Kaia Ingerdatter Sørland¹, Cristopher George Trimble¹, Elise Sandsmark², Tone Frost Bathen^1,2, Mattijs Elschot^1,2, and Martijn A. Cloos^3
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ²Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway, ³Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia

The extreme undersampling factors used in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak artefacts from the femoral arteries and veins. However, it turns out that only a subset of receiver channels is responsible for these streaks. In this work we automatically detected and removed these from the reconstruction pipeline. The method was applied to MRF acquired with various acceleration factors in seven asymptomatic volunteers, significantly reducing visible streaks in reference tissues surrounding the prostate without impairing the prostate T1 and T2 values in the MRF maps.

Krishnapriya Venugopal¹, Fatemehsadat Arzanforoosh¹, Daniëlle van Dorth², Marion Smits¹, Juan Antonio Antonio Hernandez-Tamames^1,3, Esther A.H Warnert¹, Matthias J.P van Osch², and Dirk H.J Poot^1
1Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands, ²Radiology, Leiden University Medical Center, Leiden, Netherlands, ³Medical Imaging, TU Delft, Delft, Netherlands

This study uses an MRVF approach to analyze the time evolution of a DSC hybrid -EPI (HEPI) sequence that simultaneously acquires gradient and spin echo images. HEPI properties are incorporated from the scanner into simulations including contrast agent extravasation, diffusion, and MR signal evolution and for varying the outcome parameters: CBV, mean vessel-size, and leakage. In vivo data of six glioma patients are used to compare MRVF output maps to those obtained from conventional VSI modelling. The results show reasonable agreement. Also, the noise sensitivity of both techniques was investigated.

Samo Lasič^1,2, Nathalie Just¹, and Henrik Lundell^1
1Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark, ²Random Walk Imaging, Lund, Sweden

Tensor-valued diffusion encoding can be confounded by time-dependent diffusion (TDD). Matching sensitivity to TDD or tuning of b-tensors with different shapes is needed for unbiased microstructure assessment. We present a method for tuning linear tensor encoding (LTE) to spherical tensor encoding (STE), which could be optimized for different hardware constraints. Furthermore, we introduce the spectral principle axes system (SPAS), representing spectral anisotropy of STE. The SPAS LTEs could provide an alternative to tuning and enable disentangling effects of microscopic anisotropy and TDD, useful to correlate cell shape and size.

Pavan Poojar^1,2, Enlin Qian¹, Alexis B Maddocks³, and Sairam Geethanath^1
1Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States, ²Dayananda Sagar College of Engineering, Bangalore, India, ³Columbia University Irving Medical Center, New York, NY, United States

We demonstrated the utility of tailored MR fingerprinting (TMRF) of pediatric patients with brain tumor to 1) differentiate tumor from healthy tissue using quantitative maps; 2) tailor TMRF sequence to include T₂ fluid-attenuated inversion recovery (FLAIR) contrast that could potentially substitute T₂ FLAIR sequence used in routine pediatric tumor protocol. The mean土SD difference in T₁ and T₂ values between tumor and normal tissues for 3 patients were 1.05土0.09 and 0.05土0.02 seconds respectively. The TR and flip angle trains were tailored to include T₂ FLAIR contrast for adult imaging. Current and future work includes optimizing T₂ FLAIR for the pediatric population.

Emma L Thomson^1,2, Elizabeth Powell³, Claudia A M Gandini Wheeler-Kingshott^2,4,5, and Geoff J M Parker^1,2,6
1Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ²NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, London, United Kingdom, ³Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ⁴Department of Brain & Behavioural Sciences, University of Pavia, Pavia, Italy, ⁵Brain Connectivity Centre Research Department, IRCCS Mondino Foundation, Pavia, Italy, ⁶Bioxydyn Limited, Manchester, United Kingdom

We propose the use of magnetic resonance fingerprinting (MRF), applied using a spoiled gradient echo sequence, to quantify cerebral blood volume ($$$\nu_b$$$) and inter-vascular water exchange (1/$$$\tau_b$$$), without the need for contrast agents. Through a simulation study we optimise a simulated acquisition protocol and test the sensitivity of the measurement, and its accuracy in the presence of variations in blood T₁, tissue T₁, and B₁. We demonstrate that voxel-wise simultaneous quantification of $$$\nu_b ,\tau_b , T_{1,b}, T_{1,e}$$$ and $$$B_1^+$$$ is likely feasible with an optimised acquisition.

Ja Kyung Yoon¹, Yong Eun Chung¹, Jaeseung Shin¹, Jin-Young Choi¹, Mi-Suk Park¹, and Myeong-Jin Kim^1
1Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea, Republic of

In liver MRI, diffusion-weighted imaging (DWI) allows better detection and characterization of focal lesions, but requires a relatively long scan time. The multiband variable-rate selective excitation (MB-VERSE) echoplanar imaging for DWI provides accelerated acquisition time with some expected trade-off in image quality. Qualitative and quantitative image quality of MB-VERSE images as well as focal liver lesion detectability was evaluated by three readers. The MB-VERSE sequence as well as focal liver lesion detectability showed significant sacrifice in quantitative and qualitative overall image quality, but comparable focal lesion detectability.

Gastao Cruz¹, Andrew Phair¹, Carlos Velasco¹, René M. Botnar¹, and Claudia Prieto^1
1King's College London, London, United Kingdom

Cardiac Magnetic Resonance Fingerprinting (MRF) produces co-registered, multi-parametric maps from highly accelerated acquisitions. Low rank methods leveraging temporal information have enabled highly undersampled MRF reconstructions. However, residual aliasing from these reconstructions can potentially propagate through the MRF framework into the parametric maps. Region-Optimized Virtual coils have recently been proposed for undersampled reconstructions in conventional imaging, by suppressing unwanted sources of aliasing signal. Here we combine both methods and investigate its performance in Cardiac Magnetic Resonance Fingerprinting.

Martijn Nagtegaal¹, Emiel Hartsema¹, Kirsten Koolstra², and Frans Vos^1,3
1Imaging Physics, Delft University of Technology, Delft, Netherlands, ²Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands, ³Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands

The use of high undersampling factors and short flip angle trains leads to shorter acquisition times in MR Fingerprinting acquisitions. To obtain accurate multi-component estimates from this data advanced reconstructions are required. We study a low-rank ADMM based reconstruction method that adds a multi-component constraint to the inverse reconstruction problem (MC-ADMM). This method is combined with a joint-sparsity constraint yielding higher quality multi-component estimates with k-SPIJN than with previous methods. In simulations we observed increased stability to sequence truncation and in vivo multi-component estimates contained less noise-like effects.

Imraj Ravi Devia Singh¹, Olivier Jaubert¹, Bangti Jin¹, Kris Thielemans², and Simon Arridge^1
1Department of Computer Science, University College London, London, United Kingdom, ²Institute of Nuclear Medicine, University College London, London, United Kingdom

Magnetic Resonance Fingerprinting (MRF) accelerates quantitative magnetic resonance imaging. The reconstruction can be separated into two problems: reconstruction of a set of multi-contrast images from k-space signals, and estimation of parametric maps from the set of multi-contrast images. In this study we focus on the former problem, while leveraging dictionary matching for the estimation of parametric maps. Two different sparsity promoting regularisation strategies were investigated: contrast-wise Total Variation (TV) which encourages image sparsity separately; and Total Nuclear Variation (TNV) which promotes a measure of joint edge sparsity. We found improved results using joint sparsity.

Thomas O'Reilly¹, Peter Börnert², Andrew Webb¹, and Kirsten Koolstra^1
1Leiden University Medical Center, Leiden, Netherlands, ²Philips Research Hamburg, Hamburg, Germany

Strong B₀ inhomogeneities in compact point-of-care low field permanent magnet systems can cause image distortions and reduced signal for large field-of-view images. MRF, as a flexible acquisition framework, can encode these effects into the acquisition process, to incorporate them into the reconstruction process. In this work, we use an alternating TE in the MRF sequence that supports ΔB₀ estimation and image distortion correction of the MRF data. This method reduces distortions in the relaxation time maps without needing to acquire an additional ΔB₀ map. 

Beomgu Kang¹, Hye-Young Heo^2,3, and Hyunwook Park^1
1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, ²Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Magnetization transfer contrast MR fingerprinting (MTC-MRF) is used to quantify multiple tissue parameters of free bulk water and semisolid macromolecule using pseudo-randomized MRF schedules. An optimal design of the MRF schedule is important to improve the quantification accuracy and reduce the scan time. However,  lack of the objective function that represents the encoding capability of MRF schedule hinders the reliable optimization. In this study, we propose a novel metric that represents the encoding capability of MRF schedule based on recurrent neural network. Unlike the conventional metrics based on indirect measurements, the proposed learning-based metric directly measures the tissue quantification errors.

Raffaella Fiamma Cabini^1,2, Leonardo Barzaghi^1,3, Davide Cicolari^2,4, Anna Pichiecchio^5,6, Silvia Figini^2,7, Paolo Arosio^8,9, Marta Filibian^2,10, Alessandro Lascialfari^2,4, and Stefano Carrazza^8,9
1Department of Mathematics, University of Pavia, Pavia, Italy, ²INFN, Istituto Nazionale di Fisica Nucleare - Pavia Unit, Pavia, Italy, ³Department of Neuroradiology, Advanced Imaging and Radiomics, IRCCS Mondino Foundation, Pavia, Italy, ⁴Department of Physics, University of Pavia, Pavia, Italy, ⁵Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy, ⁶Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ⁷Department of Social and Political Science, University of Pavia, Pavia, Italy, ⁸Department of Physics, University of Milano, Milano, Italy, ⁹INFN, Istituto Nazionale di Fisica Nucleare - Milano Unit, Milano, Italy, ¹⁰Centro Grandi Strumenti, University of Pavia, Pavia, Italy

We developed a Neural Network (NN) for the reconstruction of T₁ and T₂ parametric maps obtained with the Magnetic Resonance Fingerprinting (MRF) technique. The training phase was realized on experimental inputs, eliminating the use of simulated datasets and theoretical models. The set of optimal hyperparameters of the NN and the supervised training algorithm were established through an optimization procedure. The model achieved similar performances to the traditional reconstruction method, but the number of MRF images required was lower with respect to the dictionary-based method. If translated to the clinic, our results envisage a significant time shortening of MRI investigation.

Martijn Nagtegaal¹, Charles McGrath², Christian Günthner², and Manuel Bauman^3
1Imaging Physics, Delft University of Technology, Delft, Netherlands, ²Institute for Biomedical Enginering, ETH Zurich, Zurich, Switzerland, ³Philips Research Europe, Hamburg, Germany

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Gabriel Zihlmann¹, Najat Salameh¹, and Mathieu Sarracanie^1
1Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

Dictionary search for the reconstruction of MR Fingerprinting-based sequences can become a bottleneck due to the exponential growth of dictionary size with the number of parameters. Several approaches have been proposed to accelerate this search, yet the availability of efficient and scalable software implementations remains limited. Here, we extended an existing, highly optimized similarity search library to be compatible with the requirements of MR Fingerprinting dictionary search. The evaluation of our GPU implementation shows an acceleration by two orders of magnitude over the brute-force search yielding identical results for 99.9 % of the voxels.