A comparison of Sinogram Affirmed Iterative Reconstruction and filtered back projection on image quality and dose reduction in paediatric head CT: a phantom study

Background: Computed tomography (CT) is one of the most used modalities for diagnostics in paediatric populations, which is a concern as it also delivers a high patient dose. Research has focused on developing computer algorithms that provide better image quality at lower dose. The iterative reconstruction algorithm Sinogram-Affirmed Iterative Reconstruction (SAFIRE) was introduced as a new technique that reduces noise to increase image quality. Purpose: The aim of this study is to compare SAFIRE with the current gold standard, Filtered Back Projection (FBP), and assess whether SAFIRE alone permits a reduction in dose while maintaining image quality in paediatric head CT. Methods: Images were collected using a paediatric head phantom using a SIEMENS SOMATOM PERSPECTIVE 128 modulated acquisition. 54 images were reconstructed using FBP and 5 different strengths of SAFIRE. Objective measures of image quality were determined by measuring SNR and CNR. Visual measures of image quality were determined by 17 observers with different radiographic experiences. Images were randomized and displayed using 2AFC; observers scored the images answering 5 questions using a Likert scale. Results: At different dose levels, SAFIRE significantly increased SNR (up to 54%) in the acquired images compared to FBP at 80kVp (5.2-8.4), 110kVp (8.2-12.3), 130kVp (8.8-13.1). Visual image quality was higher with increasing SAFIRE strength. The highest image quality was scored with SAFIRE level 3 and higher. Conclusion: The SAFIRE algorithm is suitable for image noise reduction in paediatric head CT. Our data demonstrates that SAFIRE enhances SNR while reducing noise with a possible reduction of dose of 68%.

A balance between image quality and effective dose in orbital X-ray screening for ferromagnetic IOFBs: a pilot study

Purpose: To investigate whether standard X-ray acquisition factors for orbital radiographs are suitable for the detection of ferromagnetic intra-ocular foreign bodies in patients undergoing MRI. Method: 35 observers, at varied levels of education in radiography, attending a European Dose Optimisation EURASMUS Summer School were asked to score 24 images of varying acquisition factors against a clinical standard (reference image) using two alternative forced choice. The observers were provided with 12 questions and a 5 point Likert scale. Statistical tests were used to validate the scale, and scale reliability was also measured. The images which scored equal to, or better than, the reference image (36) were ranked alongside their corresponding effective dose (E), the image with the lowest dose equal to or better than the reference is considered the new optimum acquisition factors. Results: Four images emerged as equal to, or better than, the reference in terms of image quality. The images were then ranked in order of E. Only one image that scored the same as the reference had a lower dose. The reference image had a mean E of 3.31μSv, the image that scored the same had an E of 1.8μSv. Conclusion: Against the current clinical standard exposure factors of 70kVp, 20mAs and the use of an anti- scatter grid, one image proved to have a lower E whilst maintaining the same level of image quality and lesion visibility. It is suggested that the new exposure factors should be 60kVp, 20mAs and still include the use of an anti-scatter grid.

A narrative review on the reduction of effective dose to a paediatric patient by using different combinations of kVp, mAs and additional filtration whilst maintaining image quality

This paper reviews the literature for lowering of dose to paediatric patients through use of exposure factors and additional filtration. Dose reference levels set by The International Commission on Radiological Protection (ICRP) will be considered. Guidance was put in place in 1996 requires updating to come into line with modern imaging equipment. There is a wide range of literature that specifies that grids should not be used on paediatric patients. Although much of the literature advocates additional filtration, contrasting views on the relative benefits of using aluminium or copper filtration, and their effects on dose reduction and image quality can vary. Changing kVp and mAs has an effect on the dose to the patient and image quality. Collimation protects adjacent structures whilst reducing scattered radiation.

Reducing effective dose to a paediatric phantom by using different combinations of kVp, mAs and additional filtration whilst maintaining image quality

Purpose: To determine whether using different combinations of kVp and mAs with additional filtration can reduce the effective dose to a paediatric phantom whilst maintaining diagnostic image quality. Methods: 27 images of a paediatric AP pelvis phantom were acquired with different kVp, mAs and additional copper filtration. Images were displayed on quality controlled monitors with dimmed lighting. Ten diagnostic radiographers (5 students and 5 experienced radiographers) had eye tests to assess visual acuity before rating the images. Each image was rated for visual image quality against a reference image using 2 alternative forced choice software using a 5-point Likert scale. Physical measures (SNR and CNR) were also taken to assess image quality. Results: Of the 27 images rated, 13 of them were of acceptable image quality and had a dose lower than the image with standard acquisition parameters. Two were produced without filtration, 6 with 0.1mm and 5 with 0.2mm copper filtration. Statistical analysis found that the inter-rater and intra-rater reliability was high. Discussion: It is possible to obtain an image of acceptable image quality with a dose that is lower than published guidelines. There are some areas of the study that could be improved. These include using a wider range of kVp and mAs to give an exact set of parameters to use. Conclusion: Additional filtration has been identified as amajor tool for reducing effective dose whilst maintaining acceptable image quality in a 5 year old phantom.

Approximate entropy normalized measures for analyzing social neurobiological systems

When considering time series data of variables describing agent interactions in social neurobiological systems, measures of regularity can provide a global understanding of such system behaviors. Approximate entropy (ApEn) was introduced as a nonlinear measure to assess the complexity of a system behavior by quantifying the regularity of the generated time series. However, ApEn is not reliable when assessing and comparing the regularity of data series with short or inconsistent lengths, which often occur in studies of social neurobiological systems, particularly in dyadic human movement systems. Here, the authors present two normalized, nonmodified measures of regularity derived from the original ApEn, which are less dependent on time series length. The validity of the suggested measures was tested in well-established series (random and sine) prior to their empirical application, describing the dyadic behavior of athletes in team games. The authors consider one of the ApEn normalized measures to generate the 95th percentile envelopes that can be used to test whether a particular social neurobiological system is highly complex (i.e., generates highly unpredictable time series). Results demonstrated that suggested measures may be considered as valid instruments for measuring and comparing complexity in systems that produce time series with inconsistent lengths.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

Influence of attenuation correction in the MPI image reconstructed by the ‘Evolution for Cardiac’™

The acquisition of a Myocardial Perfusion image (MPI) is of great importance for the diagnosis of the coronary artery disease, since it allows to evaluate which areas of the heart aren’t being properly perfused, in rest and stress situations. This exam is greatly influenced by photon attenuation which creates image artifacts and affects quantification. The acquisition of a Computerized Tomography (CT) image makes it possible to get an atomic images which can be used to perform high-quality attenuation corrections of the radiopharmaceutical distribution, in the MPI image. Studies show that by using hybrid imaging to perform diagnosis of the coronary artery disease, there is an increase on the specificity when evaluating the perfusion of the right coronary artery (RCA). Using an iterative algorithm with a resolution recovery software for the reconstruction, which balances the image quality, the administered activity and the scanning time, we aim to evaluate the influence of attenuation correction on the MPI image and the outcome in perfusion quantification and imaging quality.

Development and validation of a psychometric scale for assessing PA chest image quality

Purpose - To develop and validate a psychometric scale for assessing image quality for chest radiographs.

10 kVp rule - An anthropomorphic pelvis phantom imaging study using a CR system: impact on image quality and effective dose using AEC and manual mode

Purpose: This study aims to investigate the influence of tube potential (kVp) variation in relation to perceptual image quality and effective dose for pelvis using automatic exposure control (AEC) and non-AEC in a computed radiography (CR) system. Methods and Materials: To determine the effects of using AEC and non-AEC by applying the 10 kVp rule in two experiments using an anthropomorphic pelvis phantom. Images were acquired using 10 kVp increments (60-120 kVp) for both experiments. The first experiment, based on seven AEC combinations, produced 49 images. The mean mAs from each kVp increment were used as a baseline for the second experiment producing 35 images. A total of 84 images were produced and a panel of 5 experienced observers participated for the image scoring using the 2 AFC visual grading software. PCXMC software was used to estimate the effective dose. Results: A decrease in perceptual image quality as the kVp increases was observed both in non-AEC and AEC experiments, however no significant statistical differences (p> 0.05) were found. Image quality scores from all observers at 10 kVp increments for all mAs values using non-AEC mode demonstrates a better score up to 90 kVp. Effective dose results show a statistical significant decrease (p=0.000) on the 75th quartile from 0.3 mSv at 60 kVp to 0.1 mSv at 120 kVp when applying the 10 kVp rule in non-AEC mode. Conclusion: No significant reduction in perceptual image quality is observed when increasing kVp whilst a marked and significant effective dose reduction is observed.

18F-FDG measurement in primary lung cancer: SUV normalization to different distribution volumes

Introduction: Standard Uptake Value (SUV) is a measurement of the uptake in a tumour normalized on the basis of a distribution volume and is used to quantify 18F-Fluorodeoxiglucose (FDG) uptake in tumors, such as primary lung tumor. Several sources of error can affect its accuracy. Normalization can be based on body weight, body surface area (BSA) and lean body mass (LBM). The aim of this study is to compare the influence of 3 normalization volumes in the calculation of SUV: body weight (SUVW), BSA (SUVBSA) and LBM (SUVLBM), with and without glucose correction, in patients with known primary lung tumor. The correlation between SUV and weight, height, blood glucose level, injected activity and time between injection and image acquisition is evaluated. Methods: Sample included 30 subjects (8 female and 22 male) with primary lung tumor, with clinical indication for 18F-FDG Positron Emission Tomography (PET). Images were acquired on a Siemens Biography according to the department’s protocol. Maximum pixel SUVW was obtained for abnormal uptake focus through semiautomatic VOI with Quantification 3D isocontour (threshold 2.5). The concentration of radioactivity (kBq/ml) was obtained from SUVW, SUVBSA, SUVLBM and the glucose corrected SUV were mathematically obtained. Results: Statistically significant differences between SUVW, SUVBSA and SUVLBM and between SUVWgluc, SUVBSAgluc and SUVLBMgluc were observed (p=0.000<0.05). The blood glucose level showed significant positive correlations with SUVW (r=0.371; p=0.043) and SUVLBM (r=0.389; p=0.034). SUVBSA showed independence of variations with the blood glucose level. Conclusion: The measurement of a radiopharmaceutical tumor uptake normalized on the basis of different distribution volumes is still variable. Further investigation on this subject is recommended.

In-plane displacement and strain image analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements. © 2015Computer-Aided Civil and Infrastructure Engineering.

PINPIN a-Si: H based structures for X-ray image detection using the laser scanning technique

Conventional film based X-ray imaging systems are being replaced by their digital equivalents. Different approaches are being followed by considering direct or indirect conversion, with the later technique dominating. The typical, indirect conversion, X-ray panel detector uses a phosphor for X-ray conversion coupled to a large area array of amorphous silicon based optical sensors and a couple of switching thin film transistors (TFT). The pixel information can then be readout by switching the correspondent line and column transistors, routing the signal to an external amplifier. In this work we follow an alternative approach, where the electrical switching performed by the TFT is replaced by optical scanning using a low power laser beam and a sensing/switching PINPIN structure, thus resulting in a simpler device. The optically active device is a PINPIN array, sharing both front and back electrical contacts, deposited over a glass substrate. During X-ray exposure, each sensing side photodiode collects photons generated by the scintillator screen (560 nm), charging its internal capacitance. Subsequently a laser beam (445 nm) scans the switching diodes (back side) retrieving the stored charge in a sequential way, reconstructing the image. In this paper we present recent work on the optoelectronic characterization of the PINPIN structure to be incorporated in the X-ray image sensor. The results from the optoelectronic characterization of the device and the dependence on scanning beam parameters are presented and discussed. Preliminary results of line scans are also presented. (C) 2014 Elsevier B.V. All rights reserved.

A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.