Taylor Froelich¹ and Michael Garwood^1
1Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, United States

When reconstructing with traditional Fourier-based techniques, image distortions arising from nonlinear B₁ and B₀ inhomogeneity can plague radiofrequency-based (RF) imaging methods that rely on B₁ gradients for spatial encoding. In this work, we propose a new framework for reconstructing multi-dimensional RF gradient-based images leveraging an iterative approach to solve a regularized inverse problem. The proposed methodology employs a full Bloch simulation to reconstruct an undistorted image after determination of the forward operator and measured receive coil sensitivities.

Steven T. Whitaker¹, Jon-Fredrik Nielsen², and Jeffrey A. Fessler^1
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Regions with large intravoxel B0 gradients result in a wide spread of off-resonance frequencies within each voxel, causing spins within a voxel to dephase with respect to each other. Using model-based reconstruction to account for this dephasing can help alleviate artifacts from this signal loss, but success is limited in areas of extreme dephasing. We propose a model-based reconstruction method that includes RF prephasing to help mitigate the effects of extreme dephasing. We demonstrate that the proposed approach successfully recovers signal in areas of extreme dephasing and results in lower reconstruction error than model-based reconstruction without RF prephasing.

Samuel Bianchi¹, Jakob Heinzle², Maria Engel¹, Lars Kasper^1,2, and Klaas P. Pruessmann^1
1Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zürich, Switzerland, ²Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zürich, Switzerland

Standard fMRI acquires volumes as stacks of slices with 2D sequences. Acquisition time differences within a volume can be corrected post hoc using a sinc interpolation in image space (“slice timing correction”). In 3D sequences, timing correction is not possible in image space. Here, we verify an approach that rests on a sinc interpolation of timeseries in k-space before image reconstruction. Particularly, we investigate the artifacts which get removed by timing correction and quantify effects on the power spectra of timeseries in image space for 3D EPI.

Yiming Dong¹, Malte Riedel², Kirsten Koolstra³, Matthias J.P. van Osch¹, and Peter Börnert^1,4
1C.J. Gorter Center for High Field MRI, Department of Radiology, LUMC, Leiden, Netherlands, ²University and ETH Zurich, Zurich, Switzerland, ³Division of Image Processing, Department of Radiology, LUMC, Leiden, Netherlands, ⁴Philips Research, Hamburg, Germany

Multi-shot EPI readout-approaches provide high spatial resolution at reduced geometric distortions and improved SNR in diffusion weighted imaging (DWI). As a specific challenge, multi-shot acquisition data require corrections for motion-induced, shot-specific phase errors, e.g. using additional navigator signals or appropriate self-navigation. Furthermore, proper fat-suppression is challenging in DWI, especially at B₀ critical regions, making the use of chemical-shift encoding interesting. Therefore, an iterative, model-based reconstruction algorithm with self-navigation and water/fat decomposition, is proposed in this work. In-vivo examples in the leg and head-neck regions demonstrate improved water/fat separation as compared to acquisition-navigator approaches, while measurement times can be shortened.

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 and ETH Zurich, Zurich, Switzerland, ⁴Philips Research, Hamburg, Germany

The presence of fat signals is one challenge for diffusion-weighted EPI, especially when considering the multi-peak spectrum nature of fat. In this work, we propose an improved SENSE-based water/fat separation algorithm to suppress multi-peak fat signals and apply this specifically to diffusion-weighted multi-shot EPI. The motion-induced shot-to-shot phase variations, an inevitable challenge in multi-shot DWI, are incorporated into the signal model using either a self-navigation or an extra-navigated method. The results show that the proposed SENSE-based algorithm yields good water/fat separation for non-diffusion and diffusion data with a multi-peak fat spectrum model.

Nahla M H Elsaid¹, Hemant D Tagare^1,2, and Gigi Galiana^1
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, ²Department of Biomedical Engineering, Yale University, New Haven, CT, United States

This work demonstrates the feasibility of reconstructing multiple b-value diffusion-weighted images (DWI) from a single RESOLVE k-space, where each blind was acquired with a different b-value. This multi b-value reconstruction uses the growing Constrained Alternating Minimization for Parameter mapping (g-CAMP) reconstruction method previously presented in ISMRM 2021. This can allow undersampling in the diffusion weighting dimension that is compatible with common undersampling schemes such as GRAPPA and multi-band EPI.

Junhyeok Lee¹, Junghwa Kang¹, Se-hong Oh¹, and Dong Hye Ye^2
1Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Korea, Republic of, ²Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, United States

We performed parallel MRI reconstruction from under-sampled k-space data using the Multi-Domain Neumann Network with Sensitivity Maps. The Neumann network solves the inverse problem with recursive neural networks taking account into the forward model. We adapt the Neumann network with the coil sensitivity estimation and k-space data regurlaization to take account into MR physical models. Our proposed method shows uppressed image artifacts and enhanced spatial resolution compared with GRAPPA, U-Net and Neumann network.

Haowei Xiang¹, Jeffrey A Fessler¹, and Douglas C Noll^2
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, Ann Arbor, MI, United States

Looping star is a silent MRI pulse sequence that can be used for quantitative susceptibility mapping (QSM), T2*-weighted imaging and fMRI. The conventional reconstruction approach for looping star MRI that filters out some the k-space data does not fully model the overlapping echoes, and remove potentially useful signals. This work proposes a model-based reconstruction method that can theoretically resolve the overlapping echos and improve the SNR without increasing the scan time.

I. T. Maatman¹, S. Ypma¹, M. Kachelrieß², Y. Berker², K. T. Block³, E. Van der Bijl¹, J. J. Hermans¹, M. C. Maas¹, and T. W. J. Scheenen^1
1Radboud University Medical Centre, Nijmegen, Netherlands, ²German Cancer Research Center (DKFZ), Heidelberg, Germany, ³NYU Langone Medical Center, New York, NY, United States

Motion-compensated images can be created from motion-binned undersampled radial stack-of-stars data through compressed sensing and image registration. However, for long repetition times or for many partitions, the acquisition time for one radial projection with all phase-encode steps becomes too long to sample the motion via self-gating, which leads to motion artifacts. Therefore, we estimate motion from FID-navigators and perform binning on a single-readout level to gain higher spatiotemporal resolutions. Our methods are tested on a motion phantom and volunteer with gridding and motion-compensated reconstructions. Our results show accurate detection of the motion signal and reduced motion blur in reconstructions.

Prakash Kumar¹, Yongwan Lim¹, and Krishna S. Nayak^1
1Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States

Real-time MRI (RT-MRI) captures movements and dynamic processes in the human body as they occur, without reliance on any repetition or synchronization. Many applications of RT-MRI require low-latency reconstruction for feedback or interventions (typically <200ms). Here, we investigate self-calibrating spiral Through-Time GRAPPA at 0.55T and compare its performance against viewsharing and constrained reconstruction methods. We use longer readouts, which can be used at low-field due to reduced off-resonance effects. We demonstrate RT-MRI of speech production with 13.09ms temporal resolution and 25ms reconstruction latency (after a 30s pre-computed calibration step).

Charles Iglehart¹, Ali Bilgin¹, and Manojkumar Saranathan^2
1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, ²Department of Medical Imaging, University of Arizona, Tucson, AZ, United States

We develop the motivation for and demonstrate the functionality of a temporal model-based complex multi-echo reconstruction algorithm coupled with a Complementary Stochastic Sampling procedure allowing for temporal sparsity to be leveraged . We demonstrate results for magnitude and phase images as well as parameter maps.

Jonathan Endres¹, Hoai Nam Dang², Felix Glang³, Alexander Loktyushin³, Simon Weinmüller², and Moritz Zaiss^2,3
1Universitätsklinik Erlangen, Erlangen, Germany, ²Department of Neuroradiology, Universitätsklinik Erlangen, Erlangen, Germany, ³Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany

We propose a method to automatically generate estimators for the exact encoding of a MRI signal, based on the Phase Distribution Graph of a sequence and its simulation. The estimator can then be used on a measurement to split the signal into its differently encoded parts, which enables reconstruction tailored to the sequence used. This approach can result in fewer imaging artifacts since it does not rely on any assumptions about the sequence made up front but on data obtained by a PDG simulation. This also makes it suitable for sequence optimization because it can adapt to changing sequence properties.

Diego Alves Rodrigues de Souza¹, Hervé Mathieu^1,2, Jean-Christophe Deloulme¹, and Emmanuel L. Barbier^1,2
1Univ. Grenoble Alpes - Inserm U1216 - Grenoble Institut des Neurosciences (GIN), Grenoble, France, ²Univ. Grenoble Alpes - Inserm US17 - CNRS UMS3552 - CHUGA - IRMaGe, Grenoble, France

Compressed Sensing (CS) is not routinely implemented at the preclinical level, especially in case of multiple receivers. In this study, we evaluate preclinical Kernel Low-Rank and Conventional CS reconstruction schemes at 9.4T using a 4-channel receive coil and spin-echo data. By analyzing diffusion parametric maps and white-matter tracts, we evaluate the median absolute error, the structural similarity index measure and the mean fiber length as a function of the acceleration factor and of the CS reconstruction pipeline.