Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver.

Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.

Recurrent inference machines for reconstructing heterogeneous MRI data.

Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learned from data. In clinical practice, both the underlying anatomy as well as image acquisition settings vary. For this reason, deep neural networks must be able to reapply what they learn across different measurement conditions. We propose to use Recurrent Inference Machines (RIM) as a framework for accelerated MRI reconstruction. RIMs solve inverse problems in an iterative and recurrent inference procedure by repeatedly reassessing the state of their reconstruction, and subsequently making incremental adjustments to it in accordance with the forward model of accelerated MRI. RIMs learn the inferential process of reconstructing a given signal, which, in combination with the use of internal states as part of their recurrent architecture, makes them less dependent on learning the features pertaining to the source of the signal itself. This gives RIMs a low tendency to overfit, and a high capacity to generalize to unseen types of data. We demonstrate this ability with respect to anatomy by reconstructing brain and knee scans, as well as other MRI acquisition settings, by reconstructing scans of different contrast and resolution, at different field strength, subjected to varying acceleration levels. We show that RIMs outperform CS not only with respect to quality metrics, but also according to a rating given by an experienced neuroradiologist in a double blinded experiment. Finally, we show with qualitative results that our model can be applied to prospectively under-sampled raw data, as acquired by pre-installed acquisition protocols.