KK
Kirsten Koolstra
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1
Cardiac cine MRI is a modality used to visualize the beating heart by acquiring a sequence of images within a short acquisition window. Even if data collection spans multiple heartbeats, the goal is to capture a single cardiac cycle, that is, one full heartbeat. Achieving this often requires undersampling of k-space, which leads to artifacts that degrade image quality, such as aliasing, blurring, and ghosting. This thesis investigates how deep learning can be used to reconstruct high-quality cardiac cine MR images, which are anatomically accurate and diagnostically useful. A deep learning-based reconstruction method is developed, with particular attention given to the role of training data, network architecture, and k-space sampling strategies. The models are trained using two types of datasets, each of which is transformed to mimic cardiac cine MRI acquisition characteristics and augmented to increase the data variability. The proposed model architecture consists of initial image formation, data consistency, and convolutional denoiser blocks. The denoiser block includes variations in the use and size of temporal kernels to evaluate their impact on reconstruction performance. Experiments are conducted across 4-shot and single-beat acquisition protocols, under different sampling conditions. The proposed models achieve improved reconstruction quality over the benchmark model in most scenarios. This improvement is primarily attributed to the temporal processing of the training data and the inclusion of temporal convolutions. The findings of this work show possible paths for future research in deep learning-based cardiac cine MRI reconstruction.
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Cardiac cine MRI is a modality used to visualize the beating heart by acquiring a sequence of images within a short acquisition window. Even if data collection spans multiple heartbeats, the goal is to capture a single cardiac cycle, that is, one full heartbeat. Achieving this often requires undersampling of k-space, which leads to artifacts that degrade image quality, such as aliasing, blurring, and ghosting. This thesis investigates how deep learning can be used to reconstruct high-quality cardiac cine MR images, which are anatomically accurate and diagnostically useful. A deep learning-based reconstruction method is developed, with particular attention given to the role of training data, network architecture, and k-space sampling strategies. The models are trained using two types of datasets, each of which is transformed to mimic cardiac cine MRI acquisition characteristics and augmented to increase the data variability. The proposed model architecture consists of initial image formation, data consistency, and convolutional denoiser blocks. The denoiser block includes variations in the use and size of temporal kernels to evaluate their impact on reconstruction performance. Experiments are conducted across 4-shot and single-beat acquisition protocols, under different sampling conditions. The proposed models achieve improved reconstruction quality over the benchmark model in most scenarios. This improvement is primarily attributed to the temporal processing of the training data and the inclusion of temporal convolutions. The findings of this work show possible paths for future research in deep learning-based cardiac cine MRI reconstruction.
Low field magnetic resonance imaging (MRI) scanners provide a unique low-cost alternative to conventional MRI scanners. Nevertheless, low-field scanners come with drawbacks such as reduced signal-to-noise ratio and resolution, and also distorted images caused by magnetic field inhomogeneity and non-linear gradient fields. Despite this, it still provides a more accessible way to provide MRI in resource-limited areas. The main goal of this thesis is to develop an algorithm that can reconstruct 3D data from the low-field scanner efficiently and without distortion to the image. To do this, conjugate phase reconstruction (CPR) is employed, particularly frequency segmented reconstruction (Noll, 1991) and multifrequency interpolation (Man et al., 1997).
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Low field magnetic resonance imaging (MRI) scanners provide a unique low-cost alternative to conventional MRI scanners. Nevertheless, low-field scanners come with drawbacks such as reduced signal-to-noise ratio and resolution, and also distorted images caused by magnetic field inhomogeneity and non-linear gradient fields. Despite this, it still provides a more accessible way to provide MRI in resource-limited areas. The main goal of this thesis is to develop an algorithm that can reconstruct 3D data from the low-field scanner efficiently and without distortion to the image. To do this, conjugate phase reconstruction (CPR) is employed, particularly frequency segmented reconstruction (Noll, 1991) and multifrequency interpolation (Man et al., 1997).
Correction of Field Inhomogeneities in Low-Field MRI During Image Reconstruction
Image Distortion Correction
Magnetic resonance imaging (MRI) scanners are a crucial diagnostic tool for radiologists. They are able to render two and threedimensional images of the body without exposure to harmful radiation. MRI systems are, however, costly to build and maintain. This adversely impacts access to these scanners in developing regions. In an effort to combat this problem, a lowfield MRI scanner is being developed. Conventional MRI scanners utilize a superconducting solenoid to generate the main magnetic field. The lowfield scanner, on the other hand, induces the main magnetic field through a Hallbach array of permanent neodymium magnets. While beneficial for production and maintenance costs, as well as portability, the Hallbach array is not able to generate a perfectly homogeneous magnetic field. The inhomogeneities present in the main magnetic field result in distortion of the images when reconstructed using conventional fast Fourier transform (FFT) methods. To counteract this, a reconstruction method that utilizes field information needs to be employed. In this thesis, existing methods to determine and utilize the field information to correct image distortion are explored. From this analysis, it is evident that modelbased (MB) methods are most suitable for reconstruction of data from the lowfield scanner. Current MB methods are only implemented for twodimensional reconstruction. The goal of this thesis is to expand these methods to threedimensional reconstruction. A novel MB method for threedimensional reconstruction is presented. This new method is able to circumvent memory constraints that arise from reconstruction of large data sets. Though the new method requires several hours to reconstruct a 128 × 128 × 30 data set, visual inspection indicates that an accurate result is achieved.
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Magnetic resonance imaging (MRI) scanners are a crucial diagnostic tool for radiologists. They are able to render two and threedimensional images of the body without exposure to harmful radiation. MRI systems are, however, costly to build and maintain. This adversely impacts access to these scanners in developing regions. In an effort to combat this problem, a lowfield MRI scanner is being developed. Conventional MRI scanners utilize a superconducting solenoid to generate the main magnetic field. The lowfield scanner, on the other hand, induces the main magnetic field through a Hallbach array of permanent neodymium magnets. While beneficial for production and maintenance costs, as well as portability, the Hallbach array is not able to generate a perfectly homogeneous magnetic field. The inhomogeneities present in the main magnetic field result in distortion of the images when reconstructed using conventional fast Fourier transform (FFT) methods. To counteract this, a reconstruction method that utilizes field information needs to be employed. In this thesis, existing methods to determine and utilize the field information to correct image distortion are explored. From this analysis, it is evident that modelbased (MB) methods are most suitable for reconstruction of data from the lowfield scanner. Current MB methods are only implemented for twodimensional reconstruction. The goal of this thesis is to expand these methods to threedimensional reconstruction. A novel MB method for threedimensional reconstruction is presented. This new method is able to circumvent memory constraints that arise from reconstruction of large data sets. Though the new method requires several hours to reconstruct a 128 × 128 × 30 data set, visual inspection indicates that an accurate result is achieved.