Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Medical student radiology training: What are the objectives for contemporary medical practice?

Rationale and Objectives. The authors attempt to provide a set of objectives for medical student training in radiology for contemporary medical practice. Materials and Methods. A questionnaire containing a list of educational objectives was sent to 32 radiologists in charge of medical student training in radiology at accredited residency programs in Australia and New Zealand. The importance of including each preselected objective in the curriculum was measured by respondents' agreement or disagreement on a scale of 1-6. Opportunity also was given to respondents to suggest objectives other than those presented on the questionnaire. Results. Twenty of the 32 questionnaires were returned, and a set of educational objectives was established based on the responses. The objectives were ranked in importance according to the mean score assigned to each objective by the respondents. Conclusion. This new set of educational objectives for medical student radiology training reflects recent changes in radiologic and medical practice and points to potential future developments.

Optimisation of strain rate imaging for application to stress echocardiography

We sought to improve the feasibility of strain rate imaging (SRI) during dobutamine stress echocardiography (DSE) in 56 subjects at low risk of coronary disease. The impact of several SRI changes during acquisition were studied, including: (1) changing from fundamental to harmonic imaging; (2) parallel beam-forming; (3) alteration of spatial resolution and (4) narrow sector acquisition. We assessed SR signal quality, a quantitative measure of signal noise and measurements of SRI. Of 1462 segments evaluated, 6% were uninterpretable at rest and 8% at peak stress. Signal quality was optimised by increasing temporal (p = 0.01) and spatial resolution (p<0.0001 vs. baseline imaging) at rest and peak. Increasing spatial resolution also minimised signal noise (p<0.0001). Inter-observer variability of time to peak SR and peak SR were less with high temporal and spatial resolution. SRI quality can be improved with harmonic imaging and higher temporal resolution but optimisation of spatial resolution is critical. (C) 2004 World Federation for Ultrasound in Medicine Biology.

A novel phantom and method for comprehensive 3-dimensional measurement and correction of geometric distortion in magnetic resonance imaging

A phantom that can be used for mapping geometric distortion in magnetic resonance imaging (MRI) is described. This phantom provides an array of densely distributed control points in three-dimensional (3D) space. These points form the basis of a comprehensive measurement method to correct for geometric distortion in MR images arising principally from gradient field non-linearity and magnet field inhomogeneity. The phantom was designed based on the concept that a point in space can be defined using three orthogonal planes. This novel design approach allows for as many control points as desired. Employing this novel design, a highly accurate method has been developed that enables the positions of the control points to be measured to sub-voxel accuracy. The phantom described in this paper was constructed to fit into a body coil of a MRI scanner, (external dimensions of the phantom were: 310 mm x 310 mm x 310 mm), and it contained 10,830 control points. With this phantom, the mean errors in the measured coordinates of the control points were on the order of 0.1 mm or less, which were less than one tenth of the voxel's dimensions of the phantom image. The calculated three-dimensional distortion map, i.e., the differences between the image positions and true positions of the control points, can then be used to compensate for geometric distortion for a full image restoration. It is anticipated that this novel method will have an impact on the applicability of MRI in both clinical and research settings. especially in areas where geometric accuracy is highly required, such as in MR neuro-imaging. (C) 2004 Elsevier Inc. All rights reserved.

A proposed scheme for comprehensive characterization of the measured geometric distortion in magnetic resonance imaging using a three-dimensional phantom

Recently, a 3-dimensional phantom that can provide a comprehensive, accurate and complete measurement of the geometric distortion in MRI has been developed. In this paper, a scheme for characterizing the measured geometric distortion using the 3-D phantom is described. In the proposed scheme, a number of quantitative measures are developed and used to characterize the geometric distortion. These measures encompass the overall and spatial aspects of the geometric distortion. Two specific types of volume of interest, rectangular parallelepipeds (including cubes) and spheres are considered in the proposed scheme. As an illustration, characterization of the geometric distortion in a Siemens 1.5T Sonata MRI system using the proposed scheme is presented. As shown, the proposed scheme provides a comprehensive assessment of the geometric distortion. The scheme can be potentially used as a standard procedure for the assessment of geometric distortion in MRI. (C) 2004 American Association of Physicists in Medicine.

Numerical modelling of thermal effects in rats due to high-field magnetic resonance imaging (0.5-1 GHz)

A finite-difference time-domain (FDTD) thermal model has been developed to compute the temperature elevation in the Sprague Dawley rat due to electromagnetic energy deposition in high-field magnetic resonance imaging (MRI). The field strengths examined ranged from 11.75-23.5 T (corresponding to H-1 resonances of 0.5-1 GHz) and an N-stub birdcage resonator was used to both transmit radio-frequency energy and receive the MRI signals. With an in-plane resolution of 1.95 mm, the inhomogeneous rat phantom forms a segmented model of 12 different tissue types, each having its electrical and thermal parameters assigned. The steady-state temperature distribution was calculated using a Pennes 'bioheat' approach. The numerical algorithm used to calculate the induced temperature distribution has been successfully validated against analytical solutions in the form of simplified spherical models with electrical and thermal properties of rat muscle. As well as assisting with the design of MRI experiments and apparatus, the numerical procedures developed in this study could help in future research and design of tumour-treating hyperthermia applicators to be used on rats in vivo.

Magnetic resonance imaging analysis of the upper cervical spine extensor musculature in an asymptomatic cohort: an index of fat within muscle

AIM: To establish a simple method to quantify muscle/fat constituents in cervical muscles of asymptomatic women using magnetic resonance imaging (MRI), and to determine whether there is an age effect within a defined age range. MATERIALS AND METHODS: MRI of the upper cervical spine was performed for 42 asymptomatic women aged 18-45 years. The muscle and fat signal intensities on axial spin echo T1-weighted images were quantitatively classified by taking a ratio of the pixel intensity profiles of muscle against those of intermuscular fat for the rectus capitis posterior major and minor and inferior obliquus capitis muscles bilaterally. Inter- and intra-examiner agreement was scrutinized. RESULTS: The average relative values of fat within the upper cervical musculature compared with intermuscular fat indicated that there were only slight variations in indices between the three sets of muscles. There was no significant correlation between age and fat indices. There were significant differences for the relative fat within the muscle compared with intermuscular fat and body mass index for the right rectus capitis posterior major and right and left inferior obliquus capitis muscles (p = 0.032). Intraclass correlation coefficients for intraobserver agreement ranged from 0.94 to 0.98. Inter-rater agreement of the measurements ranged from 0.75 to 0.97. CONCLUSION: A quantitative measure of muscle/fat constituents has been developed, and results of this study indicate that relative fatty infiltration is not a feature of age in the upper cervical extensor muscles of women aged 18-45 years. (C) 2005 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Method for a detailed measurement of image intensity nonuniformity in magnetic resonance imaging

In magnetic resonance imaging (MRI), the MR signal intensity can vary spatially and this spatial variation is usually referred to as MR intensity nonuniformity. Although the main source of intensity nonuniformity arises from B, inhomogeneity of the coil acting as a receiver and/or transmitter, geometric distortion also alters the MR signal intensity. It is useful on some occasions to have these two different sources be separately measured and analyzed. In this paper, we present a practical method for a detailed measurement of the MR intensity nonuniformity. This method is based on the same three-dimensional geometric phantom that was recently developed for a complete measurement of the geometric distortion in MR systems. In this paper, the contribution to the intensity nonuniformity from the geometric distortion can be estimated and thus, it provides a mechanism for estimation of the intensity nonuniformity that reflects solely the spatial characteristics arising from B-1. Additionally, a comprehensive scheme for characterization of the intensity nonuniformity based on the new measurement method is proposed. To demonstrate the method, the intensity nonuniformity in a 1.5 T Sonata MR system was measured and is used to illustrate the main features of the method. (c) 2005 American Association of Physicists in Medicine.

Live cell imaging of heavy-ion-induced radiation responses by beamline microscopy

To study the dynamics of protein recruitment to DNA lesions, ion beams can be used to generate extremely localized DNA damage within restricted regions of the nuclei. This inhomogeneous spatial distribution of lesions can be visualized indirectly and rapidly in the form of radiation-induced foci using immunocytochemical detection or GFP-tagged DNA repair proteins. To analyze faster protein translocations and a possible contribution of radiation-induced chromatin movement in DNA damage recognition in live cells, we developed a remote-controlled system to obtain high-resolution fluorescence images of living cells during ion irradiation with a frame rate of the order of seconds. Using scratch replication labeling, only minor chromatin movement at sites of ion traversal was observed within the first few minutes of impact. Furthermore, time-lapse images of the GFP-coupled DNA repair protein aprataxin revealed accumulations within seconds at sites of ion hits, indicating a very fast recruitment to damaged sites. Repositioning of the irradiated cells after fixation allowed the comparison of live cell observation with immunocytochemical staining and retrospective etching of ion tracks. These results demonstrate that heavy-ion radiation-induced changes in sub-nuclear structures can be used to determine the kinetics of early protein recruitment in living cells and that the changes are not dependent on large-scale chromatin movement at short times postirradiation. © 2005 by Radiation Research Society.

Focused, eight-element transceive phased array coil for parallel magnetic resonance imaging of the chest-theoretical considerations

A new transceive system for chest imaging for MRI applications is presented. A focused, eight-element transceive torso phased array coil is designed to investigate transmitting a focused radiofrequency field deep within the torso and to enhance signal homogeneity in the heart region. The system is used in conjunction with the SENSE reconstruction technique to enable focused parallel imaging. A hybrid finite-difference-time-domain/method-of-moments method is used to accurately predict the radiofrequency behavior inside the human torso. The simulation results reported herein demonstrate the feasibility of the design concept, which shows that radiofrequency field focusing with SENSE reconstruction is theoretically achievable. (c) 2005 Wiley-Liss, Inc.