Perceptual thresholds for differences in CT noise texture

Purpose: The average (f av) or peak (f peak) noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it. Approach: A model of CT NPS was created based on its f peak and a half-Gaussian fit (σ) to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the f peak∕σ-space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the f peak∕σ-space. NPS differences were quantified by the noise texture contrast (Ctexture), the integral of the absolute NPS difference. Results: The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for f peak alone are 0.2 lp∕cm for body and 0.4 lp∕cm for lung NPSs. For σ, these values are 0.15 and 2 lp∕cm, respectively. Thresholds change if the other parameter also changes. Different NPSs with the same f peak or f av can be discriminated. Nonradiologist observers did not need more Ctexture than radiologists. Conclusions: f peak or f av is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.</p

Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images

Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3d search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors. Approach: Sixteen non-expert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC). Results: The CNN-CADe improved the 3D search for the small microcalcification signal (delta AUC = 0.098, p = 0.0002) and the 2D search for the large mass signal (delta AUC = 0.076, p = 0.002). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D (delta delta AUC = 0.066, p = 0.035). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe (r = -0.528, p = 0.036). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit (delta delta AUC = 0.033, p = 0.133). Conclusion: The CNN-CADe brings unique performance benefits to the 3D (vs. 2D) search of small signals by reducing errors caused by the under-exploration of the volumetric data

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT

Use of Equivalent Relative Utility (ERU) to Evaluate Artificial Intelligence-Enabled Rule-Out Devices

We investigated the use of equivalent relative utility (ERU) to evaluate the effectiveness of artificial intelligence (AI)-enabled rule-out devices that use AI to identify and autonomously remove non-cancer patient images from radiologist review in screening mammography.We reviewed two performance metrics that can be used to compare the diagnostic performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and equivalent relative utility (ERU). To demonstrate the use of the two evaluation metrics, we applied both methods to a recent US-based study that reported an improved performance of the radiologist-with-device workflow compared to the one without the device by retrospectively applying their AI algorithm to a large mammography dataset. We further applied the ERU method to a European study utilizing their reported recall rates and cancer detection rates at different thresholds of their AI algorithm to compare the potential utility among different thresholds. For the study using US data, neither the PPV/NPV nor the ERU method can conclude a significant improvement in diagnostic performance for any of the algorithm thresholds reported. For the study using European data, ERU values at lower AI thresholds are found to be higher than that at a higher threshold because more false-negative cases would be ruled-out at higher threshold, reducing the overall diagnostic performance. Both PPV/NPV and ERU methods can be used to compare the diagnostic performance between the radiologist-with-device workflow and that without. One limitation of the ERU method is the need to measure the baseline, standard-of-care relative utility (RU) value for mammography screening in the US. Once the baseline value is known, the ERU method can be applied to large US datasets without knowing the true prevalence of the dataset

Natriuretic peptide activation of extracellular regulated kinase 1/2 (ERK1/2) pathway by particulate guanylyl cyclases in GH3 somatolactotropes.

The natriuretic peptides, Atrial-, B-type and C-type natriuretric peptides (ANP, BNP, CNP), are regulators of many endocrine tissues and exert their effects predominantly through the activation of their specific guanylyl cyclase receptors (GC-A and GC-B) to generate cGMP. Whereas cGMP-independent signalling has been reported in response to natriuretic peptides, this is mediated via either the clearance receptor (Npr-C) or a renal-specific NPR-Bi isoform, which both lack intrinsic guanylyl cyclase activity. Here, we report evidence of GC-B-dependent cGMP-independent signalling in pituitary GH3 cells. Stimulation of GH3 cells with CNP resulted in a rapid and sustained enhancement of ERK1/2 phosphorylation (P-ERK1/2), an effect that was not mimicked by dibutryl-cGMP. Furthermore, CNP-stimulated P-ERK1/2 occurred at concentrations below that required for cGMP accumulation. The effect of CNP on P-ERK1/2 was sensitive to pharmacological blockade of MEK (U0126) and Src kinases (PP2). Silencing of the GC-B1 and GC-B2 splice variants of the GC-B receptor by using targeted short interfering RNAs completely blocked the CNP effects on P-ERK1/2. CNP failed to alter GH3 cell proliferation or cell cycle distribution but caused a concentration-dependent increase in the activity of the human glycoprotein α-subunit promoter (αGSU) in a MEK-dependent manner. Finally, CNP also activated the p38 and JNK MAPK pathways in GH3 cells. These findings reveal an additional mechanism of GC-B signalling and suggest additional biological roles for CNP in its target tissues

Modeling Radiologists’ Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms

Purpose - To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance. Methods - Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients. The logistic regression models were subsequently used to simulate different screening programs with reader pairing based on individual true-positive rates (TPR) and/or false-positive rates (FPR). For this, retrospective results from breast cancer screening programs employing double reading in Sweden, England, and Norway were used. Outcomes of random pairing were compared against those composed of readers with similar and opposite TPRs/FPRs, with positive assessments defined by either reader flagging an examination as abnormal. Results - The analysis data sets consisted of 936,621 (Sweden), 435,281 (England), and 1,820,053 (Norway) examinations. There was good agreement between the model-predicted and observed radiologists’ TPR and FPR (r ≥ 0.969). Model-predicted negative-case disagreement rates showed high correlations (r ≥ 0.709), whereas positive-case disagreement rates had lower correlation levels due to sparse data (r ≥ 0.532). Pairing radiologists with similar FPR characteristics (Sweden: 4.50% [95% confidence interval: 4.46%–4.54%], England: 5.51% [5.47%–5.56%], Norway: 8.03% [7.99%–8.07%]) resulted in significantly lower FPR than with random pairing (Sweden: 4.74% [4.70%–4.78%], England: 5.76% [5.71%–5.80%], Norway: 8.30% [8.26%–8.34%]), reducing examinations sent to consensus/arbitration while the TPR did not change significantly. Other pairing strategies resulted in equal or worse performance than random pairing. Conclusions - Logistic regression models accurately predicted screening mammography assessments and helped explore different radiologist pairing strategies. Pairing readers with similar modeled FPR characteristics reduced the number of examinations unnecessarily sent to consensus/arbitration without significantly compromising the TPR

Paramedic assessment of pain in the cognitively impaired adult patient

<p>Abstract</p> <p>Background</p> <p>Paramedics are often a first point of contact for people experiencing pain in the community. Wherever possible the patient's self report of pain should be sought to guide the assessment and management of this complaint. Communication difficulty or disability such as cognitive impairment associated with dementia may limit the patient's ability to report their pain experience, and this has the potential to affect the quality of care. The primary objective of this study was to systematically locate evidence relating to the use of pain assessment tools that have been validated for use with cognitively impaired adults and to identify those that have been recommended for use by paramedics.</p> <p>Methods</p> <p>A systematic search of health databases for evidence relating to the use of pain assessment tools that have been validated for use with cognitively impaired adults was undertaken using specific search criteria. An extended search included position statements and clinical practice guidelines developed by health agencies to identify evidence-based recommendations regarding pain assessment in older adults.</p> <p>Results</p> <p>Two systematic reviews met study inclusion criteria. Weaknesses in tools evaluated by these studies limited their application in assessing pain in the population of interest. Only one tool was designed to assess pain in acute care settings. No tools were located that are designed for paramedic use.</p> <p>Conclusion</p> <p>The reviews of pain assessment tools found that the majority were developed to assess chronic pain in aged care, hospital or hospice settings. An analysis of the characteristics of these pain assessment tools identified attributes that may limit their use in paramedic practice. One tool - the Abbey Pain Scale - may have application in paramedic assessment of pain, but clinical evaluation is required to validate this tool in the paramedic practice setting. Further research is recommended to evaluate the Abbey Pain Scale and to evaluate the effectiveness of paramedic pain management practice in older adults to ensure that the care of all patients is unaffected by age or disability.</p

Adaptation and visual search in mammographic images

Abstract Radiologists face the visually challenging task of detecting suspicious features within the complex and noisy backgrounds characteristic of medical images. We used a search task to examine whether the salience of target features in x-ray mammograms could be enhanced by prior adaptation to the spatial structure of the images. The observers were not radiologists, and thus had no diagnostic training with the im-ages. The stimuli were randomly selected sections from nor-mal mammograms previously classified with BIRADS Den-sity scores of Bfatty ^ versus Bdense, ^ corresponding to differ-ences in the relative quantities of fat versus fibroglandular tissue. These categories reflect conspicuous differences in vi-sual texture, with dense tissue being more likely to obscure lesion detection. The targets were simulated masses corre-sponding to bright Gaussian spots, superimposed by adding the luminance to the background. A single target was random-ly added to each image, with contrast varied over five levels so that they varied from difficult to easy to detect. Reaction times were measured for detecting the target location, before or after adapting to a gray field or to random sequences of a different set of dense or fatty images. Observers were faster at detecting the targets in either dense or fatty images after adapting to the specific background type (dense or fatty) that they were searching within. Thus, the adaptation led to a facilitation of search performance that was selective for the background tex-ture. Our results are consistent with the hypothesis that adap-tation allows observers to more effectively suppress the spe-cific structure of the background, thereby heightening visual salience and search efficiency