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Eze Ahanonu¹, Kevin Johnson², Ute Goerke³, Brian Toner⁴, Vibhas Deshpande⁵, Ali Bilgin^1,2,6, and Maria Altbach^2,6
1Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, United States, ²Department of Medical Imaging, The University of Arizona, Tucson, AZ, United States, ³Siemens Healthineers, Tucson, AZ, United States, ⁴Applied Math Program, The University of Arizona, Tucson, AZ, United States, ⁵Siemens Healthineers, Austin, TX, United States, ⁶Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, United States

Keywords: Quantitative Imaging, Body

The interest in developing quantitative metrics in abdominal imaging has grown in recent years. In particular, abdominal T1 mapping plays a role in the characterization of abdominal pathologies.  However, current T1 mapping of the abdomen is limited by poor anatomical coverage, long acquisitions related to sufficient sampling of the T1 recovery curve and recovery times, and reduced T1 accuracy secondary to respiratory motion. Here we present a novel approach for free-breathing T1 mapping of the abdomen, which leverages the undersampling robustness of radial MRI and combines fast data acquisition with deep learning for accurate and efficient abdominal T1 mapping. 

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Gracyn Campbell¹, Darryl B. Sneag¹, Qian Li¹, and Ek Tsoon Tan^1
1Hospital for Special Surgery, New York, NY, United States

Keywords: Quantitative Imaging, Nerves, Deep Learning

Conventional, quantitative T2 mapping for MR neurography may depict peripheral neuropathy related changes but has insufficient spatial resolution within acceptable acquisition times (<6 min.) to mitigate motion. Alternatively, dual-echo steady-state (DESS) can simultaneously provide high resolution 3D qualitative anatomical data and quantitative T2 maps for characterizing both nerve and muscle within this targeted acquisition window. Analysis of subjects with peripheral neuropathy in the elbow/forearm region showed that DESS-T2 was higher in involved nerves and muscles, and that DL-reconstruction slightly decreased DESS-T2. Additionally, DESS enabled analysis of the magic angle effect, demonstrating a positive correlation between nerve orientation and DESS-T2 values. 

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Seonyeong Shin*^1,2, Ana-Maria Oros-Peusquens*¹, Seong Dae Yun¹, Ezequiel Farrher¹, and N. Jon Shah^1,2,3,4,5
1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany, ²RWTH Aachen University, Aachen, Germany, ³Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany, ⁴JARA - BRAIN - Translational Medicine, Aachen, Germany, ⁵Department of Neurology, RWTH Aachen University, Aachen, Germany

Keywords: Quantitative Imaging, Relaxometry, Glympathics, CSF

T2* relaxation in the brain covers a broad range of values, which can be grouped in three, roughly logarithmically spaced, intervals (short, intermediate, very long). Using a multi-echo GRE sequence to quantify T2* is time-efficient for brain parenchyma, but accurate quantification of slow-relaxing water pools, such as CSF, lengthens the acquisition time. In this work, we propose a novel sequence (ES-QUTE) that combines multi-echo acquisition with echo shifting techniques to effectively quantify the whole range of T2* relaxation times in the brain without increasing the scan time. In addition, ES-QUTE simultaneously characterises fast diffusion.

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Keywords: Quantitative Imaging, Hyperpolarized MR (Non-Gas)

We present dynamic T₂^* measurements for HP [1-¹³C]pyruvate and metabolites in a healthy human brain volunteer and two RCC patients at 3T. The T₂^* of pyruvate was shown to vary during the acquisition, whereas the T₂^* of lactate and bicarbonate was constant through time and across organs. The T₂^* of lactate was constant at gray matter (30.1±5.9ms), white matter (33.8±7.6ms), healthy kidney (38.94±6.9ms) and tumor (33.27±6.4ms), and the T₂^* of bicarbonate over whole-brain (109.5±12.8ms) and kidney (64.6±15.8ms). These relaxometry measurements will be useful for future sequence optimization and can be included in kinetic modeling to harmonize data across different TEs.

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Keywords: Quantitative Imaging, Artifacts, B1 mapping; quantitative; parameter mapping

Actual Flip angle Imaging (AFI) is an efficient B1 mapping method requiring the proper spoiling of transverse magnetization. Optimal spoiling can be achieved using large spoiling gradients enabling water diffusion-based signal decay. However, spoiling the non-aqueous signal like from fat is typically ignored in AFI optimizations. We demonstrate that infinitesimal diffusion in the fat signal makes fat spoiling in AFI unachievable in the reasonable scan time and that incomplete fat spoiling is a major source of previously unexplained AFI errors. We propose method to minimize them using a superposition model of the spoiling artifacts and chemical shift encoded fat/water separation.

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Adèle L.C. Mackowiak^1,2,3, Jérôme Yerly^1,4, Katarzyna Pierzchala⁵, Eva S. Peper^2,3, Giulia M.C. Rossi¹, and Jessica A.M. Bastiaansen^2,3
1Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ²Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), University of Bern, Bern, Switzerland, ³Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, ⁴Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, ⁵Laboratory of Functional and Metabolic Imaging, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

Keywords: Quantitative Imaging, Fat, phase-cycled bSSFP, fat fraction mapping, radial

The Signal Profiles Asymmetries for Robust multi-Compartment Quantification (SPARCQ) framework uses the off-resonance information encoded in phase-cycled bSSFP (PC-bSSFP) data to estimate fat fraction (FF). In order to strengthen previous validation work as well as open the range of applicability of the technique, in this work a 2D radial bSSFP sequence with integrated automated phase-cycling was designed and the accuracy of SPARCQ was tested in vitro on a larger FF range than previously reported. Comparisons to reference methods and sampling schemes indicate that the proposed automated 2D radial sampling scheme allows accurate FF mapping with SPARCQ while improving scan efficiency.

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Yavuz Muslu^1,2, Ty A. Cashen³, Sagar Mandava⁴, Diego Hernando^2,5, and Scott B. Reeder^1,2,5,6,7
1Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Global MR Applications and Workflow, GE Healthcare, Waukesha, WI, United States, ⁴Global MR Applications and Workflow, GE Healthcare, Atlanta, GA, United States, ⁵Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁶Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁷Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Keywords: Quantitative Imaging, Liver, Multi-Contrast

T₁ relaxation is emerging as a biomarker for the diagnosis and staging of chronic liver disease. Modified Look-Locker Inversion Recovery (MOLLI) is widely used in clinical practice for abdominal T₁ mapping; however, current methods are not corrected for fat and B₁ inhomogeneities as confounding factors and fail to provide reliable T₁ measurements. In this work, we propose a novel, free-breathing, confounder-corrected T₁ mapping method over the entire liver, by combining inversion recovery and chemical shift encoding imaging for simultaneous estimation of T₁, PDFF, R₂* and B₁+.

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Yamin Arefeen¹, Yohan Jun^2,3, Borjan Gagoski⁴, Berkin Bilgic^2,3, and Elfar Adalsteinsson^1,5,6
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Department of Radiology, Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁴Department of Radiology, Boston Children's Hospital, Boston, MA, United States, ⁵Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁶Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

Keywords: Quantitative Imaging, Pulse Sequence Design

3D-QALAS utilizes an interleaved Look-Locker acquisition with T₂-preparation pulses for full brain quantification of T₁ and T₂.  The sequence applies constant flip-angles and suffers from blurring due to k-space modulation from signal evolution during the lengthy echo-train.  This abstract improves 3D-QALAS by (1) resolving the full temporal signal with subspace reconstructions to eliminate blurring, (2) optimizing acquisition flip angles with the Cramer-Rao-Bound using simulations compatible with auto-differentiation, and (3) decreasing the number of acquisitions within a repetition time, which could enable up to 40% reduced scan time.  Simulation, phantom, and in-vivo results demonstrate the efficacy of the proposed sequence improvements.

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Hongyan Liu¹, Oscar van der Heide¹, Edwin Versteeg¹, Miha Fuderer¹, Fei Xu¹, Martijn Froeling², Cornelis A.T. van den Berg¹, and Alessandro Sbrizzi^1
1Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, Netherlands

Keywords: Quantitative Imaging, Quantitative Imaging

MR-STAT is a framework for simultaneously acquiring multi-parametric quantitative maps from one single short scan. In this work, we design a new 3D MR-STAT sequence and the corresponding two-step reconstruction strategy based on previous work. The framework is improved by designing a faster acquisition framework, and more accurate signal modeling for reconstruction. The proposed sequence takes 7 minutes after retrospectively undersampling, and is validated in a phantom experiment. Furthermore, we apply this 3D MR-STAT sequence the first time for musculoskeletal applications. Knee and lower-leg experiments of healthy volunteers are shown here.

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Keywords: Quantitative Imaging, Perfusion, Lung

The aim of this study is the evaluation of the influences of previously applied gadolinium-based contrast media, differing field strength and differing imaging sequence on the lately proposed perfusion quantification for PREFUL MRI. The results indicate severe alterations and distortions of the quantified perfusion parameters after gadolinium administration and depending on the field strength and on the applied sequence type. The results show, that future multicenter studies need to strictly adhere to an identical imaging protocol using the same field strengths to generate comparable results across all sites.

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Qing Wang¹, Qiwei Xiao¹, Elizabeth M. Fugate², Matthew M. Willmering¹, Dianna M. Lindquist², Nana S. Higano^1,2,3, Alister J. Bates^1,2,3,4, and Zackary I. Cleveland^1,2,3,4
1Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ²Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ³Pediatrics, University of Cincinnati, Cincinnati, OH, United States, ⁴Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States

Keywords: Quantitative Imaging, Preclinical, Ultra-short Echo-time (UTE), trachea

The trachea expands and retracts almost uniformly while breathing, but these dynamics are altered by congenital malformation and injury to tracheal cartilage. Tracheal collapse—tracheomalacia—is a common comorbidity in of disorders, including bronchopulmonary dysplasia (BPD), idiopathic pulmonary fibrosis (IPF) and cystic fibrosis (CF) and can cause life-threatening airway obstruction. While tracheomalacia is clinically diagnosed via bronchoscopy, no tool exists to noninvasively assess tracheal dynamics in small animal models. Here we use retrospectively gated, 3D UTE to resolve changes in tracheal caliber during tidal breathing and show these dynamics change as a function of tracheal position and breathing rate.

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Ruihao Liu^1,2, Yudu Li^2,3, Rong Guo^2,4, Yibo Zhao^2,5, Ziyu Meng¹, Huixiang Zhuang¹, Tianyao Wang⁶, Yao Li¹, Yiping P. Du¹, and Zhi-Pei Liang^2,5
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ²Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Siemens Medical Solutions USA, Inc., Urbana, IL, United States, ⁵Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁶Radiology Department, The Fifth People's Hospital of Shanghai, Shanghai, China

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence, T2 mapping

Deep learning (DL)-based methods have shown great potential for accelerating T2W imaging by using image prior generated from a companion T1W image. However, quantitative T2 mapping requires multiple T2W images acquired with multi-TE, creating practical problem for the use of DL for accelerated T2 mapping due to insufficient multi-TE training data. This work addresses this problem by using a generalized series model to map a T2W DL prior for one TE to multiple TEs, enabling effective use of T1W image prior for high-quality T2 mapping from sparsely sampled data. The method has been validated using experimental data, producing impressive results.

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Jordina Aviles Verdera^1,2, Lisa Story^1,3, Megan Hall^1,3, Tom Finck^1,4, Alexia Egloff^1,5, Shaihan Malik^1,2, Mary A Rutherford^1,2, Joseph V Hajnal ^1,2, Raphael Tomi-Tricot^1,2,6, and Jana Hutter^1,2
1Center for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ²Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ³Women's Health, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom, ⁴Department for Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar Muenchen, Munich, Germany, ⁵Radiology, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom, ⁶MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

Keywords: Quantitative Imaging, Fetus

Comprehensive anatomical and functional fetal examination at low field (0.55T) MRI, obtained using a 20min protocol in 40 pregnant women demonstrates high quality data suitable for quantitative analysis both for anatomical radiological values and for functional T2* and diffusion values, all in-line with previously reported higher field values. It opens novel avenues and widens access to fetal MRI to new groups such as the fast growing fraction of obese and overweight pregnant women, in whom comprehensive assessment by screening ultrasound may be difficult.

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Michael S Yao^1,2, Andrew Van^3,4, James Gee², Murray Grossman⁵, David J Irwin^5,6, and M Dylan Tisdall^2
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, ²Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States, ⁴Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, United States, ⁵Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, ⁶Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Quantitative Imaging, Quantitative Imaging

The acquisition of high-resolution quantitative measurements is of particular interest in studying the laminar structure and layer-specific pathology in the cerebral cortex. In this work, we propose a method to address this need by acquiring 1-D echo planar spectroscopic imaging (EPSI) as a "virtual biopsy" with 200 µm resolution along the readout direction. Our sequence yields expected spectra for common compounds and produces high-quality quantitative T₂* and off-resonance measurements in ex vivo brain tissue. Our goal is to use this quantitative virtual biopsy to discriminate laminar variations in cortical iron deposition in diseases such as frontotemporal lobar degeneration (FTLD).

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Haoyang Pei^1,2, Ding Xia¹, Xiang Xu¹, Yang Yang^1,3, Yao Wang², Fang Liu⁴, and Li Feng^1
1Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States, ³Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Keywords: Quantitative Imaging, Quantitative Imaging

Look-Locker inversion recovery (LLIR) imaging is an easy, accurate and reliable MRI method for T1 mapping. For 3D acquisition, LLIR imaging is usually performed with multiple repetitions, and additional idle time is placed between consecutive receptions. This idle time allows for signal recovery to improve SNR and ensures robustness to B1 inhomogeneity, but it also prolongs scan time. Simply eliminating the idle time reduces the accuracy of T1 quantification. In this work, a novel deep-learning approach was proposed to address this challenge, so that accurate 3D T1 maps can be generated from continuous 3D LLIR imaging without idle time.