Keywords: Image Reconstruction, Image Reconstruction, Deep learning, Self-supervised

Motivation: For low SNR training data, such as from low-field scanners, sub-sampled images reconstructed via deep learning can be susceptible to errors due to measurement noise.

Goal(s): To evaluate the performance of the proposed Robust Self-Supervised Learning via Data Undersampling (Robust SSDU), which removes corruptions due to aliasing and measurement noise in an entirely self-supervised manner.

Approach: On the fastMRI dataset and low-field dataset M4Raw, Robust SSDU was compared with a number of benchmarks including supervised training.

Results: Robust SSDU exhibited a substantially higher fidelity image restoration than standard SSDU and sharper reconstructions than competing methods that remove measurement noise.

Impact: This study demonstrates that high quality image reconstruction with deep learning is achievable when only sub-sampled, low SNR data is available for training. The proposed method could particularly impact the diagnostic potential of images acquired from low field scanners.

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence

Motivation: GRAPPA and RAKI optimize purely for data consistency, completely lacking physics-driven or model-based loss terms.

Goal(s): Recurrently feed noise amplification information into k-space interpolation networks by penalizing the online computed g-factor.

Approach: JAX-implemented GRAPPA and RAKI g-factors were estimated online in each training iteration and incorporated into the optimization as an inherent network noise amplification penalty.

Results: Networks including g-factor loss outperformed implementations optimizing only for the data consistency term. Inclusion of g-factor loss terms manifested Tikhonov regularization-like effects on image noise distribution, as revealed by difference maps to the fully sampled gold standard.

Impact: Incorporating the penalty of inherent noise amplification into k-space interpolation networks reduces reconstruction noise levels compared to implementation that optimize only for data consistency. G-factor-informed reconstructions manifest Tikhonov regularization-like effects, as revealed by noise distribution on difference maps.

Keywords: Machine Learning/Artificial Intelligence, Parallel Imaging, complex-valued convolutional neural networks, RAKI, GRAPPA, ReLU

Motivation: Robust Artificial Neural Networks for k-space Interpolation (RAKI) exhibit superior image reconstructions compared to traditional Parallel Imaging. It is crucial to thoroughly characterize RAKI to gain insights into its functionality and stimulate further enhancements.

Goal(s): Exploring how k-space interpolation with convolutional neural networks can be transformed into image domain to obtain an analytical description of noise characteristics.

Approach: The nonlinear activation in k-space is expressed as elementwise multiplication. This can be transformed into convolution in image space.

Results: The proposed image space formalism yields image reconstructions quasi-equivalent to k-space interpolation. The analytical expression of noise characteristics is in correspondence with Monte Carlo simulations.

Impact: We propose an image space formalism for k-space interpolation with convolutional neural networks. This enables an analytical expression of the noise characteristics, analogous to g-factor maps in traditional parallel imaging methods.

Keywords: Image Reconstruction, Image Reconstruction, Noise-robust method

Motivation: Deep learning-based accelerated MRI reconstruction methods have shown outstanding performance but do not consider noise. Corruption due to noise may lead to wrong diagnosis in clinical practices.

Goal(s): Propose a noise-robust reconstruction method, which reconstructs noise-free full-sampled images from noisy undersampled data.

Approach: A noise-robust reconstruction method is proposed using contrastive learning framework consisting of two stages. The first stage extracts feature representations related to the noise level, which is used in the second stage to reconstruct alias-free image.

Results: Experiment results show that the proposed method provides robust reconstruction with limited training data, yielding superior image reconstruction compared to other reconstruction methods.

Impact: The encoder trained in the first stage extracts representation features that contain content-invariant noise level information. Therefore, the trained encoder can be applied to other downstream tasks with limited amount of training data.

Keywords: Quantitative Imaging, Quantitative Imaging, phase processing, background removal, deep learning, image decomposition, phase unwrapping

Motivation: Phase images contain important information useful in many fields. However, the phase data is often wrapped into a specific range, while background or noise signal in imaging scene may bring significant interference.

Goal(s): To obtain the exact information, phase images need an accurate processing that includes the unwrapping and the background removal.

Approach:  In this paper, we propose a positive and negative learning based image decomposition network (PNnet) to accomplish the phase processing by a single network.

Results:  Experimental results demonstrate that PNnet can achieve excellent performance and efficient generalization, even for complex wrapping and inhomogeneous background.

Impact: Except magnitude images, phase data in MRI also contain important information that is useful in many fields and scenarios. This work proposed a SOTA method for phase processing with high accuracy and excellent performance.  

Keywords: Artifacts, Artifacts, susceptibility artifacts, echo planar imaging, reversed phase-encoding, deep learning, unsupervised learning

Motivation: Classical susceptibility-artifact correction methods are impractical in clinical settings given their computational burden.

Goal(s): Fast and effective correction of susceptibility artifacts in EPI via physics-driven unsupervised deep learning by utilizing phase-injected complex-valued forward-distortion.

Approach: Previous methods apply distortion correction on magnitude images, potentially yielding suboptimal performance near regions of signal dropout/pileup. We propose a novel model, compFD-Net, employing phase-injected complex forward-distortion that leverages a predicted phase image, additionally to the magnitude image and displacement field estimates, for improved capture of signal dropout/pileup artifacts in EPI images.

Results: The proposed model boosts susceptibility-artifact correction performance, notably improving predicted image and field quality.

Impact: Robust emulation of signal-dropout/pileup via the complex forward-distortion formulation boosts reliability in unsupervised artifact correction. compFD-Net facilitates rapid and performant correction of susceptibility artifacts in EPI, with possible impact in time-sensitive applications in clinical settings.

Keywords: Image Reconstruction, Cardiovascular, real-time, cardiac cine, heart, outer volume subtraction

Motivation: Real-time cine CMR provides a free-breathing ECG-free approach for heart function assessment. Nevertheless, commercially available real-time cine CMR methods without temporal regularization have limited acceleration and spatio-temporal resolutions.

Goal(s): Use deep learning (DL) to remove extra-cardiac volume that aliases into the heart and improve acceleration rates for real-time cine CMR using only spatial regularization.

Approach: We characterize pseudo-periodic ghosting artifacts arising from cardiac motion in time-interleaved sequences, then use DL to detect and remove them. This is followed by self-supervised physics-driven DL reconstruction.

Results: Proposed technique effectively estimates and removes background signal, leading to substantial image quality improvement.

Impact: We characterize and use deep learning (DL) to estimate pseudo-periodic ghosting artifacts arising from cardiac motion in time-interleaved real-time cine sequences. Background removal followed by physics-driven DL reconstruction substantially improves reconstruction at nominal R=8 for higher spatio-temporal resolution acquisitions.

Keywords: Machine Learning/Artificial Intelligence, Cardiovascular, deep learning; spiral; real-time CMR

Motivation: Real-time imaging methods are useful for patients with limited breathhold capacity or arrhythmias, but are typically limited to 2D scans that prevent evaluation of wall motion in 3D over the heart.

Goal(s): The goal of this project is to develop a technique for 3D real-time (free-breathing ungated) cine imaging.

Approach: The proposed method combines a highly undersampled 3D stack-of-spirals trajectory with a deep image prior reconstruction, which does not require ground truth training data.

Results: Real-time 3D imaging is demonstrated in healthy subjects with temporal resolutions of 36ms per volume at 1.5T and 58ms per volume at 0.55T.

Impact: Real-time 3D imaging could enable streamlined cardiac MRI exams, with whole-heart 3D cine images obtained in 10s without breathholds or gating. This technique may also simplify quantification compared to 2D real-time methods, since motion is synchronized over all partitions.

Keywords: Sparse & Low-Rank Models, Arterial spin labelling, Denoising, MRI

Motivation: Address the challenge of low SNR in arterial spin labeling (ASL) MRI that hinders its clinical and research potential.

Goal(s): Develop an advanced ASL denoising algorithm that enhances image quality and overcomes limitations in ASL due to low SNR.

Approach: Propose a Locally Adaptive low rank regularization with Collaborative data Selection (LACS) scheme that utilizes the structural characteristics of ASL images for collaborative data selection to improve low-rank modeling. The proposed low-rank regularization fundamentally performs locally adaptive PCA without explicit training.

Results: Using a single ASL image pair, LACS significantly outperformed state-of-the-art MRI denoising methods and the standard pipeline.

Impact: The proposed scheme has the potential to benefit researchers, clinicians, and patients by setting a new benchmark for ASL MRI denoising. It opens doors to exploring ASL's full clinical potential and offers opportunities for innovative research.