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Denoising reconstruction techniques can introduce nonlinear properties into computed tomography (CT) systems. These nonlinear algorithms introduce distortion which affects the assessment of the resolution of the system. The purpose of the present study was to decouple and investigate amplitude modulation and waveform distortion in reconstruction algorithms in CT. The methodology developed by Wells, J. R. and Dobbins, J. T. III [Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context. Med. Phys. 40, 091906 (2013)] was adapted to CT reconstruction algorithms. The CT simulating program ASTRA Toolbox© for MATLAB™ was used for the reconstruction of the sinusoidal wave functions. Filtered back projection and the simultaneous iterative reconstruction technique were investigated with simple nonlinear mechanisms: a median filter and a non-negative constraint, respectively. The native reconstruction algorithms were not free from nonlinear waveform distortion, however, none of the metrics showed any dependence on the contrast-to-noise ratio (CNR). Furthermore, the algorithms including nonlinear mechanisms showed a clear and specific CNR dependence, indicating the necessity for distortion analysis in nonlinear CT reconstruction., (© The Author(s) 2021. Published by Oxford University Press.)

Purpose: This study attempted to develop a method to measure the applied recursive filtration and to determine the noise reduction of four different fluoroscopic systems. The study also attempted to elucidate the importance of considering the recursive filter for quality control tests concerning signal-to-noise ratio (SNR) or image quality. The vendor's settings for recursive filtration factor (β) are, unfortunately, often not available. Hence, a method to determine the recursive filtration and associated noise reduction would be useful., Method: The recursive filter was determined by using a single fluoroscopic series and the method presented in this study. The theoretical noise reduction based on the choice of β was presented. In addition, the corresponding noise reduction, evaluated as the ratio of the standard deviation of the pixel value between a series with β equal to zero (recursive filtration off) and β > 0, was determined for different pulse rates given by pulses per second (pps), doses (mAs) and recursive filter. The images were acquired using clinically relevant radiation quality and quantity., Results: The presented method to measure the recursive filter exhibited high accuracy (1.08%) and precision (1.48%). The recursive filtration and noise reduction were measured for several settings for each vendor. The recursive filtration settings and associated recursive filtration factors for four different vendors were presented., Conclusions: This study presented an accurate method to determine applied recursive filtration, which was easy to determine. Hence, for all quality control purposes, including noise evaluation, it was possible to consider the essential noise reduction given by the settings for recursive filtration. It was also possible to compare the recursive filtration settings and associated recursive filtration within and between vendors., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: A contrast-detail phantom such as CDRAD is frequently used for quality assurance, optimization of image quality, and several other purposes. However, it is often used without considering the uncertainty of the results. The aim of this study was to assess two figure of merits (FOM) originating from CDRAD regarding the variations of the FOMs by dose utilized to create the x-ray image. The probability of overlapping (assessing an image acquired at a lower dose as better than an image acquired at a higher dose) was determined., Methods: The CDRAD phantom located underneath 12, 20, and 26 cm PMMA was imaged 16 times at five dose levels using an x-ray system with a flat-panel detector. All images were analyzed by CDRAD Analyser, version 1.1, which calculated the FOM inverse image quality figure (IQF inv ) and gave contrast detail curves for each image. Inherent properties of the CDRAD phantom were used to derive a new FOM h, which describes the size of the hole with the same diameter and depth that is just visible. Data were analyzed using heteroscedastic regression of mean and variance by dose. To ease interpretation, probabilities for overlaps were calculated assuming normal distribution, with associated bootstrap confidence intervals., Results: The proportion of total variability in IQF inv , explained by the dose (R 2 ), was 91%, 85%, and 93% for 12, 20, and 26 cm PMMA. Corresponding results for h were 91%, 89%, and 95%. The overlap probability for different mAs levels was 1% for 0.8 vs 1.2 mAs, 5% for 1.2 vs 1.6 mAs, 10% for 1.6 vs 2.0 mAs, and 10% for 2.0 mAs vs 2.5 mAs for 12 cm PMMA. For 20 cm PMMA, it was 0.5% for 10 vs 16 mAs, 13% for 16 vs 20 mAs, 14% for 20 vs 25 mAs, and 14% for 25 vs 32 mAs. For 26 cm PMMA, the probability varied from 0% to 6% for various mAs levels. Even though the estimated probability for overlap was small, the 95% confidence interval (CI) showed relatively large uncertainties. For 12 cm PMMA, the associated CI for 0.8 vs 1.2 mAs was 0.1-3.2%, and the CI for 1.2 vs 1.6 mAs was 2.1-7.8%., Conclusions: Inverse image quality figure and h are about equally related to dose level. The FOM h, which describes the size of a hole that should be seen in the image, may be a more intuitive FOM than IQF inv . However, considering the probabilities for overlap and their confidence intervals, the FOMs deduced from the CDRAD phantom are not sensitive to dose. Hence, CDRAD may not be an optimal phantom to differentiate between images acquired at different dose levels., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The purpose of the present study was to test an idea of and describe a concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow. In total over 31000 images from three ultrasound scanners from two vendors were collected retrospectively from a database. An algorithm was developed and applied to the images, 150 at a time, for detection of systematic dark regions in the superficial part of the images. The result was compared with electrical measurements (FirstCall) of the transducers, performed at times when the transducers were known to be defective. The algorithm made similar detection of horizontal nonuniformities for images acquired at different time points over long periods of time. The results showed good subjective visual agreement with the available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality control., (© 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The recent reintroduction of iterative reconstruction in computed tomography has facilitated the realisation of major dose saving. The aim of this article was to investigate the possibility of achieving further savings at a site with well-established Adaptive Statistical iterative Reconstruction (ASiR™) (GE Healthcare) brain protocols. An adult patient study was conducted with observers making visual grading assessments using image quality criteria, which were compared with the frequency domain metrics, noise power spectrum and modulation transfer function. Subjective image quality equivalency was found in the 40-70% ASiR™ range, leading to the proposal of ranges for the objective metrics defining acceptable image quality. Based on the findings of both the patient-based and objective studies of the ASiR™/tube-current combinations tested, 60%/305 mA was found to fall within all, but one, of these ranges. Therefore, it is recommended that an ASiR™ level of 60%, with a noise index of 12.20, is a viable alternative to the currently used protocol featuring a 40% ASiR™ level and a noise index of 11.20, potentially representing a 16% dose saving., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The purpose of this study was to investigate the effect of different combinations of convolution kernel and the level of Adaptive Statistical iterative Reconstruction (ASiR™) on diagnostic image quality as well as visualisation of anatomical structures in paediatric abdominal computed tomography (CT) examinations. Thirty-five paediatric patients with abdominal pain with non-specified pathology undergoing abdominal CT were included in the study. Transaxial stacks of 5-mm-thick images were retrospectively reconstructed at various ASiR levels, in combination with three convolution kernels. Four paediatric radiologists rated the diagnostic image quality and the delineation of six anatomical structures in a blinded randomised visual grading study. Image quality at a given ASiR level was found to be dependent on the kernel, and a more edge-enhancing kernel benefitted from a higher ASiR level. An ASiR level of 70 % together with the Soft™ or Standard™ kernel was suggested to be the optimal combination for paediatric abdominal CT examinations., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The aim of the present study was to investigate nodule size measurements with chest tomosynthesis (TS) and computed tomography (CT). A 26-mm thick phantom, composed of a Polylite block with embedded spheres of different materials and sizes (4-20 mm), was scanned by both CT and TS. Six observers without prior knowledge of the true diameters of the spheres independently measured the diameter of the spheres on the CT and TS images. Four observers were allowed to change the window settings and two of the observers used predetermined fixed viewing conditions. The mean relative errors for all observers and all measured spheres compared with the known diameter of the spheres were 1.4 % (standard deviation, SD: 5.4 %) on CT images and -1.1 % (SD: 5.0 %) on TS images. With regard to the four observers where the window settings were at the discretion of the observer, the mean relative errors were 1.4 % (SD: 6.4 %) on CT images and -1.7 % (SD: 5.7 %) on TS images. Regarding the two observers using identical viewing conditions the mean relative error was 1.5 % (SD: 2.8 %) on CT images and 0.2 % (SD: 2.6 %) on TS images. In conclusion, the study suggests that nodule size measurements on chest TS might be an alternative to measurements on CT.

The aim of the present study was to evaluate the use of common external image processing to compensate for differences in appearance between digital X-ray images from different vendors. Twenty posteroanterior chest radiographs were collected from each of three different modalities from different vendors (GE, Siemens and Canon) with vendor-specific image processing applied. The images were also extracted with neutral process parameters and processed with external image-processing software. Six experienced radiologists rated the quality and the similarity of the images with the original Siemens images. The externally processed GE images were rated of higher quality than the original GE images and more similar to the original Siemens images (p < 0.001). The opposite was obtained for the Canon images. The externally processed Siemens images were rated of similar quality as the original images. The present study indicates the possibility of using common external image processing to harmonise the appearance of images from different vendors, although the exposure parameters may need to be adjusted for individual vendors.

There are many ways in which imaging systems can be evaluated. The aim of the present paper is to provide an overview of a number of selected approaches to evaluating imaging systems, often encountered by the medical physicist, and discuss their validity and reliability. Specifically, it will cover (i) characterisation of an imaging system in terms of its detective quantum efficiency using linear-systems analysis; (ii) attempts to calculate relevant measures directly in images using the Rose model and the pixel signal-to-noise ratio; (iii) task-based methods incorporating human observers such as receiver-operating characteristics and (iv) visual grading-based methods using experienced radiologists as observers.

According to European and national legislation, as well as international recommendations, X-ray examinations shall be optimised. However, with limited resources and hundreds of different types of X-ray examinations, it may be difficult to prioritise among the optimisation tasks at a radiology department. This work is focused on describing a method that can be used to determine the order of which the examinations should be optimised. In the Medical Exposure Directive from 1997, the European Commission prescribes the content of an optimisation process in relation to medical exposure. A reasonable interpretation of the directive is that the assurance of medical purpose for a justified examination is superior to the need of decreased radiation dose. This was used as a basis for developing a method for prioritisation among optimisation tasks. For each examination type, the following four yes/no questions are raised: (i) Is the present image quality unacceptable? (ii) Is the examination of particular importance? (iii) Is the radiation dose suspiciously high? (iv) Are there special dose level concerns, e.g. diagnostic reference levels? Arguing that a positive response to any of the four questions results in the examination being higher prioritised than otherwise and that the questions are labelled in order of decreasing relevance, it can be shown that the resulting flow chart, determining the order of which the examinations should be optimised, can be described by a 4-bit binary scale. In this way, each examination type is given a number from 0 to 15, a higher number corresponding to the examination being prioritised higher in the optimisation work. The method was applied to a general radiology department and resulted in a well-discriminated distribution of examinations prioritised for optimisation tasks. In conclusion, taking into account both medical outcome and potential risk, the proposed method can be used to determine the order in which examinations at a radiology department should be optimised.

The purpose of the present work was to investigate the reliability of subjective assessments of the low-contrast visibility in constancy control of computed tomography (CT). Axial CT images of a low-contrast phantom were acquired on an 8-slice multi-detector CT scanner at nine tube current settings ranging from 75 to 440 mA. Five medical physicists assessed the visibility of the low-contrast details in two sessions. In the first session, containing 54 images, the visibility was rated on an absolute scale by determining the number of visible details in each contrast group in each image. In the second session, 180 image pairs were presented to the observers with the task of determining if the two images had been acquired under identical conditions or not. In the absolute session, both the intra- and inter-observer variabilities were high. In the relative session, the variability was smaller, but an exposure difference of 50 % was needed for all observers to correctly identify a change in all cases. In conclusion, the present study indicates that subjective assessments of the low-contrast visibility in constancy control of CT are not reliable.

The aim of this work was to calculate synthetic digital chest tomosynthesis projections using a computer simulation model based on the Monte Carlo method. An anthropomorphic chest phantom was scanned in a computed tomography scanner, segmented and included in the computer model to allow for simulation of realistic high-resolution X-ray images. The input parameters to the model were adapted to correspond to the VolumeRAD chest tomosynthesis system from GE Healthcare. Sixty tomosynthesis projections were calculated with projection angles ranging from +15 to -15 degrees. The images from primary photons were calculated using an analytical model of the anti-scatter grid and a pre-calculated detector response function. The contributions from scattered photons were calculated using an in-house Monte Carlo-based model employing a number of variance reduction techniques such as the collision density estimator. Tomographic section images were reconstructed by transferring the simulated projections into the VolumeRAD system. The reconstruction was performed for three types of images using: (i) noise-free primary projections, (ii) primary projections including contributions from scattered photons and (iii) projections as in (ii) with added correlated noise. The simulated section images were compared with corresponding section images from projections taken with the real, anthropomorphic phantom from which the digital voxel phantom was originally created. The present article describes a work in progress aiming towards developing a model intended for optimisation of chest tomosynthesis, allowing for simulation of both existing and future chest tomosynthesis systems.

With the increasing use of computed tomography (CT) for urography examinations, the indications for 'conventional' projection urography have changed and are more focused on high-contrast details. The purpose of the present study was to optimise the beam quality for urography examinations performed with a Gd(2)O(2)S:Tb flat-panel detector for the new conditions. Images of an anthropomorphic phantom were collected at different tube voltages with a CXDI-40G detector (Canon Inc., Tokyo, Japan). The images were analysed by radiologists and residents in a visual grading characteristics (VGCs) study. The tube voltage resulting in the best image quality was 55 kV, which therefore was selected for a clinical study. Images from 62 patients exposed with either 55 or 73 kV (original tube voltage) at constant effective doses were included. The 55-kV images underwent simulated dose reduction to represent images collected at 80, 64, 50, 40 and 32 % of the original dose level. All images were included in a VGC study where the observers rated the visibility of important anatomical landmarks. For images collected at 55 kV, an effective dose of approximately 85 % resulted in the same image quality as for images collected at 73 kV at 100 % dose. In conclusion, a low tube voltage should be used for conventional urography focused on high-contrast details. The study indicates that using a tube voltage of 55 kV instead of 73 kV for a Gd(2)O(2)S:Tb flat-panel detector, the effective dose can be reduced by approximately 10-20 % for normal-sized patients while maintaining image quality.

The imaging properties of an imaging system can be described by its detective quantum efficiency (DQE). Using the modulation transfer function calculated from measured line spread functions and the normalized noise power spectrum calculated from uniformity images, DQE was calculated with the number of photons emitted from a plane source as a measure for the incoming SNR2. Measurements were made with 99mTc, using three different pulse height windows at 2 cm and 12 cm depths in water with high resolution and all purpose collimators and with two different crystal thicknesses. The results indicated that at greater depths a 15% window is the best choice. The choice of collimator depends on the details in the organ being investigated. There is a break point at 0.5 cycles cm-1 and 1.2 cycles cm-1 at 12 cm and 2 cm depths, respectively. A difference was found in DQE between the two crystal thicknesses, with a slightly better result for the thick crystal for measurements at 12 cm depth. At 2 cm depth, the thinner crystal was slightly better for frequencies over 0.5 cm-1. The determination of DQE could be a method to optimize the parameters for different nuclear medicine investigations. The DQE could also be used in comparing different gamma camera systems with different collimators to obtain a figure of merit.

Based on a recently described method for determining the two-dimensional presampling modulation transfer function (MTF), the aperture mask method, a method for determining the two-dimensional detective quantum efficiency (DQE) of a digital radiographic system was developed. The method was applied to a new computed radiography (CR) system and comparisons with one-dimensional determinations of the presampling MTF and the DQE were performed. The aperture mask method was shown to agree with the conventional tilted slit method for determining the presampling MTF along the axes. For the particular CR system studied, the mean of one-dimensional determinations of the DQE in orthogonal directions led to a representative measure of the average DQE behavior of the system up to the Nyquist frequency along the axes, but a deviation was observed above this frequency. In conclusion, the method developed for determining the two-dimensional DQE can be used to determine the imaging properties of a digital radiographic detector system over almost the entire frequency domain, the exception being the lowest frequencies (< or = 0.1 mm(-1)) at which the validity and the reliability of the method are low.