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José P. Marques¹, Dennis Den Hollander¹, David G Norris¹, and Kwok-Shing Chan^1
1Donders Institute for Brain Cognition adn Behaviour, Radboud University, Nijmegen, Netherlands

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping

In this abstract we demonstrate a simple framework that builds up on deep learning infrastructure to perform quantitative susceptibility mapping and correct for susceptibility related effects on R2* maps, taking the advantage of high GPU computational efficiency. Asking the Adam optimizer to point you in the right direction results in a gradient descent method that can perform field mapping, background field removal, QSM and reduce macroscopic intravoxel dephasing artifacts in R2* maps with most operations being performed in under 60 seconds even for 0.8mm isotropic whole brain multi-echo data.

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Catarina N. Carvalho¹, Teresa M. Correia^2,3, and Rita G. Nunes^1,4
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal, Lisbon, Portugal, ²School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom, London, United Kingdom, ³Center of Marine Sciences - CCMAR, Faro, Portugal, Faro, Portugal, ⁴Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College, London, UK, London, United Kingdom

Keywords: Quantitative Imaging, Relaxometry

Model-based deep learning approaches have shown promising results to accelerate T2 relaxometry, but most adopt a pure exponential curve to model the signal, which does not account for indirect and stimulated echoes. A PhasE graph sigNal and Gradients QUantitative Inference MachiNe (PENGUIN) is proposed, which implements a dictionary of pre-calculated echo-modulation curves following the Extended Phase Graph (EPG) formulation and respective gradients as the inputs of a Recurrent Inference Machine to perform accurate T₂ mapping from the reconstructed images. PENGUIN is 25-fold faster than a pattern recognition approach with a T2 dictionary step of 2 ms.

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Markus Zimmermann¹, Zaheer Abbas¹, Yannic Sommer¹, Alexander Lewin¹, Shukti Ramkiran¹, Ana-Maria Oros-Peusquens¹, Seong Dae Yun¹, and N. Jon Shah^1,2,3,4
1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany, Jülich, Germany, ²Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Germany, Jülich, Germany, ³JARA - BRAIN - Translational Medicine, Aachen, Germany, Aachen, Germany, ⁴Department of Neurology, RWTH Aachen University, Aachen, Germany, Aachen, Germany

Keywords: Sparse & Low-Rank Models, Image Reconstruction, Brain, Relaxometry, Quantitative Susceptibility mapping

The development of fast, accurate, and robust methods for multiparametric quantitative MRI (qMRI) at ultrahigh field strength remains an important topic of research. Here, we present a novel qMRI technique for the simultaneous quantification of water content, T₁, T₂^*, and magnetic susceptibility, termed QRAGE. The proposed method combines a highly undersampled multi-echo MPnRAGE sequence with a model-based reconstruction approach. It acquires 171 different contrasts with full brain coverage and 1 mm isotropic resolution within 7:20 min from which the parametric maps are estimated. The accuracy and precision of QRAGE are demonstrated by comparison to gold-standard reference methods.

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Filippo Carlo Michelotti¹, Yuliya Kupriyanova¹, Tim Mori¹, Thomas Küstner², Geronimo Heilmann¹, Maria Bombrich¹, Clara Moeser¹, Martin Schön¹, Michael Roden¹, and Vera Schrauwen-Hinderling^1
1Institute for Clinical Diabetelogy, German Diabetes Center (DDZ) Leibniz Center for Diabetes Research, Düsseldorf, Germany, ²Medical Image and Data Analysis (MIDAS.lab), University Hospital of Tübingen, Diagnostics and Interventional Radiology, Tübingen, Germany

Keywords: Quantitative Imaging, Liver, Machine Learning, Segmentation

This work focuses on an empirical approach to determine water T₁ from multiparametric MR images, including T₁, PDFF and T₂^* maps. To this end, a multiple linear regression model was fit to describe the deviation in MOLLI T₁ based on PDFF and T₂^* values, which were measured in phantoms built at increasing lipids and iron content. This method was validated on a cohort of healthy volunteers and diabetes subjects (n=45). Further investigations were conducted to elucidate the relationship between MOLLI T₁ values, before and after correction for hepatic lipid and iron content, and liver stiffness measured by MR elastography.

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Simone Hufnagel¹, Patrick Schuenke¹, Jeanette Schulz-Menger^2,3,4, Tobias Schaeffter^1,5,6, and Christoph Kolbitsch^1
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, ²Charité Medical Faculty University Medicine, Berlin, Germany, ³Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), Charité Humboldt University Berlin, DZHK partner site Berlin, Berlin, Germany, ⁴Department of Cardiology and Nephrology, HELIOS Klinikum Berlin Buch, Berlin, Germany, ⁵School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, ⁶Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany

Keywords: Quantitative Imaging, Heart, Super-Resolution Reconstruction

Cardiac T1 mapping provides valuable information for the diagnosis of a variety of heart diseases. However, due to SNR and scan time limitations, often only 2D imaging with a low through-plane resolution covering a few slices of the left ventricle is possible. In this work, a super-resolution reconstruction approach is presented aiming towards whole-heart 1.3 mm isotropic T1 mapping within less than three minutes acquisition time. The proposed approach provided a whole-heart cardiac T1 map including the atria and the right ventricle with improved visualization of small structures and overall image quality.

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Nicolás Garrido^1,2, Andrew Phair³, Ronal Coronado^1,4, Haikun Qi⁵, Claudia Prieto^1,3,4, and René M Botnar^1,2,3
1Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile, ²Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ³King's College London, London, United Kingdom, ⁴Electrical Engineering Department, Pontificia Universidad Católica de Chile, Santiago, Chile, Santiago, Chile, ⁵ShanghaiTech University, Shanghai, China

Keywords: Quantitative Imaging, Myocardium, Motion Correction

Cardiac T₁-T₂ mapping provides information about focal and diffuse fibrosis and inflammation of the myocardium. A recently proposed free-running 3D mapping technique allows time efficient and simultaneous whole-heart T₁-T₂ mapping within a single scan, with retrospective respiratory motion correction. However, this approach loses the information about the temporal contrast evolution and does not reconstruct multi-contrast 3D whole-heart images, which may carry useful clinical information in patients with myocardial infarction and, acute and subacute thrombus. In this work, we propose to extend this approach to enable joint T₁-T₂ mapping, CINE, and multi-contrast 3D whole-heart imaging from a single free-running scan.

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Nadège Corbin^1,2, Trotier J. Aurélien ¹, Laurent Petit³, Silvio Sarubbo⁴, Sylvain Miraux¹, and Emeline J. Ribot^1
1Centre de Résonance Magnétique et Systèmes Biologiques, UMR5536, CNRS, University of Bordeaux, Bordeaux, France, ²Wellcome Center for Human Neuroimaging, UCL Queen Square Institute of Neurology, University Coleege of London, London, United Kingdom, ³Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France, ⁴Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, Santa Chiara Hospital, Trento, Italy

Keywords: Quantitative Imaging, Image Reconstruction, myelin water fraction

Multi-compartment T2 imaging suffers from long acquisition time. Undersampling the k-space combined with advanced iterative reconstructions could be beneficial to reach a reasonable scan duration. This work investigates the suitability of linear subspace-based reconstruction for myelin water fraction and intra-extracellular T2 mapping. Our findings suggest that subspace-based reconstruction for intra-extra cellular T2 and myelin water fraction mapping can be reliably used in combination with spatial regularization enforcing sparsity. The temporal basis can be built from extended phase graph simulations and should include at least 12 components, especially for myelin water fraction mapping.  

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Shachar Moskovich¹, Oshrat Shtangel¹, and Aviv Mezer^1
1The Hebrew University, Jerusalem, Israel

Keywords: Quantitative Imaging, Signal Representations

Weighted MRI images are widely used in both clinical and open-source datasets, while quantitative mapping is not always feasible. The ratio of T1 and T2 weighted images was previously suggested as a semi-quantitative measurement. We propose two additional weighted ratios T1w/PDw and ln(T2w/PDw), as semi-quantitative proxies for R1 and R2, which we tested on phantom and human data. We found that the new ratios accurately represent the quantitative parameters in both datasets. 

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Shujian Li¹, Jieliang Lin², and Jingliang Cheng^1
1the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Advanced Technical Support, Philips Healthcare, Beijing, China

Keywords: Quantitative Imaging, Microstructure

This study conducted an initial investigation of the feasibility of MOLLI-based T1 mapping and DWI by using mono-exponential, bi-exponential, and DKI models for the noninvasive preoperative evaluation of cervical cancer. Our findings indicated that both T1 mapping and non-mono-exponential model DWI can be used to discriminate cervical cancer from normal cervical tissue and adenocarcinoma from SCC. Our results also achieved a significant information gain for identifying SCC grade by combining native T1 and MKmean. Moreover, the maximum or minimum values of diffusion parameters within the whole lesion had advantages over the mean values in the prediction of SCC grade.

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Zhitao Li¹, Ding Xia^2,3, Shreyeas Vasanawala¹, and Li Feng^2,3
1Department of Radiology, Stanford University, Stanford, CA, United States, ²BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Keywords: Quantitative Imaging, Liver, fat, Dixon

Fat represents a major confounding factor for T1 mapping of the liver. Recently, a number of novel magnetization-prepared multiecho imaging approaches have been proposed to obtain water-only T1 mapping, and they all hold great potential for clinical applications. However, several questions have raised along with this this trend, such as (1) whether concurrent estimation of R2star and the number of echoes is necessary and whether it would affect the quantification of water T1; and (b) whether the number of echoes would affect T1 estimation. This work aimed to investigate these two questions with both numerical simulation and in-vivo experiments.

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Jingjia Chen¹, Ding Xia², Kai Tobias Block³, Chunlei Liu¹, and Li Feng^2
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ²Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Center for Advanced Imaging Innovation and Research (CAI2R), NYU Grossman School of Medicine, New York, NY, United States

Keywords: Quantitative Imaging, Multi-Contrast

This work demonstrates the feasibility of simultaneous estimation of fat/water-separated T1, proton density fat fraction (PDFF), R2*, and quantitative susceptibility mapping (QSM) of the liver using free-breathing GraspT1-Dixon MRI from a single rapid acquisition with an inversion-recovery (IR)-prepared multi-echo stack-of-stars sequence. For fat/water-separated T1 mapping, water-only images are generated from multi-echo images at different inversion times (TIs), from which a water-specific T1 map is estimated. For other parameters, acquired data from all TIs are averaged to generate a single set of multi-echo images, from which PDFF, R2*, and QSM are estimated. 

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Yi-Chun Wang^1,2, Roberto Salvati², John Connell², Natali Van Zijl¹, Tom Waddell², Daniel Bulte¹, and Michael Brady^2
1University of Oxford, Oxford, United Kingdom, ²Perspectum, Oxford, United Kingdom

Keywords: Quantitative Imaging, Liver, cT1, T2*, PDFF, volume, future liver remnant

Understanding the interplay between quantitative MRI metrics is crucial for reliable clinical assessment of liver health. This study utilised Bayesian networks to visualise hidden relationships between cT1, T2*, proton density fat fraction (PDFF), volume and future liver remnant (FLR). Analysing the directionality between Bayesian networks on a pre-operative dataset with 130 participants and a post-operative dataset with 90 participants, clear causal relationships from PDFF to cT1 and from PDFF to volume were found, which are supported by published literature. An additional discovery is the potential for correlation between metrics to help strengthen the clinical utility of cT1 after surgery.

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Christopher Elliot Vaughn^1,2, N Reid Bolding³, Mark A Griswold⁴, and William A Grissom^1,2
1Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ²Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ³Physics, Case Western Reserve University, Cleveland, OH, United States, ⁴Radiology, Case Western Reserve University, Cleveland, OH, United States

Keywords: Quantitative Imaging, Data Acquisition

We demonstrate a new extension of Selective Encoding through Nutation and Fingerprinting (SENF) which is a RF spatial encoding technique that also encodes quantitative information. This SENF method uses quadratic RF phase modulation sequence with an off-resonant Bloch-Siegert pulse to induce the Bloch-Siegert shift to simultaneously encode spatial and quantitative information. We validate this method on a 47.5mT low-field scanner and in simulation.

Sen Jia¹, Lixian Zou¹, Zhilang Qiu², Yongquan Ye³, Haifeng Wang¹, Chao Zou¹, Ye Li¹, Jian Xu³, Xin Liu¹, Hairong Zheng¹, and Dong Liang^1,4
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China, ²Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ³UIH America, Houston, TX, United States, ⁴Medical AI Research Center, Shenzhen Institutes of Advanced Technology, Shenzhen, China

Keywords: Quantitative Imaging, Relaxometry, Susceptibility

The MULTIPLEX technique could quantify the T1/T2*/PD/Susceptibility maps in a single 3D scan but leads to a long scan time due to the dual-TR, dual-flip angle, and multi-echo signal acquisition strategy. Wave-CAIPI acceleration with SENSE reconstruction is limited by noise amplification at high acceleration factors and is susceptible to artifacts from inaccurate coil sensitivity maps. This work develops a L1 regularized Wave-SPIRiT reconstruction to achieve 9-fold accelerated MULTIPLEX imaging in 3 minutes. The L1 regularized coil-by-coil reconstruction also benefits the Multi-Dimensional Integration (MDI) quantification to achieve comparable accuracy and robustness as the reference scan with 55% reduction of scan time.

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Giorgia Milotta¹, Isobel Green², Jonathan Roiser³, and Martina Callaghan^1
1Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, ²Harvard Medical School, Boston, MA, United States, ³Institute of Cognitive Neuroscience, University College London, London, United Kingdom

Keywords: Quantitative Imaging, Tissue Characterization, Multi-Parameter Mapping

The habenula has attracted much interest in neuroscience studies because it plays an important role in the reward circuitry of the brain and is implicated in psychiatric conditions. However, imaging the habenula remains challenging due to its sub-cortical location and small size, with few reports analysing its microstructural composition in vivo. To address this gap in the literature, we performed a multi-parametric characterisation of the microstructure of the habenula by quantifying relaxation rates (R₁, R₂*), water content (PD) and a marker of macromolecular content (MTsat), most notably myelin, in a cohort of 20 healthy participants.

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Chenyang Dai¹, Jiechao Wang¹, Qizhi Yang¹, Zhigang Wu², Congbo Cai¹, and Shuhui Cai^1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, ²MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Keywords: Quantitative Imaging, Brain, Multi-slice information sharing; modulation pattern; overlapping-echo detachment imaging

Multi-parametric quantitative magnetic resonance imaging (mqMRI) has important applications in clinic. Multiple overlapping-echo detachment (MOLED) imaging can achieve single-shot mqMRI. However, the existing methods mainly focus on single-slice reconstruction. To improve the reconstruction quality of parametric maps by exploring data redundancy among adjacent slices, we proposed a multi-slice information sharing method via multiple modulation patterns of MOLED k-space and deep neural network. The results show that our method can effectively utilize the correlation information among adjacent slices and improve the reconstruction quality compared to the single-slice reconstruction method.

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Yohan Jun^1,2, Yamin Arefeen³, Jaejin Cho^1,2, Xiaoqing Wang^1,2, Michael Gee^2,4, Borjan Gagoski^2,5, and Berkin Bilgic^1,2,6
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, ⁵Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁶Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

Keywords: Quantitative Imaging, Quantitative Imaging

The 3D-quantification using an interleaved Look-Locker acquisition sequence with T₂ preparation pulse (3D-QALAS) has been developed and used for acquiring high-resolution T₁, T₂, and PD maps from five measurements within each repetition time. However, it assumes that each k-space data is acquired instantly at the first echo train length index neglecting T₁ and T₂ relaxation during the acquisition, which might cause blurring and biases in the reconstructed maps. In this study, we propose to reconstruct accurate quantitative T₁ and T₂ maps with reduced blurring compared to the conventional QALAS method using our proposed zero-shot deep subspace reconstruction method (i.e., Zero-DeepSub).

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Fei Xu¹, Hongyan Liu¹, Stefano Mandija¹, Oscar van den Heide¹, Edwin Versteeg¹, Miha Fuderer¹, Cornelis A.T. van den Berg¹, and Alessandro Sbrizzi^1
1Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands, Utrecht, Netherlands

Keywords: Quantitative Imaging, Contrast Agent, MR-STAT; Contrast Enhancement Imaging

Gadolinium-based contrast agents (GBCAs) uptake has the ability to facilitate disease diagnosis. In this work, we implement MR-STAT for fast contrast agent uptake quantification by applying keyhole acquisition and regularized reconstruction. We first analyze the accuracy of the proposed method on gadolinium-doped gel phantoms and observe that accurate T1 mapping of post-injection can be achieved with a keyhole factor of 25%. Furthermore, we investigate the impact of lower (20% vs 100%) GBCA dose administration on simulated clinical data. The quantification of pathologic T1 change for low-dose administration was comparable to that for full-dose.

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Hyunyeol Lee^1,2, Jing Xu², and Felix W Wehrli^2
1School of Electronics Engineering, Kyungpook National University, Daegu, Korea, Republic of, ²Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Quantitative Imaging, Metabolism

In the original qBOLD, it is challenging to separate deoxyhemoglobin’s contribution to R₂' from other sources modulating the voxel signal. Further, extracting DBV and Y_v from measured R₂' is a nontrivial task. It was recently shown that the constrained qBOLD method was able to properly separate the several confounding factors, yielding the expected contrast for both Y_v and DBV maps across the entire brain, and, together with a separate measurement of CBF, leading to whole-brain 3D CMRO₂ maps within physiologically plausible ranges. Here, we validated the new 3D qBOLD method with respect to repeatability and hypercapnic gas breathing challenges.

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Steen Moeller¹, Casey P. Johnson^1,2, Erick O. Buko^1,2, Ferenc Toth², Greg Metzger¹, Silvia Mangia¹, Shalom Michaeli¹, Sara Ponticorvo¹, Antonietta Canna¹, Kamil Ugurbil¹, and Mehmet Akcakaya^1,3
1Radiology, University of Minnesota, Minneapolis, MN, United States, ²Veterinary Clinical Sciences, University of Minnesota, Minneapolis, MN, United States, ³Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States

Keywords: Quantitative Imaging, Data Processing

The concept of transform processing domain with locally low rank denoising is proposed as T-NORDIC and demonstrated for MSK and brain applications. The improvements on quantitative maps may be leveraged for faster acquisitions by relaxing the number of averages needed to obtain sufficient SNR for high resolution acquisitions and for application of low rank denoising to common clinical acquisitions.