Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Cardiovascular MRI

Motivation: Cardiac cine MRI reconstruction is a natural high-dimensional problem that poses great challenges to deep learning.

Goal(s): To develop a new deep learning method that can work efficiently in cardiac cine MRI, even with limited training data.

Approach: In this work, the proposed method DeepSSL significantly alleviates training and generalization challenges of deep learning in cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling.

Results: Extensive results show that DeepSSL can work efficiently even with highly limited training data (5~10 cases), and provides state-of-the-art reconstructions while reduces data demand by up to 75%. It further shows robustness in prospective real-time MRI.

Impact: The proposed deep separable spatiotemporal learning (DeepSSL) significantly alleviates the training and generalization challenges of deep learning in high-dimensional cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling.

Keywords: AI/ML Image Reconstruction, Cardiovascular

Motivation: Cardiac CINE provides dynamic images of the heart for morphology and function assessment. Single-heartbeat CINE enables faster acquisition times and the study of heart rate variations, but conventional reconstruction methods incur significant computational cost.

Goal(s): This study aims to speed up single-heartbeat CINE reconstruction by using deep learning reconstruction.

Approach: We propose a novel, rapid, end-to-end deep learning pipeline for motion estimation and motion-corrected single-heartbeat CINE reconstruction with golden-angle radial acquisition.

Results: The network reconstructs each CINE slice in ~40 seconds (400 times faster than state-of-the-art), with comparable image quality, achieving SSIM values ranging from 0.75 to 0.84 across cardiac phases and slices.

Impact: The proposed approach enables reconstruction of single-heartbeat golden-angle radial CINE acquisition in ~40 seconds, making it clinically feasible. Single-heartbeat CINE could reduce scan times, achieve acquisitions of multiple slices in a single breath-hold and be robust to heart rate variations.

Keywords: AI/ML Image Reconstruction, Cancer, 4D-MRI\Enhancement\Temporal-compensation

Motivation: Four-dimensional Magnetic Resonance Imaging (4D-MRI) shows promise for motion management in abdominal radiotherapy. However, the prevalent undersampling often hampers its image quality. 

Goal(s): To enhance the image quality of 4D-MRI, we propose Tco-SEN, a deep-learning model to exploit its properties. 

Approach: Tco-SEN employs a two-stage architecture and a customized loss penalty, enabling effective restoration of detailed features and preservation of anatomical structures.

Results:  Compared to state-of-the-art algorithms, Tco-SEN significantly enhances image quality by improving spatial resolution, reducing motion artifacts and noise, and preserving delicate structures. Furthermore, our method enhances the accuracy of subsequent motion modeling in 4D-MRI, highlighting its potential for clinical applications.

Impact: Tco-SEN effectively improves the image quality of 4D-MRI, benefiting more accurate tumor delineation and motion estimation. This advancement promotes the application of 4D-MRI in cancer radiotherapy, ultimately enhancing the accuracy of abdominal cancer radiation treatment.

Keywords: AI/ML Image Reconstruction, Cardiovascular, Self-Supervised learning, Feature learning

Motivation: Most existing deep learning-based MR image reconstruction methods are supervised learning, relying on fully-sampled images, which is challenging to acquire in practice.

Goal(s): We aim to leverage undersampled data in a self-supervised reconstruction framework to enhance expressibility and model performance.

Approach: We use information maximization methods to learn sampling-invariant features from undersampled images and incorporate them in a self-supervised reconstruction network.

Results: The proposed method can learn sampling-invariant features from undersampled data, which enhance the reconstruction performance, enabling self-supervised MR image reconstruction for up to 16× undersampling.

Impact: The proposed self-supervised feature learning strategy can extract sampling-invariant features from undersampled images, effectively assisting the reconstruction of undersampled cardiac cine MR imaging without requiring fully-sampled images. This feature learning strategy may also be advantageous for other downstream tasks.

Keywords: AI/ML Image Reconstruction, New Trajectories & Spatial Encoding Methods

Motivation: Sampling patterns in deep learning (DL) or compressed sensing (CS) based accelerated dynamic MRI reconstructions are typically chosen heuristically. k-t sampling patterns can be optimized to capture the spatio-temporal characteristics of dynamic MRI data more efficiently.

Goal(s): Our objective is to develop a method for optimizing k-t sampling patterns for dynamic MRI.

Approach: We extend the recently developed AutoSamp framework to dynamic MRI setting to jointly optimize k-t sampling and reconstruction. We test our method on a cardiac cine dataset.

Results: DL reconstruction with optimized k-t patterns using the proposed method produces higher quality results with reduced spatial and temporal artifacts.

Impact: Dynamic MRI reconstructions with learned sampling patterns improves reconstruction quality. The learned patterns can also provide insights about designing general k-t MRI sampling patterns.

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

Motivation:
Neural implicit k-space representations (NIK) enable binning-free respiratory-resolved MR reconstructions in a data-driven manner. The multi-dimensionality of MR, i.e., provided in dual-echo acquisitions, is expected to improve reconstruction performance and allows for further echo-processing.

Goal(s): A Dual-Echo-NIK that takes advantage of the redundant data present in two echoes and enables subsequent water-fat-separation.

Approach: We propose three Dual-Echo-NIK variants trained (1) individually, (2) jointly and (3) in an echo-modulated way. Motion-resolved echo and water-fat reconstructions are evaluated on a free-breathing phantom simulation and in-vivo.

Results: Quantitative simulations demonstrate improved performance for the modulated Dual-Echo-NIK. In-vivo reconstructions reveal sharper reconstructions when both echoes are utilized.

Impact: The Dual-Echo Neural Implicit k-space Representations indicate how echo information can lead to improved motion-resolved reconstructions, including subsequent water-fat separations. Echo-modulation can further enhance reconstruction performance and offers the potential to reduce acquisition times for training data.

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Unsupervised Learning, Image Reconstruction, Dynamic MRI

Motivation: Current unsupervised dynamic-MRI reconstruction algorithms based on DIP uses very low-dimensional latent variables and a single generator for direct non-linear mapping, which may limit the performance.

Goal(s): To propose a new model and algorithm for unsupervised dynamic MRI reconstruction.

Approach: We propose a novel Graph-Image-Prior(GIP) model, which uses branched CNN generators to recover the image structure, and use a Graph-Neural-Network(GNN) to discover the best spatio-temporal manifold. Besides, we devise an ADMM algorithm to alternately optimize the dynamic image and network.

Results: The proposed method achieves the state-of-art performance even compared with supervised deep-learning methods, without the need for any fully-sampled data.

Impact: The proposed Graph-Image-Prior(GIP) scheme is a new unsupervised image reconstruction model, which has a significant value for further research. Besides, GIP is promising to be used in other multi-frame MRI reconstruction applications where fully-sampled data is scarce or unavailable.

Keywords: AI/ML Image Reconstruction, Image Reconstruction

Motivation: State-of-the-art motion-resolved 4D MRI techniques lack sufficient spatial resolution and efficient acquisition and reconstruction for application in clinical practice.

Goal(s): To develop HD-Movienet, a deep learning-based method to efficiently acquire and reconstruct 4D MRI with approximately 1mm isotropic resolution using 3D radial acquisitions.

Approach: HD-Movienet uses accelerated half-spoke (UTE) and full-spoke (T1-weighted) 3D radial kooshball acquisition and image-time-coil deep learning 4D reconstruction without k-space data consistency.

Results: HD-Movienet can enable 4D MRI with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds to image patients with lung tumors.

Impact: Deep learning-based HD-Movienet reconstruction enables motion-resolved 4D MRI technique with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds for robust radiation-free imaging of patients with mobile tumors.

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction

Motivation: Interactive real-time MRI requires low reconstruction latencies. Deep learning-based methods are promising, but unrolled networks like the Variational Network have longer inference times than purely image-based methods.

Goal(s): Design and train a Variational Network with high reconstruction quality and inference times suitable for interactive real-time applications.

Approach: Modify the Variational Network architecture such that few unrolling steps are sufficient for high reconstruction quality with short inference times.

Results: The proposed architectural modifications allowed to halve the number of unrolling steps without compromising image quality, therefore enabling considerably shortened reconstruction times.

Impact: Two modifications to an unrolled Variational Network architecture for MRI reconstruction are proposed. These enable reconstructing interactive real-time cardiac cine MRI with high reconstruction quality while maintaining minimal reconstruction latency.