5 research outputs found
Super-resolution in brain Diffusion Weighted Imaging (DWI)
Abstract. Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the partial volume effect (PVE) that may completely alter the spatial resolution and therefore the visualization of microanatomical details. In this work, a new superresolution method will be presented, taking advantage of the redundant structural patterns that shape the brain. The proposed method couples low-high resolution information and explores different directional spaces that might exploit the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demostrates an improvement of about 3 dB when using the typical PSNR.Las im谩genes de Difusi贸n Ponderada (DWI por sus siglas en ingl茅s) han probado ser de gran utilidad en proceso de an谩lisis de la arquitectura del cerebro y en investigaciones acerca de la organizaci贸n de tractos y la conectividad neuronal. Sin embargo, el uso cl铆nico de la im谩genes DW est谩 limitado actualmente por algunos artefactos propios de la adquisici贸n, tales como el efecto de volumen parcial (PVE), que afecta la resoluci贸n espacial y por ende la visualizaci贸n de detalles microanat贸micos. En este trabajo de tesis se presenta un nuevo m茅todo de super-resoluci贸n que aprovecha lo redundante de los patrones estructurales que dan forma al cerebro el cerebro. El m茅todo propuesto acopla informaci贸n de alta /baja resoluci贸n y explora diferentes espacios de representaci贸n para las caracter铆sticas direccionales y el contenido espectral de las DWI. Una comparaci贸n con m茅todos cl谩sicos de interpolaci贸n demuestra una mejora de cerca de 3dB usando como m茅trica el PSNR.Maestr铆
Super resoluci贸n (SR) en im谩genes de resonancia magn茅tica DWI de cerebro usando estimaci贸n bayesiana
En la presente tesis, se propone un m茅todo bayesiano de S煤per resoluci贸n (SR) que obtiene im谩genes de alta resoluci贸n (HR) DWI a partir de im谩genes degradadas de baja resoluci贸n (LR), tratando de recuperar un m谩ximo de la informaci贸n en alta frecuencia. Bajo la formuaci贸n bayesiana, la imagen desconocida de alta resoluci贸n (HR), el proceso de adquisici贸n y los par谩metros del modelo son modelados como procesos estoc谩sticos. El t茅rmino de verosimilitud es modelado usando una distribuci贸n gausiana para estimar el error entre la representaci贸n y las observaciones. El t茅rmino a priori se modela como una distribuci贸n gausiana multivariada en el que los pesos del vecindario corresponden a variables intermedias que se introducen con dos prop贸sitos: modelar las relaciones locales con una distribuci贸n Laplaciana y utilizar la informaci贸n m谩s relevante de su vecindario. En consecuencia, la matriz de covarianza de los pesos de este prior se aproxima por variables latentes que se calculan de las relaciones locales modeladas con una Laplaciana. Los resultados experimentales muestran que el m茅todo supera la l铆nea base por 2.56 dB usando como m茅trica el PSNR para una colecci贸n de 35 casos.Abstract: In this thesis, a Bayesian super resolution (SR) method obtains high resolution (HR) brain Diffusion-Weighted Magnetic Resonance Imaging (DMRI) images from degraded low resolution (LR) images. Under a Bayesian formulation, the unknown HR image, the acquisition process and the unknown parameters are modeled as stochastic processes. The likelihood model is modeled using a Gaussian distribution to estimate the error between the representation and the observations. The prior is introduced as a Multivariate Gaussian Distribution, for which the inverse of the covariance matrix is approximated by Laplacian-like functions that model the local relationships, capturing thereby non-homogeneous relationships between neighbor intensities. Experimental results show the method outperforms the base line by 2.56 dB when using PSNR as a metric of quality in a set of 35 cases.Maestr铆
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Deep learning assisted MRI guided attenuation correction in PET
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological
and functional details of the tissue at the molecular level. However, the acquired PET images
have some limitations such as the attenuation. PET attenuation correction is an essential step to
obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners,
magnetic resonance (MR) images are used to address the problem of PET attenuation correction.
The MR images segmentation is one simple and robust approach to create pseudo computed
tomography (CT) images, which are used to generate attenuation coefficient maps to correct the
PET attenuation. Recently, deep learning has been proposed and used as a promising technique
to efficiently perform MR and various medical images segmentation.
In this research work, deep learning guided segmentation approaches have been proposed
to enhance the bone class segmentation of MR brain images in order to generate accurate
pseudo-CT images. The first approach has introduced the combination of handcrafted features
with deep learning features to enrich the set of features. Multiresolution analysis techniques,
which generate multiscale and multidirectional coefficients of an image such as contourlet and
shearlet transforms, are applied and combined with deep convolutional neural network (CNN)
features. Different experiments have been conducted to investigate the number of selected
coefficients and the insertion location of the handcrafted features.
The second approach aims at reducing the segmentation algorithm鈥檚 complexity while
maintaining the segmentation performance. An attention based convolutional encode-decoder
network has been proposed to adaptively recalibrate the deep network features. This attention based
network consists of two different squeeze and excitation blocks that excite the features
spatially and channel wise. The two blocks are combined sequentially to decrease the number
of network鈥檚 parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A
pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w
images. Multiple fine tuning approaches and experiments have been conducted to study the best
fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and
T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been
used to carry an objective evaluation to measure the segmentation performance of the results
obtained by the three proposed methods. The first and second approaches have been validated
with other studies in the literature that applied deep network based segmentation technique to
perform MR based attenuation correction for PET images. The proposed methods have shown
an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC)
from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%.
The proposed excitation-based CNN has decreased the model complexity by decreasing the
number of trainable parameters by more than 46% where less computing resources are required
to train the model. The proposed hybrid transfer learning method has shown its superiority to
build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied
transfer learning methods especially with the bone class where the DSC is increased from 0.3841
to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than
transfer learning using open and conservative fine tuning
Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances
This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD
Imaging Sensors and Applications
In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome