Mobile Dermatoscope Type in Patient-Performed Teledermoscopy

To assess the performance of routine cone-beam computed tomography (CBCT) quality assurance (QA) at predicting and diagnosing clinically recognizable linac CBCT image quality issues. Monthly automated linac CBCT image quality QA data were acquired on eight Varian linacs (Varian Medical Systems, Palo Alto, CA) using the CATPHAN 500 series phantom (The Phantom Laboratory, Inc., Greenwich, NY) and Total QA software (Image Owl, Inc., Greenwich, NY) over 34months between July 2014 and May 2017. For each linac, the following image quality metrics were acquired: geometric distortion, spatial resolution, Hounsfield Unit (HU) constancy, uniformity, and noise. Quality control (QC) limits were determined by American Association of Physicists in Medicine (AAPM) expert consensus documents Task Group (TG-142 and TG-179) and the manufacturer acceptance testing procedure. Clinically recognizable CBCT issues were extracted from the in-house incident learning system (ILS) and service reports. The sensitivity and specificity of CATPHAN QA at predicting clinically recognizable image quality issues was investigated. Sensitivity was defined as the percentage of clinically recognizable CBCT image quality issues that followed a failing CATPHAN QA. Quality assurance results are categorized as failing if one or more image quality metrics are outside the QC limits. The specificity of CATPHAN QA was defined as one minus the fraction of failing CATPHAN QA results that did not have a clinically recognizable CBCT image quality issue in the subsequent month. Receiver operating characteristic (ROC) curves were generated for each image quality metric by plotting the true positive rate (TPR) against the false-positive rate (FPR). Over the study period, 18 image quality issues were discovered by clinicians while using CBCT to set up the patient and five were reported prior to x-ray tube repair. The incidents ranged from ring artifacts to uniformity problems. The sensitivity of the TG-142/179 limits was 17% (four of the prior monthly QC tests detected a clinically recognizable image quality issue). The area under the curve (AUC) calculated for each image quality metric ROC curve was: 0.85 for uniformity, 0.66 for spatial resolution, 0.51 for geometric distortion, 0.56 for noise, 0.73 for HU constancy, and 0.59 for contrast resolution. Automated monthly QA is not a good predictor of CBCT image quality issues. Of the available metrics, uniformity has the best predictive performance, but still has a high FPR and low sensitivity. The poor performance of CATPHAN QA as a predictor of image quality problems is partially due to its reliance on region-of-interest (ROI) based algorithms and a lack of a global algorithm such as correlation. The manner in which image quality issues occur (trending toward failure or random) is still not known and should be studied further. CBCT image quality QA should be adapted based on how CBCT is used clinically.

Various impacts on image qualities are described from display panels, multi‐screens, viewing distances, viewing angles, ambient light, viewer's human behavior, and image characteristics for the integration of image enhancement methods with efficient programmability. Image quality issues on LCD BLU dimming are discussed as a case study.

Purpose. To illustrate image quality issues that may impact quantitative measurements used to assess treatment response in a multi-site clinical trial. Background. Quantitative longitudinal measurements on breast MRI are used to assess patient response to neoadjuvant treatment in the multi-site I-SPY 2 TRIAL. A standardized MR protocol is distributed to sites prior to site initiation. Previous work presented image quality issues1, which may affect measurements that rely on automated computerized methods. Functional tumor volume (FTV), a primary imaging biomarker monitoring treatment response, was more predictive of pathological complete response for protocol adherent exams compared to non-adherent exams2. Image quality may also impact background parenchymal enhancement (enhancement level of normal fibroglandular tissue), which is being investigated as a secondary imaging biomarker. This presentation will show example images demonstrating the challenges of quantitative MR analysis in a multi-site clinical trial. Image quality issues include:. Motion. Motion due to patient movement during the MRI may result in skin, fat, and breast tissue being poorly defined and blurry. Mis-registration between pre- and post-contrast can cause errors in measurements. Threshold variation. For image processing, two signal intensity thresholds are applied to pixels within the region of interest (ROI), which may be adjusted when less than 50% of the tumor is segmented. The percent enhancement threshold is lowered when poorly enhancing areas of tumor are not segmented. The background threshold is lowered when a bright pixel within the ROI of the pre-contrast images causes relatively darker areas of the tumor to be poorly segmented. Scan duration variation. Scan duration is the time required to scan each T1-weighted acquisition phase. Scan duration variations occur if scan duration is outside the specified protocol range or differs from the scan duration of the patient’s baseline MRI. Longer scan duration may result in overestimation of FTV. Field of View. Field of view (FOV) is the anatomical area being imaged and is directly related to spatial resolution, which plays a key role in the FTV measurement. Incorrect FTV monitoring can occur if FOV is inconsistent between visits, outside the specified protocol range, or is overlarge for the patient. Discussion. The image quality required for measurements used to assess treatment response in I-SPY 2 differs from the image quality that is acceptable for diagnostic evaluation, including BIRADS category, longest diameter measurements, and visual assessment of washout characteristics. In I-SPY 2, 21 sites use a variety of MRI platforms. Since quantitative measurements are increasingly used to monitor treatment response and guide clinical decision-making, high quality images are essential. Strategies must be implemented to minimize imaging issues and further refine the quantitative measurements. Ongoing studies are examining the impact of image quality factors on accuracy of treatment response prediction. References. 1.Gibbs J et al. Abstract PS11-08: Operational standardization and quality assurance yield high acceptance rate for breast MRI in the I-SPY 2 TRIAL. Poster Session Abstracts. American Association for Cancer Research; 2021. 2.Onishi N et al. Impact of MRI Protocol Adherence on Prediction of Pathological Complete Response in the I-SPY 2 Neoadjuvant Breast Cancer Trial. Tomography. 2020 Jun. Table 1.Image quality issues observed in 1,749 I-SPY 2 MRIs submitted June 2019 to June 2021Image Quality IssueExams (percentage of total)Motion568 (32%)Threshold variation104 (6%)Scan duration variation83 (5%)FOV229 (13%) Citation Format: Teffany Joy Bareng, Jessica E Gibbs, Natsuko Onishi, David C Newitt, Barbara LeStage, I-SPY 2 TRIAL Imaging Working Group, I-SPY 2 TRIAL Coordinators, Nola M Hylton. Challenges of achieving high image quality on breast MRI for quantitative measurements in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-04.

To subjectively assess radiographs obtained with photostimulable phosphor (PSP) plates exposed to clinical levels of ambient light prior to read-out to potentially set a safe limit for acceptable image quality. Six dental regions of a dry human skull were X-rayed using PSP plates from VistaScan and Express under four exposure times: 0.1, 0.2, 0.32, and 0.4 s. Before read-out, the PSP plates were exposed to ambient light for 0, 5, 10, 30, 60, and 90 s. Six observers were asked to classify the 288 resulting radiographs as acceptable or unacceptable based on the identification of anatomical structures and global image quality. The number of answers classifying radiographs as unacceptable was used to calculate a rejection rate; a pairwise comparison for better image quality was further conducted among radiographs considered acceptable. Reproducibility was tested by having 25% of all experimental groups reassessed. Intra- and interobserver agreement ranged from 0.87 to 1.00 and from 0.81 to 0.92, respectively. Exposure of PSP plates to ambient light increased rejection rates mostly as of 10 s. In the pairwise comparison, subtle differences were observed between radiographs obtained with PSP plates not exposed and those exposed to ambient light for 5 s. Ambient light exposure of PSP plates impairs the image quality of radiographs. A safe limit of ambient light exposure of 5 s for VistaScan and Express should be considered. Ambient light exposure of PSP plates within safe limits can avoid retakes and reduce unnecessary patient exposure to X-rays.

Effect of 3 different disinfection methods on the image quality of photostimulable phosphor plates

Image quality in mobile displays is considerably influenced by ambient light. In the daylight condition, images on mobile displays are darkly perceived by the human visual system due to the limited dynamic range of display, which causes loss of luminance and details. In this paper, we propose readability enhancement of displayed images under ambient light by enhancing both luminance and details. We design a weighted optimization framework, which contains the data term for luminance enhancement and the gradient term for detail enhancement. In the data term, we use an ambient light nonlinear intensity-transfer function considering display properties, ambient light, and image contents. In the gradient term, we employ a threshold versus intensity adaptation function based on the degree of ambient light adaptation to compensate for gradient distortions. We solve the optimization framework using a numerical solver and achieve image enhancement in displayed images. Experimental results demonstrate that the proposed method significantly improves readability of mobile displays under ambient light by enhancing luminance and details of images.

Cutaneous melanoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†.

Digital chest radiography: quality assurance.

BackgroundThe Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS.MethodsThe cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed.ResultsQualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good.When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality.ConclusionsIt is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality.Trial registrationClinicalTrials.gov, NCT03188172, Registered on 15 June 2017.Critical relevance statementGood overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines.Key points• A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered.• Quantitative image quality metrics were developed and correlated with radiological assessment.• SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment.Graphical

P024] The enlightening of radiologists

Tumor Grade Improves the Prognostic Ability of American Joint Committee on Cancer Stage in Patients With Penile Carcinoma

The quality of an image affects its utility for various analytic tasks. For security screening of baggage, the quality of the X-ray image will affect the ability of human operators to detect and identify relevant objects. This paper presents a recent protocol aimed at the development of a perception-based standard for assessing the quality of x-ray images of baggage. This standard provides a quantitative method for assessing x-ray image quality from the display, as presented to security officers. Furthermore, it provides a framework for understanding how different variables (belt speed, scanner orientation, degree of clutter in the image, ambient lighting, etc) affect the quality of images taken from X-Ray scanners at security checkpoints. The paper presents the protocol that was performed, summarizes the analysis and findings, and presents a method for employing the results to assess performance of a scanner system.

Cells to Surgery Quiz: May 2019

Digital cameras are increasingly used in automotive applications. As these cameras are integrated into active safety systems, image quality becomes ever more important. The captured image information is limited not only by the sensitivity and signal-to-noise ratio of the image sensor, but also by such image capture conditions as ambient lighting, camera-object distance, and relative camera-object velocity. For example, the detection of a pedestrian at night might be further impeded by the headlights of oncoming traffic. Image quality performance characterizes the ability of the imaging system to capture vital image information under application-typical capture conditions. Characterizing objective image quality performance requires clearly defined image quality attributes and metrics. This paper introduces the basic concepts of objective and subjective image quality, reviews existing image quality metrics and standards that have been developed for digital still and video applications, and explores their applicability for automotive uses where image information is interpreted by a human observer or machine vision application. The automotive photospace is introduced as a useful tool to characterize the automotive image capture conditions, and distinguish them from other still and video applications. As automotive imaging becomes more widespread, early standardization of image quality is important. This will enable automotive camera suppliers and the automotive industry to communicate in a common language when specifying imaging systems so that sufficient image quality under application conditions is ensured.

We present a motion-resistant three-wavelength spatial frequency domain imaging (SFDI) system with ambient light suppression using an 8-tap complementary metal-oxide semiconductor (CMOS) image sensor (CIS) developed at Shizuoka University. The system addresses limitations in conventional SFDI systems, enabling reliable measurements in challenging imaging scenarios that are closer to real-world conditions. Our study demonstrates a three-wavelength SFDI system based on an 8-tap CIS. We demonstrate and evaluate the system's capability of mitigating motion artifacts and ambient light bias through tissue phantom reflectance experiments and in vivo volar forearm experiments. We incorporated the Hilbert transform to reduce the required number of projected patterns per wavelength from three to two per spatial frequency. The 8-tap image sensor has eight charge storage diodes per pixel; therefore, simultaneous image acquisition of eight images based on multi-exposure is possible. Taking advantage of this feature, the sensor simultaneously acquires images for planar illumination, sinusoidal pattern projection at three wavelengths, and ambient light. The ambient light bias is eliminated by subtracting the ambient light image from the others. Motion artifacts are suppressed by reducing the exposure and projection time for each pattern while maintaining sufficient signal levels by repeating the exposure. The system is compared to a conventional SFDI system in tissue phantom experiments and then in vivo measurements of human volar forearms. The 8-tap image sensor-based SFDI system achieved an acquisition rate of 9.4 frame sets per second, with three repeated exposures during each accumulation period. The diffuse reflectance maps of three different tissue phantoms using the conventional SFDI system and the 8-tap image sensor-based SFDI system showed good agreement except for high scattering phantoms. For the in vivo volar forearm measurements, our system successfully measured total hemoglobin concentration, tissue oxygen saturation, and reduced scattering coefficient maps of the subject during motion (16.5cm/s) and under ambient light (28.9lx), exhibiting fewer motion artifacts compared with the conventional SFDI. We demonstrated the potential for motion-resistant three-wavelength SFDI system with ambient light suppression using an 8-tap CIS.