31 research outputs found

    Breast Tomosynthesis: Aspects on detection and perception of simulated lesions

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    The aim of this thesis was to investigate aspects on detectability of simulated lesions (microcalcifications and masses) in digital mammography (DM) and breast tomosynthesis (BT). Perception in BT image volumes were also investigated by evaluating certain reading conditions. The first study concerned the effect of system noise on the detection of masses and microcalcification clusters in DM images using a free-response task. System noise has an impact on image quality and is related to the dose level. It was found to have a substantial impact on the detection of microcalcification clusters, whereas masses were relatively unaffected. The effect of superimposed tissue in DM is the major limitation hampering the detection of masses. BT is a three-dimensional technique that reduces the effect of superimposed tissue. In the following two studies visibility was quantified for both imaging modalities in terms of the required contrast at a fixed detection performance (92% correct decisions). Contrast detail plots for lesions with sizes 0.2, 1, 3, 8 and 25 mm were generated. The first study involved only an in-plane BT slice, where the lesion centre appeared. The second study repeated the same procedure in BT image volumes for 3D distributed microcalcification clusters and 8 mm masses at two dose levels. Both studies showed that BT needs substantially less contrast than DM for lesions above 1 mm. Furthermore, the contrast threshold increased as the lesion size increased for both modalities. This is in accordance with the reduced effect of superimposed tissue in BT. For 0.2 mm lesions, substantially more contrast was needed. At equal dose, DM was better than BT for 0.2 mm lesions and microcalcification clusters. Doubling the dose substantially improved the detection in BT. Thus, system noise has a substantial impact on detection. The final study evaluated reading conditions for BT image volumes. Four viewing procedures were assessed: free scroll browsing only or combined with initial cine loops at frame rates of 9, 14 and 25 fps. They were viewed on a wide screen monitor placed in vertical or horizontal positions. A free-response task and eye tracking were utilized to record the detection performance, analysis time, visual attention and search strategies. Improved reading conditions were found for horizontally aligned BT image volumes when using free scroll browsing only or combined with a cine loop at the fastest frame rate

    TASK SPECIFIC EVALUATION METHODOLOGY FOR CLINICAL FULL FIELD DIGITAL MAMMOGRAPHY

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    Purpose: The purpose of this dissertation is to evaluate the image quality of clinical Full Field Digital Mammography (FFDM) systems. This is done by evaluating image acquisition performance of clinical FFDM in a comprehensive way that accounts for scatter, focal spot un-sharpness, detector blur and anti-scatter grid performance using an anthropomorphic phantom. Additionally we intend to provide a limited evaluation of the effects that image processing in clinical FFDM has in signal detectability. Methodology: We explored different strategies and a variety of mathematical model observers in order to evaluate the performance of clinical FFDM systems under different conditions. To evaluate image acquisition performance, we tested a system-model-based Hotelling observer (SMHO) model on a bench-top system using a uniform anthropomorphic phantom for an signal known exactly background known exactly (SKE/BKE) task. We then applied this concept on two clinical FFDM systems to compare their performance. In a limited study to evaluate the effects of image processing in the detectability of FFDM, we implemented the channelized Hotelling observer (CHO) model on clinically realistic images of an anatomical phantom for an SKE/BKE task. Results: Even though the two systems use different detection technologies, there was no significant difference between their image acquisition performances quantified by the Contrast-Detail (CD) curves. We applied the CHO model to investigate the image processing algorithms used in GE Senographe DS FFDM system. For the particular SKE/BKE task with rotationally symmetric signals, the image processing tends to contribute to a non-significant reduction of system detectability. Conclusion: We provided a complete description of FFDM system performance including the image acquisition chain and post-acquisition image processing. We demonstrated the simplicity and effectiveness of both the MFHO and CHO methods in a clinical setting

    A comparative study evaluating the performance of diagnostic radiography units and protocols for paediatric and adult chest radiography examinations

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    Purpose: Little is known about the variations in image quality (IQ) and radiation dose for paediatric and adult chest radiography (CXR), between and within hospitals. Large variations in IQ could influence the diagnostic accuracy, and variations in radiation dose could affect the risk to patients. This thesis aims to develop, validate and then use a novel method for comparing IQ and radiation dose for paediatric and adult CXR imaging examinations and report variation between a series of public hospitals. Method: A Figure of Merit (FOM) concept was used for the purposes of comparing IQ and radiation dose, between and within hospitals. Low contrast detail (LCD) detectability, using the CDRAD 2.0 phantom, was utilised as the main method for IQ evaluation. The validity of utilising LCD detectability, using CDRAD 2.0 phantom, for evaluating visual IQ, simulated lesion visibility (LV) and CXR optimisation studies, was investigated. This was done by determining the correlation between the LCD detectability and visual measures of IQ and LV for two lesions with different locations and visibility in the Lungman chest phantom.The CDRAD 2.0 phantom and two anthropomorphic phantoms (adult Lungman and the neonatal Gammex phantom) were used to simulate the chest region. Radiographic acquisitions were conducted on 17 X-ray units located in eight United Kingdom (UK) public hospitals within the North-west of England using their existing CXR protocols. The CDRAD 2.0 phantom was combined with different thicknesses of Polymethyl methacrylate (PMMA) slabs to simulate the chest regions of 5 different age groups: neonate, 1, 5, 10 years and adults. A Lungman phantom, with and without the fat jacket, was used to simulate average and larger sized patients. IQ was evaluated using a number of methods, including: 1) physically, by calculating LCD detectability as represented by an image quality figure inverse (IQFinv) using the CDRAD analyser software; 2) using images acquired from the anthropomorphic phantoms – for this, a relative visual grading analysis (VGA) method was used. Additionally, signal to noise ratios (SNR), contrast to noise ratios (CNR) and conspicuity indices (CI) were calculated for all phantom image data in this study. Incident air karma (IAK) was measured using a solid-state dosimeter. Results: Regarding the validation of utilising LCD detectability for evaluating visual IQ and LV, and CXR optimisation studies, a strong positive correlation (r = 0.91; p < 0.001) was observed between IQFinv and the visual IQ scores from the Lungman phantom. A good correlation was observed between IQFinv and visual LV from the Lungman phantom for both lesions (lesion 1 (with low visibility) (r = 0.79; p < 0.001); lesion 2 (with high visibility) (r =0.68; p < 0.001), respectively). Considerable variation in standard imaging protocols/techniques, radiation dose, IQ and FOM were observed between the hospitals, while within hospital variation was lower. A weak correlation between IQ and radiation dose was observed across most of the age groups studied. Conclusion: A novel method has been established to evaluate and compare IQ and radiation dose between and within hospitals based on an FOM concept. This combines IQ and radiation dose into a single factor and is the first of its kind to reported within the field of medical imaging. It can be confirmed that LCD detectability using the CDRAD 2.0 phantom is valid for evaluating visual IQ and LV and can be of use within routine quality assurance and optimisation studies in digital radiography. Further radiation dose optimisation for the paediatric age groups and adult group, especially in hospitals /X-ray machines with low IQ and high IAK, are required

    A comparison of fixed tube current (FTC) and automatic tube current modulation (ATCM) CT methods for abdominal scanning : implications on radiation dose and image quality

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    PURPOSE: There has been a huge increase in the use of abdominal CT scanning in recent years. This has contributed to an increase in radiation dose administered to patients. Abdominal CT scans generally require higher exposure factors when compared to other anatomical regions. This drives a need for urgent optimisation of the radiation dose and image quality for abdominal CT examinations. The aim of this thesis is to evaluate Fixed Tube Current (FTC) and Automatic Tube Current Modulation (ATCM) on image quality and radiation dose during abdominal CT examinations across a range of scanning parameters. MATERIALS AND METHODS: Using a Toshiba Aquilion 16 CT scanner (120 kVp, 0.5 seconds tube rotation), an adult ATOM dosimetry and abdominal anthropomorphic phantom were exposed to a series of FTC and ATCM CT protocols with variations in tube current as follows: FTC - 100, 200, 250, 300 and 400mA; ATCM - low dose+, low dose, standard, quality and high quality. The pitch factors evaluated included were 0.688, 0.938 &amp; 1.438 and the detector configurations included were 0.5×16 mm, 1.0×16 mm and 2.0×16 mm. Radiation doses for nine abdominal organs were directly measured using the Metal Oxide Semiconductor Field Effect Transistors (MOSFET). Effective dose (ED) was measured and estimation comprised of three methods: mathematical modelling with k-factors and dose length product DLP, direct with MOSFET and indirectly with Monte Carlo simulation (ImPACT). Effective risk (ER) was estimated using MOSFET data and Brenner’s equations / BEIR VII 2006 report. The raw data for ATCM radiation dose was corrected using an equivalence equation. The ATCM corrected and uncorrected data were compared against FTC. Image quality was assessed using SNR (five abdominal organs) and a relative visual grading analysis (VGA) method (five different axial images). Image quality evaluation was performed by the researcher after testing agreement between against five different observers. RESULTS: There were no significant differences in the mean radiation doses between FTC and corrected ATCM across a range of acquisition protocols (P&gt;0.05). This was with the exception of the 300mA/quality protocols, and for a fast pitch factor with 0.5×16mm detector configurations. These had significantly lower doses for FTC (P&lt;0.05). These differences were up to 13% for the mean abdominal organ doses, effective doses and the effective risk. In addition, for all acquisition parameters, the mean radiation dose was significantly higher (P&lt;0.05; 17%-23%) for uncorrected ATCM when compared to FTC. In terms of image quality, there were no differences in SNR values between FTC and ATCM for the majority of acquisition protocols, excepting the higher mean SNR value (P&lt;0.05) for the FTC at 100mA/low dose + and 200 mA/ low dose (pancreas, left and right kidneys). Conversely, the mean SNR values were significantly higher (P&lt;0.05) for the ATCM scans for 300mA/quality and fast pitch factor (1.438) (liver, spleen and pancreas) than FTC. Finally, relative VGA scores for both FTC and ATCM demonstrated no significant difference, except for ‘quality’ ATCM scans (image # 1, image # 2) and a fast pitch factor (1.438) for image #2 and #3. CONCLUSION: FTC and corrected ATCM were generally similar in terms of radiation dose and image quality except for some acquisition parameters; 300mA/quality tube current and fast (1.483) pitch factor FTC was lower than the corrected ATCM. However, the uncorrected ATCM produced higher radiation dose when compared with FTC techniques. In addition, FTC and ATCM generally produced similar SNR, again with the exception of some protocols. The SNR was higher for FTC than ATCM at lower tube current (pancreas, left and right kidneys), at 300mA/quality and fast pitch factor (1.438) SNR values for ATCM higher than FTC (liver and spleen). However, the ATCM technique is able to produce higher mean relative VGA scores for upper and middle abdominal organs. Further investigation of image quality and radiation dose difference between FTC and ATCM is required

    How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?

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    YesCardiologists use x-ray image sequences of the moving heart acquired in real-time to diagnose and treat cardiac patients. The amount of radiation used is proportional to image quality; however, exposure to radiation is damaging to patients and personnel. The amount by which radiation dose can be reduced without compromising patient care was determined. For five patient image sequences, increments of computer-generated quantum noise (white + colored) were added to the images, frame by frame using pixel-to-pixel addition, to simulate corresponding increments of dose reduction. The noise adding software was calibrated for settings used in cardiac procedures, and validated using standard objective and subjective image quality measurements. The degraded images were viewed next to corresponding original (not degraded) images in a two-alternativeforced- choice staircase psychophysics experiment. Seven cardiologists and five radiographers selected their preferred image based on visualization of the coronary arteries. The point of subjective equality, i.e., level of degradation where the observer could not perceive a difference between the original and degraded images, was calculated; for all patients the median was 33% 15% dose reduction. This demonstrates that a 33% 15% increase in image noise is feasible without being perceived, indicating potential for 33% 15% dose reduction without compromising patient care.Funded in part by Philips Healthcare, the Netherlands. Part of this work has been performed in the project PANORAMA, co-funded by grants from Belgium, Italy, France, the Netherlands, and the United Kingdom, and the ENIAC Joint Undertaking

    Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity.

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    Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study this problem, we used a task which involved nine textured spheres moving in depth, eight simulating the observer's forward motion while the ninth, the target, moved independently with a different speed towards or away from the observer. Capitalizing on the high temporal resolution of magnetoencephalography (MEG) we trained a Support Vector Classifier (SVC) using the sensor-level data to identify correct and incorrect responses. Using the same MEG data, we addressed the dynamics of cortical processes involved in the detection of the independently moving object and investigated whether we could obtain confirmatory evidence for the brain activity patterns used by the classifier. Our findings indicate that response correctness could be reliably predicted by the SVC, with the highest accuracy during the blank period after motion and preceding the response. The spatial distribution of the areas critical for the correct prediction was similar but not exclusive to areas underlying the evoked activity. Importantly, SVC identified frontal areas otherwise not detected with evoked activity that seem to be important for the successful performance in the task. Dynamic connectivity further supported the involvement of frontal and occipital-temporal areas during the task periods. This is the first study to dynamically map cortical areas using a fully data-driven approach in order to investigate the neural mechanisms involved in the detection of moving objects during observer's self-motion.R01 NS104585 - NINDS NIH HHS; U01 EB023820 - NIBIB NIH HHSPublished versio

    Análisis y propuesta de métricas de calidad de imagen médica que mimetizan al observador humano

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    La investigación que se presenta en este documento se centra en el paradigma de la percepción automática de la calidad de imagen médica, y en la correlación de dicha percepción con la percepción humana. El análisis de la calidad de imagen médica tiene un lugar central en el diseño de sistemas de imagen para diagnóstico. El objetico de este análisis es, usualmente, el de diseñar una métrica capaz de evaluar la calidad de imagen percibida por un observador, una IQM por sus siglas en inglés (Image Quality Metric). Más aún, el objetivo de un gran número de investigadores es el de desarrollar métricas automatizadas capaces de reproducir los resultados que produciría un observador humano ante dichas imágenes. De forma prácticamente universal, estas métricas se desarrollan como programas informáticos, desarrollados en uno u otro lenguaje de programación. Hasta el momento solo se han obtenido éxitos parciales. El número existente de aproximaciones a este problema y, por tanto, el número de algoritmos desarrollado es elevado; sin embargo, sigue siendo una cuestión abierta. En la literatura médica se encuentran dos aproximaciones claramente diferenciadas; una de ellas está basada en modelos de la función visual humana o en modelos ideales de observador (bien juntos o por separado). Estos modelos tratan de reproducir el procesado de la imagen en el observador desde su captación en el ojo hasta su procesado de alto nivel en el cerebro. Son modelos muy complejos, con una validez limitada y no han mostrado respuestas satisfactorias y, sobre todo, generalizables. Son estudios y modelos típicos en el campo de la imagen médica. Por otro lado, los especialistas del mundo de las Telecomunicaciones han analizado la calidad de imagen desde un punto de vista más amplio, más enfocado en estudios de imágenes naturales (aquellas presentes en el entorno natural humano), y tanto en estudios de imagen fija como en vídeo. Muchos de estos análisis están basados en aproximaciones “top‐down” al sistema visual humano. Estos modelos proponen hipótesis de carácter general acerca del funcionamiento del sistema visual humano y construyen modelos del mismo basándose en dichas hipótesis. Algunos de estos estudios han propuesto y desarrollado métricas muy bien correlacionadas con la percepción humana. Es quizá sorprendente que, hasta hace unos años, ha habido muy pocos estudios sobre la aplicación de estas métricas al campo de la imagen médica. Dentro de este acercamiento, la métrica que ha tenido más éxito ha sido, sin ningún género de dudas, SSIM, presentada por Wang, Bovik y Simoncelli en el año 2004. Esta métrica se basa en la teoría propuesta por Wang y Bovik sobre el funcionamiento del sistema visual humano. Esta teoría afirma que nuestro sistema visual está especialmente adaptado para extraer información estructural de una imagen. Es una aproximación en la que se parte de una teoría del funcionamiento general del sistema visual humano, en lugar de deducir un esquema de funcionamiento a partir de sus elementos funcionales. A partir de esta métrica se ha desarrollado una amplia familia de índices que comparte la estructura básica con SSIM y que ha obtenido correlaciones crecientes entre los resultados de dichas métricas y los resultados del observador humano. Actualmente es la métrica más usada para medir la calidad de imagen percibida en la industria del vídeo por cable y por satélite..
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