Machine Learning: Practical Approaches to Reconstruction
Mehmet Akçakaya1,2

1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States


Machine learning methods have found wide use in MRI reconstruction, with a recent focus on artificial neural networks, in particular convolutional neural networks. In this talk, we will overview both model-based and data-driven machine learning approaches for reconstruction. We will also consider practical aspects of implementing deep artificial neural networks for MRI reconstruction.

Target Audience:

Engineers and scientists, who are interested in using machine learning for advanced reconstruction of accelerated acquisitions, or are interested in understanding the nuances and implications of performing such acceleration.


Understand the current practical approaches for machine learning based reconstructions in accelerated MRI; and understand how they compare to other approaches.


Machine learning (ML) based techniques have gathered interest as a possible means to improve reconstruction quality with different undersampling patterns. Recent efforts have focused on the use of artificial neural networks (ANNs) for data-driven learning, and in particular on deep learning. Our purpose is to look at both model-based and data-driven ML methods that have been used for MRI reconstruction.

Methods and Results:

Model-based ML techniques have been used in MRI reconstruction, primarily for image regularization. Earlier works have utilized a dictionary model to represent “blocks” of image data in a sparse manner (1,2). These methods were used in conjunction with a compressed sensing framework (3,4) to improve upon reconstruction using sparsity in pre-defined transform domains. Such dictionaries may be learned for a given dataset or from a training database, ultimately generating an adaptive linear transformation of the data, while the overall reconstruction process is non-linear. Similar ideas were explored in other works (5,6).

The utility of non-linear processing was also explored from other perspectives. Specifically, the non-linear GRAPPA approach (7) utilized the so-called kernel methods (8) to improve k-space interpolation used in GRAPPA (9). The main idea of kernel methods is to map the data to a higher-dimensional feature space, using pre-defined non-linear transforms. In this feature space, linear estimation is performed, which corresponds to a non-linear operation in the underlying data space. This method was shown to reduce parallel imaging artifacts in certain applications. However, the choice of the non-linear transforms, i.e. kernels, is heuristic, thus not leading to a fully data-driven approach.

Another line of work utilizes non-linear manifold learning for dynamic MRI reconstruction (10-12). These methods model the imaging data as a low-dimensional non-linear “surface,” which is characterized by a few underlying unknown parameters. The manifold structure can be learned in multiple ways, including Laplacian eigenmaps (13) that learn local geometry, as well as kernel methods (8) that allow regularization in higher-dimensional feature spaces. These two approaches were used for dynamic MRI reconstruction in (11) and (12), respectively. These manifold learning methods also utilize pre-determined non-linearities, either to determine local distances among points on the manifold or as mappings to the feature space.

Recent efforts have focused on more data-driven approaches that aim to learn non-linear relationships from the MRI data. Most of these methods utilize artificial neural networks, along with large training databases of images/measurements to perform the learning. A common line of approach learns to represent the main features of the underlying images using convolutional neural networks (CNNs) (14-18). In effect, these methods extend on the earlier dictionary models, by incorporating more complicated data-driven non-linear models. Deep learning of the CNNs is performed using imaging databases, and various ideas including unfolding of reconstruction iterations (14) and generative adversarial networks (17) have also been explored.

Deep learning approaches have also been used to infer other parts of the MRI acquisition system. In (19), the process of generating images from raw data was studied for various MRI acquisitions. A neural network with fully-connected layers, trained on a large database, was utilized to provide a uniform reconstruction framework for various acquisition schemes. In (20), CNNs were used to learn the redundancies among the channels of the receiver coil-array from scan-specific ACS data, without the need for training databases. Thus, this work extends on the linear convolutions (9) and kernel-based approaches (7) used previously for parallel imaging.

Implementation of MRI reconstruction techniques based on neural network approaches are subject to certain other practical considerations. As we study these different approaches, we will also look at different loss functions, optimization approaches for minimizing such losses, backpropagation, and other implementation points.


ML methods have found wide use in MRI reconstruction, with a recent focus on ANNs, in particular CNNs. We will provide an overview of both model-based and data-driven ML approaches, while also studying practical aspects of implementing deep CNNs for MRI reconstruction. ML holds a significant promise in improving MRI reconstructions, but careful consideration should be given to understanding its utility and limitations, along with its advantages.


The author’s work is supported by NIH R00HL111410, NIH P41EB015894, NIH U01EB025144, and NSF CAREER CCF-1651825.


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Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)