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Haddad L, Saleme H, Howarth N, Tack D. Reject Analysis in Digital Radiography and Computed Tomography: A Belgian Imaging Department Case Study. J Belg Soc Radiol 2023; 107:100. [PMID: 38144871 PMCID: PMC10742225 DOI: 10.5334/jbsr.3259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/25/2023] [Indexed: 12/26/2023] Open
Abstract
Objective Reject analysis is usually performed in digital radiography (DR) for quality assurance. Data for computed tomography (CT) rejects remains sparse. The aim of this study is to help provide a straightforward benchmark for reject analysis of both DR and CT. Materials and methods This retrospective observational study included 107,277 DR and 20,659 CT during 18 months in a tertiary care center. Rejected acquisitions were retrieved by Dose Archiving and Communication System (DACS). The DR and CT reject analysis included reject rates, reasons for rejection and supplementary radiation dose associated with these rejects. Results 8,904 rejected DR and 514 rejected CT were retrieved. The DR reject rate was 8.3% whereas the CT reject rate was 2.5%. The cumulative effective dose (ED) of DR rejects was 377.3 mSv while the cumulative ED of CT rejects was 1267.4 mSv. The major reason for rejects was positioning for both DR (61%) and CT (44%). Conclusion This study helps constitute a simple reproducible method to analyze both DR and CT rejects simultaneously. Although CT rejects are less often monitored than DR rejects, the radiation dose associated with CT rejects is much higher, which emphasizes the need to systematically monitor both DR and CT rejects. Investigating the reasons and the most frequently rejected examinations gives an opportunity for improvement of imaging techniques in cooperation with technologists.
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Affiliation(s)
| | - Hanna Saleme
- Department of Radiology, Epicura La Madeleine, Rue Maria Thomée, 1, 7800 Ath, Belgium
| | - Nigel Howarth
- Department of Radiology, Hislanden –Clinique des Grangettes, 7 Chemin des Grangettes, 1224 Chênes-Bougeries, Switzerland
| | - Denis Tack
- Department of Radiology, Epicura La Madeleine, Rue Maria Thomée, 1, 7800 Ath, Belgium
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Wei S, Qiu R, Pu Y, Hu A, Niu Y, Wu Z, Zhang H, Li J. A semi-supervised learning-based quality evaluation system for digital chest radiographs. Med Phys 2023; 50:6789-6800. [PMID: 37543992 DOI: 10.1002/mp.16663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/03/2023] [Accepted: 07/20/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential step in medical examinations. However, manual evaluation can be time-consuming, labor-intensive, and prone to interobserver differences, making it less reliable. PURPOSE To alleviate the workload of radiographic technologists and enhance the efficiency of radiograph quality evaluation, it is crucial to develop rapid and reliable quality evaluation methods and establish a set of quantitative evaluation standards. To address this, we have proposed a quality evaluation system for digital radiographs that utilizes deep learning techniques to achieve fast and precise evaluation. METHODS The evaluation of frontal chest radiograph quality involves assessing patient positioning through semantic segmentation and foreign body detection. For lung, scapula, and clavicle segmentation in digital chest radiographs, a residual connection-based convolutional neural network π-ResUNet, was proposed. Criteria for patient positioning evaluation were established based on the segmentation and manual evaluation results. A convolutional neural network, FasterRCNN, was utilized to detect and localize foreign bodies in digital chest radiographs. To enhance the performance of both neural networks, a semi-supervised learning (SSL) strategy was implemented by incorporating a consistency loss that leverages a large number of unlabeled digital radiographs. We also trained the network using the fully supervised learning (FSL) strategy and compared their performance on the test set. The ChestXRay-14 and object-CXR datasets were used throughout the process. RESULTS By comparing with the manual annotation, the proposed network, trained using the SSL method, achieved a high Dice similarity coefficient (DSC) of 0.96, 0.88, and 0.88 for lung, scapula, and clavicle segmentation, respectively, outperforming the network trained with the FSL method. In addition, for foreign body detection, the proposed SSL method was superior to the FSL method, achieving an AUC (Area under receiver operating characteristic curve, Area under ROC curve) of 0.90 and an FROC (Free-response ROC) of 0.77 on the test dataset. CONCLUSIONS The experimental results show that our proposed system is well-suited for radiograph quality evaluation, with the semi-supervised learning method further improving the network's performance. The proposed method can evaluate the quality of a chest radiograph from two aspects-patient positioning and foreign body detection-within 1 s, offering a promising tool in radiograph quality evaluation.
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Affiliation(s)
- Shuoyang Wei
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
- Department of Radiotherapy, Peking Union Medical College Hospital, Beijing, China
| | - Rui Qiu
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Yanheng Pu
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Ankang Hu
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Yantao Niu
- Beijing Tongren Hospital, CMU, Beijing, China
| | - Zhen Wu
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Hui Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Junli Li
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
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Bantas G, Sweeney R, Mdletshe S. Digital radiography reject analysis: A comparison between two radiology departments in New Zealand. J Med Radiat Sci 2023; 70:137-144. [PMID: 36657740 PMCID: PMC10258640 DOI: 10.1002/jmrs.654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION Image reject analysis (RA) in direct digital radiography (DDR) is an important quality indicator tool. Analysis of rejected images is a component of quality assurance (QA) programmes, with the overall aim of reducing patient radiation dose. This study aimed to compare differences in image rejection rates (RR) and the reasons for rejection between two radiology departments. METHODS A retrospective quantitative descriptive study of images performed across the two radiology departments (RAD 1 and RAD 2) acquired with DDR systems between the beginning of February and the end of May 2021 was undertaken. Collected data included the medical imaging technologist (MIT) selection of image rejection reasons for different anatomic regions and compared between the two radiology departments. RESULTS A total of 47,046 images and 29,279 images were acquired at RAD 1 and RAD 2, respectively, with an overall image rejection rate of 7.86% at RAD 1 and 5.91% at RAD 2. The primary reason for image rejections was positioning errors, 79.4% and 77.3% recorded at RAD 1 and RAD 2, respectively. Significant differences were demonstrated between the two radiology departments for image rejection rates and selected reasons for rejection for most anatomical body groups. CONCLUSION The implementation of image RA remains a key part of QA in radiology departments utilising DDR systems. This study recommends interventions based on image RRs for examinations taking into consideration the department-specific variations and imaging protocols used.
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Affiliation(s)
- Gabriela Bantas
- Department of Anatomy and Medical Imaging, School of Medical and Health SciencesThe University of AucklandAucklandNew Zealand
| | - Rhonda‐Joy Sweeney
- Department of Anatomy and Medical Imaging, School of Medical and Health SciencesThe University of AucklandAucklandNew Zealand
| | - Sibusiso Mdletshe
- Department of Anatomy and Medical Imaging, School of Medical and Health SciencesThe University of AucklandAucklandNew Zealand
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Long Z, Walz-Flannigan AI, Littrell LA, Schueler BA. Technical note: Four-year experience with utilization of DICOM metadata analytics in clinical digital radiography practice. Med Phys 2023; 50:831-836. [PMID: 36542418 DOI: 10.1002/mp.16170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/13/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Digital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization. PURPOSE This technical note aims to report the 4-year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution. METHODS Thirty-eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization. RESULTS For systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post-acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior-anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, eμ , based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques. CONCLUSIONS This analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data-driven quality standardization and optimization.
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Affiliation(s)
- Zaiyang Long
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Beth A Schueler
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Akhter Y, Singh R, Vatsa M. AI-based radiodiagnosis using chest X-rays: A review. Front Big Data 2023; 6:1120989. [PMID: 37091458 PMCID: PMC10116151 DOI: 10.3389/fdata.2023.1120989] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 01/06/2023] [Indexed: 04/25/2023] Open
Abstract
Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.
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Chen X, Deng Q, Wang Q, Liu X, Chen L, Liu J, Li S, Wang M, Cao G. Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks. Front Public Health 2022; 10:891766. [PMID: 35558524 PMCID: PMC9087032 DOI: 10.3389/fpubh.2022.891766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard. Materials and Methods A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance. Results The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively. Conclusion This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.
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Affiliation(s)
- Xiao Chen
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qingshan Deng
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiang Wang
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xinmiao Liu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jinjin Liu
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuangquan Li
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guoquan Cao
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Elameer AS, Jaber MM, Abd SK. Radiography image analysis using cat swarm optimized deep belief networks. JOURNAL OF INTELLIGENT SYSTEMS 2021; 31:40-54. [DOI: 10.1515/jisys-2021-0172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
Abstract
Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.
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Affiliation(s)
- Amer S. Elameer
- Biomedical Informatics College, University of Information Technology and Communications (UOITC) , Baghdad , Iraq
| | - Mustafa Musa Jaber
- Department of Computer Science, Dijlah University Collage , Baghdad , 00964 , Iraq
- Department of Computer Science, Al-Turath University College , Baghdad , Iraq
| | - Sura Khalil Abd
- Department of Computer Science, Dijlah University Collage , Baghdad , 00964 , Iraq
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Rose S, Viggiano B, Bour R, Bartels C, Kanne JP, Szczykutowicz TP. Applying a New CT Quality Metric in Radiology: How CT Pulmonary Angiography Repeat Rates Compare Across Institutions. J Am Coll Radiol 2021; 18:962-968. [PMID: 33741373 DOI: 10.1016/j.jacr.2021.02.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To quantify overall CT repeat and reject rates at five institutions and investigate repeat and reject rates for CT pulmonary angiography (CTPA). METHODS In this retrospective study, we apply an automated repeat rate analysis algorithm to 103,752 patient examinations performed at five institutions from July 2017 to August 2019. The algorithm identifies repeated scans for specific scanner and protocol combinations. For each institution, we compared repeat rates for CTPA to all other CT protocols. We used logistic regression and analysis of deviance to compare CTPA repeat rates across institutions and size-based protocols. RESULTS Of 103,752 examinations, 1,447 contained repeated helical scans (1.4%). Overall repeat rates differed across institutions (P < .001) ranging from 0.8% to 1.8%. Large-patient CTPA repeat rates ranged from 3.0% to 11.2% with the odds (95% confidence intervals) of a repeat being 4.8 (3.5-6.6) times higher for large- relative to medium-patient CTPA protocols. CTPA repeat rates were elevated relative to all other CT protocols at four of five institutions, with strong evidence of an effect at two institutions (P < .001 for each; odds ratios: 2.0 [1.6-2.6] and 6.2 [4.4-8.9]) and somewhat weaker evidence at the others (P = .005 and P = 0.011; odds ratios: 2.2 [1.3-3.8] and 3.7 [1.5-9.1], respectively). Accounting for size-based protocols, CTPA repeat rates differed across institutions (P < .001). DISCUSSION The results indicate low overall repeat rates (<2%) with CTPA rates elevated relative to other protocols. Large-patient CTPA rates were highest (eg, 11.2% at one institution). Differences in repeat rates across institutions suggest the potential for quality improvement.
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Affiliation(s)
- Sean Rose
- Department of Medical Physics, University of Wisconsin Madison, Madison, Wisconsin
| | - Ben Viggiano
- Department of Radiology, University of Wisconsin Madison, Madison, Wisconsin
| | - Robert Bour
- Department of Radiology, University of Wisconsin Madison, Madison, Wisconsin
| | - Carrie Bartels
- Department of Radiology, University of Wisconsin Madison, Madison, Wisconsin
| | - Jeffery P Kanne
- Vice Chair of Quality and Safety, Department of Radiology, University of Wisconsin, Madison, Wisconsin
| | - Timothy P Szczykutowicz
- Department of Medical Physics, University of Wisconsin Madison, Madison, Wisconsin; Department of Radiology, University of Wisconsin Madison, Madison, Wisconsin; Department of Biomedical Engineering, University of Wisconsin Madison, Madison, Wisconsin.
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Abstract
OBJECTIVE. Repeated imaging is an unnecessary source of patient radiation exposure, a detriment to patient satisfaction, and a waste of time and money. Although analysis of rates of repeated and rejected images is mandated in mammography and recommended in radiography, the available data on these rates for CT are limited. MATERIALS AND METHODS. In this retrospective study, an automated repeat-reject rate analysis algorithm was used to quantify repeat rates from 61,102 patient examinations obtained between 2015 and 2018. The algorithm used DICOM metadata to identify repeat acquisitions. We quantified rates for one academic site and one rural site. The method allows scanner-, technologist-, protocol-, and indication-specific rates to be determined. Positive predictive values and sensitivity were estimated for correctly identifying and classifying repeat acquisitions. Repeat rates were compared between sites to identify areas for targeted technologist training. RESULTS. Of 61,102 examinations, 4676 instances of repeat scanning contributed excess radiation dose to patients. Estimated helical overlap repeat rates were 1.4% (95% CI, 1.2-1.6%) for the rural site and 1.1% (95% CI, 1.0-1.2%) for the academic site. Significant differences in rates of repeat imaging required because of bolus tracking (11.6% vs 4.3%; p < 0.001) and helical extension (3.3% vs 1.8%; p < 0.001) were observed between sites. Positive predictive values ranged from 91% to 99% depending on the reason for repeat imaging and site location. Sensitivity of the algorithm was 92% (95% CI, 87-96%). Rates tended to be highest for emergent imaging procedures and exceeded 9% for certain protocols. CONCLUSION. Our multiinstitutional automated quantification of repeat rates for CT provided a useful metric for unnecessary radiation exposure and identification of technologists in need of training.
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Development of a tool to aid the radiologic technologist using augmented reality and computer vision. Pediatr Radiol 2018; 48:141-145. [PMID: 28866805 DOI: 10.1007/s00247-017-3968-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/26/2017] [Accepted: 08/15/2017] [Indexed: 10/18/2022]
Abstract
This technical innovation describes the development of a novel device to aid technologists in reducing exposure variation and repeat imaging in computed and digital radiography. The device consists of a color video and depth camera in combination with proprietary software and user interface. A monitor in the x-ray control room displays the position of the patient in real time with respect to automatic exposure control chambers and image receptor area. The thickness of the body part of interest is automatically displayed along with a motion indicator for the examined body part. The aim is to provide an automatic measurement of patient thickness to set the x-ray technique and to assist the technologist in detecting errors in positioning and motion before the patient is exposed. The device has the potential to reduce the incidence of repeat imaging by addressing problems technologists encounter daily during the acquisition of radiographs.
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Technologist-Directed Repeat Musculoskeletal and Chest Radiographs: How Often Do They Impact Diagnosis? AJR Am J Roentgenol 2017; 209:1297-1301. [PMID: 28898128 DOI: 10.2214/ajr.17.18030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Radiologic technologists may repeat images within a radiographic examination because of perceived suboptimal image quality, excluding these original images from submission to a PACS. This study assesses the appropriateness of technologists' decisions to repeat musculoskeletal and chest radiographs as well as the utility of repeat radiographs in addressing examinations' clinical indication. MATERIALS AND METHODS We included 95 musculoskeletal and 87 chest radiographic examinations in which the technologist repeated one or more images because of perceived image quality issues, rejecting original images from PACS submission. Rejected images were retrieved from the radiograph unit and uploaded for viewing on a dedicated server. Musculoskeletal and chest radiologists reviewed rejected and repeat images in their timed sequence, in addition to the studies' remaining images. Radiologists answered questions regarding the added value of repeat images. RESULTS The reviewing radiologist agreed with the reason for rejection for 64.2% of musculoskeletal and 60.9% of chest radiographs. For 77.9% and 93.1% of rejected radiographs, the clinical inquiry could have been satisfied without repeating the image. For 75.8% and 64.4%, the repeated images showed improved image quality. Only 28.4% and 3.4% of repeated images were considered to provide additional information that was helpful in addressing the clinical question. CONCLUSION Most repeated radiographs (chest more so than musculoskeletal radiographs) did not add significant clinical information or alter diagnosis, although they did increase radiation exposure. The decision to repeat images should be made after viewing the questionable image in context with all images in a study and might best be made by a radiologist rather than the performing technologist.
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Walz-Flannigan A. Features to Consider When Selecting New Digital Radiology Systems. J Am Coll Radiol 2017; 14:528-530. [DOI: 10.1016/j.jacr.2016.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 12/08/2016] [Accepted: 12/12/2016] [Indexed: 11/16/2022]
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