538 research outputs found
A Multimodal Deep Network for the Reconstruction of T2W MR Images
Multiple sclerosis is one of the most common chronic neurological diseases
affecting the central nervous system. Lesions produced by the MS can be
observed through two modalities of magnetic resonance (MR), known as T2W and
FLAIR sequences, both providing useful information for formulating a diagnosis.
However, long acquisition time makes the acquired MR image vulnerable to motion
artifacts. This leads to the need of accelerating the execution of the MR
analysis. In this paper, we present a deep learning method that is able to
reconstruct subsampled MR images obtained by reducing the k-space data, while
maintaining a high image quality that can be used to observe brain lesions. The
proposed method exploits the multimodal approach of neural networks and it also
focuses on the data acquisition and processing stages to reduce execution time
of the MR analysis. Results prove the effectiveness of the proposed method in
reconstructing subsampled MR images while saving execution time.Comment: 29th Italian Neural Networks Workshop (WIRN 2019
Investigating microstructural variation in the human hippocampus using non-negative matrix factorization
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses
DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI
Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection
and assessment of tumor aggressiveness by using a Gadolinium-based contrast
agent (GBCA). However, GBCA is known to have potential toxic effects. This risk
can be avoided if we obtain DCE-MRI images without using GBCA. We propose,
DCE-former, a transformer-based neural network to generate early and late
response prostate DCE-MRI images from non-contrast multimodal inputs (T2
weighted, Apparent Diffusion Coefficient, and T1 pre-contrast MRI).
Additionally, we introduce (i) a mutual information loss function to capture
the complementary information about contrast uptake, and (ii) a frequency-based
loss function in the pixel and Fourier space to learn local and global
hyper-intensity patterns in DCE-MRI. Extensive experiments show that DCE-former
outperforms other methods with improvement margins of +1.39 dB and +1.19 db in
PSNR, +0.068 and +0.055 in SSIM, and -0.012 and -0.013 in Mean Absolute Error
for early and late response DCE-MRI, respectively.Comment: Accepted at IEEE ISBI 202
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
In this paper, we propose a bi-modality medical image synthesis approach
based on sequential generative adversarial network (GAN) and semi-supervised
learning. Our approach consists of two generative modules that synthesize
images of the two modalities in a sequential order. A method for measuring the
synthesis complexity is proposed to automatically determine the synthesis order
in our sequential GAN. Images of the modality with a lower complexity are
synthesized first, and the counterparts with a higher complexity are generated
later. Our sequential GAN is trained end-to-end in a semi-supervised manner. In
supervised training, the joint distribution of bi-modality images are learned
from real paired images of the two modalities by explicitly minimizing the
reconstruction losses between the real and synthetic images. To avoid
overfitting limited training images, in unsupervised training, the marginal
distribution of each modality is learned based on unpaired images by minimizing
the Wasserstein distance between the distributions of real and fake images. We
comprehensively evaluate the proposed model using two synthesis tasks based on
three types of evaluate metrics and user studies. Visual and quantitative
results demonstrate the superiority of our method to the state-of-the-art
methods, and reasonable visual quality and clinical significance. Code is made
publicly available at
https://github.com/hustlinyi/Multimodal-Medical-Image-Synthesis
Generative Models for Preprocessing of Hospital Brain Scans
I will in this thesis present novel computational methods for processing routine clinical brain scans. Such scans were originally acquired for qualitative assessment by trained radiologists, and present a number of difficulties for computational models, such as those within common neuroimaging analysis software. The overarching objective of this work is to enable efficient and fully automated analysis of large neuroimaging datasets, of the type currently present in many hospitals worldwide. The methods presented are based on probabilistic, generative models of the observed imaging data, and therefore rely on informative priors and realistic forward models. The first part of the thesis will present a model for image quality improvement, whose key component is a novel prior for multimodal datasets. I will demonstrate its effectiveness for super-resolving thick-sliced clinical MR scans and for denoising CT images and MR-based, multi-parametric mapping acquisitions. I will then show how the same prior can be used for within-subject, intermodal image registration, for more robustly registering large numbers of clinical scans. The second part of the thesis focusses on improved, automatic segmentation and spatial normalisation of routine clinical brain scans. I propose two extensions to a widely used segmentation technique. First, a method for this model to handle missing data, which allows me to predict entirely missing modalities from one, or a few, MR contrasts. Second, a principled way of combining the strengths of probabilistic, generative models with the unprecedented discriminative capability of deep learning. By introducing a convolutional neural network as a Markov random field prior, I can model nonlinear class interactions and learn these using backpropagation. I show that this model is robust to sequence and scanner variability. Finally, I show examples of fitting a population-level, generative model to various neuroimaging data, which can model, e.g., CT scans with haemorrhagic lesions
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Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.
The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery
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