Cory R. Wyatt¹, Kaveh R. Sharzehi², Erin R. Gilbert³, Brett R. Sheppard³, and Alexander R. Guimaraes^1
1Diagnostic Radiology, Oregon Health and Science University, Portland, OR, United States, ²Gastroenterology and Hepatology, Oregon Health and Science University, Portland, OR, United States, ³Surgery, Oregon Health and Science University, Portland, OR, United States

T₁ relaxation mapping has been shown to demonstrate significant differences in the pancreas of patients with chronic pancreatitis.  However, T₂ and T₁ρ relaxation times have been largely unexplored in pancreas, due to difficulty acquiring these values in the abdomen. In this study, magnetic resonance fingerprinting (MRF) techniques are applied to simultaneously acquire T₁, T₂, and T1ρ relaxation times in the pancreas of healthy volunteers and patients with clinically diagnosed chronic pancreatitis (CP).  A significant increase in T₁ relaxation was found with near significant increases in T₂ and T₁ρ relaxation times in CP patients.

Gastao Cruz¹, Carlos Velasco¹, Olivier Jaubert¹, Haikun Qi¹, René M. Botnar¹, and Claudia Prieto^1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Cardiac Magnetic Resonance Fingerprinting (MRF) has been proposed for simultaneous myocardial T1, T2 and fat fraction quantification using ECG-triggering, mid-diastolic acquisition window and a 3-echo gradient echo sequence. Here we extend this framework to further enable T2* quantification. This is achieved with an 8-echo sequence with increased acquisition window (to acquire sufficient data within a breath-hold) and a low-rank motion correction reconstruction to correct for cardiac motion within this increased window. The proposed approach enables simultaneous mapping of T1, T2, T2* and fat fraction within a single breath-hold with similar quality to conventional (sequential) single parameter approaches.

Constance G.F. Gatefait¹, Kirsten M. Kerkering¹, Sebastian Schmitter¹, and Christoph Kolbitsch^1
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany

Cardiac magnetic resonance fingerprinting (cMRF) is a promising framework for quantitative assessment of various cardiomyopathies. One major challenge is cardiac motion. The majority of cMRF methods use ECG triggering and gating to select only certain cardiac phases to reconstruct cMRF data.  In this study, we propose an iterative motion-correction approach utilizing the entire cardiac MRF acquisition. Obtained results show an improvement in obtained maps and consistent quantifications of T1 and T2.

Thomas James Fletcher¹, Carlos Velasco¹, Talent Fong¹, Gastão Cruz¹, René Michael Botnar¹, and Claudia Prieto^1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Dictionary generation for multi-parametric cardiac Magnetic Resonance Fingerprinting (MRF) is a significant bottleneck as subject-specific dictionaries must be created accounting for the subject’s heart rate variability and dictionaries grow exponentially with the number of parameters considered. Here we propose a feedforward neural network to generate cardiac MRF dictionaries for T₁, T₂ and T_1ρ. The proposed approach was tested on simulations and in-vivo data, generating dictionaries in 3 seconds. The proposed method achieved a good match to dictionaries generated with Extended Phase Graph (EPG) simulations with mean relative errors for myocardium T₁, T₂ and T_1ρ ranging from 1.7% to 5.1%.

Telly Ploem¹, Jaap Boon¹, Ingo Hermann^1,2, Cole S. Simpson³, Joe F Juffermans⁴, Tom M. Piscaer⁵, Hildo J Lamb⁴, Nazli Tümer⁶, Joao Tourais¹, and Sebastian Weingärtner^1
1Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands, ²Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ³Department of Mechanical Engineering, Stanford University, Stanford, CA, United States, ⁴Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, ⁵Orthopaedic Surgery, Erasmus University Medical Centre, Rotterdam, Netherlands, ⁶Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands

Quantitative tissue characterization of the articular cartilage is a promising method for early assessment of degeneration. However, current techniques have limited spatial coverage and are at risk of missing localized alterations. In this work, we implemented and validated an MRF-EPI sequence for the simultaneous T1 and T2* quantification with whole knee coverage across 16 slices in 3 minutes. Initial evaluation in phantom, ex vivo animal tissue and in one healthy subject show promising results compared with conventional methods.

Victoria YuiWen Yu¹, Ergys Subashi¹, Can Wu¹, Peter Koken², Mariya Doneva², Ricardo Otazo¹, and Ouri Cohen^1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Philips Healthcare, Hamburg, Germany

We demonstrated the feasibility of golden-angle radial MR fingerprinting for high-resolution quantitative multi-parametic prostate MRI. The variation in T1 and T2 values for different scan times within the normal prostate and the required number of spokes per temporal time frame required for robust quantitation parameter mapping were examined. A decrease in average T1 and T2 values was observed as scan time increased. Radial spokes of 2 or more per temporal frame achieved parameter maps that are in good agreement with reported normal prostate transition zone values.

Christian Guenthner¹, Peter Koken², Peter Boernert^2,3, and Sebastian Kozerke^1
1University and ETH Zurich, Zurich, Switzerland, ²Philips Research, Hamburg, Germany, ³Leiden University Medical Center, Leiden, Netherlands

We have investigated the feasibility of concurrent water/fat separation and T1/T2 mapping using spoiled FISP-MR Fingerprinting on a lower-field 0.75T MRI. Water/fat separation is performed in k-space and combined with seven-peak fat-spectrum deblurring and B0-deblurring using multi-frequency interpolation. Matching is performed for water and fat separately and takes B1+ inhomogeneities into account. At 0.75T, T1 was 491ms (liver), 911ms (spleen), 958ms (kidney), 744ms (muscle), and 195ms (fat); and T2 was 77ms (liver), 91ms (spleen), 111ms (kidney), 50ms (muscle), and 105ms (fat).

Abdul Moiz Hassan¹, Rana Muhammad Saad¹, Irfan Ullah¹, and Hammad Omer^1
1Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University, Islamabad, Pakistan

Magnetic Resonance Fingerprinting (MRF) has a limited use in clinics due to a considerable reconstruction time and large memory requirements. This paper utilizes clustering in MRF Dictionary to reduce the reconstruction time and memory requirements for MRF image reconstruction. The proposed method is further optimized for parallel processing to significantly reduce the pattern matching time with minimum memory usage by incorporating a multi-core GPU framework. As an outcome, the MRF reconstruction time is accelerated, keeping the SNR of the resulting images in a clinically acceptable range.

Megan E Poorman¹, Zydrunas Gimbutas², Dan Ma³, Andrew Dienstfrey², and Kathryn E Keenan^1
1Physical Measurement Laboratory, National Institute of Standards & Technology, Boulder, CO, United States, ²Information Technology Laboratory, National Institute of Standards & Technology, Boulder, CO, United States, ³Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

This study demonstrated functionality of an error analysis framework applied to understand the impacts of variations in the MR Fingerprinting pipeline. Our preliminary analysis using both simulation and experimental methods showed small differences in the accuracy and precision of the reconstructed property maps with choice of k-space to image space reconstruction method. We demonstrated the need for a better understanding of error propagation within the pipeline, improved quantitative metrics of error, and future work will include a full Monte Carlo analysis using this framework. 

Sebastian Flassbeck^1,2 and Jakob Assländer^1,2
1Dept. of Radiology, Center for Biomedical Imaging, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research, New York, NY, United States

In this work, we investigate the influence of eddy currents on hybrid state free precession (HSFP) sequences with 3D radial (koosh-ball) readout trajectories and propose a simulated annealing-based algorithm to minimize their impact.

The proposed approach of reordering the temporal succession of radial spokes successfully minimized the influence of eddy currents on HSFP experiments. Although these results were shown in the context of a HSFP experiment, we believe that this approach could find a broader application in multi-shot bSSFP sequences. 

David Heesterbeek^1,2, Frans Vos^1,3, Martin van Gijzen², and Martijn Nagtegaal^1
1Department of Imaging Physics, Delft University of Technology, Delft, Netherlands, ²Department of Numerical Analysis, Delft University of Technology, Delft, Netherlands, ³Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands

Quantitative MRI and especially MR Fingerprinting make use of complicated acquisition schemes and signal models to measure tissues parameters. The sequence choice is crucial for the noise robustness for parameter estimations of different tissues. Multi-component (MC) signal models for MRF are of importance to estimate partial volume effects or myelin water fractions for example. We propose to use the Cramér-Rao bound to assess and optimise the multi-component parameter estimations for MRF. The optimised flip angle and TR patterns for MC-MRF were highly structured which was also observed for the optimisation based on the single-component model, but structural differences were noticed.

Ronal Manuel Coronado^1,2, Gabriel Manuel della Maggiora^1,2, Carlos Manuel Castillo-Passi^1,2, Gastão Cruz ³, Sergio Manuel Uribe^1,2, Cristian Manuel Tejos^1,2, Claudia Prieto^2,3,4, and Pablo Manuel Irarrazaval^2,4
1Centro de Imagenes Biomedicas-Universidad Catolica de Chile, Santiago, Chile, ²Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, ³School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁴Centro de Imagenes Biomedicas- Pontificia Universidad Catolica de Chile, Santiago, Chile

Magnetic Resonance Fingerprinting (MRF) acquisitions with balanced Steady State Free Precession (bSSFP) and spiral trajectories are prone to off-resonance artifacts. Thus, those artifacts hinder the reconstruction of the tissue maps (T1 and T2). In this work, we propose a model based on  Generative Adversarial Networks (GANs) mixed with transformer blocks to decrease these artifacts. Our method improved the NMSE for both quantitative maps T1 and T2. Heavily reducing the effects of the off-resonance in comparison to classical bSSFP-MRF.

Ergys Subashi¹, Victoria Yu¹, Can Wu¹, Peter Koken², Mariya Doneva², Ricardo Otazo¹, and Ouri Cohen^1
1Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Philips Research, Hamburg, Germany

The acquisition of quantitative MRI biomarkers with sufficiently high spatiotemporal resolution and coverage remains challenging. This is particularly difficult in organs of the abdomen, where respiratory, pulsative, and peristaltic motion may introduce biases in the expected evolution of signal intensity. This work describes an implementation of free-breathing MR-fingerprinting (MRF) for quantitative imaging of the abdomen. The method relies on golden-angle radial sampling combined with compressed sensing and parallel imaging. We compare MRF-derived parametric maps in vivo as a function of reconstruction algorithm and in free-breathing (FB) and breath-hold (BH) acquisitions. 

Yong Chen¹, Wei-Ching Lo^2,3, Andrew Dupuis², Rasim Boyacioglu¹, Michael Hansen⁴, and Mark Griswold^1
1Radiology, Case Western Reserve University, Cleveland, OH, United States, ²Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ³Siemens Medical Solutions, Boston, MA, United States, ⁴Health Futures, Microsoft Research, Seattle, WA, United States

In this study, we proposed an efficient online map reconstruction framework for 3D MRF using a GPU-based reconstruction. Both SVD compression and 2D NUFFT were performed mostly during data acquisition and GPU computation was further implemented to accelerate coil combination and pattern matching. Our simulation results demonstrated that 1) accurate T1 and T2 mapping was obtained with the proposed method and 2) rapid MRF tissue mapping can be achieved in ~ 0.7 sec per slice, which enables rapid volumetric quantitative imaging using MRF.

Kathleen Ropella-Panagis¹, Jesse Hamilton¹, and Nicole Seiberlich^1
1Department of Radiology, University of Michigan, Ann Arbor, MI, United States

Increasing the spatial resolution of MR Fingerprinting can result in longer acquisition times due to the need to sample a larger extent of k-space. In this work, a novel data sampling scheme consisting of interleaved high- and low-resolution spiral trajectories is introduced to achieve high-resolution maps during a shortened acquisition time, which could have advantages for sequences that require a breath-hold. 

Evan Scope Crafts¹ and Bo Zhao^1,2
1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, ²Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States

Optimal design of acquisition parameters utilizing the Cramer-Rao bound provides improved SNR efficiency for MR fingerprinting experiments. The early work demonstrates that smooth magnetization evolutions resulting from constraining the flip angle variation lead to improved estimation performance. Here we introduce a new formulation, in which we constrain the sequence of acquisition parameters in the low-dimensional spline space. The proposed formulation enforces smooth magnetization evolutions with significantly reduced degrees of freedom. Compared to the state-of-the-art experiment design approach, it improves the computational efficiency by two orders of magnitude, while achieving a similar or slightly better SNR efficiency of the imaging experiments.

Tian Li¹, Di Cui², Ge Ren¹, Edward S. Hui³, and Jing Cai^1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, ²Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, ³Department of Rehabilitation Science, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

Magnetic resonance fingerprinting (MRF) is an imaging technique that effectively samples the transient state signal to achieve the goal of fast multi-parameter measurement. However, the applications of MRF only focus on static images, mostly brain image. Therefore, this study aims to investigate the feasibility of different acquisition schemes of four-dimensional magnetic resonance fingerprinting (4D-MRF) by computer simulation.

Cory R. Wyatt¹ and Alexander R. Guimaraes^2
1Oregon Health and Science University, Portland, OR, United States, ²Diagnostic Radiology, Oregon Health and Science University, Portland, OR, United States

3D magnetic resonance fingerprinting (MRF) techniques have been developed to efficiently acquire 3D volumes of quantitative parameters. Most techniques are based on applications of 2D trajectories rotated or stacked in 3D k-space.  By acquiring data across all of 3D k-space each TR, we believe that efficient imaging and quantification can be obtained. In this study, seiffert spirals are used to acquire 3D k-space in an MRF acquisition of an isotropic 3D volume for the quantification of T₁ and T₂ relaxation times. High resolution T₁ and T₂ maps of a human brain were acquired in less than 3 minutes.

Evan Cummings^1,2, Yuchi Liu², Kathleen Ropella-Panagis², Jesse Hamilton^1,2, and Nicole Seiberlich^1,2
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Radiology, University of Michigan, Ann Arbor, MI, United States

This abstract proposes a method for estimating T₁, T₂, and R₂* values from rosette MRF data. R₂* and B₀ can be extracted from a series of images generated by subdividing the rosette data into partial readouts, and T₁ and T₂ are determined using the standard MRF pattern matching pipeline. This method was tested on the ISMRM/NIST MRI system phantom to assess the accuracy of the quantitative relaxation measurements.

Enlin Qian¹ and Sairam Geethanath^1
1Columbia Magnetic Resonance Research Center, New York, NY, United States

This work develops an end to end, open source, vendor neutral package that allows for fast prototyping of magnetic resonance fingerprinting using Pulseq. In this work, a previously published sequence was implemented in Pulseq, and data were acquired on ISMRM/NIST phantom. The raw data were reconstructed and matched with an extended phase graph simulated dictionary. Automated region of interest analysis was performed to extract T₁ and T₂ estimation for each sphere of the phantom. The results show that T₁ and T₂ estimations for 17 spheres of T₁ and T₂ arrays are within 20% of gold standard measurements.