Rushdi Zahid Rusho¹, Wahidul Alam¹, Abdul Haseeb Ahmed², Stanley J. Kruger³, Mathews Jacob², and Sajan Goud Lingala^1,3
1Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, United States, ²Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States, ³Department of Radiology, The University of Iowa, Iowa City, IA, United States

We propose a novel rapid dynamic speech MRI scheme that leverages multi-coil acquisitions from a dedicated 16 channel airway coil, variable density spirals, and manifold regularization. The variable density spirals enables self-navigation to extract the Laplacian manifold matrix from low spatial but high temporal resolution data. Our scheme  allows for efficient exploitation of similarities between image frames that are distant in time without the need of explicit binning. We demonstrate robust reconstructions on free running speech data containing complex spatio-temporal dynamics at a temporal resolution of 15 ms/frame.

Yongwan Lim¹, Asterios Toutios¹, Yannick Bliesener¹, Ye Tian¹, Krishna S. Nayak¹, and Shrikanth Narayanan^1
1University of Southern California, Los Angeles, CA, United States

We introduce the first-ever public domain real-time MRI raw dataset for the study of human speech production. The dataset consists of raw, multi-receiver-coil MRI data with non-Cartesian, spiral sampling trajectory and reconstructed images derived using a reference reconstruction method along with synchronized audio for 72 subjects performing 32 linguistically motivated speech tasks. This dataset can be used to develop traditional and machine learning / artificial intelligence approaches for dynamic image reconstruction in the context of fast aperiodic motion, which is currently an unsolved problem, as well as for artifact correction, feature extraction, and direct extraction of linguistically relevant biomarkers.

Yaewon Kim¹, Hsin-Yu Chen¹, Adam W. Autry¹, Javier Villanueva-Meyer¹, Susan M. Chang², Yan Li¹, Peder E. Z. Larson¹, Jeffrey R. Brender³, Murali C. Krishna³, Duan Xu¹, Daniel B. Vigneron^1,2, and Jeremy W. Gordon^1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States, ²Department of Neurological Surgery, University of California, San Francisco, CA, United States, ³Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

Quantifying metabolism in hyperpolarized (HP) ¹³C MRI can be challenging because of low signal-to-noise ratio for downstream metabolites. To overcome this limitation, we investigated a new patch-based singular value decomposition method to denoise dynamic imaging data and tested it in numerical simulations and on 6 HP [1-¹³C]pyruvate EPI human brain datasets. The sensitivity enhancement provided by denoising significantly improved quantification of metabolite dynamics. With denoising, [1-¹³C]pyruvate and its metabolites [1-¹³C]lactate and [¹³C]bicarbonate had ≥5-fold sensitivity gain, improving the number of quantifiable voxels for mapping pyruvate-to-bicarbonate conversion rates (k_PB) by 2-fold, and providing whole-brain coverage for mapping pyruvate-to-lactate conversion rates (k_PL).

Chenyang Zhao¹, Xingfeng Shao¹, Lirong Yan¹, and Danny JJ Wang^1
1Laboratory of Functional MRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

The intrinsically low SNR of ASL techniques is a main limitation that hinders their clinical translations. This work presented a novel denoising algorithm for dynamic MRI termed KWIA and evaluated its use in multi-delay ASL and ASL-based 4D dMRA. KWIA improves SNR without compromising spatial and temporal resolution by progressively increasing the neighboring time frames for view-shared averaging for more distal k-space regions. Experimental results showed that KWIA can provide significant SNR improvement that enables better visualization and quantification for both multi-delay ASL and ASL-based 4D dMRA as well as other dynamic MRI techniques.

Mengye Lyu¹, Guoxiong Deng¹, Yali Zheng¹, Yilong Liu^2,3, and Ed X. Wu^2,3
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, ²Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, ³Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Super-resolution (SR) is useful to reduce scan time and/or enhance MR images for better visual perception. High-resolution reference images may improve super-resolution quality, but most previous studies focused on using references from the same subject. Here, we use an image search module to find similar images from other subjects and use transformer based neural networks to learn and transfer the relevant textures to the output. We demonstrate that this approach can outperform single-image super-resolution, and is feasible to achieve high-quality super-resolution at large factors. As the reference images are not limited within a subject, it potentially has wide applications.

Yiming Dong¹, Kirsten Koolstra², Malte Riedel³, Matthias J.P. van Osch¹, and Peter Börnert^1,4
1C.J. Gorter Center for High Field MRI, Department of Radiology, LUMC, Leiden, Netherlands, ²Division of Image Processing, Department of Radiology, LUMC, Leiden, Netherlands, ³University of Lübeck, Lübeck, Germany, ⁴Philips Research Hamburg, Hamburg, Germany

Multi-shot EPI enables high-resolution diffusion-weighted imaging with reduced geometric distortions. However, fat is often a confounding factor in EPI, especially in regions with severe B₀ inhomogeneities. For the proposed method, data is acquired using TE-shifted interleaved EPI and 2D-navigators to sense the motion-induced shot phases. The reconstruction includes a multi-peak fat model and corrects for the fat frequency-specific chemical shift displacements in phase-encoding direction by a time-efficient image-space formulation. In-vivo results show that the proposed algorithm provides improved water/fat separation compared with the conventional technique and fat-suppressed acquisition.

Sophia Kronthaler¹, Christof Boehm¹, Kilian Weiss², Marcus R. Makowski¹, and Dimitrios C. Karampinos^1
1Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany, ²Philips Healthcare, Hamburg, Germany

Chemical shift encoding-based (CSE) water-fat separation techniques are becoming more common in the study of bone marrow changes as they allow the simultaneous assessment of tissue fat-fraction and R₂^*. A typical acquisition strategy in CSE-MRI aims to minimize the first TE to increase SNR. However, the R₂^* decay in the presence of trabecular bone microstructure is known to be nontrivial due to the occurrence of the static dephasing regime. The present work investigates, with the help of UTE acquisitions, the quantification errors of R₂^* and PDFF maps in trabecularized bone marrow due to the presence of the static dephasing regime.

Romain Froidevaux¹, Markus Weiger¹, and Klaas Paul Pruessmann^1
1Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland

Short-T₂ imaging needs immediate and rapid encoding, as provided by zero echo time (ZTE) MRI. However, in ZTE, excitation and early encoding occur simultaneously and preclude data acquisition in the k-space center, leading to local undersampling or gap. One way of retrieving the missing data involves algebraic reconstruction, but it is limited to small gaps and thus requires short RF pulses that restrain achievable SNR and contrast options. Here, we demonstrate a method for algebraic reconstruction of large gaps, based on the knowledge of excitation pulses. It enables the use of longer pulses and overcomes ZTE flip angle limitations.

Ayse Sila Dokumaci¹, Fraser R. Aitken¹, Jan Sedlacik¹, Philippa Bridgen¹, Raphael Tomi-Tricot^1,2, Tom Wilkinson¹, Ronald Mooiweer¹, Sharon Giles¹, Joseph V. Hajnal¹, Shaihan Malik¹, Jonathan O'Muircheartaigh¹, and David W. Carmichael^1
1Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, ²MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

The MP2RAGE sequence is typically used at 7T to produce UNI image with maximised contrast between WM-GM and GM-CSF while mitigating B₁^- field variability. It can also be optimised to obtain Fluid and White Matter Suppression (FLAWS) images but this is typically done separately.  Here, the Extended Phase Graph formalism was used to optimise both FLAWS and UNI images at 7T within one acquisition while minimising B₁⁺ sensitivity. Different combinations were tested in healthy subjects with 0.65mm isotropic resolution demonstrating that UNI and FLAWS images could be obtained together while largely maintaining image quality.  

Alexis Reymbaut^1
1Random Walk Imaging AB, Lund, Sweden

Tensor-valued diffusion encoding has allowed for increased specificity in diffusion MRI, probing the diffusion patterns of water molecules in vivo along new dimensions. From an intuitive standpoint, a versatile sampling scheme should be sensitive to a diverse set of diffusion profiles in any given voxel. However, while optimization strategies based on electrostatic repulsion achieve this for conventional diffusion sampling scheme, no equivalent optimization exists for tensor-valued diffusion data. In this work, we derive an optimization strategy based on maximizing the Frobenius distance between b-tensors. Its evaluation in silico demonstrates that it increases the accuracy of diffusion tensor distribution imaging.