Breast imaging: is the sonographic descriptor of orientation valid for magnetic resonance imaging?

Purpose: To evaluate if the Breast Imaging Reporting and Data System (BI-RADS) ultrasound descriptor of orientation can be used in magnetic resonance imaging (MRI). Materials and Methods: We conducted a retrospective study to evaluate breast mass lesions identified by MRI from 2008 to 2010 who had ultrasound (US) and histopathologic confirmation. Lesions were measured in the craniocaudal (CC), anteroposterior (AP), and transverse (T) axes and classified as having a nonparallel orientation, longest axis perpendicular to Cooper's ligaments, or in a parallel orientation when the longest axis is parallel to Cooper's ligaments. The MR image data were correlated with the US orientation according to BI-RADS and histopathological diagnosis. Results: We evaluated 71 lesions in 64 patients. On MRI, 27 lesions (38.0%) were nonparallel (8 benign and 19 malignant), and 44 lesions (62.0%) were parallel (33 benign and 11 malignant). There was significant agreement between the lesion orientation on US and MRI (kappa value = 0.901). The positive predictive values (PPV) for parallel orientation malignancy on MR and US imaging were 70.4% and 73.1%, respectively. Conclusion: A descriptor of orientation for breast lesions can be used on MRI with PPV for malignant lesions similar to US. J. Magn. Reson. Imaging 2012; 36:13831388. (C) 2012 Wiley Periodicals, Inc.

On the development of novel cocaine-analogues for in vivo imaging of the dopamine transporter status

The present thesis is concerned with the development of novel cocaine-derived dopamine transporter ligands for the non-invasive exploration of the striatal and extra-striatal dopamine transporter (DAT) in living systems. The presynaptic dopamine transporter acquires an important function within the mediation of dopaminergic signal transduction. Its availability can serve as a measure for the overall integrity of the dopaminergic system. The DAT is upregulated in early Parkinson’s disease (PD), resulting in an increased availability of DAT-binding sites in the striatal DAT domains. Thereby, DAT imaging has become an important routine diagnostic tool for the early diagnosis of PD in patients, as well as for the differentiation of PD from symptomatically similar medical conditions. Furthermore, the dopaminergic system is involved in a variety of psychiatric diseases. In this regard, DAT-selective imaging agents may provide detailed insights into the scientific understanding of the biochemical background of both, the progress as well as the origins of the symptoms. DAT-imaging may also contribute to the determination of the dopaminergic therapeutic response for a given medication and thereby contribute to more convenient conditions for the patient. From an imaging point of view, the former demands a high availability of the radioactive probe to facilitate broad application of the modality, whereas the latter profits from short-lived probes, suitable for multi-injection studies. Therefore, labelling with longer-lived 18F-fluoride and in particular the generator nuclide 68Ga is worthwhile for clinical routine imaging. In contrast, the introduction of a 11C-label is a prerequisite for detailed scientific studies of neuronal interactions. The development of suitable DAT-ligands for medical imaging has often been complicated by the mixed binding profile of many compounds that that interact with the DAT. Other drawbacks have included high non-specific binding, extensive metabolism and slow accumulation in the DAT-rich brain areas. However, some recent examples have partially overcome the mentioned complications. Based on the structural speciality of these leads, novel ligand structures were designed and successfully synthesised in the present work. A structure activity relationship (SAR) study was conducted wherein the new structural modifications were examined for their influence on DAT-affinity and selectivity. Two of the compounds showed improvements in in vitro affinity for the DAT as well as selectivity versus the serotonin transporter (SERT) and norepinephrine transporter (NET). The main effort was focussed on the high-affinity candidate PR04.MZ, which was subsequently labelled with 18F and 11C in high yield. An initial pharmacological characterisation of PR04.MZ in rodents revealed highly specific binding to the target brain structures. As a result of low non-specific binding, the DAT-rich striatal area was clearly visualised by autoradiography and µPET. Furthermore, the radioactivity uptake into the DAT-rich brain regions was rapid and indicated fast binding equilibrium. No radioactive metabolite was found in the rat brain. [18F]PR04.MZ and [11C]PR04.MZ were compared in the primate brain and the plasma metabolism was studied. It was found that the ligands specifically visualise the DAT in high and low density in the primate brain. The activity uptake was rapid and quantitative evaluation by Logan graphical analysis and simplified reference tissue model was possible after a scanning time of 30 min. These results further reflect the good characteristics of PR04.MZ as a selective ligand of the neuronal DAT. To pursue 68Ga-labelling of the DAT, initial synthetic studies were performed as part of the present thesis. Thereby, a concept for the convenient preparation of novel bifunctional chelators (BFCs) was developed. Furthermore, the suitability of novel 1,4,7-triazacyclononane based N3S3-type BFCs for biomolecule-chelator conjugates of sufficient lipophilicity for the penetration of the blood-brain-barrier was elucidated.

Tetraamine-derived bifunctional chelators for technetium-99m labelling: synthesis, bioconjugation and evaluation as targeted SPECT imaging probes for GRP-receptor-positive tumours

Owing to its optimal nuclear properties, ready availability, low cost and favourable dosimetry, (99m)Tc continues to be the ideal radioisotope for medical-imaging applications. Bifunctional chelators based on a tetraamine framework exhibit facile complexation with Tc(V)O(2) to form monocationic species with high in vivo stability and significant hydrophilicity, which leads to favourable pharmacokinetics. The synthesis of a series of 1,4,8,11-tetraazaundecane derivatives (01-06) containing different functional groups at the 6-position for the conjugation of biomolecules and subsequent labelling with (99m)Tc is described herein. The chelator 01 was used as a starting material for the facile synthesis of chelators functionalised with OH (02), N(3) (04) and O-succinyl ester (05) groups. A straightforward and easy synthesis of carboxyl-functionalised tetraamine-based chelator 06 was achieved by using inexpensive and commercially available starting materials. Conjugation of 06 to a potent bombesin-antagonist peptide and subsequent labelling with (99m)Tc afforded the radiotracer (99m)Tc-N4-BB-ANT, with radiolabelling yields of >97% at a specific activity of 37 GBq micromol(-1). An IC(50) value of (3.7+/-1.3) nM was obtained, which confirmed the high affinity of the conjugate to the gastrin-releasing-peptide receptor (GRPr). Immunofluorescence and calcium mobilisation assays confirmed the strong antagonist properties of the conjugate. In vivo pharmacokinetic studies of (99m)Tc-N4-BB-ANT showed high and specific uptake in PC3 xenografts and in other GRPr-positive organs. The tumour uptake was (22.5+/-2.6)% injected activity per gram (% IA g(-1)) at 1 h post injection (p.i.). and increased to (29.9+/-4.0)% IA g(-1) at 4 h p.i. The SPECT/computed tomography (CT) images showed high tumour uptake, clear background and negligible radioactivity in the abdomen. The promising preclinical results of (99m)Tc-N4-BB-ANT warrant its potential candidature for clinical translation.

Getting in touch-3D printing in Forensic Imaging

With the increasing use of medical imaging in forensics, as well as the technological advances in rapid prototyping, we suggest combining these techniques to generate displays of forensic findings. We used computed tomography (CT), CT angiography, magnetic resonance imaging (MRI) and surface scanning with photogrammetry in conjunction with segmentation techniques to generate 3D polygon meshes. Based on these data sets, a 3D printer created colored models of the anatomical structures. Using this technique, we could create models of bone fractures, vessels, cardiac infarctions, ruptured organs as well as bitemark wounds. The final models are anatomically accurate, fully colored representations of bones, vessels and soft tissue, and they demonstrate radiologically visible pathologies. The models are more easily understood by laypersons than volume rendering or 2D reconstructions. Therefore, they are suitable for presentations in courtrooms and for educational purposes.

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.

Instructor training institute for NHTSA curriculum package: emergency medical technician-dispatcher. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Emergency medical services statutes. U.S. state and territorial survey.

National Highway Traffic Safety Administration, Washington, D.C.

Emergency medical technician instructor training institute. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Pediatric emergency medical services training program. Final report.

Mode of access: Internet.

First responders emergency medical care training: CIM course revision. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Training program for emergency medical technician: dispatcher. Instructor lesson plans.

Mode of access: Internet.