Heuristic method for photo-detectors localization over continuous crystal scintillation cameras

The construction process of large scintillation cameras with continuous crystals is a complex problem affected by multiple variables, from the physical construction of the scintillation crystal to the dispersion in the sensibility of photomultiplier tubes. The selection of these variables have a strong impact on the final performance of the scintillation camera. A new method to select photomultiplier is presented in this paper, regarding its gain, making use of k-means clustering and genetic algorithms. The proposed algorithm was tested and applied in the construction of six scintillation cameras for the Argentinean positron emission tomogaph prototype (AR-PET), a problem with more than 10 +600 possible configurations. The resulting configuration of the scintillation cameras reduced the software-free calibration FWHM of the camera from 20% to 15% average.Fil: Rodríguez Colmeiro, Ramiro Germán. Comisión Nacional de Energía Atómica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Verrastro, Claudio Abel. Comisión Nacional de Energía Atómica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentin

Semi-Automated Stereo Image Patches Generation and Labeling Method Based on Perspective Transformations

In computer vision, Wide Baseline Stereo (WxBS) refers to Vision System configurations on which their images come from cameras with non parallel and widely separated views. One common task in reconstruction algorithms of WxBS consists of subvididing the stereo images in multiple image patches and then associate homologous patches between homologous images. Multiple approaches can be used to associate homologous patches. To train and test supervised learning algorithms for this tasks, a labeled dataset is required. In this work, a semi-automated method to generate patches and their labels from WxBS images is presented. It allows to calculate thousands of positive and negative pairs of patches with a score of correspondence between a pair of potentially homologous image patches. This method largely solves the problems of traditional approach, which requires a lot of hand labeled work and time. To apply the method, images from different viewpoints of objects with planar faces and their corner locations are required.Sociedad Argentina de Informática e Investigación Operativ

Redes Generativas Adversarias 3D de Crecimiento Progresivo para generación de mapas de atenuación de PET

International audienc

Hacia la sinterización de mapas de atenuación de cuerpo completo para tomografía por emisión de positrones de [18F] FDG usando redes neuronales profundas

The correction of attenuation effects in Positron Emission Tomography (PET) imaging is fundamental to ob-tain a correct radiotracer distribution. However direct measurement of this attenuation map is not error-free and normally results in additional ionization radiation dose to the patient. Here, we explore the task of whole body attenuation map generation using 3D deep neural networks. We analyze the advantages that an adversarial training can provide to such models. The networks are trained to learn the mapping from non attenuation corrected [18F]-fluorodeoxyglucose PET images to a synthetic Computerized Tomography (sCT) and also to label the input voxel tissue. Then the sCT image is further refined using an adversarial training scheme to recover higher frequency details and lost structures using context information. This work is trained and tested on public available datasets, containing several PET images from different scanners with different ra-diotracer administration and reconstruction modalities. The network is trained with 108 samples and validated on 10 samples. The sCT generation was tested on 133 samples from 8 distinct datasets. The resulting mean absolute error of the tested networks is 96 ± 20 HU and 103 ± 18 HU with a peak signal to noise ratio of 19.3 ± 1.7 dB and 18.6 ± 1.5 dB, for the base model and adversarial model respectively. The attenuation correction is tested by means of attenuation sinograms, obtaining a line of response attenuation mean error lower than 1% with a standard deviation lower than 8%. The proposed deep learning topologies are capable of generating whole body attenuation maps from uncorrected PET image data. Moreover, the accuracy of both methods holds in the presence of data from multiple sources and modalities and are trained on publicly available datasets. Finally, while the adversarial layer enhances visual appearance of the produced samples, the 3D U-Net achieves higher metric performance.La corrección de los efectos de la atenuación en las imágenes de Tomografía por Emisión de Positrones (PET) es fundamental para obtener la correcta distribución del radio trazador. Sin embargo la medición directa del mapa de atenuación no esta libre de errores y normalmente resulta en la absorción de una dosis superior de radiación ionizante por parte del paciente. Aquí, exploramos la tarea de la generación del mapa de atenuación de cuerpo completo usando redes neuronales profundas 3D. Se analizan las ventajas que un entrenamiento adversario puede proveer a estos modelos. Las redes son entrenadas para aprender la conversión desde una imagen de [18F]- fluorodeoxyglucosa PET sin corrección de atenuación a una imagen sintética de Tomografía Computada (sCT) y además obtener una etiqueta del tipo de tejido ´ en los voxeles de la imagen. Luego la imagen de sCT es refinada usando un entrenamiento de tipo adversario para recobrar detalles de alta frecuencia y estructuras perdidas usando información contextual. Este trabajo es entrenado y probado sobre conjuntos de datos públicos, conteniendo distintas imágenes PET de diferentes tomógrafos, distintos modos de administración de dosis y modos de reconstrucción. La red es entrenada con 108 muestras y validada con 10 muestras. La generación del sCT fue probada con ´ 133 muestras de 8 conjuntos de datos independientes. El error medio absoluto de las redes es de 96±20 HU y 103±18 HU con una relación señal ruido pico de ˜ 103 ± 18 HU y 18.6±1.5 dB para el modelo base y el modelo adversario respectivamente. La corrección de atenuación es probada por medio de sinogramas, obteniendo un error medio en la atenuación de las líneas de respuesta menor al 1% con un desvió estándar menor al 8%. Las topologías de aprendizaje profundo propuestas son capaces de generar mapas de atenuación de cuerpo completo a partir de imágenes PET sin corregir. Además, la exactitud de los métodos se sostiene en presencia de datos de múltiples fuentes y modalidades y son entrenadas en conjuntos de datos públicos. Finalmente, mientras se observa que el entrenamiento adversario mejora la apariencia visual de los mapas generados, la topologa 3D U-Net obtiene mejor rendimiento en las métricas.Fil: Rodríguez Colmeiro, Ramiro Germán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universite de Technologie de Troyes; Francia. Universidad Tecnológica Nacional; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Verrastro, Claudio Abel. Universidad Tecnológica Nacional; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Minsky, Daniel Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Grosges, Thomas. Universite de Technologie de Troyes; Franci

Reconstruction of positron emission tomography images using adaptive sliced remeshing strategy

Purpose: The reconstruction of positron emission tomography images is a computationally intensive task which benefits from the use of increasingly complex physical models. Aiming to reduce the computational burden by means of a reduced system matrix, we present a list mode reconstruction approach based on maximum likelihood-expectation maximization and a sliced mesh support. Approach: The reconstruction strategy uses a fully 3D projection along series of 2D meshes arranged in the axial plane of the scanner. These series of meshes describe the continuous volumetric activity using a piece-wise linear function interpolated from the mesh elements. The mesh support is automatically adapted to the underlying structure of the activity by means of a remeshing process. This process finds a high-quality compact mesh representation constrained to a controlled interpolation error. Results: The method is tested using a Monte Carlo simulation of a Hoffman brain phantom and a National Electrical Manufacturers Association image quality phantom acquisition, using different sets of statistics. The reconstructions are compared against a voxelized reconstruction under different conditions, achieving similar or superior results. The number of parameters needed to reconstruct the image in voxel and mesh support is also compared, and the mesh reconstruction permits to reduce the number of nodes used to represent a complex image. Conclusions: The proposed reconstruction strategy reduces the number of parameters needed to describe the activity distribution by more than one order of magnitude for similar voxel size and with similar accuracy than state-of-the-art methods.Fil: Rodríguez Colmeiro, Ramiro Germán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Verrastro, Claudio Abel. Universidad Tecnológica Nacional; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Minsky, Daniel Mauricio. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Grosges, Thomas. Université de Technologie de Troye; Franci