12,492 research outputs found

    ‘Do you see what I see?’ Medical imaging: the interpretation of visual information

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    Röntgen's discovery of x-rays in 1895, gave to medicine the extraordinary benefit of being able to see inside the living body without surgery. Over time, technology has added to the sophistication of imaging processes in medicine and we now have a wide range of techniques at our disposal for the investigation and early detection of disease. But radiology deals with visual information; and like any information this requires interpretation. It is a practical field and medical images are used to make inferences about the state of peoples' health. These inferences are subject to the same variability and error as any decision-making process and so the criteria for the success of medical imaging are based not entirely on the images themselves but on the performance of the decision-makers. Research in the accuracy of medical imaging must draw on techniques from a wide range of disciplines including physics, psychology, computing, neuroscience and medicine in attempting to better understand the processes involved in visual decision-making in this context and to minimise diagnostic error

    A novel method for comparing radiation dose and image quality, between and within different X-ray units in a series of hospitals

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    Objectives: To develop a novel method for comparing radiation dose and image quality (IQ) to evaluate adult chest X-ray (CXR) imaging among several hospitals. Methods: CDRAD 2.0 phantom was used to acquire images in eight hospitals (17 digital X-ray units) using local adult CXR protocols. IQ was represented by image quality figure inverse (IQFinv), measured using CDRAD analyser software. Signal to noise ratio (SNR), contrast to noise ratio (CNR) and conspicuity index (CI) were calculated as additional measures of IQ. Incident air kerma (IAK) was calculated using a solid-state dosimeter for each acquisition. Figure of merit (FOM) was calculated to provide a single estimation of IQ and radiation dose. Results: IQ, radiation dose and FOM varied considerably between hospitals and X-ray units. For IQFinv, the mean (range) between and within the hospitals were 1.42 (0.83-2.18) and 1.87 (1.52-2.18), respectively. For IAK, the mean (range) between and within the hospitals were 93.56 (17.26 to 239.15) ”Gy and 180.85 (122.58-239.15) ”Gy, respectively. For FOM, the mean (range) between and within hospitals were 0.05 (0.01 to 0.14) and 0.03 (0.02-0.05), respectively. Conclusions: The suggested method for comparing IQ and dose using FOM concept along with the new proposed FOM, is a valid, reliable and effective approach for monitoring and comparing IQ and dose between and within hospitals. It is also can be beneficial for the optimisation of X-ray units in clinical practice. Further optimisation for the hospitals /X-ray units with low FOM are required to minimise radiation dose without degrading IQ

    Comparative analysis of radiation dose and low contrast detail detectability using routine paediatric chest radiography protocols

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    Objectives: To compare low contrast detail (LCD) detectability and radiation dose for routine paediatric chest X-ray (CXR) imaging protocols among various hospitals. Methods: CDRAD 2.0 phantom and medical grade polymethyl methacrylate (PMMA) slabs were used to simulate the chest region of four different paediatric age groups. Radiographic acquisitions were undertaken on 17 X-ray machines located in eight hospitals using their existing CXR protocols. LCD detectability represented by image quality figure inverse (IQFinv) was measured physically using the CDRAD analyser software. Incident air kerma (IAK) measurements were obtained using a solid-state dosimeter. Results: The range of IQFinv, between and within the hospitals, was 1.40-4.44 and 1.52-2.18, respectively for neonates; 0.96-4.73 and 2.33-4.73 for a 1-year old; 0.87-1.81 and 0.98-1.46 for a 5-year old and 0.90-2.39 and 1.27-2.39 for a 10-year old. The range of IAK, between and within the hospitals, was 8.56-52.62 ”Gy and 21.79-52.62 ”Gy, respectively for neonates; 5.44-82.82 ”Gy and 36.78-82.82 ”Gy for a 1-year old; 10.97-59.22 ”Gy and 11.75-52.94 ”Gy for a 5-year old and 13.97-100.77 ”Gy and 35.72-100.77 ”Gy for a 10-year old. Conclusions: Results show considerable variation, between and within hospitals, in the LCD detectability and IAK. Further radiation dose optimisation for the four paediatric age groups, especially in hospitals /X-ray rooms with low LCD detectability and high IAK, are required. Keywords: Paediatric chest radiography, CDRAD phantom, low contrast detail detectability and radiation dose

    Optimisation of the digital radiographic imaging of suspected non-accidental injury.

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    Aim: To optimise the digital (radiographic) imaging of children presenting with suspected non-accidental injury (NAI).;Objectives: (i) To evaluate existing radiographic quality criteria, and to develop a more suitable system if these are found to be inapplicable to skeletal surveys obtained in suspected NAI. (ii) To document differences in image quality between conventional film-screen and the recently installed Fuji5000R computed radiography (CR) system at Great Ormond Street Hospital for Children, (iii) To document the extent of variability in the standard of skeletal surveys obtained in the UK for suspected NAI. (iv) To determine those radiographic parameters which yield the highest diagnostic accuracy, while still maintaining acceptable radiation dose to the child, (v) To determine how varying degrees of edge-enhancement affect diagnostic accuracy. (vi) To establish the accuracy of soft compared to hard copy interpretation of images in suspected NAI.;Materials and Methods: (i) and (ii) Retrospective analysis of 286 paediatric lateral spine radiographs by two observers based on the Commission of European Communities (CEC) quality criteria, (iii) Review of the skeletal surveys of 50 consecutive infants referred from hospitals throughout the United Kingdom (UK) with suspected NAI. (iv) Phantom studies. Leeds TO. 10 and TO. 16 test objects were used to compare the relationship between film density, exposure parameters and visualisation of object details, (iv) Clinical study. Anteroposterior and lateral post mortem skull radiographs of six consecutive infants were obtained at various exposures. Six observers independently scored the images based on visualisation of five criteria, (v) and (vi) A study of diagnostic accuracy in which six observers independently interpreted 50 radiographs from printed copies (with varying degrees of edge-enhancement) and from a monitor.;Results: The CEC criteria are useful for optimisation of imaging parameters and allow the detection of differences in quality of film-screen and digital images. There is much variability in the quality and number of radiographs performed as part of skeletal surveys in the UK for suspected NAI. The Leeds test objects are either not sensitive enough (TO. 10) or perhaps over sensitive (TO. 16) for the purposes of this project. Furthermore, the minimum spatial resolution required for digital imaging in NAI has not been established. Therefore the objective interpretation of phantom studies is difficult. There is scope for reduction of radiation dose to children with no effect on image quality. Diagnostic accuracy (fracture detection) in suspected NAI is generally low, and is not affected by image display modality.;Conclusions: The CEC quality criteria are not applicable to the assessment of clinical image quality. A national protocol for skeletal surveys in NAI is required. Dedicated training, close supervision, collaboration and consistent exposure of radiologists to cases of NAI should improve diagnostic accuracy. The potential exists for dose reduction when performing skeletal surveys in children and infants with suspected NAI. Future studies should address this issue

    Learning to detect chest radiographs containing lung nodules using visual attention networks

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    Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies

    Image quality evaluation in X-ray medical imaging based on Thiel embalmed human cadavers

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    An investigation into the validity of utilising the CDRAD 2.0 phantom for optimisation studies in digital radiography

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    Objectives: To determine if a relationship exists between low contrast detail (LCD) detectability using the CDRAD 2.0 phantom, visual measures of image quality (IQ) and simulated lesion visibility (LV) when performing digital chest radiography (CXR). Methods: Using a range of acquisition parameters, a CDRAD 2.0 phantom was used to acquire a set of images with different levels of image quality. LCD detectability using the CDRAD 2.0 phantom, represented by an image quality figure inverse (IQFinv) metric, was determined using the phantom analyser software. A Lungman chest phantom was loaded with two simulated lesions, of different sizes / placed in different locations, and was imaged using the same acquisition factors as the CDRAD phantom. A relative visual grading analysis (VGA) was used by seven observers for IQ and LV evaluation of the Lungman images. Correlations between IQFinv, IQ and LV were investigated. Results: Pearson’s correlation demonstrated a strong positive correlation (r=0.91; p<0.001) between the IQ and the IQFinv. Spearman’s correlation showed a good positive correlation (r=0.79; p<0.001) and (r=0.68; p<0.001) between the IQFinv and the LV for the first lesion (left upper lobe) and the second lesion (right middle lobe), respectively. Conclusions: From results presented in this study, the automated evaluation of LCD detectability using CDRAD 2.0 phantom is likely to be a suitable option for IQ and LV evaluation in digital CXR optimisation studies
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