Additional Artifacts Research Articles

Clustering and Classification of Time Series Sound Data

This scientific article addresses two critical tasks in data analysis—time series classification and clustering, particularly focusing on heart sound recordings. One of the main challenges in analyzing time series lies in the difficulty of comparing different series due to their variability in length, shape, and amplitude. Various algorithms were employed to tackle these tasks, including the Long Short-Term Memory (LSTM), KNN, recurrent neural network for classification and the K-means and DBSCAN methods for clustering. The study emphasizes the effectiveness of these methods in solving classification and clustering problems involving time series data containing heart sound recordings. The results indicate that LSTM is a powerful tool for time series classification due to its ability to retain contextual information over time. In contrast, KNN demonstrated high accuracy and speed in classification, though its limitations became apparent with larger datasets. For clustering tasks, the K-means method proved to be more effective than DBSCAN, showing higher clustering quality based on metrics such as silhouette score, Rand score, and others. The data used in this research were obtained from the UCR Time Series Archive, which includes heart sound recordings from various categories: normal sounds, murmurs, additional heart sounds, artifacts, and extra systolic rhythms. The analysis of results demonstrated that the chosen classification and clustering methods could be effectively used for diagnosing heart diseases. Furthermore, this research opens up new opportunities for further improvement in data processing and analysis methods, particularly in developing new medical diagnostic tools. Thus, this work illustrates the effectiveness of machine learning algorithms for time series analysis and their significance in improving cardiovascular disease diagnosis.

Extraction and reduction of parameters from hand-drawn Archimedes spirals for clinical tremor assessment

BackgroundPatients’ hand-drawn Archimedes spirals are widely used in the neurological community to grade tremors. These spirals are either drawn on paper and Xeroxed/scanned into digital images or digitizing tablets are used for the drawings. This process introduces artifacts such as variable widths of the drawn lines with varying pixel grey scale values. Xeroxing introduces additional artifacts resulting from paper misalignments. These artifacts and the presence of the reference spiral in the image complicate an automatic extraction of a mathematical spiral signal from the image. New methodsWe introduce a mathematical mapping that transforms the image pixels of the patient's hand-drawn spiral into a one-dimensional discrete signal that can be used for mathematical analysis. ResultsA cohort of 18 hand-drawn spirals with various artifacts is used to validate our method.We extract the parameters of the discrete signals and show that the signals can be represented by truncating to as few as 150 parameters with a truncation RMS error of 6.26 % across the cohort. Using only 150 features makes machine learning a viable option for future applications. Furthermore, our method can be used to evaluate the frequency and the amplitude of the tremor. Comparison with existing methodsIn existing methods, the patient draws the spiral on a digitizing tablet, and features are extracted from this data for machine learning. We recognize that a vast majority of hospitals are still using the pencil-and-paper approach, and there is an abundance of ready-to-be-mined tremor-related data already stored as paper or digitized drawings. Our procedure is equally applicable to Xeroxed documents as well as files generated from digital tablets. ConclusionsWe have validated a new procedure requiring minimal user intervention to automatically extract a patient's hand-drawn spiral as a discrete mathematical one-dimensional signal from a scanned image or a file from a digital tablet.

Towards to Theory of the X-ray Diffraction Tomography of Crystals with Nano-Sized Defects

X-ray diffraction tomography is an innovative method that is widely used to obtain 2D-phase-contrast diffraction images and their subsequent 3D-reconstruction of structural defects in crystals. The most frequent objects of research are linear and helical dislocations in a crystal, for which plane wave diffraction images are the most informative, since they do not contain additional interference artifacts unrelated to the images of the defects themselves. In this work the results of modeling and analysis of 2D plane wave diffraction images of a nano-dimensional Coulomb-type defect in a Si(111) thin crystal are presented based on the construction of numerical solutions of the dynamic Takagi-Taupin equations. An adapted physical expression for the elastic displacement field of the point defect, which excludes singularity at the defect location in the crystal, is used. A criterion for evaluating the accuracy of numerical solutions of the Takagi-Taupin equations is proposed and used in calculations. It is shown that in the case of the Coulomb-type defect elastic displacement field, out of the two difference algorithms for solving the Takagi-Taupin equations used in their numerical solution, only the algorithm for solving the Takagi-Taupin equations where the displacement field function enters in exponential form is acceptable in terms of the required accuracy-duration of the calculations.

Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint

PurposeMulti-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. MethodsWe firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. ResultsThrough theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). ConclusionThe SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.

The Music Education Legacy of William Frederick Cardin, or Pejawah: ‘A Language All America Should Know’

William Frederick (“Fred”) Cardin served as a director of instrumental music in the Reading, Pennsylvania, School District from 1930 until his retirement in June 1960. An accomplished performer and composer, Cardin studied at the Curtis School of Music and the Conservatoire Américaine in Paris, France. He is remembered as an outstanding educator and conductor both in the public schools and with community organizations. Cardin was also known as Pejawah, his Native American name. To gain further insight regarding Cardin’s musical legacy and how it reflects his heritage, interviews were conducted with former students and colleagues. Additional artifacts were collected pertaining to his education, influences, and accomplishments. Cardin pursued a focused mission through compositions and performances to promote Native American music and culture while also demonstrating aptitude in music composition, performance, and conducting. Cardin is one individual who pursued a successful career in music and music education while navigating the issues of racial discrimination.

Edge-Oriented Compressed Video Super-Resolution.

Due to the proliferation of video data in Internet of Things (IoT) systems, in order to reduce the data burden, most social media platforms typically employ downsampling to reduce the resolution of high-resolution (HR) videos before video coding. Consequently, the loss of detail and the introduction of additional artifacts seriously compromise the quality of experience (QoE). Recently, the task of compressive video super-resolution (CVSR) has garnered significant attention, aiming to simultaneously eliminate compression artifacts and enhance the resolution of compressed videos. In this paper, we propose an edge-oriented compressed video super-resolution network (EOCVSR), which focuses on reconstructing higher-quality details, to effectively address the CVSR task. Firstly, we devised a motion-guided alignment module (MGAM) to achieve precise bi-direction motion compensation in a multi-scale manner. Secondly, we introduced an edge-oriented recurrent block (EORB) to reconstruct edge information by combining the merits of explicit and implicit edge extraction. In addition, benefiting from the recurrent structure, the receptive field of EOCVSR can be enhanced and the features can be effectively refined without introducing additional parameters. Extensive experiments conducted on benchmark datasets demonstrate that our method surpasses the performance of state-of-the-art (SOTA) approaches in both quantitative and qualitative evaluations. Our approach can provide users with high-quality and cost-effective HR videos by integrating with sensors and codecs.

Author's Reply to Comment by Greaves et al. on “Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations”

AbstractIn an attempt to understand the findings presented in the Comment by Greaves et al. (2023, https://doi.org/10.1029/2023GL103539), we followed their data analysis methodology, omitting the hot and cold‐load calibrations that are an important part of the standard SOFIA GREAT instrument calibration procedure. This process requires scaling of the Venus off‐source spectra by an arbitrary factor, which in turn introduces residuals of the intrinsic receiver bandpass shape as spurious components in the resulting line/continuum spectra. Although these additional artifacts can be reduced via Fourier‐domain spectral filtering, their removal depends on an ill‐constrained interpolation of the Venus continuum across the PH3 spectral line positions, resulting in an unreliable final spectrum. We therefore conclude that the PH3 lines claimed to be detected in the Comment by Greaves et al. (2023, https://doi.org/10.1029/2023GL103539) originate from data/analysis artifacts, and confirm our original result that there is no evidence for phosphine in the SOFIA Venus data.

The Paleoindian and Archaic Occupation of Grants, New Mexico: A Review and Reanalysis of the Grants San Jose Sites and Projectile Point Collections

In the late 1930s Joseph Toulouse Jr., Kirk Bryan, and Bryan McCann published two articles describing the archaeology and geology of seventeen preceramic sites near Grants, New Mexico. George Agogino, Jim Hester, and William Roosa periodically revisited a subset of these sites during the 50s and 60s, publishing three brief articles and collecting additional artifacts. Despite the importance of these sites to the history of preceramic research in Northern New Mexico, no complete inventory or analysis of the projectile point collections has been undertaken to date. In this article, we provide: (1) a brief history of the Grants San Jose collections, (2) an inventory of diagnostic projectile points, (3) an analysis of raw materials represented and geochemical analysis of a sample of volcanic dart points, and (4) a brief discussion of the collections’ implications for the pre-ceramic archaeology of the Grants area.

Managing occupational impulsive noise from explosive depth hardening operations

The assessment and identification of measures to eliminate or reduce risks from occupational exposure to impulsive noise for the protection of hearing of employees in industry is an important yet comparatively neglected technical area. The aim of this work is to examine the current best practice to assess auditory hazards associated with impulsive blast noise generated by an infrastructure test process at a UK-based test site. A summary of the literature is presented on the measurement methodologies, hearing conservation and relevant guidance for the selection of hearing protection within the context of near-field impulsive noise. The current UK best practice recommends the adapted HML method from Defence Standard 0027:2015, using a C-peak measurement of exposure to compare with the national regulations. On-site measurements of personnel exposure were made at the DNV Spadeadam Testing and Research Site (STaR), of an industrial process known as Explosive Depth Hardening. Analyses of the trials data show that the C-peak personnel exposures resulted in acceptable levels below the 140 dBCpk limit within the national regulations when using the prescribed hearing protection, according to the adapted HML method. Waveforms recorded of repeated charge sizes were uncorrelated and additional signal artefacts were present. It is concluded that the adapted HML assessment is unclear on how excessive low-frequency content and multiple pressure peaks are accounted for in the assessment of hearing protector performance. The implication of this work is that further research is required to improve current best practice to quantify the uncertainties introduced by the additional characteristics found within real impulsive noise exposures.

Self-supervised deep learning for joint 3D low-dose PET/CT image denoising

Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET) and low-dose computed tomography (LDCT) has been widely explored. However, previous methods have focused only on single modality denoising, neglecting the possibility of simultaneously denoising LDPET and LDCT using only one neural network, i.e., joint LDPET/LDCT denoising. Moreover, DL-based denoising methods generally require plenty of well-aligned LD-normal-dose (LD-ND) sample pairs, which can be difficult to obtain. To this end, we propose a self-supervised two-stage training framework named MAsk-then-Cycle (MAC), to achieve self-supervised joint LDPET/LDCT denoising. The first stage of MAC is masked autoencoder (MAE)-based pre-training and the second stage is self-supervised denoising training. Specifically, we propose a self-supervised denoising strategy named cycle self-recombination (CSR), which enables denoising without well-aligned sample pairs. Unlike other methods that treat noise as a homogeneous whole, CSR disentangles noise into signal-dependent and independent noises. This is more in line with the actual imaging process and allows for flexible recombination of noises and signals to generate new samples. These new samples contain implicit constraints that can improve the network’s denoising ability. Based on these constraints, we design multiple loss functions to enable self-supervised training. Then we design a CSR-based denoising network to achieve joint 3D LDPET/LDCT denoising. Existing self-supervised methods generally lack pixel-level constraints on networks, which can easily lead to additional artifacts. Before denoising training, we perform MAE-based pre-training to indirectly impose pixel-level constraints on networks. Experiments on an LDPET/LDCT dataset demonstrate its superiority over existing methods. Our method is the first self-supervised joint LDPET/LDCT denoising method. It does not require any prior assumptions and is therefore more robust.

Context-Aware Expert for Software Architecture Recovery (CAESAR): An automated approach for recovering software architectures

Software architecture recovery approaches help to reconstruct the architecture of complex software systems. However, the manual effort and expertise required to use such techniques are high, as the user demands detailed knowledge about software architecture recovery process and preparing additional software artifacts to use as input for such approaches. Such practices lack consideration for the context standards of the recovered software. Additionally, proper module detection rules that are aware of the modeled system’s context should be studied to enhance the cluster quality of the detected modules. We introduce a novel approach to software architecture recovery by implementing an automated knowledge-based rules solution that considers the context of the developed software while identifying and clustering that system’s software modules. Such solution is aided by a novel code distance model that is utilized to enhance the cohesiveness of the recovered software modules, improving the recovered software architecture quality, and enhancing readability through providing visualizations at different abstraction levels for the recovered architecture. The proposed method has been evaluated on two software systems with varying sizes and from different contexts and involved experienced participants. The results demonstrate significant improvements in cohesion and coupling of the produced clusters compared to alternative architecture recovery approaches.

An arrowhead made of meteoritic iron from the late Bronze Age settlement of Mörigen, Switzerland and its possible source

A search for artefacts made of meteoritic iron has been performed in archaeological collections in the greater area of the Lake of Biel, Switzerland. A single object made of meteoritic iron has been identified, an arrowhead with a mass of 2.9 g found in the 19th Century in the late Bronze Age (900–800 BCE) lake dwelling of Mörigen, Switzerland. The meteoritic origin is definitely proven by combining methods extended and newly applied to an archaeological artefact. Elemental composition (7.10–8.28 wt% Ni, 0.58–0.86 wt% Co, ∼300 ppm Ge), primary mineralogy consisting of the associated Ni-poor and Ni-rich iron phases kamacite (6.7 wt% Ni) and taenite (33.3 wt% Ni), and the presence of cosmogenic 26Al (1.7−0.4+0.5 dpm/kg). The Ni-rich composition below the oxidized crust and the marked difference to meteorites from the nearby (4–8 km) Twannberg iron meteorite strewn field is confirmed by muon induced X-ray emission spectrometry (8.28 wt% Ni). The Ni-Ge-concentrations are consistent with IAB iron meteorites, but not with the Twannberg meteorite (4.5 wt% Ni, 49 ppm Ge). The measured activity of 26Al indicates derivation from an iron meteorite with a large (2 t minimum) pre-atmospheric mass. The flat arrowhead shows a laminated texture most likely representing a deformed Widmanstätten pattern, grinding marks on the surface and remnants of wood-tar. Among just three large European IAB iron meteorites with fitting chemical composition, the Kaalijarv meteorite (Estonia) is the most likely source because this large crater-forming fall event happened at ∼1500 years BC during the Bronze Age and produced many small fragments. The discovery and subsequent transport/trade of such small iron fragments appears much more likely than in case of buried large meteorite masses. Additional artefacts of the same origin may be present in archaeological collections.

Analysis of in-person general practice respiratory consultations: assessing translatability to telehealth.

The COVID-19 pandemic saw many GPs adopt telehealth as a consultation modality to minimise disease transmission. Patients presenting with respiratory ailments were particularly affected by this transition, given the overlap of general respiratory symptoms with those of COVID-19. It is unclear if the rapid transition to telehealth has compromised the ability to conduct certain tasks that were possible during in-person consultations. To investigate the extent to which tasks observed during in-person GP consultations are replicable in telehealth, focusing on patients with respiratory concerns. Twenty-six respiratory consultations were extracted from a database of 281 consultations collated from various general practices in the UK. Interactions between GPs and respiratory patients were assessed through in-depth transcript review and de-identified video analysis. Then, tasks performed and physical artefacts used during the consultations were identified and ranked in terms of their translatability to telehealth, using a newly developed scoring system. Overall, the translatability to telehealth score for these respiratory consultations was 6.7/10, suggesting that many tasks can be replicated over telehealth, but that they might require additional physical artefacts to support this. However, some tasks are not currently amenable to telehealth (for example, auscultation). While many aspects of respiratory consultations are replicable over telehealth, some tasks cannot be replicated at this stage.

Performance analysis of single-focus phase singularity based on elliptical reflective annulus quadrangle-element coded spiral zone plates

Optical vortices generated by the conventional vortex lens are usually disturbed by the undesired higher-order foci, which may lead to additional artifacts and thus degrade the contrast sensitivity. In this work, we propose an efficient methodology to combine the merit of elliptical reflective zone plates (ERZPs) and the advantage of spiral zone plates (SZPs) in establishing a specific single optical element, termed elliptical reflective annulus quadrangle-element coded spiral zone plates (ERAQSZPs) to generate single-focus phase singularity. Differing from the abrupt reflectance of the ERZPs, a series of randomly distributed nanometer apertures are adopted to realize the sinusoidal reflectance. Typically, according to our physical design, the ERAQSZPs are fabricated on a bulk substrate; therefore, the new idea can significantly reduce the difficulty in the fabrication process. Based on the Kirchhoff diffraction theory and convolution theorem, the focusing performance of ERAQSZPs is calculated. The results reveal that apart from the capability of generating optical vortices, ERAQSZPs can also integrate the function of focusing, energy selection, higher-order foci elimination, as well as high spectral resolution together. In addition, the focusing properties can be further improved by appropriately adjusting the parameters, such as zone number and the size of the consisted primitives. These findings are expected to direct a new direction toward improving the performance of optical capture, x-ray fluorescence spectra, and forbidden transition.

Superpixel-Based Stripe Noise Removal for Satellite Imageries

This work introduces a novel noise removal algorithm for satellite imageries based on superpixel segmentation followed by statistics-based filtering. The algorithm worked in three main steps. First, the noisy input image was divided into subregions by employing simple linear iterative clustering (SLIC)-based superpixel segmentation. Then, the statistical property of each subregion was calculated, including their standard deviations and maximum values. Last, an adaptive statistics-based stripe noise removal was performed for each subregion by constructing adaptive filter sizes according to calculated properties. The algorithm was tested using real satellite imageries taken by the LAPAN-A2 and LAPAN-A3 satellites. Its performance was then compared to three existing methods in terms of image quality and computation speed. Extensive experiments on two datasets of 3-channel images captured by the LAPAN-A2 satellite showed that the algorithm was capable of reducing the stripe pattern as measured using the peak-signal-to-noise-ratio (PSNR) metric without introducing additional artifacts, which commonly appeared on over-corrected regions. Moreover, compared to existing methods, the proposed algorithm ran 42 to 103 times faster and provided better image quality by 2.46%, measured using the structural similarity metric (SSIM). The code of this work and the datasets used for the testing are publicly available on www.github.com/dancingpixel/SPSNR.

A nonlinear scaling-based normalized metal artifact reduction to reduce low-frequency artifacts in energy-integrating and photon-counting CT.

Metal within the scan plane can cause severe artifacts when reconstructing X-ray computed tomography (CT) scans. Both in clinical use and recent research, normalized metal artifact reduction (NMAR) has established as the reference method for correcting metal artifacts, but NMAR introduces inconsistencies within the sinogram, which can cause additional low-frequency artifacts after image reconstruction. This paper introduces an extension to NMAR by applying a nonlinear scaling function (NLS-NMAR) to reduce low-frequency artifacts, which get introduced by the reconstruction of interpolation-edge-related sinogram inconsistencies in the normalized sinogram domain. After linear interpolation of the metal trace, an NLS function is applied in the prior-normalized sinogram domain to reduce the impact of the interpolation edges during filtered backprojection. After sinogram denormalization and image reconstruction, the low frequencies of the NLS image are combined with different high frequencies to restore anatomic details. An anthropomorphic dental phantom with removable metal inserts was utilized on two different CT systems to quantitatively assess the artifact reduction performance in terms of HU deviations and the root-mean-square-error within relevant regions of interest. Clinical dental examples were assessed to qualitatively demonstrate the problem of the interpolation-related blooming as well as to demonstrate the performance of the NLS function to reduce respective artifacts. To quantitatively prove HU consistency, HU values were assessed in central ROIs in the clinical cases. In addition, single clinical cases of a hip replacement and pedicle screws in the spine are shown to demonstrate the method's results in other body regions. The NLS-NMAR can minimize the effect of interpolation-related sinogram inconsistencies and thus reduce resulting hyperdense blooming artifacts. In the phantom results, the reconstructions with the NLS-NMAR-corrected low frequencies demonstrate the lowest error. In the qualitative assessment of the clinical data, the NLS-NMAR shows a tremendous enhancement in image quality, also performing best within all assessed images series. The NLS-NMAR provides a small yet effective extension to conventional NMAR by reducing low-frequency hyperdense metal trace-interpolation-related artifacts in computed tomography.

Chromatic confocal sensor-based on-machine measurement for microstructured optical surfaces featuring a self-aligned spiral center.

Chromatic confocal sensor-based on-machine measurement is effective for identifying and compensating for form errors of the ultra-precisely machined components. In this study, an on-machine measurement system was developed for an ultra-precision diamond turning machine to generate microstructured optical surfaces, for which the sensor probe adopts a uniform spiral scanning motion. To avoid the tedious spiral center alignment, a self-alignment method was proposed without additional equipment or artefact, which identified the deviation of the optical axis to the spindle axis by matching the measured surface points and the designed surface. The feasibility of the proposed method was demonstrated by numerical simulation with full consideration of noises and system dynamics. Practically, taking a typical microstructured surface as an example, the on-machine measured points were reconstructed after calibrating the alignment deviation, which was then verified by off-machine white light interferometry measurement. Avoiding tedious operations and special artefacts may significantly simplify the on-machine measurement process, thereby greatly improving the efficiency and flexibility for the measurement.

A cross-scale framework for low-light image enhancement using spatial–spectral information

This paper presents a spatial–spectral low-light enhancement approach based on the neural network. Considering that the Image Signal Processor (ISP) which non-linearly maps RAW data to RGB images may introduce additional noise and artifacts, we propose to conduct the enhancement task directly on RAW data. To this end, we propose a cross-scale framework to map the input low-light RAW data to visually pleasing RGB images. The cross-scale framework consists of three branches for low-level, mid-level, and high-level representations of input images. We embed paired Fast Fourier Convolution (FFC) and Transformer in each level for global and local feature analysis and aggregation. Specifically, the FFC enlarges the receptive field of our neural network, exploring the pixel correlations in the spectral space; the following Transformer uses self-attention for feature selection and aggregation in the spatial space. As a result, spatial–spectral information within an image is jointly utilized for the final fusion. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art low-light enhancement methods in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Moreover, the proposed method produces more aesthetically pleasing RGB images than other methods.

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.

Constructing Immunized Stego-Image for Secure Steganography via Artificial Immune System

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.