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Image quality control in digital radiology

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  • Save American Journal of Biomedical Engineer ing 2013, 3(6A): 8-14 DOI: 10.5923/s.ajbe.201310.02 Image Quality Control in Digital Radiology Susana Cândido1, Luís Pedro Vie ira Ribe iro2,*, Anabe la Magalhães Ribe iro3, António Fernando Lagem Abrantes4, João Pedro Pinheiro5, Rui Pedro Pereira Almeida6, Kevin Barros Azeve do7 1BSc, Algarve´s University Health School (ESSUAlg), Algarve, Portugal 2PhD, M ember of the Research Center of Sports and Physical Activity (CIDAF) of Coimbra University, Professor and M ember of the Center for Health Studies (CES) of Algarve´s University Health School (ESSUAlg), Algarve, Portugal 3M Sc, Coordinator Radiographer, Hospitalar Center of Barlavento Algarvio, Professor and M ember of the Center for Health Studies (CES) of Algarve´s University Health School (ESSUAlg), Algarve, Portugal 4PhD, M ember of the Research Center of Sociologic Studies of Lisbon´s Nova University (Cesnova), Professor and M ember of the Center for Health Studies (CES) of Algarve´s University Health School (ESSUAlg), Dir ector of the Radiology Department and professor at ESSUAlg, Algarve, Portugal 5Post-graduate, M Sc student at the National Public Health School, Professor of the Radiology Department at Algarve´s University Health School ( ESSUAlg), Algarve, Portugal 6Post-graduate, M ember of the Center for Health Studies (CES), PhD Student at Beira Interior University, Professor of the Radiology Department at Algarve´s University Health School (ESSUAlg), Algarve, Portugal 7Post-graduate, M ember of the Center for Health Studies (CES), PhD Student at Cranfield University, Professor of the Radiology Department at Algarve´s University Health School (ESSUAlg), Algarve, Portugal Abstract Image Quality Control is an impo rtant factor that contributes to the improvement of patient care and overall diagnostic accuracy. Our purpose was to elaborate Quality Control Charts and demonstrate the importance of image quality control in a radio logy department. A total of 37 rando m samples, composed of 30 x-ray exams each, were selected and analyzed. Primarily, data about image non-conformit ies were co mp iled to make three d istinct Quality Control Charts. Secondly, improvement and corrective actions were suggested. Our results allowed us to identify and account for different types of non-conformit ies found on x-ray images. This illustrates the importance and necessity for the imp lementation of an adequate Image Quality Control in Digital Radio logy. Keywords Image Quality, Quality Control, Control Chart, Non-conformit ies 1. Introduction Nowadays medical imag ing is essential and greatly used as an aid to medical d iagnoses. Through the evolution of med ical imaging equip ment there is an increased association with digital technology, the imp lementation of image quality control becoming essential. The radiological image must have the quality needed for med ical diagnosis. It should be obtained on the first attempt in order to avoid repetit ion of exams and the consequent exposure of patients to a higher dose of radiat ion. Technical errors are present when a radiological e xa m is not performed us ing th e ap p rop riat e p roto co ls , o r is inap p rop riat ely processed[1]. To assess the quality of th e rad io log ical images produced it is necessary to monitor the production process. This monitoring can be accomplished through the development of quality control charts previously defined, where nonconformities can be identified. Through research conducted on quality management and quality aof radiological images, the vast majority of studies performed are concerned with rad iological equip ment quality control[2][3] to assess their functionality[4]. It is very important for equipment to comply with legal requirements[4], but it is also essential that the quality control of the radiolog ical image enco mpasses the entire production process and therefore radiographer performance cannot be forgotten. Thus, there is a need to accurately assess whether the images produced, possess enough quality to be used in a clinical d iagnosis. The main objective is to study the quality control of dig ital radiographic images, through the elaboration of Quality Control Charts. As specific objectives: To assess the quality of digital radiographic images; To plan and suggest corrective and improvement actions; To contribute to the improvement of image quality and medical d iagnostic. * Corresponding author: (Luís Pedro Vieira Ribeiro) Published online at Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved 1.1. Research Questions 1. Which quality control chart best suits a radiology department? American Journal of Biomedical Engineer ing 2013, 3(6A): 8-14 9 2. W ill there be a large number of non-conformit ies? 3. What are the most common types of non-conformit ies? 4. Which corrective actions should be suggested to improve image quality? 2. Methods In this section we present the methodology followed in conducting this research. We characterize the type of study, its location, the sample, the instrument, ethical issues and the procedures for collecting, processing and analyzing data. 2.1. Type of Study This is a case study that employs quantitative methods as we have to quantify the non-conformit ies existing on digital radiological images. 2.2. Location All data was collected fro m the rad iology department of one major public hospital. 2.3. Sample The target population of this study comprises a series of conventional radiology exams. The set of exams concerns the follo wing anatomical structures: Thorax, Abdomen and Foot x-rays. The sample is a stratified random sample. The probabilistic sample is a selection procedure in which each element of the population has the same probability of being s elected [5]. The X-ray exams selected were performed to the following anatomical structures: Chest (postero-anterior (PA), anteroposterior (AP) and lateral incidence), abdomen (PA and AP) and Foot (AP and internal oblique). This study comprises 37 samples of conventional radiology exams composed of 30 elements each. 640 are chest exams, 340 abdomen exams and 130 foot exams. The criteria to classify an exam as Conform (C) or Non-conform (NC) were made using guidelines on how to perform and classify radiology exams[6][7]. We considered conform when they are present in the study, in other words, requirements were obtained (quality criteria), prev iously established, as listed below: • Image processing: Rad iographer ID, Side identification, Adequate Contrast; • Correct Patient Positioning: no overlapped structures, complete v iew fro m the study area; • Image artefacts: absence of metallic artefacts, clean Image Plate (IP), no movement artefacts. 2.6. Ethical Issues In accordance to institutional guidelines, the approval of this study was obtained fro mthe review board and the data of the patients selected were kept confidential. 2.7. Data Processing and Analysis The collected data was introduced in Microsoft Excel Office 2007, to calculate the control limits and construct the Control Charts by type p, np and c (Table 1). These charts classify a product as Conform or Non-conform and assess the overall image quality. Taking into account the sample size Type p Control Charts were chosen to determine the average percentage of non-conform exams and Type c Control Charts to identify the total number of Non-conform exams on each sample [8][9]. Table 1. Formulas for calculating the Control Charts Limits by type p, np and c 2.4. Variables The type of variable used is an attribute variable. 2.5. Instrument A specific checklist was made for this study. Contained in the header of the instrument sheet there is the following informat ion: date, hour and the number of the e xa m. On the instrument there is informat ion about: • Total nu mber of selected exams; • Type of exam; • Anatomica l St ructure; • Conform Image; • Non-conform Image. In the end of the sheet there is information regarding the total number o f conform and non-conform exams in the sample, as well as information about the rad iographer´s shift. The program used for image visualization was the “Magic Web System”. We also made determine the Warning Control Limits for the standard deviation (δ) – inferior and superior to the central limit (Table 2). Table 2. Formulas used for calculating the Warning Control Limits For the analysis of the sample distribution trend, 4 of 8 10 Susana Cândido et al.: Image Quality Control in Digital Radiology rules states by ISO 8258:1991 were selected. The quality process is out of statistical control when at least one of the following rules occurs: 1. Ru le nº1: One dot/point above the control limits; 2. Ru le nº2: 9 consecutive points on the same side of the Central Line; 3. Ru le nº3: 6 consecutive points upward or downward; 4. Ru le nº4: 14 point upwards or downwards alternately; 3. Results In this section we present the results of our research. Firstly, the data used to determine the Control Limits of the three control charts (Control Charts p, np and c). Secondly, we demonstrate the respective Control Graphics. Th irdly, the data related with the number of Non-conformity exams and the type of Non-conformities found. Graphic 2. Type np Control Charts • Type c Control Charts A Type c Control Chart was made based on the total number of Non-conformit ies of each sample. The values for the Central Line (CL), Superior Central Limit (SCL) and the Lo wer Central Limit were 24.43, 39.26 and 9.60 respectively (Graphic 3). 3.1. Control Charts Preparati on By grouping all data, we built several Control Charts: • Type p Control Chart A Type p Control Chart was made based on the proportion of Non-conformity exams of the samp les. To ensure the quality and statistical control of our samples, an adjusted Type p Control Chart was made in order to eliminate outliers. The values of the Central Limit (CL), Superior Central Limit (SCL) and Lo wer Central Limit (LCL) were set to 59%, 86% and 32% respectively (Graphic 1). The adjusted Type p Control Chart does not present any sample outside the Control Limits, indicating that the quality of our samp les is under statistic control. These values will be used as the standard Quality Control referential for Type p Control Charts. Graphic 3. Type c Control Chart There are no samp les outside the control limits (Graphic 3), therefore it can be used as the standard Quality Control referential for Type c Control Charts. 3.2. Anal ysis of x-ray Exams Collected Of all 1110 x-ray exams observed (37 samples, co mposed of 30 elements each) of the chest, abdomen and foot, there is a larger nu mber of Non-conform exams than Conform exams with 57% and 43% respectively (Table 3). Of the 640 chest x-rays analysed, 40% were were Conform and 60% were Non-conform. Of the 340 abdomen x-rays analysed, 46% are Conform and 54% are Non-conform. Relat ively to foot x-rays, 51% are Conform and 49% are Non-conform (Table 3). Graphic 1. Adjusted Type p Chart composed of 29 Samples • Type np Control Charts A Type np Control Chart was made based on the total number of non-conformity exams of each sample. The values for the Central Line (CL), Superior Central Limit (SCL) and the Lo wer Central Limit were 17, 23.79 and 10.21 respectively (Graphic 2). There are no samp les outside the control limits (Graphic 2), therefore it can be used as the standard Quality Control referential for Type np Control Charts. Table 3. Conform and Non-conform exams according to anatomical st ruct ure Anatomical Stru ctu re Chest Abdomen Feet Tot al Conform Exams Total Pe rcentag e 259 40% 156 46% 66 51% 481 43% Non-conformity Exams Total Pe rcentage 381 60% 184 54% 64 49% 629 57% As for the observed anatomical structures, the larger number of Non-conform exams correspond to chest x-rays (60%), followed by abdomen x-rays with 54% and foot x-rays with 49% (Table 3). American Journal of Biomedical Engineer ing 2013, 3(6A): 8-14 11 3.3. Anal ysis of the Type of Non-conformi ties Identified An x-ray exam was considered Non-conform when at least one or more images, that constitute the radiographic study, did not meet the quality criteria set for this study (Table 4). Non-conformit ies were g rouped into 3 groups: 1. Incomplete/incorrect image process; 2. Incorrect Positioning; 3. Artefacts; Image processing is divided into 2 subgroups – Non-conformit ies that do not affect image interpretation (i.e. “right” or “left” side marking and radiographer´s identification) and Non-conformit ies that affect image quality (i.e. inadequate contrast). For Incorrect Positioning the following Non-conformities were identified : overlapped anatomical structures, missing anatomical structures and patient rotation. The Artefacts group had the following Non-conformit ies: metallic artefacts, Image Plate (IP) dirt/grains and movement artefacts. Table 4. Classification of Non-conformities Types of Non-conformities Incomplete or incorrect processing Radiographer ID missing De signation Image was not archived properly. Collimation, contrast, side identification and radiographers id missing Image does not have ident ificat ion No side identification Inadequate Contrast Incorrect Patient Posi tioning Artefacts Right or Left sign missing Image is overexposed or underexposed compromising diagnosis At least on criteria for image evaluation related to anatomical structures is missing (i.e. overlapped structures, missing or incomplete) Unwanted image or external structure Metallic Artefacts Dirt on Image Plate (IP) Movement Artefacts Unexpected metallic artefacts Image presents unwanted dirt (i.e. white dots) Image shows signs of patient movement or incorrect definition of anatomical struct ures (i.e. blurred image) A total of 904 Non-conformities were identified: 64.16% are related with incorrect or incomplete image processing, 27.10% to incorrect positioning and 8.74% to artefacts (Table 5). Incorrect or incomplete image processing, are responsible for mo re than half o f Non-conformities in the samp le. Taking into account the group subdivision into Non-conformities that affect and Non-conformities that do not affect image quality and interpretation, 61.84% of Non-conformities identified correspond to criteria that do not affect image quality and interpretation. Table 6 represents the analysis of the number of non-conformit ies identified for different anatomica l structures (Chest, abdomen and foot x-rays). Table 5. Non-conformities identified Non -confo rmi ty Group Incomplete / In co rrect Processin g Type of Noncon fo rmity No side ID No Radiographer ID Inadequate Contrast nº n/c 289 262 21 Fre quency 64.84% 2.32% Overlapped Structures 26 In co rrect Posi tioning In comp let e/Missin g St ruct ures 141 Patient Rotation 78 27.10% Artefacts Met allic Dirt on IP 5 73 8.74% Movement 1 Table 6. Non-conformities by anatomical structure N/C Group Type of N/C Ches Abdo t men Foot In com plete No side 212 67 18 or ident ificat ion 41 incorre ct No Radiographer ID 141 80 processing Inadequate Contrast 15 4 2 Incorrect Overlapped Structures 25 0 1 Patien t Missing Structures 74 60 7 Posi tioning Patient Rotation 48 27 3 Artefacts Met allic IP Dirt Movement Tot al 1 4 0 48 22 3 1 0 0 565 264 75 Chest x-rays correspond to the greater number of Non-conformit ies of the sample (63%), fo llo wed by abdomen x-rays (29%) and foot x-rays (8%). Fo r all exams analysed, regarding incomplete or incorrect processing, there is a large number of Non-conformit ies in images with no side and no Radiographer’s identification (Chest – 353; Abdomen – 147; Foot – 59). The second group with more Non-conformities are images with missing anatomical structures, follo wed by images showing patient rotation. On chest x-rays there is a large number o f images with missing anatomical structures (74 Non-conformit ies), mainly the pulmonary apexes and the costo-phrenic angles. As regards the 60 Non-conform abdomen x-rays, there are coexisting images in which the spine is not on the centre of the image. On foot x-rays there are 7 images with anatomical structures missing, or not completely visualized. Most missing anatomical structures are due to incorrect patient positioning in relation to the equip ment or central x-ray beam. On chest x-rays there are 48 Non-conformit ies related to patient rotation, represented by the lack of equidistance between the external extremit ies of the clavicles and the central line of the column. On abdomen x-rays (27 Non-conformit ies) patient rotation causes incomplete anatomical structures, such as the iliac bone. 12 Susana Cândido et al.: Image Quality Control in Digital Radiology On foot x-rays there are 3 Non-conformities caused by patient rotation. The most important artefact present on x-rays are dirt artefacts on the Image Plate (IP) (Chest – 48; Abdomen – 22; Foot – 3); There are a large nu mber of Non-conformit ies on chest x-ray, however, these are the most common type of exam performed (Graphic 4). Radi ographer´s Work Schedule All e xa ms were selected according to radiographer´s work schedule and shifts. The morning shift is fro m 8h 00 a.m. to 2h00 p.m., the afternoon shift is from 2h 00 p.m. to 8h00 p.m. and night shift is fro m 8h00 p.m. to 8h00 a.m. Each shift corresponds to a total of 10 exams. On g raphic 5 conformities and non-conformities are grouped by shifts. There are more Non-conform exams on every shift than Conformity e xa ms (Graphic 5). The night shift represents the larger nu mber of Non Conform e xams, as well as the larger number of Non-conformities. 4. Discussion A study on the evaluation of mammography in Australia, used two different benchmarks for evaluating mammography exams: the PGMI and the EA R (10). The first classified images as Perfect, Good, Moderate and Inadequate and the latter as Excellent, Acceptable and Repeat. In spite of the great subjectivity of these two benchmarks, the importance of radiologica l image quality in the detection and identification of breast tumors was clearly shown. Another study assessed technical errors in intraoral peri-apical radiographs[11] where 82.74% presented technical errors, but 50,51% were acceptable. Th is results shows us the importance of image quality improvement, because even if some images are acceptable for med ical diagnosis, its interpretation becomes much more difficult. Graphic 4. Non-conformities identified by anatomical structure (Chest, abdomen and Foot x-rays) Graphic 5. Distribution of Conform Exams, Non-conform exams and number of Non-Conformities American Journal of Biomedical Engineer ing 2013, 3(6A): 8-14 13 In our research the Control Chart that better suits the Image Quality Control of a Radiology Depart ment is the Type p Control Chart, based on the proportion of Conform and Con-conform exams with 70% for the Central Limit, 95% for the Superior Control Limit and 45% for the Inferior Control Limit. However, due to an abnormal variat ion new Control limits were set as well as Warning Limits according to ISO 8259 (8) with 59% for the Central Limit, 86% for the Superior Control Limit and 32% for the Inferio r Control Limit. To identify outliers, the types of Non-conformities outside control were identified. A total of 76 Non-conformit ies related with images absent side or Radiographers identificat ion, followed by the incorrect positioning group with 40 Non-conformit ies were identified. The Type p Control Chart presents an extremely high value, allo wing for a high “error tolerance”. In other words, it is acceptable that in a sample o f 30 exams, an 86% of Non-conform exams is considered acceptable. It is also worthy to note that despite this value, 61.84% of Non-conformit ies identified correspond to criteria that do not affect image interpretation and diagnosis (Table 5). According to the labour instructions set by the radiology department, where the study was performed, all exams must have the Radiographers Identificat ion as well as a mark labelling the “right” or “left” side o f the anato mical structure. In the other groups, 27.10% correspond to incorrect patient positioning and 8.74% to artefacts. Chest x-rays represent the exams with mo re Non-conformities (63%), followed by abdomen exams (29%) and foot exams (8%). These results may be explained by the fact that chest x-rays are the most common type of exam. Other studies have a much lower “erro r tolerance” but they also present a higher number of Non-conformities related with incorrect positioning, image processing and inadequate image contrast[12]. As recommend when is used this type of instruments[13], a fishbone diagram also called Cause–and–Effect Diagram was constructed to help identify the probable causes that led to incorrect positioning. The main causes were bedridden patients and children who could not cooperate with the radiographer. Although the Quality Control of Radiographic is not present in many radiology departments, taking recently its first steps, the existence of adequate Digital Image Quality Control is crucial. In the only study of this type carried out in a Pub lic Portuguese Hospital, the imp lementation of an image quality control program allo wed for a reduction of Non-conform exams of 39.78%[12]. 5. Conclusions This type of control allo ws us to find out the causes of variability when it is out of statistical control, making corrective actions easier to apply. We can infer there is a necessity for improvement standardization in rad iographic imag ing that can be achieved through the imp lementation of a Quality Control of Radiographic Images. 5.1. Li mitati ons • The virtual absence of studies related to this field of research, making results comparisons difficu lt; • Since this is a case study it is not possible to extrapolate results to other radiology departments; • Due to schedule limitat ions only three anatomical structures were studied; 5.2. Improvement Suggestions • This study focused on 3 main types of radiographic exams (chest, abdomen and foot x-rays). Further research is needed to implement the same methodology on other anatomica l structures. 5.3. Recommendations To min imize the occurrence of non-conformit ies, corrective actions must be applied, such as: • The expansion of Quality Control Charts and Results through several institutions; • The performance of x-ray exams fo llo wing established g u id elin es ; • Always take into account collimation and the irradiated area of the patient; • Periodic Image Plate maintenance; REFERENCES [1] Donnelly, L. F., & Strife JL. Performance-Based Assessment of Radiology Faculty: A Practical Plan to Promote Improvement and M eet JCAHO Standards. American Journal of Roentgenology. 2005;1398–401. [2] Osibote, A., Azevedo, A., Carvalho, A., Khoury, H., Oliveira, R., & Silva M . Exposição de pacientes e qualidade da imagem em radiografias de tórax: uma avaliação crítica. Radiologia Brasileira. 2007;(40):119–22. [3] Cunha, G., M achado, N., & Teixeira N. Controlo Estatístico do Processo - monitorização do desempenho de equipamento radiológico. In: Edições Sílabo L, editor. QuaLidade Numa Perspectiva M ulti e Interdisciplinar. Lisboa; 2010. p. 186–91. [4] M inistério da Justiça. Decreto-Lei n.o 140/2004 de 8 de Junho[Internet]. Lisboa; 2004. Available ackhtmlfiles/dl140.html The existence of adequate quality control carried out on [5] Fortin, M .F., Côté, J., & Filion F. Fundamentos e Etapas do radiological images, allows greater uniformity of the final product and decreases the variability of exam execution, Processo de Investigação. 2nd ed. Loures: Lusodidacta; 2009. p. 1–540. regardless of the Radiographer performing the exam. [6] Practice guideline for digital radiography. In Practice 14 Susana Cândido et al.: Image Quality Control in Digital Radiology Guidelines and Technical Standards. Reston, Va: American College of Radiology; 2007:23-57. [7] ACR practice guideline for the performance of pediatric and adult chest radiography. In: Practice Guidelines and Technical Standards. Reston, Va: American College of Radiology; 2006:233-238. [8] Shewhart control charts. ISO 8258:1991. 1991. [9] Pires AR. Qualidade: sistemas de gestão da qualidade. 3rd ed. Lisboa: Sílabo, LDA; 2004. [10] M oreira, C., Svoboda, K., Poulos, A., Taylor, R., Page, A., & Rickard M . Comparison of the validity and reliability of two image classification systems for the assessment of mammogram quality. Journal of M edical Screening. 2005;(12):38–42. [11] Carvalho, P. L., Neves, A. C.,M edeiros, J.M ., Zöllner, N. A., Carlos, R. L., & Almeida E.T. Erros técnicos nas radiografias intrabucais realizadas por alunos de graduação. Revista Gaúcha de Odontologia. 2009;2(57):151–5. [12] Proença JA. Implementação do controlo da qualidade da imagem radiológica digital. Revista da Faculdade de Ciências e Tecnologias. 2009;6–19. [13] Nancy R. Tague’s The Quality Toolbox, Second Edition, ASQ Quality Press, 2004, pages 247–249.

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