Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI

Slow acquisition has been one of the historical problems in dynamic magnetic resonance imaging (dMRI), but the rise of compressed sensing (CS) has brought numerous algorithms that successfully achieve high acceleration rates. While CS proposes random sampling for data acquisition, practical CS applications to dMRI have typically relied on random variable-density (VD) sampling patterns, where masks are drawn from probabilistic models, which preferably sample from the center of the Fourier domain. In contrast to this model-driven approach, we propose the first data-driven, scalable framework for optimizing sampling patterns in dMRI. Through a greedy algorithm, this approach allows the data to directly govern the search for a mask that exhibits good empirical performance. Previous greedy approach, designed for static MRI, required very intensive computations, prohibiting their direct application to dMRI, and we address this issue by resorting to a stochastic greedy algorithm that exploits only a fraction of resources compared to the previous approach without sacrificing the reconstruction accuracy. A thorough comparison on in vivo datasets shows the inefficiency of model-based approaches in terms of sampling performance and suggests that our data-driven sampling approach could fully enable the potential of CS applied to dMRI.

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI employ variable-density (VD) sampling, where samples are drawn at random based on a parametric probability model which focuses on the center of the Fourier domain. In stark contrast to this model-driven sampling approaches, we propose a data-driven framework for optimizing sampling in parallel (multi-coil) MRI. Our approach does not assume any structure in the data, and instead optimizes a performance metric (e.g. PSNR) for any given reconstruction algorithm, based on our earlier learning-based sampling framework previously applied to 2D MRI which we also extend to 3D MRI setting in this work by employing lazy evaluations in the greedy algorithm. We show boosted performance for the parallel MRI based on this sampling approach and highlight the inefficiency of variable density approaches. This suggests that data-driven sampling methods could be the key to unlocking the full power of CS applied to MRI.

IEEE2019

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

Despite being a powerful medical imaging technique which does not emit any ionizing radiation, magnetic resonance imaging (MRI) always had the major problem of long scanning times that can take up to an hour depending on the application. It also requires uncomfortable breath-holds due to the slow acquisition, sedation of children and repeated scans in the cases of degraded image quality due to body motion. Recent years have seen new image reconstruction techniques that need less amount of acquired data (i.e., accelerated scans) for reconstructing MRI images: parallel imaging and compressed sensing (CS). Although much work has been done on the reconstruction side, there has been relatively less work on experimental design, i.e., which parts of the Fourier domain to acquire during the scan, an essential factor that considerably affects the performance of image reconstructions. The state-of-the-art experimental designs use random subsampling either based on parametric models or heuristical adaptive models. The requirement of extensive parameter tuning and the random nature of the performance render these methods impractical and unreliable for clinical use. Can we systematically use the data acquired during past MRI scans for the design of accelerated scans with a reliable image quality? This problem is the focus of this thesis which proposes a data-driven scan design approach and training procedures which efficiently and effectively learn the structure inherent in the data, and accordingly, design the scans that directly acquire only the most relevant information during acquisition given an acceleration rate constraint. As a result, this boosts the performance of the existing state-of-the-art compressive sensing techniques on real-world datasets. Moreover, this approach provides strong theoretical guarantees by using tools from statistical learning theory. The intensive computational training procedures of our approach are made feasible by large-scale implementations on a parallel computing cluster. In return, this approach avoids any dependence on parametric or heuristic models and provides a reliably consistent image reconstruction performance for accelerated scans. Our approach is flexible and capable of giving deterministic scan designs specific to the anatomy, to the acceleration rate in use, to the reconstruction algorithm and the scan settings such as static/dynamic and parallel imaging. Apart from measurement designs for MRI, this thesis also considers the reconstruction problem. In particular, we focus on the inverse problems that involve a mixture of regularizers in the objective function, exploiting multiple structures at the same time. For these problems, we propose a reliable and systematic optimization framework and illustrate its effectiveness. Finally, in the last part of the thesis, we present a data-driven model and an optimization method for the design of nearly isometric, linear and dimensionality reducing embeddings.

EPFL2019

Color Demosaicing by Estimating Luminance and Opponent Chromatic Signals in the Fourier Domain

Sabine Süsstrunk

We propose a new method for color demosaicing based on a mathematical model of spatial multiplexing of color. We demonstrate that a one-color per pixel image can be written as the sum of luminance and chrominance. In case of a regular arrangement of colors, such as with the Bayer color filter array (CFA), luminance and chrominance are well localized in the spatial frequency domain. Our algorithm is based on selecting the luminance and chrominance signal in the Fourier domain. This simple and efficient algorithm gives good results, comparable with the Bayesian approach to demosaicing. Additionally, this model allows us to demonstrate that the Bayer CFA is the most optimal arrangement of three colors on a square grid. Visual artifacts of the reconstruction can be clearly explained as aliasing between luminance and chrominance. Finally, this framework also allows us to control the trade-off between algorithm efficiency and quality in an explicit manner.

2002

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

EPFL2019