Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging.

OBJECTIVES: This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. METHODS: A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. RESULTS: For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. CONCLUSIONS: This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. KEY POINTS: • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

OBJECTIVES: To study how radiologists' perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. METHODS: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. RESULTS: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). CONCLUSIONS: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. KEY POINTS: • Lower spatial resolution and increased quantum noise affected the radiologists' perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.

A mixed-methods feasibility study of an arts-based intervention for patients receiving maintenance haemodialysis.

BACKGROUND: Haemodialysis can negatively impact quality of life and mental health. Arts-based interventions used successfully in other settings to improve health and well-being, could help address the impact of haemodialysis. This study aimed to evaluate the feasibility and acceptability of conducting a randomised controlled trial (RCT) of an arts-based intervention for patients receiving haemodialysis. METHODS: A parallel convergent mixed-methods design was used, including a pilot cluster RCT and qualitative process evaluation. Phase 1 evaluated recruitment and retention rates through a pilot cluster RCT at a single haemodialysis unit in Northern Ireland. Participants included patients who received haemodialysis for ESKD, were over the age of 18 and had the capacity to consent. These participants were randomised to the intervention or control group according to their haemodialysis shift. The intervention involved six one-hour, one-to-one facilitated arts sessions during haemodialysis. Phase 2 explored intervention and trial acceptability through a qualitative process evaluation using semi-structured interviews based on the RE-AIM framework. Participants included 13 patients who participated in phase 1 of the study, including 9 participants from the experimental group and four participants from the control group, and nine healthcare professionals who were present on the unit during implementation. RESULTS: Out of 122 outpatient haemodialysis patients, 94 were assessed as eligible for participation. Twenty-four participants were randomised, meaning 80% of the target sample size was recruited and the attrition rate at 3 months was 12.5% (n = 3). Participants viewed the arts as more accessible and enjoyable than anticipated following implementation. All participants who started the intervention (n = 11) completed the full six sessions. Qualitative benefits of the intervention suggest improvements in mental well-being. Patient choice and facilitation were important factors for successful implementation. CONCLUSION: An arts-based intervention for patients receiving haemodialysis is acceptable for both patients and healthcare professionals, and a definitive trial is feasible. The intervention may help improve mental-wellbeing in patients receiving haemodialysis, but this requires further investigation in a definitive trial. TRIAL REGISTRATION: The trial was prospectively registered on clinicaltrials.gov on 14/8/2018, registration number NCT03629496 .

Breast cancer detection rates using four different types of mammography detectors.

OBJECTIVE: To compare the performance of different types of detectors in breast cancer detection. METHODS: A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening. RESULTS: No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors' FoMs were significantly lower than for DR detectors. The calcification cluster's FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15% and 22% lower, respectively, than for DR detectors. CONCLUSION: Cancer detection is affected by detector type, and the use of CR in mammography should be reconsidered. KEY POINTS: The type of mammography detector can affect the cancer detection rates. CR detectors performed worse than DR detectors in mammography. Needle phosphor CR performed better than powder phosphor CR. Calcification clusters detection is more sensitive to detector type than other cancers.

The diet of Myotis lucifugus across Canada: assessing foraging quality and diet variability.

Variation in prey resources influences the diet and behaviour of predators. When prey become limiting, predators may travel farther to find preferred food or adjust to existing local resources. When predators are habitat limited, local resource abundance impacts foraging success. We analysed the diet of Myotis lucifugus (little brown bats) from Nova Scotia (eastern Canada) to the Northwest Territories (north-western Canada). This distribution includes extremes of season length and temperature and encompasses colonies on rural monoculture farms, and in urban and unmodified areas. We recognized nearly 600 distinct species of prey, of which ≈30% could be identified using reference sequence libraries. We found a higher than expected use of lepidopterans, which comprised a range of dietary richness from ≈35% early in the summer to ≈55% by late summer. Diptera were the second largest prey group consumed, representing ≈45% of dietary diversity early in the summer. We observed extreme local dietary variability and variation among seasons and years. Based on the species of insects that were consumed, we observed that two locations support prey species with extremely low pollution and acidification tolerances, suggesting that these are areas without environmental contamination. We conclude that there is significant local population variability in little brown bat diet that is likely driven by seasonal and geographical changes in insect diversity, and that this prey may be a good indicator of environment quality.

The effect of image processing on the detection of cancers in digital mammography.

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

Radiation doses received in the UK breast screening programmes 2019-2023.

OBJECTIVE: To report the latest UK mammography dose survey results and to compare radiation doses from digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in UK breast screening. METHODS: Anonymized exposure factors were collected for 111 152 screening cases and 5113 assessment cases from 405 x-ray sets across the United Kingdom using an online submission system linked to a national database of mammography quality control data. Output and beam quality measurements from each set were combined with exposure data to estimate mean glandular doses (MGD). RESULTS: FFDM doses increased by ∼10% compared to the 2016-2019 national survey but compressed breast thicknesses (CBT) remained similar. DBT doses were 34%-40% higher than FFDM overall and 34% higher than FFDM for breasts 50-60 mm thick. We found a possible overestimation of PMMA breast equivalent thicknesses at low CBTs, but the evidence was not conclusive. CONCLUSION: Recent changes to the mix of x-ray models in use in UK breast screening have resulted in higher FFDM breast doses. DBT doses in the NHSBSP are on average higher than FFDM by ∼34%-40%. ADVANCES IN KNOWLEDGE: This is the first national study to report DBT and FFDM MGDs in UK breast screening.

Capability and reliability of deep learning models to make density predictions on low-dose mammograms.

Purpose: Breast density is associated with the risk of developing cancer and can be automatically estimated using deep learning models from digital mammograms. Our aim is to evaluate the capacity and reliability of such models to predict density from low-dose mammograms taken to enable risk estimates for younger women. Approach: We trained deep learning models on standard-dose and simulated low-dose mammograms. The models were then tested on a mammography dataset with paired standard- and low-dose images. The effect of different factors (including age, density, and dose ratio) on the differences between predictions on standard and low doses is analyzed. Methods to improve performance are assessed, and factors that reduce the model quality are demonstrated. Results: We showed that, although many factors have no significant effect on the quality of low-dose density prediction, both density and breast area have an impact. The correlation between density predictions on low- and standard-dose images of breasts with the largest breast area is 0.985 (0.949 to 0.995), whereas that with the smallest is 0.882 (0.697 to 0.961). We also demonstrated that averaging across craniocaudal-mediolateral oblique (CC-MLO) images and across repeatedly trained models can improve predictive performance. Conclusions: Low-dose mammography can be used to produce density and risk estimates that are comparable to standard-dose images. Averaging across CC-MLO and model predictions should improve this performance. The model quality is reduced when making predictions on denser and smaller breasts.

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions.Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained.Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases.Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.

Successes and challenges in extracting information from DICOM image databases for audit and research.

In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.

Special Section Guest Editorial: Introduction to the JMI Special Issue on Advances in Breast Imaging.

The editorial introduces the JMI Special Issue on Advances in Breast Imaging.

Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set.

OBJECTIVE: To pilot a process for the independent external validation of an artificial intelligence (AI) tool to detect breast cancer using data from the NHS breast screening programme (NHSBSP). METHODS: A representative data set of mammography images from 26,000 women attending 2 NHS screening centres, and an enriched data set of 2054 positive cases were used from the OPTIMAM image database. The use case of the AI tool was the replacement of the first or second human reader. The performance of the AI tool was compared to that of human readers in the NHSBSP. RESULTS: Recommendations for future external validations of AI tools to detect breast cancer are provided. The tool recalled different breast cancers to the human readers. This study showed the importance of testing AI tools on all types of cases (including non-standard) and the clarity of any warning messages. The acceptable difference in sensitivity and specificity between the AI tool and human readers should be determined. Any information vital for the clinical application should be a required output for the AI tool. It is recommended that the interaction of radiologists with the AI tool, and the effect of the AI tool on arbitration be investigated prior to clinical use. CONCLUSION: This pilot demonstrated several lessons for future independent external validation of AI tools for breast cancer detection. ADVANCES IN KNOWLEDGE: Knowledge has been gained towards best practice procedures for performing independent external validations of AI tools for the detection of breast cancer using data from the NHS Breast Screening Programme.

Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system.

PURPOSE: Undertaking observer studies to compare imaging technology using clinical radiological images is challenging due to patient variability. To achieve a significant result, a large number of patients would be required to compare cancer detection rates for different image detectors and systems. The aim of this work was to create a methodology where only one set of images is collected on one particular imaging system. These images are then converted to appear as if they had been acquired on a different detector and x-ray system. Therefore, the effect of a wide range of digital detectors on cancer detection or diagnosis can be examined without the need for multiple patient exposures. METHODS: Three detectors and x-ray systems [Hologic Selenia (ASE), GE Essential (CSI), Carestream CR (CR)] were characterized in terms of signal transfer properties, noise power spectra (NPS), modulation transfer function, and grid properties. The contributions of the three noise sources (electronic, quantum, and structure noise) to the NPS were calculated by fitting a quadratic polynomial at each spatial frequency of the NPS against air kerma. A methodology was developed to degrade the images to have the characteristics of a different (target) imaging system. The simulated images were created by first linearizing the original images such that the pixel values were equivalent to the air kerma incident at the detector. The linearized image was then blurred to match the sharpness characteristics of the target detector. Noise was then added to the blurred image to correct for differences between the detectors and any required change in dose. The electronic, quantum, and structure noise were added appropriate to the air kerma selected for the simulated image and thus ensuring that the noise in the simulated image had the same magnitude and correlation as the target image. A correction was also made for differences in primary grid transmission, scatter, and veiling glare. The method was validated by acquiring images of a CDMAM contrast detail test object (Artinis, The Netherlands) at five different doses for the three systems. The ASE CDMAM images were then converted to appear with the imaging characteristics of target CR and CSI detectors. RESULTS: The measured threshold gold thicknesses of the simulated and target CDMAM images were closely matched at normal dose level and the average differences across the range of detail diameters were -4% and 0% for the CR and CSI systems, respectively. The conversion was successful for images acquired over a wide dose range. The average difference between simulated and target images for a given dose was a maximum of 11%. CONCLUSIONS: The validation shows that the image quality of a digital mammography image obtained with a particular system can be degraded, in terms of noise magnitude and color, sharpness, and contrast to account for differences in the detector and antiscatter grid. Potentially, this is a powerful tool for observer studies, as a range of image qualities can be examined by modifying an image set obtained at a single (better) image quality thus removing the patient variability when comparing systems.

Effect of image quality on calcification detection in digital mammography.

PURPOSE: This study aims to investigate if microcalcification detection varies significantly when mammographic images are acquired using different image qualities, including: different detectors, dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. METHODS: One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography (CR) imaging system at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. RESULTS: There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from 0.84 to 0.63 and the ROC area decreased from 0.91 to 0.79 (p < 0.0001). This corresponded to a 30% drop in lesion sensitivity at a NLF equal to 0.1. Detection was also sensitive to the dose used. There was no significant difference in detection between the two image processing algorithms used (p > 0.05). It was additionally found that lower threshold gold thickness from CDMAM analysis implied better cluster detection. The measured threshold gold thickness passed the acceptable limit set in the EU standards for all image qualities except half dose CR. However, calcification detection varied significantly between image qualities. This suggests that the current EU guidelines may need revising. CONCLUSIONS: Microcalcification detection was found to be sensitive to detector and dose used. Standard measurements of image quality were a good predictor of microcalcification cluster detection.

The relationship between age of digital mammography systems and number of reported faults and downtime.

PURPOSE: To investigate the relationship between age of mammographic x-ray equipment, and number of reported faults and related consequences. METHODS: A centralised online fault reporting database is used by all UK breast screening programmes to collate faults with mammography equipment. Data on faults occurring in 2018 and 2019 for digital x-ray imaging systems were analysed. The effect of the age of mammography systems on the number of equipment faults, and the consequences of these faults was examined. The number of days downtime, number of cancelled appointments, number of repeated images, and number of recalled participants were used to quantify the severity of faults. RESULTS: This analysis covers a two year period and includes 4271 faults and 522 individual x-ray sets. On average, an x-ray set was 6.1 years old at the time when a fault occurred. 77% of x-ray sets experienced five of fewer annual faults. X-ray sets of nine years old had the highest average number of annual faults. Systems of ten years old had the highest average number of days downtime per year, and the highest average number of cancellations per year. The indicated primary use of 48% of the x-ray sets included in this analysis was screening, but a disproportionate 87% of cancelled appointments occurred due to faults on these units compared to those used primarily for assessment, or for a mixture of assessment and screening. CONCLUSIONS: Information from this unique dataset can be used to support guidance on equipment replacement programmes for mammographic x-ray sets.

Development of an algorithm to convert mammographic images to appear as if acquired with different technique factors.

Purpose: We set out a fully developed algorithm for adapting mammography images to appear as if acquired using different technique factors by changing the signal and noise within the images. The algorithm accounts for difference between the absorption by the object being imaged and the imaging system. Approach: Images were acquired using a Hologic Selenia Dimensions x-ray unit for the validation, of three thicknesses of polymethyl methacrylate (PMMA) blocks with or without different thicknesses of PMMA contrast objects acquired for a range of technique factors. One set of images was then adapted to appear the same as a target image acquired with a higher or lower tube voltage and/or a different anode/filter combination. The average linearized pixel value, normalized noise power spectra (NNPS), and standard deviation of the flat field images and the contrast-to-noise ratio (CNR) of the contrast object images were calculated for the simulated and target images. A simulation study tested the algorithm on images created using a voxel breast phantom at different technique factors and the images compared using local signal level, variance, and power spectra. Results: The average pixel value, NNPS, and standard deviation for the simulated and target images were found to be within 9%. The CNRs of the simulated and target images were found to be within 5% of each other. The differences between the target and simulated images of the voxel phantom were similar to those of the natural variability. Conclusions: We demonstrated that images can be successfully adapted to appear as if acquired using different technique factors. Using this conversion algorithm, it may be possible to examine the effect of tube voltage and anode/filter combination on cancer detection using clinical images.

Radiation doses in the United Kingdom breast screening programmes 2016-2019.

OBJECTIVES: To record the radiation doses involved in UK breast screening and to identify any changes since previous publications related to technical factors and the population screened. METHODS: Mammographic exposure factors for 68,998 women imaged using 411 X-ray sets spread across the UK were compiled. Local output and half value layer measurements for each X-ray set were used to estimate mean glandular dose (MGD) using the standard UK method. RESULTS: Mean MGDs in digital mammography have increased by 11% since 2010-12 for both medio-lateral oblique (MLO) and cranio-caudal (CC) views. The mean compressed breast thickness (CBT) has increased (4.8% CC, 5.2% MLO) over the same period. The mean MLO CBT value of 62.4 ± 0.1 mm is outside the 50 to 60 mm range used for diagnostic reference levels. The increase in MGD is consistent with the CBT changes. The mean MGD in the 50 to 60 mm CBT range is 1.44 ± 0.03 mGy for MLO views. CBT varies with age and peaks at 51. CONCLUSIONS: Mean CBT has increased with time, and this has increased mean MGDs for digital mammography. CBT also varies with age. ADVANCES IN KNOWLEDGE: Updated average MGDs in the UK are provided. There is evidence that breast size is increasing in the UK and that mean CBT is affected by age-related changes in the breast.

Correction: Abdi et al. Visual Evaluation of Image Quality of a Low Dose 2D/3D Slot Scanner Imaging System Compared to Two Conventional Digital Radiography X-ray Imaging Systems. Diagnostics 2021, 11, 1932.

In the original publication [...].

Evaluation of a Programme of Online Arts Activities for Patients with Kidney Disease during the COVID-19 Pandemic.

Patients living with end-stage kidney disease (ESKD) have been seriously impacted by the COVID-19 pandemic. As these patients are considered extremely clinically vulnerable, they were advised to 'shield' at home, with limited face-to-face contact and support for the duration of the pandemic. Living with ESKD impacts heavily on patients' mental health and wellbeing, and this extended period of isolation and loneliness is likely to have a further negative effect on patients' mental wellbeing. The Renal Arts Group (RAG), Queen's University Belfast, aims to improve the quality of life of those living with ESKD and the extended renal community through engagement with the arts. We developed an initiative, funded by the Economic and Social Research Council, and carried out an evaluation. The initiative included a programme of online arts-based activities that built upon the work of RAG and provided mental wellbeing support for patients who faced an extended, lonely period of self-isolation. We worked with experienced arts practitioners to identify appropriate activities and developed five workshops and tutorials that were delivered online. We received positive feedback from participants who found the activities to be enjoyable, beneficial to their mental wellbeing and were interested in undertaking further activities online. We conducted interviews with the arts facilitators and identified three themes for consideration when developing online arts activities for the renal community. Participants reported that the activities benefited their mental wellbeing, were enjoyable and provided an opportunity to meet others with shared interests. The arts facilitators reported experiences around accessibility, audience engagement, impact on health and wellbeing and facilitator experience, that should be considered when developing online arts activities for the renal community. This evaluation will inform future work in this area, and the arts tutorial videos developed as part of this project will remain available online for members of the renal community to access.

Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms.

Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged. Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.