Magnetic resonance imaging reconstruction using machine learning and motion compensation

A computer-implemented method of reconstructing a sequence of Magnetic Resonance Imaging (MRI) images, the method comprising:

• obtaining, via one or more processors, a sequence of MRI measurement datasets that have been acquired during a measurement time duration during which a patient movement occurs, each respective one of MRI measurement datasets of the sequence of MRI measurement datasets being acquired using an under-sampling trajectory in k-space and a receiver coil array;
• and
• performing, via one or more processors, an iterative process to obtain a sequence of reconstructed MRI images based on the MRI measurement datasets,
• wherein the iterative process comprises, for each iteration of the multiple iterations of the iterative process, a regularization operation and a data-consistency operation to obtain a respective current MRI image,
• wherein the data-consistency operation is based on differences between the MRI measurement datasets and synthesized MRI measurement datasets, the synthesized MRI measurement datasets being based on a k-space representation of a prior image of the multiple iterations, an undersampling trajectory, and a sensitivity map associated with the receiver coil array,
• wherein an input to the regularization operation comprises, for each iteration of the multiple iterations, a concatenation of multiple prior images obtained from a previous iteration of the multiple iterations, the multiple prior images being associated with multiple motion states of the patient movement and with multiple points in time throughout the measurement time duration, and
• wherein the regularization operation comprises, for each iteration of the multiple iterations, a first convolution of the input in spatial domain, a second convolution of the input in time domain, and a third convolution of the input in motion-state domain.

A computer-readable storage medium comprising program code that, when executed by at least one processor, cause the at least one processor to reconstruct a sequence of Magnetic Resonance Imaging (MRI) images by:

• obtaining a sequence of MRI measurement datasets that have been acquired during a measurement time duration during which patient movement occurs, MRI measurement datasets of the sequence of MRI measurement datasets being acquired using a respective under-sampling trajectory in k-space and a receiver coil array;
• and
• performing an iterative process to obtain a sequence of reconstructed MRI images based on the MRI measurement datasets,
• wherein the iterative process comprises, for each iteration of the multiple iterations of the iterative process, a regularization operation and a data-consistency operation to obtain a respective current MRI image,
• wherein the data-consistency operation is based on differences between the MRI measurement datasets and synthesized MRI measurement datasets, the synthesized MRI measurement datasets being based on a k-space representation of a prior MRI image of the multiple iterations, an undersampling trajectory, and a sensitivity map associated with the receiver coil array, and
• wherein an input to the regularization operation comprises, for each iteration of the multiple iterations, a concatenation of multiple prior images obtained from a previous iteration of the multiple iterations, the multiple prior images being associated with multiple motion states of the patient movement and with multiple points in time throughout the measurement time duration, the multiple prior images being warped from their respective motion states to a reference motion state.