335 research outputs found

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Feasibility of functional MRI on point-of-care MR platforms

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    Magnetic resonance imaging (MRI) has proven to be a clinically valuable tool that can produce anatomical and functional images with improved soft tissue contrast compared to other imaging modalities. There has recently been a surge in low- and mid-field scanners due to hardware developments and innovative acquisition techniques. These compact scanners are accessible, offer reduced siting requirements and can be made operational at a reduced cost. This thesis aims to implement blood-oxygen-level-dependent (BOLD) resting-state functional MRI (fMRI) at such a mid-field point-of-care scanner. The availability of this technique can be beneficial to get neurological information in cases of traumatic brain injury, stroke, epilepsy, and dementia. This technique was previously not implemented at low- and mid-field since signal-to-noise ratio and the contrast scale with field strength. Studies were conducted to gauge the performance of an independent component analysis (ICA) based platform (GraphICA) to analyze artificially added noisy resting state functional data previously collected with a 3T scanner. This platform was used in later chapters to preprocess and perform functional connectivity studies with data from a mid-field scanner. A single echo gradient echo echoplanar imaging (GE-EPI) sequence is typically used for BOLD-based fMRI. Task-based fMRI experiments were performed with this sequence to gauge the feasibility of this technique on a mid-field scanner. Once the feasibility was established, the sequence was further optimized to suit mid-field scanners by considering all the imaging parameters. Resting-state experiments were conducted with an optimized single echo GE-EPI sequence with reduced dead time on a mid-field scanner. Temporal and image signal-to-noise ratio were calculated for different cortical regions. Along with that, functional connectivity studies and identification of resting-state networks were performed with GraphICA which demonstrated the feasibility of this resting-state fMRI at mid-field. The reliability and repeatability of the identified networks were assessed by comparing the networks identified with 3T data. Resting-state experiments were conducted with a multi-echo GE-EPI sequence to use the dead time due to long T2* at mid-field effectively. Temporal signal-to-noise was calculated for different cortical regions. Along with that, functional connectivity studies and identification of resting-state networks were performed with GraphICA which demonstrated the feasibility of this resting-state fMRI at mid-field

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients

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    Introduction: Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure—compared to water-based methods alone—but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods: In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results: Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion: In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR

    Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

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    Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Enhancing precision in human neuroscience

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    Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience

    Simulation and Synthesis for Cardiac Magnetic Resonance Image Analysis

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