Accelerated Real-time Cine and Flow under In-magnet Staged Exercise

Background: Cardiovascular magnetic resonance imaging (CMR) is a well-established imaging tool for diagnosing and managing cardiac conditions. The integration of exercise stress with CMR (ExCMR) can enhance its diagnostic capacity. Despite recent advances in CMR technology, ExCMR remains technically challenging due to motion artifacts and limited spatial and temporal resolution. Methods: This study investigates the feasibility of biventricular functional and hemodynamic assessment using real-time (RT) ExCMR during a staged exercise protocol in 26 healthy volunteers. We introduce a coil reweighting technique to minimize motion artifacts. In addition, we identify and analyze heartbeats from the end-expiratory phase to enhance the repeatability of cardiac function quantification. To demonstrate clinical feasibility, qualitative results from five patients are also presented. Results: Our findings indicate a consistent decrease in end-systolic volume (ESV) and stable end-diastolic volume (EDV) across exercise intensities, leading to increased stroke volume (SV) and ejection fraction (EF). Coil reweighting effectively reduces motion artifacts, improving image quality in both healthy volunteers and patients. The repeatability of cardiac function parameters, demonstrated by scan-rescan tests in nine volunteers, improves with the selection of end-expiratory beats. Conclusions: The study demonstrates that RT ExCMR with in-magnet exercise is a feasible and effective method for dynamic cardiac function monitoring during exercise. The proposed coil reweighting technique and selection of end-expiratory beats significantly enhance image quality and repeatability.

Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction

High-quality training data are not always available in dynamic MRI. To address this, we propose a self-supervised deep learning method called deep image prior with structured sparsity (DISCUS) for reconstructing dynamic images. DISCUS is inspired by deep image prior (DIP) and recovers a series of images through joint optimization of network parameters and input code vectors. However, DISCUS additionally encourages group sparsity on frame-specific code vectors to discover the low-dimensional manifold that describes temporal variations across frames. Compared to prior work on manifold learning, DISCUS does not require specifying the manifold dimensionality. We validate DISCUS using three numerical studies. In the first study, we simulate a dynamic Shepp-Logan phantom with frames undergoing random rotations, translations, or both, and demonstrate that DISCUS can discover the dimensionality of the underlying manifold. In the second study, we use data from a realistic late gadolinium enhancement (LGE) phantom to compare DISCUS with compressed sensing (CS) and DIP and to demonstrate the positive impact of group sparsity. In the third study, we use retrospectively undersampled single-shot LGE data from five patients to compare DISCUS with CS reconstructions. The results from these studies demonstrate that DISCUS outperforms CS and DIP and that enforcing group sparsity on the code vectors helps discover true manifold dimensionality and provides additional performance gain.

Surface Coil Intensity Correction for MRI

Modern MRI scanners utilize one or more arrays of small receive-only coils to collect k-space data. The sensitivity maps of the coils, when estimated using traditional methods, differ from the true sensitivity maps, which are generally unknown. Consequently, the reconstructed MR images exhibit undesired spatial variation in intensity. These intensity variations can be at least partially corrected using pre-scan data. In this work, we propose an intensity correction method that utilizes pre-scan data. For demonstration, we apply our method to a digital phantom, as well as to cardiac MRI data collected from a commercial scanner by Siemens Healthineers. The code is available at https://github.com/OSU-MR/SCC.

Motion-robust free-running cardiovascular MRI

PURPOSE: To present and validate an outlier rejection method that makes free-running cardiovascular MRI (CMR) more motion robust. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers as an auxiliary variable that is added to the measured data. We enforce MR physics-guided group-sparsity on the auxiliary variable and jointly estimate it along with the image using an iterative algorithm. For validation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and another outlier rejection method using two simulation studies. Then, CORe is compared to CS using five 3D cine and ten rest and stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the outlier rejection method in terms of normalized mean squared error (NMSE) and structural similarity index (SSIM) across 50 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. The flow consistency evaluation in 4D flow images show that CORe yields more consistent flow measurements, especially under exercise stress. CONCLUSION: An outlier rejection method is presented and validated using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications. CODE: MATLAB implementation code is available on GitHub at https://github.com/syedmurtazaarshad/motion-robust-CMR

A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging

Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by collecting data below the Nyquist rate. As an ill-posed inverse problem, many plausible solutions exist, yet the majority of deep learning approaches generate only a single solution. We instead focus on sampling from the posterior distribution, which provides more comprehensive information for downstream inference tasks. To do this, we design a novel conditional normalizing flow (CNF) that infers the signal component in the measurement operator's nullspace, which is later combined with measured data to form complete images. Using fastMRI brain and knee data, we demonstrate fast inference and accuracy that surpasses recent posterior sampling techniques for MRI. Code is available at https://github.com/jwen307/mri_cnf/

MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

PURPOSE: To present and validate a self-supervised MRI reconstruction method that does not require fully sampled k-space data. METHODS: ReSiDe is inspired by plug-and-play (PnP) methods and employs a denoiser as a regularizer. In contrast to traditional PnP approaches that utilize generic denoisers or train deep learning-based denoisers using high-quality images or image patches, ReSiDe directly trains the denoiser on the image or images being reconstructed from the undersampled data. We introduce two variations of our method, ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements. More importantly, the trained denoisers in ReSiDe-M are stored for PnP recovery without further training. To improve robustness, the denoising strength in ReSiDe-S and ReSiDe- M is auto-tuned using the discrepancy principle. RESULTS: Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. ReSiDe-S and ReSiDe-M outperform other methods in terms of reconstruction signal-to-noise ratio and structural similarity index measure for Studies I and II and in terms of expert scoring for Study III. CONCLUSION: A self-supervised image reconstruction method is presented and validated in both static and dynamic MRI applications. These developments can benefit MRI applications where availability of fully sampled training data is limited.

A Regularized Conditional GAN for Posterior Sampling in Inverse Problems

In inverse problems, one seeks to reconstruct an image from incomplete and/or degraded measurements. Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, superresolution, inpainting, and other applications. It is often the case that many image hypotheses are consistent with both the measurements and prior information, and so the goal is not to recover a single ``best'' hypothesis but rather to explore the space of probable hypotheses, i.e., to sample from the posterior distribution. In this work, we propose a regularized conditional Wasserstein GAN that can generate dozens of high-quality posterior samples per second. Using quantitative evaluation metrics like conditional Fr\'{e}chet inception distance, we demonstrate that our method produces state-of-the-art posterior samples in both multicoil MRI and inpainting applications.

Denoising Generalized Expectation-Consistent Approximation for MRI Image Recovery

To solve inverse problems, plug-and-play (PnP) methods have been developed that replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN). Although such methods have been successful, they can be improved. For example, denoisers are usually designed/trained to remove white Gaussian noise, but the denoiser input error in PnP algorithms is usually far from white or Gaussian. Approximate message passing (AMP) methods provide white and Gaussian denoiser input error, but only when the forward operator is a large random matrix. In this work, for Fourier-based forward operators, we propose a PnP algorithm based on generalized expectation-consistent (GEC) approximation -- a close cousin of AMP -- that offers predictable error statistics at each iteration, as well as a new DNN denoiser that leverages those statistics. We apply our approach to magnetic resonance imaging (MRI) image recovery and demonstrate its advantages over existing PnP and AMP methods.

Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI

For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.

Cardiac and respiratory motion extraction for MRI using Pilot Tone-a patient study

Background:The Pilot Tone (PT) technology allows contactless monitoring of physiological motion during the MRI scan. Several studies have shown that both respiratory and cardiac motion can be extracted from the PT signal successfully. However, most of these studies were performed in healthy volunteers. In this study, we seek to evaluate the accuracy and reliability of the cardiac and respiratory signals extracted from PT in patients clinically referred for cardiovascular MRI (CMR). Methods: Twenty-three patients were included in this study, each scanned under free-breathing conditions using a balanced steady-state free-precession real-time (RT) cine sequence on a 1.5T scanner. The PT signal was generated by a built-in PT transmitter integrated within the body array coil. For comparison, ECG and BioMatrix (BM) respiratory sensor signals were also synchronously recorded. To assess the performances of PT, ECG, and BM, cardiac and respiratory signals extracted from the RT cine images were used as the ground truth. Results: The respiratory motion extracted from PT correlated positively with the image-derived respiratory signal in all cases and showed a stronger correlation (absolute coefficient: 0.95-0.09) than BM (0.72-0.24). For the cardiac signal, the precision of PT-based triggers (standard deviation of PT trigger locations relative to ECG triggers) ranged from 6.6 to 81.2 ms (median 19.5 ms). Overall, the performance of PT-based trigger extraction was comparable to that of ECG. Conclusions: This study demonstrates the potential of PT to monitor both respiratory and cardiac motion in patients clinically referred for CMR.