The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.
These settings, adjusted for x-ray tube loading limits and clinically acceptable image quality, should provide a useful option for optimizing patient dose to image quality in cardiac x-ray imaging. The same optimal x-ray beam spectra were found for both the tin and copper details, suggesting that iodine contrast based imaging and visualization of interventional devices could potentially be optimized for dose using similar x-ray beam spectra.
Background: Physicians use fixed C-arm fluoroscopy equipment with many interventional radiological and cardiological procedures. The associated effective dose to a patient is generally considered low risk, as the benefit-risk ratio is almost certainly highly favorable. However, X-ray-induced skin injuries may occur due to high absorbed patient skin doses from complex fluoroscopically guided interventions (FGI). Suitable action levels for patient-specific follow-up could improve the clinical practice.There is a need for a refined metric regarding follow-up of X-ray-induced patient injuries and the knowledge gap regarding skin dose-related patient information from fluoroscopy devices must be filled. The most useful metric to indicate a risk of erythema, epilation or greater skin injury that also includes actionable information is the peak skin dose, that is, the largest dose to a region of skin. Materials and Methods: The report is based on a comprehensive review of best practices and methods to estimate peak skin dose found in the scientific literature and situates the importance of the Digital Imaging and Communication in Medicine (DICOM) standard detailing pertinent information contained in the Radiation Dose Structured Report (RDSR) and DICOM image headers for FGI devices. Furthermore, the expertise of the task group members and consultants have been used to bridge and discuss different methods and associated available DICOM information for peak skin dose estimation.
Objectives: This study aimed to determine the impact on radiation dose and image quality of a new cardiac interventional X-ray system for trans-catheter aortic valve implantation (TAVI) patients compared to the previously-used cardiac X-ray system. Methods: Patient dose and image data were retrospectively collected from a Philips AlluraClarity (new) and Siemens Axion Artis (reference) X-ray system. Patient dose area product (DAP) and fluoroscopy duration of 41 patient cases from each X-ray system were compared using a Wilcoxon test. Ten patient aortograms from each X-ray system were scored by 32 observers on a continuous scale to assess the clinical image quality at the given phase of the TAVI procedure. Scores were dichotomised by acceptability and analysed using a Chi-squared test. Results: Significant reductions in patient dose (p ,, 0.001) were found for the new system with no significant change in fluoroscopy duration (p 5 0.052); procedure DAP reduced by 55%, fluoroscopy DAP by 48% and "cine" acquisition DAP by 61%. There was no significant difference between image quality scores of the two X-ray systems (p 5 0.06). Conclusions:The new cardiac X-ray system demonstrated a very significant reduction in patient dose with no loss of clinical image quality. Advances in Knowledge: The huge growth of TAVI may impact on the radiation exposure of cardiac patients and particularly on operators including anaesthetists; cumulative exposure of interventional cardiologists performing high volume TAVI over 30-40 years may be harmful. The Phillips Clarity upgrade including improved image enhancement and optimised X-ray settings significantly reduced radiation without reducing clinically acceptable image quality.
This work presents a methodology to optimize the selection of multiple parameter levels of an image acquisition, degradation, or post-processing process applied to stimuli intended to be used in a subjective image or video quality assessment (QA) study. It is known that processing parameters (e.g. compression bit-rate) or technical quality measures (e.g. peak signal-to-noise ratio, PSNR) are often non-linearly related to human quality judgment, and the model of either relationship may not be known in advance. Using these approaches to select parameter levels may lead to an inaccurate estimate of the relationship between the parameter and subjective quality judgments -the system's quality model. To overcome this, we propose a method for modeling the relationship between parameter levels and perceived quality distances using a paired comparison parameter selection procedure in which subjects judge the perceived similarity in quality. Our goal is to enable the selection of evenly sampled parameter levels within the considered quality range for use in a subjective QA study. This approach is tested on two applications: (1) selection of compression levels for laparoscopic surgery video QA study, and (2) selection of dose levels for an interventional X-ray QA study. Subjective scores, obtained from the follow-up single stimulus QA experiments conducted with expert subjects who evaluated the selected bit-rates and dose levels, were roughly equidistant in the perceptual quality space -as intended. These results suggest that a similarity judgment task can help select parameter values corresponding to desired subjective quality levels.
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