21 research outputs found
Histogram Equalization for Improving Quality of Low-resolution Ultrasonography Images
The current development of digital image processing techniques have been very rapid. Application of digital image processing both hardware and software are available with a variety of features as a form of superiority. Medical ultrasonography is one of the results of digital image processing technology. It is a kind of diagnostic imaging technique with ultrasonic that is used to produce images of internal organs and muscles, size, structure, and wound pathology, which makes this technique is useful for checking organ. However the images produced by low resolution ultrasonography device is not fully produce clear information. In this research we use histogram equalization to improve image quality. In this paper we emphasize on the comparison of the two methods in the histogram equalization, namely Enhance Contrast Using Histogram Equalization (ECHE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The results showed that CLAHE give the best results, with the parameter value Nbins 256 and Distribution Rayleigh with MSE value 9744.80 and PSNR value 8.284150
Applications of Physically Accurate Deep Learning for Processing Digital Rock Images
Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis.
Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques.
Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks
Advanced capabilities for planar X-ray systems
Mención Internacional en el título de doctorThe past decades have seen a rapid evolution towards the use of digital detectors
in radiology and a more flexible robotized movement of the system components,
X-ray tube and detector. This evolution opened the possibility for incorporating
advanced capabilities in these planar X-ray systems, and for providing new valuable
diagnostic information compared to the previous technology. Some of the current
challenges for radiography are to obtain more quantitative images and to reduce the
inherent superposition of tissues because of the 2D nature of the technique.
Dual energy radiography, based on the acquisition of two images at different
source voltages, enables a separate characterization of soft tissue and bone structures.
Its benefits over conventional radiography have been proven in different applications,
since it improves information content without adding significant extra
acquisition time or radiation dose.
In a different direction, a really disruptive advance would be to obtain 3D imaging
with systems designed just for planar images. The incorporation of tomographic
capabilities into these systems would have to deal with the acquisition of a limited
number of projections, with non-standard geometrical configurations.
This thesis presents original contributions in these two directions: dual energy
radiography and 3D imaging with X-ray systems designed for planar imaging. The
work is framed in a line of research of the Biomedical Imaging and Instrumentation
Group from the Bioengineering and Aerospace Department of University Carlos III
de Madrid working jointly with the University Hospital Gregorio Marañón, focused
on the advance of radiology systems. This research line is carried out in collaboration
with the group of Computer Architecture, Communications and Systems (ARCOS),
from the same university, the Imaging Research Laboratory (IRL) of the University
of Washington and the research center CREATIS, France. The research has a clear
focus on technology transfer to the industry through the company Sedecal, a Spanish
multinational among the 10 best world companies in the medical imaging field.
The first contribution of this thesis is a complete novel protocol to incorporate
dual energy capabilities that enable quantitative planar studies. The proposal is
based on the use of a preliminary calibration with a very simple and low-cost phantom
formed by two parts that represent soft tissue and bone equivalent materials.
This calibration is performed automatically with no strict placement requirements.
Compared to current Dual-energy X-ray Absorptiometry (DXA) systems, 1) it provides
real mass-thickness values directly, enabling quantitative planar studies instead
of relative comparisons, and 2) it is based on an automatic preliminary calibration without the need of interaction of an experienced technician.
The second contribution is a novel protocol for the incorporation of tomographic
capabilities into X-ray systems originally intended for planar imaging. For this purpose,
we faced three main challenges.
First, the geometrical trajectory of equipment follows non-standard circular orbits,
thus posing severe difficulties for reconstruction. To handle this, the proposed
protocol comprises a new geometrical calibration procedure that estimates all the
system parameters per-projection.
Second, the reconstruction of a limited number of projections from a reduced angular
span leads to severe artifacts when using conventional reconstruction methods.
To deal with these limited-view data, the protocol includes a novel advanced reconstruction
method that incorporates the surface information of the sample, which
can be extracted with a 3D light surface scanner. These data are introduced as an
imposed constraint following the Split Bregman formulation. The restriction of the
search space by exploiting the surface-based support becomes crucial for a complete
recovery of the external contour of the sample and surroundings when the angular
span is extremely reduced. The modular, efficient and flexible design followed for its
implementation allows for the reconstruction of limited-view data with non-standard
trajectories.
Third, the optimization of the acquisition protocols has not yet explored with
these systems. This thesis includes a study of the optimum acquisition protocols
that allowed us to identify the possibilities and limitations of these planar systems.
Using the surface-constrained method, it is possible to reduce the total number of
projections up to 33% and the angular span down to 60 degrees.
The contributions of this thesis open the way to provide depth and quantitative
information very valuable for the improvement of radiological diagnosis. This could
impact considerably the clinical practice, where conventional radiology is still the
imaging modality most used, accounting for 80-90% of the total medical imaging
exams. These advances open the possibility of new clinical applications in scenarios
where 1) the reduction of the radiation dose is key, such as lung cancer screening or
Pediatrics, according to the ALARA criteria (As Low As Reasonably Achievable),
2) a CT system is not usable due to movement limitations, such as during surgery
or in an ICU and 3) where costs issues complicate the availability of CT systems,
such as rural areas or underdeveloped countries.
The results of this thesis has a clear application in the industry, since it is part
of a proof of concept of the new generation of planar X-ray systems that will be
commercialized worldwide by the company SEDECAL (Madrid, Spain).Los últimos años están viendo un rápido avance de los sistemas de radiología hacia el
uso de detectores digitales y a una mayor flexibilidad de movimientos de los principales
componentes del sistema, el tubo de rayos X y el detector. Esta evolución abre
la posibilidad de incorporar capacidades avanzadas en sistemas de imagen plana por
rayos X proporcionando nueva información valiosa para el diagnóstico. Dos retos en
radiografía son obtener imágenes cuantitativas y reducir la superposición de tejidos
debida a la naturaleza proyectiva de la técnica.
La radiografía de energía dual, basada en la adquisición de dos imágenes a diferente
kilovoltaje, permite obtener imágenes de tejido blando y hueso por separado.
Los beneficios de esta técnica que aumenta la cantidad de información sin añadir
un tiempo de adquisición o de dosis de radiación extra significativos frente al uso de
radiografía convencional, han sido demostrados en diferentes aplicaciones.
En otra dirección, un avance realmente disruptivo sería la obtención de imagen
3D con sistemas diseñados únicamente para imagen plana. La incorporación de capacidades
tomográficas en estos sistemas tendría que lidiar con la adquisición de un
número limitado de proyecciones siguiendo trayectorias no estándar.
Esta tesis presenta contribuciones originales en esas dos direcciones: radiografía
de energía dual e imagen 3D con sistemas de rayos X diseñados para imagen plana.
El trabajo se encuadra en una línea de investigación del grupo de Imagen Biomédica
e Instrumentación del Departamento de Bioingeniería e Ingeniería Aerospacial de
la Universidad Carlos III de Madrid junto con el Hospital Universitario Gregorio
Marañon, centrada en el avance de sistemas de radiología. Esta línea de investigación
se desarollada en colaboración con el grupo Computer Architecture, Communications
and Systems (ARCOS), de la misma universidad, el grupo Imaging Research Laboratory
(IRL) de la Universidad de Washington y el centro de investigación CREATIS,
de Francia. Se trata de una línea de investigación con un claro enfoque de transferencia
tecnológica a la industria a través de la compañía SEDECAL, una multinacional
española de entre las 10 líderes del mundo en el campo de la radiología.
La primera contribución de esta tesis es un protocolo completo para incorporar
capacidades de energía dual que permitan estudios cuantitativos de imagen plana.
La propuesta se basa en una calibración previa con un maniquí simple y de bajo coste
formado por dos materiales equivalentes de tejido blando y hueso respectivamente.
Comparado con los sistemas actuales DXA (Dual-energy X-ray Absorptiometry),
1) proporciona valores reales de tejido atravesado, 2) se basa en una calibración
automática que no requiere la interacción de un técnico con gran experiencia. La segunda contribución es un protocolo nuevo para la incorporación de capacidades
tomográficas en sistemas de rayos X originariamente diseñados para imagen
plana. Para ello, nos enfrentamos a tres principales dificultades.
En primer lugar, las trayectorias que pueden seguir la fuente y el detector en
estos sistemas no constituyen órbitas circulares estándares, lo que plantea retos importantes
en la caracterización geométrica. Para solventarlo, el protocolo propuesto
incluye una calibración geométrica que estima todos los parámetros geométricos del
sistema para cada proyección.
En segundo lugar, la reconstrucción de un número limitado de proyecciones
adquiridas en un rango angular reducido da lugar a artefactos graves cuando se
reconstruye con algoritmos convencionales. Para lidiar con estos datos de ángulo
limitado, el protocolo incluye un nuevo método avanzado de reconstrucción que incorpora
la información de superficie de la muestra, que se puede se obtener con un
escáner 3D. Esta información se impone como una restricción siguiendo la formulación
de Split Bregman, para compensar la falta de datos. La restricción del espacio
de búsqueda a través de la explotación del soporte basado en superficie, es crucial
para una recuperación completa del contorno externo de la muestra cuando el rango
angular es extremadamente pequeño. El diseño modular, eficiente y flexible de la
implementación propuesta permite reconstruir datos de ángulo limitado obtenidos
con posiciones de fuente y detector no estándar.
En tercer lugar, hasta la fecha, no se ha explorado la optimización del protocolo
de adquisición con estos sistemas. Esta tesis incluye un estudio de los protocolos
óptimos de adquisición que permitió identificar las posibilidades y limitaciones de
estos sistemas de imagen plana. Gracias al método de reconstrucción basado en
superficie, es posible reducir el número total de proyecciones hasta el 33% y el rango
angular hasta 60 grados.
Las contribuciones de esta tesis abren la posibilidad de proporcionar información
de profundidad y cuantitativa muy valiosa para la mejora del diagnóstico radiológico.
Esto podría impactar considerablemente en la práctica clínica, donde la radiología
convencional es todavía la modalidad de imagen más utilizada, abarcando el 80-
90% del total de los exámenes de imagen médica. Estos avances abren la posibilidad
de nuevas aplicaciones clínicas en escenarios donde 1) la reducción de la dosis de
radiación es clave, como en screening de cáncer de pulmón, de acuerdo con el criterio
ALARA (As Low As Reasonably Achievable), 2) no se puede usar un sistema
TAC por limitaciones de movimiento como en cirugía o UCI, o 3) el coste limita la
disponibilidad de sistemas TAC, como en zonas rurales o en países subdesarrollados.
Los resultados de esta tesis presentan una clara aplicación industrial, ya que
son parte de un prototipo de la nueva generación de sistemas planos de rayos X que
serán distribuidos mundialmente por la compañía SEDECAL.This thesis has been developed as part of several research projects with public funding:
- DPI2016-79075-R. ”Nuevos escenarios de tomografía por rayos X”, IP: Mónica
Abella García, Ministerio de Economía y Competitividad, 01/01/2017-31/12/2019,
147.620 e.
- ”Nuevos escenarios de tomografía por rayos X (NEXT) DPI2016-79075-R.
Ministerio de Economía”, Industria y Competitividad. (Universidad Carlos
III de Madrid). 30/12/2016-29/12/2019. 147.620 e.
(…)
- FP7-IMI-2012 (GA-115337), ”PreDict-TB: Model-based preclinical development
of anti-tuberculosis drug combinations”. FP7-IMI - Seventh Framework
Programme (EC-EFPIA). Unión Europea. (Universidad Carlos III de Madrid).
01/05/2012-31/10/2017.
(…)
- TEC2013-47270-R, ”Avances en Imagen Radiológica (AIR)”, Ministerio de
Economía y Competitividad”, 01/01/2014-31/12/2016. IP: Mónica Abella Garcia
and Manuel Desco Menéndez. 160.204 e
(…)
- RTC-2014-3028-1, ”Nuevos Escenarios Clínicos con Radiología Avanzada (NECRA)”,
Ministerio de Economía y Competitividad, 01/06/2014-31/12/2016 IP: Mónica
Abella García. 2014-2016. 219.458,96 e
- IDI-20130301, ”Nuevo sistema integral de radiografía (INNPROVE: INNovative
image PROcessing in medicine and VEterinary)”, IP: Mónica Abella García
and Manuel Desco Menéndez. Ministerio de Economía y Competitividad.
Subcontratación CDTI, 14/01/2013-31/03/2015. Total: 1.860.629e (UC3M:
325.000e). (Art. 83)
- IPT-2012-0401-300000 INNPACTO 2012, ”Tecnologías para Procedimientos
Intraoperatorios Seguros y Precisos. XIORT. MINECO. (Universidad Carlos
III de Madrid). 01/01/2013-31/12/2015.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Doménec Ros Puig.- Secretario: Cyril Riddell.- Vocal: Yannick Boursie
Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty
ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY
By Rebecca Nichole Mahon, MS
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University.
Virginia Commonwealth University, 2018
Major Director: Dr. Elisabeth Weiss,
Professor,
Department of Radiation Oncology
Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted.
Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions
Reduction of Limited Angle Artifacts in Medical Tomography via Image Reconstruction
Artifacts are unwanted effects in tomographic images that do not reflect the nature of the object. Their widespread occurrence makes their reduction and if possible removal an important subject in the development of tomographic image reconstruction algorithms. Limited angle artifacts are caused by the limited angular measurements, constraining the available tomographic information. This thesis focuses on reducing these artifacts via image reconstruction in two cases of incomplete measurements from: (1) the gaps left after the removal of high density objects such as dental fillings, screws and implants in computed tomography (CT) and (2) partial ring scanner configurations in positron emission tomography (PET). In order to include knowledge about the measurement and noise, prior terms were used within the reconstruction methods. Careful consideration was given to the trade-off between image blurring and noise reduction upon reconstruction of low-dose measurements.Development of reconstruction methods is an incremental process starting with testing on simple phantoms towards more clinically relevant ones by modeling the respective physical processes involved. In this work, phantoms were constructed to ensure that the proposed reconstruction methods addressed to the limited angle problem. The reconstructed images were assessed qualitatively and quantitatively in terms of noise reduction, edge sharpness and contrast recovery.Maximum a posteriori (MAP) estimation with median root prior (MRP) was selected for the reconstruction of limited angle measurements. MAP with MRP successfully reduced the artifacts caused by limited angle data in various datasets, tested with the reconstruction of both list-mode and projection data. In all cases, its performance was found to be superior to conventional reconstruction methods such as total-variation (TV) prior, maximum likelihood expectation maximization (MLEM) and filtered backprojection (FBP). MAP with MRP was also more robust with respect to parameter selection than MAP with TV prior.This thesis demonstrates the wide-range applicability of MAP with MRP in medical tomography, especially in low-dose imaging. Furthermore, we emphasize the importance of developing and testing reconstruction methods with application-specific phantoms, together with the properties and limitations of the measurements in mind