94 research outputs found

    Characterisation of computed tomography devices and optimisation of clinical protocols based on mathematical observers

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    The technological evolutions of diagnostic X-ray imaging modalities enable to radiologists improve diagnosis quality and patient care. In this context, the number of X-ray examinations like conventional radiography, fluoroscopy or computed tomography (CT), is increasingly used in patient care. The risk associated with the use of ionizing radiation in medical imaging is the risk of inducing cancer, a risk which is by the Linear No-Threshold model traditionally developed for patient radiation protection. In addition, CT imaging contributes to roughly 70 % of the total annual effective dose delivered by X-ray imaging to the population. Because of this, many efforts have been made to decrease patient exposure to ensure that the risk benefit balance clearly lies on the benefit side. Nevertheless, while the risk of inducing cancer cannot be neglected, the major risk for the patient, if the justification process is respected, was the non-detection of a pathological lesion. The goal of this work was to propose a strategy to optimise patient exposure while maintaining diagnostic accuracy using a task-based methodology that is pertinent in a clinical context when dealing with CT imaging. In this context, objective image quality should be developed and should take into account the following four elements: (1) It should be linked to a task; (2) the properties of signals and backgrounds have to be defined in accordance with their statistical properties; (3) the observer should be specified and (4) a figure of merit should be precisely defined and quantified. In this sense, model observers, which are mathematical tools potentially used as a surrogate for human observers are well suited to objectively estimate image quality at the diagnostic accuracy level. They can indeed perform a task (e.g. lesion detection) for a given type of image and signal (e.g. noisy uniform background) and allow a quantitative performance estimation using for example the area under the receiver operating characteristic curve. In addition, the advantage of model observers is that they are economical, both in terms of time and money and they are consistent unlike the human observers. This work shows that using a task-based approach to benchmark CT units and clinical protocols in terms of image quality and patient exposure becomes feasible with model observers. Such an approach may be useful for adequately and quantitatively comparing clinically relevant image quality and to estimate the potential for further dose reductions offered by the latest technological developments. The methodology developed during this PhD thesis enables medical physicists to convert clinically relevant information defined by radiologists into task-based image quality criteria

    Doctor of Philosophy

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    dissertationSingle Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), a noninvasive and effective method for diagnosing coronary artery disease (CAD), is the most commonly performed SPECT procedure. Hence, it is not surprising that there is a tremendous market need for dedicated cardiac SPECT scanners. In this dissertation, a novel dedicated stationary cardiac SPECT system that using a segmented-parallel-hole collimator is investigated in detail. This stationary SPECT system can acquire true dynamic SPECT images and is inexpensive to build. A segmented-parallel-hole collimator was designed to fit the existing general-purpose SPECT cameras without any mechanical modifications of the scanner while providing higher detection sensitivity. With a segmented-parallel-hole collimator, each detector was segmented to seven sub-detector regions, providing seven projections simultaneously. Fourteen view-angles over 180 degree were obtained in total with two detectors positioned at 90 degree apart. The whole system was able to provide an approximate 34-fold gain in sensitivity over the conventional single-head SPECT system. The potential drawbacks of the stationary cardiac SPECT system are data truncation from small field of view (FOV) and limited number of view angles. A tailored maximum-likelihood expectation-maximization (ML-EM) algorithm was derived for reconstruction of truncated projections with few view angles. The artifacts caused by truncation and insufficient number of views were suppressed by reducing the image updating step sizes of the pixels outside the FOV. The performance of the tailored ML-EM algorithm was verified by computer simulations and phantom experiments. Compared with the conventional ML-EM algorithm, the tailored ML-EM algorithm successfully suppresses the streak artifacts outside the FOV and reduces the distortion inside the FOV. At 10 views, the tailored ML-EM algorithm has a much lower mean squared error (MSE) and higher relative contrast. In addition, special attention was given to handle the zero-valued projections in the image reconstruction. There are two categories of zero values in the projection data: one is outside the boundary of the object and the other is inside the object region, which is caused by count starvation. A positive weighting factor c was introduced to the ML-EM algorithm. By setting c>1 for zero values outside the projection, the boundary in the image is well preserved even at extremely low iterations. The black lines, caused by the zero values inside the object region, are completely removed by setting 0< c<1. Finally, the segmented-parallel-hole collimator was fabricated and calibrated using a point source. Closed-form explicit expressions for the slant angles and rotation radius were derived from the proposed system geometry. The geometric parameters were estimated independently or jointly. Monte Carlo simulations and real emission data were used to evaluate the proposed calibration method and the stationary cardiac system. The simulation results show that the difference between the estimated and the actual value is less than 0.1 degree for the slant angles and the 5 mm for the rotation radius, which is well below the detector's intrinsic resolution

    Task-based Optimization of Administered Activity for Pediatric Renal SPECT Imaging

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    Like any real-world problem, the design of an imaging system always requires tradeoffs. For medical imaging modalities using ionization radiation, a major tradeoff is between diagnostic image quality (IQ) and risk to the patient from absorbed dose (AD). In nuclear medicine, reducing the AD requires reducing the administered activity (AA). Lower AA to the patient can reduce risk and adverse effects, but can also result in reduced diagnostic image quality. Thus, ultimately, it is desirable to use the lowest AA that gives sufficient image quality for accurate clinical diagnosis. In this dissertation, we proposed and developed tools for a general framework for optimizing RD with task-based assessment of IQ. Here, IQ is defined as an objective measure of the user performing the diagnostic task that the images were acquired to answer. To investigate IQ as a function of renal defect detectability, we have developed a projection image database modeling imaging of 99mTc-DMSA, a renal function agent. The database uses a highly-realistic population of pediatric phantoms with anatomical and body morphological variations. Using the developed projection image database, we have explored patient factors that affect IQ and are currently in the process of determining relationships between IQ and AA in terms of these found factors. Our data have shown that factors that are more local to the target organ may be more robust than weight for estimating the AA needed to provide a constant IQ across a population of patients. In the case of renal imaging, we have discovered that girth is more robust than weight (currently used in clinical practice) in predicting AA needed to provide a desired IQ. In addition to exploring the patient factors, we also did some work on improving the task simulating capability for anthropomorphic model observer. We proposed a deep learning-based anthropomorphic model observer to fully and efficiently (in terms of both training data and computational cost) model the clinical 3D detection task using multi-slice, multi-orientation image sets. The proposed model observer is important and could be readily adapted to model human observer performance on detection tasks for other imaging modalities such as PET, CT or MRI

    Iterative reconstruction in CT : using mathematical model observers to determine low dose images' trustworthiness

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    La tomodensitométrie (TDM) est une technique d'imagerie pour laquelle l'intérêt n'a cessé de croitre depuis son apparition au début des années 70. De nos jours, l'utilisation de cette technique est devenue incontournable, grâce entre autres à sa capacité à produire des images diagnostiques de haute qualité. Toutefois, et en dépit d'un bénéfice indiscutable sur la prise en charge des patients, l'augmentation importante du nombre d'examens TDM pratiqués soulève des questions sur l'effet potentiellement dangereux des rayonnements ionisants sur la population. Parmi ces effets néfastes, l'induction de cancers liés à l'exposition aux rayonnements ionisants reste l'un des risques majeurs. Afin que le rapport bénéfice-risques reste favorable au patient il est donc nécessaire de s'assurer que la dose délivrée permette de formuler le bon diagnostic tout en évitant d'avoir recours à des images dont la qualité est inutilement élevée. Ce processus d'optimisation, qui est une préoccupation importante pour les patients adultes, doit même devenir une priorité lorsque l'on examine des enfants ou des adolescents, en particulier lors d'études de suivi requérant plusieurs examens tout au long de leur vie. Enfants et jeunes adultes sont en effet beaucoup plus sensibles aux radiations du fait de leur métabolisme plus rapide que celui des adultes. De plus, les probabilités des évènements auxquels ils s'exposent sont également plus grandes du fait de leur plus longue espérance de vie. L'introduction des algorithmes de reconstruction itératifs, conçus pour réduire l'exposition des patients, est certainement l'une des plus grandes avancées en TDM, mais elle s'accompagne de certaines difficultés en ce qui concerne l'évaluation de la qualité des images produites. Le but de ce travail est de mettre en place une stratégie pour investiguer le potentiel des algorithmes itératifs vis-à-vis de la réduction de dose sans pour autant compromettre la qualité du diagnostic. La difficulté de cette tâche réside principalement dans le fait de disposer d'une méthode visant à évaluer la qualité d'image de façon pertinente d'un point de vue clinique. La première étape a consisté à caractériser la qualité d'image lors d'examen musculo-squelettique. Ce travail a été réalisé en étroite collaboration avec des radiologues pour s'assurer un choix pertinent de critères de qualité d'image. Une attention particulière a été portée au bruit et à la résolution des images reconstruites à l'aide d'algorithmes itératifs. L'analyse de ces paramètres a permis aux radiologues d'adapter leurs protocoles grâce à une possible estimation de la perte de qualité d'image liée à la réduction de dose. Notre travail nous a également permis d'investiguer la diminution de la détectabilité à bas contraste associée à une diminution de la dose ; difficulté majeure lorsque l'on pratique un examen dans la région abdominale. Sachant que des alternatives à la façon standard de caractériser la qualité d'image (métriques de l'espace Fourier) devaient être utilisées, nous nous sommes appuyés sur l'utilisation de modèles d'observateurs mathématiques. Nos paramètres expérimentaux ont ensuite permis de déterminer le type de modèle à utiliser. Les modèles idéaux ont été utilisés pour caractériser la qualité d'image lorsque des paramètres purement physiques concernant la détectabilité du signal devaient être estimés alors que les modèles anthropomorphes ont été utilisés dans des contextes cliniques où les résultats devaient être comparés à ceux d'observateurs humain, tirant profit des propriétés de ce type de modèles. Cette étude a confirmé que l'utilisation de modèles d'observateurs permettait d'évaluer la qualité d'image en utilisant une approche basée sur la tâche à effectuer, permettant ainsi d'établir un lien entre les physiciens médicaux et les radiologues. Nous avons également montré que les reconstructions itératives ont le potentiel de réduire la dose sans altérer la qualité du diagnostic. Parmi les différentes reconstructions itératives, celles de type « model-based » sont celles qui offrent le plus grand potentiel d'optimisation, puisque les images produites grâce à cette modalité conduisent à un diagnostic exact même lors d'acquisitions à très basse dose. Ce travail a également permis de clarifier le rôle du physicien médical en TDM: Les métriques standards restent utiles pour évaluer la conformité d'un appareil aux requis légaux, mais l'utilisation de modèles d'observateurs est inévitable pour optimiser les protocoles d'imagerie. -- Computed tomography (CT) is an imaging technique in which interest has been quickly growing since it began to be used in the 1970s. Today, it has become an extensively used modality because of its ability to produce accurate diagnostic images. However, even if a direct benefit to patient healthcare is attributed to CT, the dramatic increase in the number of CT examinations performed has raised concerns about the potential negative effects of ionising radiation on the population. Among those negative effects, one of the major risks remaining is the development of cancers associated with exposure to diagnostic X-ray procedures. In order to ensure that the benefits-risk ratio still remains in favour of the patient, it is necessary to make sure that the delivered dose leads to the proper diagnosis without producing unnecessarily high-quality images. This optimisation scheme is already an important concern for adult patients, but it must become an even greater priority when examinations are performed on children or young adults, in particular with follow-up studies which require several CT procedures over the patient's life. Indeed, children and young adults are more sensitive to radiation due to their faster metabolism. In addition, harmful consequences have a higher probability to occur because of a younger patient's longer life expectancy. The recent introduction of iterative reconstruction algorithms, which were designed to substantially reduce dose, is certainly a major achievement in CT evolution, but it has also created difficulties in the quality assessment of the images produced using those algorithms. The goal of the present work was to propose a strategy to investigate the potential of iterative reconstructions to reduce dose without compromising the ability to answer the diagnostic questions. The major difficulty entails disposing a clinically relevant way to estimate image quality. To ensure the choice of pertinent image quality criteria this work was continuously performed in close collaboration with radiologists. The work began by tackling the way to characterise image quality when dealing with musculo-skeletal examinations. We focused, in particular, on image noise and spatial resolution behaviours when iterative image reconstruction was used. The analyses of the physical parameters allowed radiologists to adapt their image acquisition and reconstruction protocols while knowing what loss of image quality to expect. This work also dealt with the loss of low-contrast detectability associated with dose reduction, something which is a major concern when dealing with patient dose reduction in abdominal investigations. Knowing that alternative ways had to be used to assess image quality rather than classical Fourier-space metrics, we focused on the use of mathematical model observers. Our experimental parameters determined the type of model to use. Ideal model observers were applied to characterise image quality when purely objective results about the signal detectability were researched, whereas anthropomorphic model observers were used in a more clinical context, when the results had to be compared with the eye of a radiologist thus taking advantage of their incorporation of human visual system elements. This work confirmed that the use of model observers makes it possible to assess image quality using a task-based approach, which, in turn, establishes a bridge between medical physicists and radiologists. It also demonstrated that statistical iterative reconstructions have the potential to reduce the delivered dose without impairing the quality of the diagnosis. Among the different types of iterative reconstructions, model-based ones offer the greatest potential, since images produced using this modality can still lead to an accurate diagnosis even when acquired at very low dose. This work has clarified the role of medical physicists when dealing with CT imaging. The use of the standard metrics used in the field of CT imaging remains quite important when dealing with the assessment of unit compliance to legal requirements, but the use of a model observer is the way to go when dealing with the optimisation of the imaging protocols

    TOWARDS FURTHER OPTIMIZATION OF RECONSTRUCTION METHODS FOR DUAL-RADIONUCLIDE MYOCARDIAL PERFUSION SPECT

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    Coronary artery disease (CAD) is the most prevalent type of heart disease and a leading cause of death both in the United States and worldwide. Myocardial perfusion SPECT (MPS) is a well-established and widely-used non-invasive imaging technique to diagnose CAD. MPS images the distribution of radioactive perfusion agent in the myocardium to assess the myocardial perfusion status at rest and stress state and allow diagnosis of CAD and allow differentiation of CAD and previous myocardial infarctions. The overall goal of this dissertation was to optimize the image reconstruction methods for MPS by patient-specific optimization of two advanced iterative reconstruction methods based on simulations of realistic patients population modeling existing hardware and previously optimized dual-isotope simultaneous-acquisition imaging protocols. After optimization, the two algorithms were compared to determine the optimal reconstruction methods for MPS. First, we developed a model observer strategy to evaluate image quality and allow optimization of the reconstruction methods using a population of phantoms modeling the variability seen in human populations. The Hotelling Observer (HO) is widely used to evaluate image quality, often in conjunction with anthropomorphic channels to model human observer performance. However, applying the HO to non- multivariate-normally (MVN) distributed, such as the output from a channel model applied to images with variable signals and background, is not optimal. In this work, we proposed a novel model observer strategy to evaluate the image quality of such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Next, the Linear Discriminant (LD) is applied to estimate test statistics for each sub-ensemble, and a single area under the receiver operating characteristics curve (AUC) is calculated using the pooled test statistics from all the sub-ensembles. The AUC serves as the figure of merit for performance on the defect detection task. The proposed multi-template LD was compared to other model observer strategies and was shown to be a practical, theoretically justified, and produced higher AUC values for non-MVN data such as that arising from the clinically-realistic SKS task used in the remainder of this work. We then optimized two regularized statistical reconstruction algorithms. One is the widely used post-filtered ordered subsets-expectation maximization (OS-EM) algorithm. The other is a maximum a posteriori (MAP) algorithm with dual-tracer prior (DTMAP) that was proposed for dual-isotope MPS study and was expected to outperform the post-filtered OS-EM algorithm. Of importance, we proposed to investigate patient-specific optimization of the reconstruction parameters. To accomplish this, the phantom population was divided into three anatomy groups based on metrics that expected to affect image noise and resolution and thus the optimal reconstruction parameters. In particular, these metrics were the distance from the center of the heart to the face of the collimator, which is directly related to image resolution, heart size, and counts from the myocardium, which is expected to determine image noise. Reconstruction parameters were optimized for each of these groups using the proposed model observer strategy. Parameters for the rest and stress images were optimized separately, and the parameters that achieve the highest AUC were deemed optimal. The results showed that the proposed group-wise optimization method offered slightly better task performance than using a single set of parameters for all the phantoms. For DTMAP, we also applied the group-wise optimization approach. The extra challenges for DTMAP optimization are that it has three parameters to be optimized simultaneously, and it is substantially more computationally expensive than OS-EM. Thus, we adopted optimization strategies to reduce the size of the parameter search space. In particular, we searched in two parameter ranges expected to give result in good image quality. We also reduced the computation burden by exploiting limiting behavior of the penalty function to reduce the number of parameters that need to be optimized. Despite this effort, the optimized DTMAP had poorer task performance compared to the optimized OS-EM algorithm. As a result, we studied the limitations of the DTMAP algorithm and suggest reasons of its worse performance for the task investigated. The results of this study indicate that there is benefit from patient-specific optimization. The methods and optimal patient-specific parameters may be applicable to clinical MPS studies. In addition, the model observer strategy and the group-wise optimization approach may also be applicable both to future work in MPS and to other relevant fields

    Virtual clinical trials in medical imaging: a review

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    The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

    Modelos de observador aplicados a la detectabilidad de bajo contraste en tomografía computarizada

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, leída el 15/01/2016. Tesis formato europeo (compendio de artículos)Introduction. Medical imaging has become one of the comerstones in modem healthcare. Computed tomography (CT) is a widely used imaging modality in radiology worldwide. This technique allows to obtain three-dimensional volume reconstructions ofdifferent parts of the patient with isotropic spatial resolution. Also, to acquire sharp images of moving organs, such as the heart orthe lungs, without artifacts. The spectrum ofindications which can be tackled with this technique is wide, and it comprises brain perfusion, cardiology, oncology, vascular radiology, interventionism and traumatology, amongst others. CT is a very popular imaging technique, widely implanted in healthcare services worldwide. The amount of CT scans performed per year has been continuously growing in the past decades, which has led to a great benefit for the patients. At the same time, CT exams represent the highest contribution to the collective radiation dose. Patient dose in CT is one order ofmagnitude higher than in conventional X-ray studies. Regarding patient dose in X-ray imaging the ALARA criteria is universally accepted. It states that patient images should be obtained using adose as low as reasonably achievable and compatible with the diagnostic task. Sorne cases ofpatients' radiation overexposure, most ofthem in brain perfusion procedures have come to the public eye and hada great impact in the USA media. These cases, together with the increasing number ofCT scans performed per year, have raised a red flag about the patient imparted doses in CT. Several guidelines and recommendation for dose optimization in CT have been published by different organizations, which have been included in European and National regulations and adopted by CT manufacturers. In CT, the X-ray tube is rotating around the patient, emitting photons in beams from different angles or projections. These photons interact with the tissues in the patient, depending on their energy and the tissue composition and density. A fraction of these photons deposit all or part of their energy inside the patient, resulting in organs absorbed dose. The images are generated using the data from the projections ofthe X-ray beam that reach the detectors after passing through the patient. Each proj ection represents the total integrated attenuation of the X-ray beam along its path. A CT protocol is defined as a collection of settings which can be selected in the CT console and affect the image quality outcome and the patient dose. They can be acquisition parameters such as beam collimation, tube current, rotation time, kV, pitch, or reconstruction parameters such as the slice thickness and spacing, reconstruction filter and method (filtered back projection (FBP) or iterative algorithms). All main CT manufacturers offer default protocols for different indications, depending on the anatomical region. The user can frequently set the protocol parameters selecting amongst a range of values to adapt them to the clinical indication and patient characteristics, such as size or age. The selected settings in the protocol affect greatly image quality and dose. Many combinations ofsean parameters can render an appropriate image quality for a particular study. Protocol optimization is a complex task in CT because most sean protocol parameters are intertwined and affect image quality and patient dose...Introducción. La imagen médica se ha convertido en uno de los pilares en la atención sanitaria actual. La tomografía computarizada (TC) es una modalidad de imagen ampliamente extendida en radiología en todo el mundo. Esta técnica permite adquirir imágenes de órganos en movimiento, como el corazón o los pulmones, sin artefactos. También permite obtener reconstrucciones de volúmenes tridimensionales de distintas partes del cuerpo de los pacientes. El abanico de indicaciones que pueden abordarse con esta técnica es amplio, e incluye la perfusión cerebral, cardiología, oncología, radiología vascular, intervencionismo y traumatología, entre otras. La TC es una técnica de imagen muy popular, ampliamente implantada en los servicios de salud de hospitales de todo el mundo. El número de estudios de TC hechos anualmente ha crecido de manera continua en las últimas décadas, lo que ha supuesto un gran beneficio para los pacientes. A la vez, los exámenes de TC representan la contribución más alta a la dosis de radiación colectiva en la actualidad. La dosis que reciben los pacientes en un estudio de TC es un orden de magnitud más alta que en exámenes de radiología convencional. En relación con la dosis a pacientes en radiodiagnóstico, el criterio ALARA es aceptado universalmente. Expone que las imágenes de los pacientes deberían obtenerse utilizando una dosis tan baja como sea razonablemente posible y compatible con el objetivo diagnóstico de la prueba. Algunos casos de sobreexposición de pacientes a la radiación, la mayoría en exámenes de perfusión cerebral, se han hecho públicos, lo que ha tenido un gran impacto en los medios de comunicación de EEUU. Estos accidentes, junto con el creciente número de exámenes TC anuales, han hecho aumentar la preocupación sobre las dosis de radiación impartidas a los pacientes en TC. V arias guías y recomendaciones para la optimización de la dosis en TC han sido publicadas por distintas organizaciones, y han sido incluidas en normas europeas y nacionales y adoptadas parcialmente por los fabricantes de equipos de TC. En TC, el tubo de rayos-X rota en tomo al paciente, emitiendo fotones en haces desde distintos ángulos o proyecciones. Estos fotones interactúan con los tejidos en el paciente, en función de su energía y de la composición y densidad del tejido. Una fracción de estos fotones depositan parte o toda su energía dentro del paciente, dando lugar a la dosis absorbida en los órganos. Las imágenes se generan usando los datos de las proyecciones del haz de rayos-X que alcanzan los detectores tras atravesar al paciente. Cada proyección representa la atenuación total del haz de rayos-X integrada a lo largo de su trayectoria. Un protocolo de TC se define como una colección de opciones que pueden seleccionarse en la consola del equipo y que afectan a la calidad de las imágenes y a la dosis que recibe el paciente. Pueden ser parámetros de adquisición, tales como la colimación del haz, la intensidad de corriente, el tiempo de rotación, el kV, el factor de paso parámetros de reconstrucción como el espesor y espaciado de corte, el filtro y el método de reconstrucción (retroproyección filtrada (FBP) o algoritmos iterativos). Los principales fabricantes de equipos de TC ofrecen protocolos recomendados para distintas indicaciones, dependiendo de la región anatómica. El usuario con frecuencia fija los parámetros del protocolo eligiendo entre un rango de valores disponibles, para adaptarlo a la indicación clínica y a las características del paciente, tales como su tamaño o edad. Las condiciones seleccionadas en el protocolo tienen un gran impacto en la calidad de imagen y la dosis. Múltiples combinaciones de los parámetros pueden dar lugar a un nivel de calidad de imagen apropiado para un estudio en concreto. La optimización de los protocolos es una tarea compleja en TC, ya que la mayoría de los parámetros del protocolo están relacionados entre sí y afectan a la calidad de imagen y a la dosis que recibe el paciente...Depto. de Radiología, Rehabilitación y FisioterapiaFac. de MedicinaTRUEunpu
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