Applications of the golden angle in cardiovascular MRI

The use of radial trajectories has been seen as a potential solution to highly efficient cardiovascular magnetic resonance imaging (MRI). By acquiring a broad range of spatial frequencies per repetition time, the acquisition is time-efficient and robust against motion. Of particular interest is the golden angle profile order, which promises a near-uniform k-space coverage for an arbitrary number of readouts, enabling flexible data resorting, which is critical for efficient cardiovascular MRI. In Study I the use of 2D golden angle profile ordering is explored for imaging pulmonary embolisms. The insensitivity to motion and flow is used to reduce the artifacts that otherwise degrade images of the pulmonary vasculature when imaging with thin slices. It was found that the proposed technique could improve the image quality. Another source of artifacts arises when gradients are rapidly switched, and local induction of eddy currents may perturb spin equilibrium. In Study II, we propose a generalized golden angle profile orderings in 3D which reduces eddy-current artifacts. We demonstrate the efficacy of our generalization through numerical simulations, phantom imaging and imaging of a healthy volunteer. In Study III an improved 2D golden angle profile ordering was explored which resulted in a higher degree of k-space uniformity after physiological binning. This novel profile ordering was used in combination with a phase-contrast readout to enable quantification of myocardial tissue velocity and transmitral blood flow velocity, which are essential parameters for diastolic function assessment. When compared to echocardiography, it was found that MRI could accurately quantify myocardial tissue velocity, whereas transmitral blood flow velocity was underestimated. Study IV explored a further development of Study III by proposing a 3D version of the improved profile ordering. This novel ordering was used to acquire whole-heart functional images during free-breathing in less than one minute. Together, these results indicate that golden-angle-based imaging has the potential to improve cardiovascular MRI in several areas

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.Comment: Submitted to Magnetic Resonance in Medicine; 29 pages with 10 figures and 1 tabl

Generalized super-resolution 4D Flow MRI \unicode{x2013} using ensemble learning to extend across the cardiovascular system

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinical level input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.Comment: 10 pages, 5 figure

Self-calibrated through-time spiral GRAPPA for real-time, free-breathing evaluation of left ventricular function

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175229/1/mrm29462.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175229/2/mrm29462_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175229/3/mrm29462-sup-0001-supinfo.pd

Detection of acute pulmonary embolism using native repeated magnetic resonance imaging acquisitions under free-breathing and without respiratory or cardiac gating. A diagnostic accuracy study

Objectives: Computed tomography pulmonary angiography (CTPA) is the gold standard diagnostic method for patients with suspected pulmonary embolism (PE), but it has its drawbacks, including exposure to ionizing radiation and iodinated contrast agent. The present study aims to evaluate the diagnostic performance of our in-house developed non-contrast MRI protocol for PE diagnosis in reference to CTPA. Methods: 107 patients were included, all of whom underwent MRI immediately before or within 36 hours after CTPA. Additional cases examined only with MRI and a negative result were added to reach a PE prevalence of approximately 20%. The protocol was a non-contrast 2D steady-state free precession (SSFP) sequence under free-breathing, without respiratory or cardiac gating, and repeated five times to capture the vessels at different breathing/cardiac phases. The MRIs were blinded and read by two radiologists and the results were compared to CTPA. Results: Of the 243 patients included, 47 were positive for PE. Readers 1 and 2 demonstrated 89% and 87% sensitivity, 100% specificity, 98% accuracy and Cohen’s kappa of 0.88 on patient level. In the per embolus comparison, readers 1 and 2 detected, 60 and 59/61 (98, 97%) proximal, 101 and 94/113 (89, 83%) segmental, and 5 and 2/32 (16, 6%) subsegmental emboli, resulting in 81 and 75% sensitivity respectively. Conclusion: The repeated 2D SSFP can reliably be used for the diagnosis of acute PE at the proximal and segmental artery levels

CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison. Keywords: CG-SENSE; MRI; NUFFT; image reconstruction; nonuniform sampling; reproducibility

ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"

Challange data (brain/heart) and supplementary data (cardiac/rawdata_sprial) for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge".Funding information: Austrian Academy of Sciences, DOC-Fellowship: 24966; NIH (NIBIB) awards R01EB024532, P41EB017183, R21EB027241, U24EB029240