacturers were also highly significant (p < 0.001), with mean values of 120.9 (SD, 3.5), 124.6 (3.3), 126.9 (4.4), and 123.9 (3.4) HU for manufacturers A, B, C, and D, respectively. For the cortical bone surrogate, differences in attenuation values across all manufacturers were also highly significant (p < 0.001), with mean values of 939.0 (14.2), 874.3 (13.3), 897.6 (11.3), and 912.7 (13.4) HU for manufacturers A, B, C, and D, respectively. CONCLUSION. CT scanners made by different manufacturers show systematic offsets in attenuation measurement when compared with each other. Knowledge of these off-sets is useful for optimizing the accuracy of opportunistic diagnosis of osteoporosis.

Diffraction tomography with a deep image prior.

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.

Dual-energy computed tomography for evaluation of breast cancer: value of virtual monoenergetic images reconstructed with a noise-reduced monoenergetic reconstruction algorithm.

To evaluate the image quality and lesion visibility of virtual monoenergetic images (VMIs) reconstructed using a new monoenergetic reconstruction algorithm (nMERA) for evaluation of breast cancer. Forty-two patients with 46 breast cancers who underwent 4-phasic breast contrast-enhanced computed tomography (CT) using dual-energy CT (DECT) were enrolled. We selected the peak enhancement phase of the lesion in each patient. The selected phase images were generated by 120-kVp-equivalent linear blended (M120) and monoenergetic reconstructions from 40 to 80keV using the standard reconstruction algorithm (sMERA: 40, 50, 60, 70, 80) and nMERA (40 +, 50 +, 60 +, 70 +, 80 +). The contrast-to-noise ratio (CNR) was calculated and objectively analyzed. Two independent readers subjectively scored tumor visibility and image quality each on a 5-point scale. The CNR at 40 + and tumor visibility scores at 40 + and 50 + were significantly higher than those on M120. The CNR at 50 + was not significantly different from that on M120. However, the overall image quality score at 40 + was significantly lower than that at 50 + and on M120 (40 + vs M120, P < 0.0001 and 40 + vs 50 +, P = 0.0001). VMI reconstructed with nMERA at 50keV is preferable for evaluation of patients with breast cancer.

X-Ray Microtomography Using Compensation Filters with Complicated Form

The influence of an aluminum compensation filter of complicated form on the signal-tonoise ratio in microtomography of geological cores was investigated. When scanning the core, a compensation filter in the form of an aluminum parallelepiped with a hole approximately equal to the diameter of the core was used. The filter is fixed rigid over the object table, the core is placed in the hole and rotates inside the filter. It is shown that the filter provides the maximal contrast of the useful signal over the entire dynamic range of the scintillation panel detector. After normalizing the projection of the core onto the filter projection without the core, the obtained projections were used in the standard reconstruction algorithm for cone beam tomography. The use of a compensation filter made it possible to increase the signal-to-noise ratio of the resulting tomograms by more than two times, as compared with the case of a conventional flat filter made of aluminum.

Feasibility study of using one-tenth mSv radiation dose in young children chest CT with 80 kVp and model-based iterative reconstruction

CT has become a routine imaging modality based on its excellent ability of displaying lung structures and diseases. But, how to reduce radiation dose of routine CT examination is a concern for radiologists. Our study aimed to evaluate the feasibility of using 80kVp and a model-based iterative reconstruction (MBIR) algorithm to achieve one-tenth mSv dose chest CT in infants and young children. Thirty-two cases (study group, average age 1.71 ± 1.01 years) underwent non-contrast chest CT examination at low dose with 80 kV, 4mAs and was reconstructed with MBIR (LD-MBIR) and the standard adaptive statistical iterative reconstruction (ASIR) algorithm (LD-ASIR); another group (control group) of 32 children underwent routine-dose chest CT with 100 kV and was reconstructed with ASIR only (RD-ASIR). The subjective and objective image quality of the three groups were measured and statistically compared. The radiation dose for the low dose scan was 0.09 ± 0.02 mSv, 6% of the routine dose. All LD-MBIR images were diagnostically acceptable. Compared with the RD-ASIR images, the LD-MBIR images were similar in noise in the left ventricle, muscles, lung field, on-par in displaying large airways, lung lucency and mediastinum, but were inferior in displaying lung marking, small airways and mediastinum. Thus, MBIR images with low dose in pediatric chest CT can be used in the diagnosis for lung field and air way disorders in infants and young children.

Application of iterative reconstruction algorithms to mitigate CT-artefacts when measuring fiber reinforced polymer materials

In general, fiber reinforced polymer materials are being used in case of challenging mechanical requirements. Important parameters determining performance of such polymer composites are properties of the components as well as the quality of the fiber-polymer interface. For example, porosity or voids particularly at the interface will influence the overall performance of the composite negatively. Therefore, it is of importance to know the actual porosity value to e.g. calculate and predict the mechanical properties. In principle, a CT scan can provide the means to determine the amount, size, and shape of the voids in an X-ray transparent material. The difficulty, which arises in fully organic based i.e. carbon fiber reinforced polymer composites, is the small difference between absorption of X-rays in the carbon fibers versus the surrounding polymer matrix. At certain conditions, when single fibers need to be resolved and voids surrounding the fibers may exist, CT reconstruction artefacts introduced by standard algorithms are visible as “virtual” voids in large quantities severely limiting the correct determination of the actual porosity. On the other hand, in glass fiber reinforced polymer materials, the large density difference between fibers and matrix causes artefacts to appear, too. Specific experimental settings necessary to deal with the high absorption of the glass fibers (beam hardening enforces the use of X-ray filters) cause very low absorption differences between air and the polymer matrix and thus hardly provide information on the existence of voids. Iterative reconstruction techniques, however, are more robust against the encountered CT-reconstruction artifacts and may enable accurate determination of important sample features in polymer composite materials, and especially allow for accurate determination of voids or porosity. This work explores the use of two iterative versus standard reconstruction algorithms (CGLS and SIRT versus FDK) and validates the results with cross-sectional Optical Microscopy. The paper shows that iterative reconstruction algorithms outperform the standard reconstruction algorithm with respect to mitigating the influence of the CT reconstruction artefacts.

High-attenuation artifact reduction in breast tomosynthesis using a novel reconstruction algorithm

PurposeTo assess the effect on reducing the out-of-plane artifacts from metal objects in breast tomosynthesis (BT) using a novel artifact-reducing reconstruction algorithm in specimen radiography. Methods and MaterialsThe study was approved by the Regional Ethical Review Board. BT images of 18 partial- and whole mastectomy specimens from women with breast cancer were acquired before and after a needle was inserted close to the lesion. The images were reconstructed using both a standard reconstruction algorithm, and a novel algorithm; the latter uses pre-segmentation to remove highly attenuating artifact-inducing objects from projection images before reconstruction. Images were separately reconstructed with and without segmentation, and combined into an artifact-reduced reconstruction. Standard and artifact-reduced BT-algorithms were compared visually and quantitatively using clinical images of mastectomy specimens and a physical anthropomorphic phantom. Six readers independently assessed the visibility of the lesion with and without artifact-reduction in a side-by-side comparison. A quantitative analysis was performed, comparing the signal-difference to background ratio (SDBR) and artifact spread function (ASF) between the two reconstruction methods. ResultsThe magnitude of out-of-plane artifacts was clearly reduced with the novel reconstruction compared to BT-images without artifact reduction. Lesion masking by artifacts was largely averted; tumour visibility was comparable to standard BT images without a needle. In 76 ± 8% (standard deviation) of cases overall, readers could confidently state needle location. The same figure was 94 ± 6% for whole mastectomy cases, compared to 62 ± 17% for partial mastectomies. With metal artifact reduction, SDBR increased by 97% in the phantom, and by 69% in the mastectomies. The artifact spread function was substantially narrower. ConclusionArtifact reduction in BT using a novel reconstruction method enables qualitatively and quantitatively improved clinical use of BT when metal artifacts can be a limiting factor such as in tomosynthesis-guided biopsy.

CellSTORM-Cost-effective super-resolution on a cellphone using dSTORM.

High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for “imaging on a budget”. Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that “cellSTORM” paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.

Machine Learning in KM3NeT

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.

Diagnostic efficacy of model-based iterative reconstruction algorithm in an assessment of coronary artery in comparison with standard hybrid-Iterative reconstruction algorithm: dose reduction and image quality.

To evaluate the image quality and radiation dose exposure of low-dose coronary CTA (cCTA) study, reconstructed with the new model-based iterative reconstruction algorithm (IMR), compared with standard hybrid-iterative reconstruction (iDose4) cCTA in patients with suspected coronary artery disease. Ninety-eight patients with an indication for coronary CT study were prospectively enrolled. Fifty-two patients (study group) underwent 256-MDCT low-dose cCTA (80kV; automated-mAs; 60mL of CM, 350 mgL/mL) with prospective ECG-triggering acquisition and IMR. A control group of 46 patients underwent 256-MDCT standard prospective ECG-gated protocol (100kV; automated-mAs; 70mL of CM, 400 mgL/mL; iDose4). Subjective and objective image quality (attenuation value, SD, SNR and CNR) were evaluated by two radiologists subjectively. Radiation dose exposure was quantified as DLP, CTDIvol and ED. Mean values of mAs were significantly lower for IMR-cCTA (167 ± 62 mAs) compared to iDose-cCTA (278 ± 55 mAs), p < 0.001. With a significant reduction of 38% in radiation dose exposure (DLP: IMR-cCTA 91.7 ± 26mGy cm vs. iDose-cCTA 148.6 ± 35mGy cm; p value < 0.001), despite the use of different CM, we found higher mean attenuation values of the coronary arteries in IMR group compared to iDose4 (mean density in LAD: 491HU IMR-cCTA vs. 443HU iDose-cCTA; p = 0.03). We observed a significant higher value of SNR and CNR in study group due to a lower noise level. Qualitative analysis did not reveal any significant differences between the two groups (p = 0.23). Low-dose cCTA study combined with IMR reconstruction allows to correctly evaluate coronary arteries disease, offering high-quality images and significant radiation dose exposure reduction (38%), as compared to standard cCTA protocol.

Study of the associated production of the Higgs boson with a top quark pair in a boosted regime in the ATLAS experiment

The measurements of the \(pp\rightarrow t\bar{t} H\) signal strength (\(\mu = \sigma_{obs}/\sigma_{t\bar{t} H_{SM}}\)) and its upper limit at a center-of-mass energy of pp collision of 13TeV are presented in this paper. The pp data collected with the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.5 fb-1, have been subjected to a detailed analysis. The \( t\bar{t} H\) signal strength and its upper limit have been measured searching for the decays \(H\rightarrow b \bar{b}\) and \( t\bar{t} \rightarrow l \nu b q \bar{q}' \bar{b}\). Two different analysis approaches have been used in order to analyse the same data in two different ways. In the first (namely “resolved”), the procedure uses standard identification and reconstruction algorithms, meaning that the individual partons from the initial hard process are reconstructed as separate objects. In the second (namely “combined”), the events are separated in two exclusive regimes, including events containing the hadronically decaying top quark (\(t \rightarrow q q' b\)) with a low transverse momentum (\(p_{\rm T} < 250\) GeV) and the boosted ones with the opposite requirement, where the partially overlapped jets coming from high pT tops are suitably identified, reconstructed and tagged. The boosted sample is sensitive to a significantly different kinematic region and provides additional information. The measured signal strength is \( 1.4 \pm 0.5\) and \( 1.2 \pm 0.5\), respectively, for the resolved and the combined analysis. A signal strength larger than 2.3 and 2.0 can be excluded at the 95% confidence level respectively for the resolved and the combined analysis. Both results are compatible with the prediction of the Standard Model (\(\mu = 1\)). Previous results in the search for the \(t\bar{t} H\) process were reported by the ATLAS Collaboration and a boosted reconstruction in the channel was previously done by the CMS Collaboration.

Dual-Energy Computed Tomography in Patients With Small Hepatocellular Carcinoma: Utility of Noise-Reduced Monoenergetic Images for the Evaluation of Washout and Image Quality in the Equilibrium Phase.

This study aimed to evaluate the utility of virtual monoenergetic images for detecting washout of small (≤2 cm) hepatocellular carcinoma (HCC) in the equilibrium phase. We performed 120-kVp-equivalent linear-blended (M120) and monoenergetic reconstructions from 40 to 90 keV by standard (40, 50, 60, 70, 80, 90) and novel noise-reduced (nMERA: 40+, 50+, 60+, 70+, 80+, 90+) monoenergetic reconstruction algorithms. Image quality and tumor visibility of delayed washout of HCCs in the equilibrium phase were compared between standard monoenergetic reconstruction algorithm and nMERA by objective and subjective analyses. Contrast-to-noise ratio of the tumor at 40+ was the highest, whereas the score of tumor visibility peaked at 50+. The score of overall image quality at 40+ was significantly lower than those on all other image series, and the image quality among other image series were not significantly different. Virtual monoenergetic image reconstructed with nMERA 50+ was most appropriate to detect washout of small HCCs.

"Hyperdense artery sign" in early ischemic stroke: diagnostic value of model-based reconstruction approach in comparison with standard hybrid iterative reconstruction algorithm.

Hyperdense artery sign is the earliest sign of ischemic stroke on non-enhanced computed tomography and it can be visible long before parenchymal changes. The aim of our study was to compare diagnostic value of model-based iterative reconstruction algorithm (IMR) with that of iterative reconstruction algorithm (iDose4) in identifying hyperdense artery sign. We selected 56 consecutive patients suspected for ischemic stroke, who underwent a NCCT and that demonstrated a vessel occlusion at angio-CT or developed ischemic lesion at follow-up CT. Two readers randomly analyzed images of NCCT reconstructed both with iDose4 (4mm) and IMR (2mm), reporting presence of hyperdense artery sign (0: no; 1: yes; 2: not sure). They rated image quality on a 4-point scale (1: unacceptable; 4: more than average) and recorded HU values of clot and of normal vessel and measured noise index, CNR and SNR. Mean values of CTDI, DLP, and ED were respectively of 43mGy, 819.7mGycm, and 1.72mSv. By analyzing the IMR reconstruction, both readers were able to recognize hyperdense vessel sign in 55/56 patients, while only in 12/56 patients were identified with iDose. IMR obtained better rating of image quality (mean score for IMR 3.32 vs 2.53 for iDose), higher clot density (57.2 vs 46.7 HU), lower noise index (5 vs 2), higher CNR and SNR (respectively 4.2 vs 2 and 16.8 vs 8.5). Model-based approach significantly increases sensitivity in detecting hyperdense artery sign, offering higher SNR and CNR in brain CT images in comparison with standard hybrid reconstruction algorithm.