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Hendrik Mattern^1,2, Falk Lüsebrink^1,3,4, and Oliver Speck^1,2,5,6
1Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, ²German Center for Neurodegenerative Disease, Magdeburg, Germany, ³Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Magdeburg, Germany, ⁴Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ⁵Center for Behavioral Brain Sciences, Magdeburg, Germany, ⁶Leibniz Institute for Neurobiology, Magdeburg, Germany

Keywords: Data Analysis, Aging

In this explorative study, the effect of imaging parameters, image quality, circadian rhythm, and aging on FreeSurfer’s estimates is investigated. To that end, the human phantom, an openly available data of a single subject scanned at 7T over 7 years with various MR protocols, is used.

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Zhixing Wang¹, Xiaozhi Cao², Kun Qing³, Xue Feng¹, John P. Mugler⁴, and Craig H. Meyer^1,4
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ²Department of Radiology, Stanford University, Stanford, CA, United States, ³Radiation Oncology, City of Hope, Duarte, CA, United States, ⁴Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, United States

Keywords: Data Acquisition, Data Acquisition

This study provides an alternative approach to conventional diffusion-weighted (DW) EPI-based sequences. A 2D single-shot (SS) diffusion-prepared (DP) turbo-spin-echo (TSE) sequence, combined with spiral-ring trajectories and magnitude stabilizers, dubbed “SS-DP-SPRING TSE”, was developed for distortion- and motion artifact-free diffusion imaging. Compared to a SS-DW-EPI sequence, this method is less sensitive to B₀-inhomogeneity and thus provides DW-images with improved geometric fidelity.

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Qiang Liu^1,2, Lipeng Ning¹, Congyu Liao^3,4, Shaik Imam⁵, Scott Peltier⁶, Borjan Gagoski⁷, Berkin Bilgic^8,9,10, William A. Grissom⁵, Maxim Zaitsev¹¹, Jon-Fredrik Nielsen¹², and Yogesh Rathi^1,13
1Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, ²School of Biomedical Engineering, Southern Medical University, Guangzhou, China, ³Department of Radiology, Stanford University, Stanford, CA, United States, ⁴Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ⁵Institute of Imaging Science, Department of Biomedical Engineering, Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States, ⁶Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ⁷Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States, ⁸Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁹Department of Radiology, Harvard Medical School, Cambridge, MA, United States, ¹⁰Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ¹¹Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany, ¹²Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ¹³Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States

Keywords: Data Acquisition, Software Tools

Using on the open-source pulse sequence programming platform Pulseq, we successfully developed and implemented the gSlider sequence on a 3T scanner without the vendor-specific sequence development environment. One-millimeter whole-brain isotropic dMRI data was acquired using the gSlider implementation from Pulseq and Siemens and test-retest variability was evaluated. The test-retest reliability in fractional anisotropy improved dramatically for the Pulseq implementation while it was comparable for both implementations for mean diffusivity. We demonstrated the feasibility of implementing advanced sequences using the open-source Pulseq platform and expect that it will be easily transferred to and executed on different vendors. 

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Joan Chick¹, Evanthia Kousi¹, Andreas Wetscherek¹, Julie Hughes², Georgina Hopkinson², Jessica Gough^2,3, Rosalyne Westley^2,3, Radhouene Neji⁴, Alex Dunlop¹, Simeon Nill¹, Henry Mandeville^2,3, Katharine Aitken^2,3, Dow-Mu Koh^2,3, and Uwe Oelfke^1
1Joint Department of Physics, The Institute of Cancer Research and Royal Marsden Hospital, London, United Kingdom, ²Royal Marsden Hospital, London, United Kingdom, ³The Institute of Cancer Research, London, United Kingdom, ⁴MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom

Keywords: Data Acquisition, Radiotherapy, Respiratory Correlated MRI

Respiratory correlated 4D-MRI has the potential to be a valuable tool in radiotherapy. This work aims to optimise and validate a 4D-MRI golden angle stack of stars radial sequence for measuring respiratory motion of abdominal organs. A 4D motion phantom is used to compare 4D-MRI against 4D-CT. For craniocaudal movement, motion is underestimated in 4D-MRI but motion estimation improves for non-axial acquisitions. This could be due to the alignment of the motion direction with the cartesian phase encoding direction for axial stack of stars acquisitions. 

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Gael Saib¹, Zeynep Demir¹, Paul Taylor², S. Lalith Talagala³, and Alan P. Koretsky^1
1NINDS/LFMI, National Institutes of Health, Bethesda, MD, United States, ²NIMH/SSCC, National Institutes of Health, Bethesda, MD, United States, ³NINDS/NMRF, National Institutes of Health, Bethesda, MD, United States

Keywords: Visualization, Neurofluids, CSF

Perivascular spaces (PVS) dilatation has been recently linked to aging and neurodegenerative diseases. T₂-weighted ultra-high field MRI allows to image the PVS burden at high contrast and spatial resolution non-invasively. However, most of sub-voxel sized PVS cannot be resolved due to partial volume effects and remaining signal from blood and tissue. High-resolution 3D-TSE with reduced flip angle allowed the visualization of PVS with a very high CSF-to-tissue ratio of ~37:1 at 7T.

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Serhat Ilbey¹, Michael Bock¹, and Ali Caglar Özen^1
1Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany

Keywords: Pulse Sequence Design, New Trajectories & Spatial Encoding Methods

XTE is a general implementation of various sequences, namely UTE, ZTE, SPI, PETRA, and gmPETRA. Each sequence type and their combinations can be realized within XTE by adjusting only a few parameters. We provide phantom and in vivo images acquired with XTE for different sequence settings.

Zhongsen Li¹, Aiqi Sun², Shuai Wang¹, Chuyu Liu¹, Sirui Wu¹, Haozhong Sun¹, Rui Guo³, Haiyan Ding¹, and Rui Li^1
1Center for Biomedical Imaging Research, Tsinghua University, Beijing, China, ²Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ³School of Medical Technology, Beijing Institute of Technology, Beijing, China

Keywords: Data Acquisition, Cardiovascular, golden angle radial, eddy-current suppresion, profile order

Golden angle radial trajectory suffers from eddy-current when combined with trueFISP sequence. If additional navigators are required, how to keep golden ratio sampling while maintain stable steady-state is still a problem to be solved. In this work, we propose a novel radial profile order, named "swin golden angle"(swinGA), which is able to achieve golden ratio sampling, stable trueFISP signal, and navigator acquisition simultaneously. We validated the proposed profile in static phantom study and in-vivo study for free-running cardiac imaging. The results show that the proposed swinGA significantly outperforms other sampling profiles and achieves good image quality.

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Evanthia Kousi¹, Joan Chick¹, Andreas Wetscherek¹, Julie Hughes², Georgina Hopkinson², Jessica Gough^2,3, Rosalyne Westley^2,3, Radhouene Neji⁴, Simeon Nill¹, Dow-Mu Koh^2,3, and Uwe Oelfke^1
1Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom, ²The Royal Marsden NHS Foundation Trust, London, United Kingdom, ³The Institute of Cancer Research, London, United Kingdom, ⁴MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom

Keywords: Data Analysis, Radiotherapy, 4D-MRI, Respiratory correlated MRI, radiotherapy planning

In this abstract we present our initial results from the validation of a respiratory correlated 4D MRI golden angle stack of stars radial sequence prior to its clinical implementation and highlight potential experimental pitfalls to avoid that may impact motion range estimations. 

Correct phantom set up reduced motion underestimations by 3%-18% for the simulated respiratory waveforms considered. Larger motion discrepancies observed for the low amplitude motion which improved by increasing the number of bins.

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Nuwan D. Nanayakkara¹, Liesel-Ann Meusel¹, Nicole D. Anderson^1,2, and J. Jean Chen^1,3,4
1Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada, ²Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada, ³Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, ⁴Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Keywords: Data Analysis, fMRI (task based), Cerebrovascular reactivity, CVR

Cerebrovascular reactivity (CVR) can be estimated from the BOLD response to a vasoactive stimulus such as a breath-holding (BH) approximated by a sinusoidal regressor. We proposed a robust approach for estimating CVR using the frequency spectrum of BOLD data itself, without regressions or correlations with external recordings. We also estimated regional CVR time delay patterns using the BOLD signal phase relative to a non-brain reference. We demonstrate our pipeline in 18 healthy adults.

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Yan Tan¹, Zeliang Liu², Xiaochun Wang¹, and Hui Zhang^1
1First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China, China, ²LinFen People's Hospital, Linfen, Shanxi Province, China, China

Keywords: Data Analysis, Tumor, diffusion kurtosis imaging, nomogram, astrocytoma, overall survival, treatment

To establish a nomogram by integrating DKI, molecular and clinical risk factors for personalized OS estimation in astrocytomas, and explore the nomogram-based treatment benefits. Astrocytomas are the most common brain tumors with poor prognoses. The detection of risk factors may aid in individualizing therapeutic plans and improve survival. It is concluded the nomogram based on DKI, molecular and clinical risk factors could achieve the individualized OS estimation of astrocytomas with excellent performance. For high-risk patients, surgery plus chemo and/or radiotherapy were recommended; while for low-risk patients, additional chemo and/or radiotherapy did not increase survival benefits.

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Yuguang Meng¹, James J. Lah², Jason W. Allen^1,2, and Deqiang Qiu^1
1Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States, ²Department of Neurology, Emory University, Atlanta, GA, United States

Keywords: Data Acquisition, Artifacts

         In gradient-echo imaging, the motion effects on the reconstructed images could be exacerbated by the temporal B₀ field changes due to respiration and the subjects’ unintentional position/posture changes during scanning. An efficient 3D spiral navigator independent of the multi-echo gradient-echo (mGRE) acquisition train was designed and implemented in a 3D mGRE sequence. The results showed that although there were unintentional movements during acquisitions, the temporal B₀ changes could be significant at 3.0 T. The design provides flexible TE options for mGRE in obtaining simultaneous T₁-weighted and T₂^*-weighted contrasts, quantitative T₂^* and/or quantitative susceptibility mapping.

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Veronica P Dell'Acqua¹, Chantal M W Tax^1,2, Malwina Molendowska¹, Greg D Parker¹, Derek K Jones^1,3, Muhamed Barakovic^1,4,5,6,7, and Erick Jorge Canales-Rodriguez^6
1Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, ²University Medical Center Utrecht, Image Sciences Institute, Utrecht, Netherlands, ³Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia, ⁴Translational Imaging in Neurology (ThINk) Basel, University Hospital Basel and University of Basel, Basel, Switzerland, ⁵MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland, ⁶Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁷Roche Pharma Research & Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland

Keywords: Data Analysis, Relaxometry, Validation, Axon Diameter, Diffusion-Relaxation

In-vivo quantification of axon diameter is an attractive and debated topic in the MRI community. The possibility to resolve submicrometric axon diameters non-invasively yields the potential to push further the boundaries in research and clinics but yet, further work is needed to better explore and validate the existing approaches to estimate the inner axon diameter. Recently, the feasibility of estimating the axon diameter from the intra-axonal transverse relaxation time has been investigated combining a diffusion-relaxation protocol and histological data. In the present study, we apply this approach in a larger in vivo population to assess variability across participants.

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Kuan Zhang¹, Daehun Kang¹, Maria A Halverson¹, Shengzhen Tao², Steven A Messina¹, MyungHo In¹, Joshua D Trzasko¹, John III Huston¹, Matthew A Bernstein¹, and Yunhong Shu^1
1Radiology, Mayo Clinic, Rochester, MN, United States, ²Radiology, Mayo Clinic, Jacksonville, FL, United States

Keywords: Data Acquisition, Data Acquisition, FGATIR, Deep brain stimulation, Compact 3T, MRI sequence optimization

Deep brain stimulation (DBS) is used for neurological disorder treatment but requires precise image-based targeting. The fast gray matter acquisition T1 inversion recovery (FGATIR) sequence was developed to visualize DBS targets, such as the mammillothalamic tract (MTT). We investigated the imaging protocol of FGATIR through simulation, phantom experiment, and volunteer scanning to improve the contrast-to-noise ratio. Adjustments to inversion time, flip angle, and receive bandwidth were made to achieve the highest MTT-thalamus contrast. A compact 3T scanner was also leveraged to further maximize the contrast as the high-performance gradients reduce repetition time, thereby decreased the acquisition window of FGATIR. 

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Thomas Jochmann^1,2, Fahad Salman², Ademola Adegbemigun², Jens Haueisen¹, and Ferdinand Schweser^2,3
1Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, ²Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Keywords: Image Reconstruction, Quantitative Susceptibility mapping

We studied the dependency of quantitative susceptibility mapping to scaling the resolution and amplitude in a brain phantom with and without tissue microstructure. We found that nonsusceptibility frequency contributions cause a resolution- and region-dependent bias to quantitative susceptibility mapping.

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Yuchou Chang¹, Gulfam Ahmed Saju¹, Jasina Yu¹, Reza Abiri², Zhiqiang Li³, and Tianming Liu^4
1Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, United States, ²Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States, ³Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, United States, ⁴Computer Science, University of Georgia, Athens, GA, United States

Keywords: Image Reconstruction, Parallel Imaging

The background phase variations exist in coil sensitivities. Optimized phase distribution can minimize the noise of the parallel MRI reconstruction. However, phase variation may be originated from different factors, and it is difficult to be modeled. A random feature method is proposed to model phase variations in coil sensitivities. Through a linear reconstruction using the random phase feature, background noise can be suppressed. Augmented phase features make the linear reconstruction better remove background noise.

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Nolan Meyer^1,2, Myung-Ho In¹, Maria Halverson¹, John Huston III¹, Matt Bernstein¹, and Joshua Trzasko^1
1Radiology, Mayo Clinic, Rochester, MN, United States, ²Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States

Keywords: Image Reconstruction, Signal Representations

A model-based image reconstruction (MBIR) framework is developed for highly accelerated point spread function (PSF) encoded echo planar imaging (EPI). The reconstruction accounts for known physics, and utilizes a subspace representation with local low-rank (LLR) regularization along with a variable splitting solution. Comparisons with images obtained through standard methods demonstrate considerable artifact reduction and sharpness preservation through MBIR. MBIR accommodates arbitrary sampling trajectories along the PSF-encoding dimension, enabling reconstruction of quality images from less than one minute of scan time; and we demonstrate performance boosts with nonstandard trajectories precluded by conventional reconstruction methods. 

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Thanh-Duc Nguyen¹, Saurabh Garg¹, Nasrin Akbari¹, Saqib Basar¹, Sean London², Yosef Chodakiewitz², Rajpaul Attariwala^1,2, and Sam Hashemi^1,2
1Voxelwise Imaging Technology Inc., Vancouver, BC, Canada, ²Prenuvo, Vancouver, BC, Canada

Keywords: Machine Learning/Artificial Intelligence, Heart, Deep learning segmentation, cardiomegaly

Despite its ubiquity, cardiac assessment at non-cardiac-gated MRI has yet to be standardized, leading to misdiagnosis. This paper examines the feasibility of AI for automatic 3D volumetric cardiac quantification used to detect and stratify cardiomegaly on non-cardiac-gated torso MRI. Using AI, we automatically measured the cardiothoracic ratio and 3D cardiac volumetric features as indicators for detections of cardiomegaly conditions. Large-scale results on 3485 normal-heart individuals revealed the effect of aging on cardiac features in both men and women. AI findings on non-cardiac-gated imaging offer opportunistic useful information, increasing the diagnostic precision, and allowing potentially useful imaging monitoring for cardiomegaly-associated conditions.

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Adiraju Karthik¹, Apoorwa Devappa², Aakaar Kapoor³, Dharmesh Singh⁴, and Dileep Kumar^4
1Department of Radiology, Sprint Diagnostics, Jubilee Hills, Hyderabad, India, ²Department of Radiology, Mahadevappa Rampure Medical College, Kalaburagi, India, ³Department of Radiology, City X-Rays Scan & Clinical Private Limited, New Delhi, India, ⁴Central Research Institute, Global Scientific Collaborations, United Imaging Healthcare, New Delhi, India

Keywords: Data Acquisition, Body, AI-assisted compressed sensing, T2-weighted Imaging

T2-weighted imaging (T2WI) is an essential diagnostic tool for several diseases. However, one of the challenges faced by patients and radiology departments is the longer scanning time of MR examinations. Recent advancements in artificial intelligence (AI) and deep learning techniques have made it able to acquire images quickly while preserving high-quality image resolution. In this study, the efficacy of a deep learning-based reconstruction technique termed AI-Assisted Compressed Sensing (ACS) was evaluated qualitatively and quantitatively using T2WI in routine clinical settings for brain, spine, knee, kidney and liver. 

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Praitayini Kanakaraj¹, Leon Y Cai², Francois Rheault³, Baxter P Rogers⁴, Adam Anderson^2,4, Kurt G Schilling⁴, and Bennett A Landman^1,2,4,5
1Department of Computer Science, Vanderbilt University, Nashville, TN, United States, ²Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ³Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada, ⁴Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ⁵Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States

Keywords: Data Processing, Diffusion/other diffusion imaging techniques

Gradient nonlinearity correction is well-established but not straightforward to implement in existing diffusion software packages due to it producing gradient tables that vary by voxel. We propose a simple, practical approach that approximates full correction by: (1) scaling the diffusion signal and (2) resampling the gradient orientations. Our approach results in uniform gradients across the corrected image and provides the key advantage of seamless integration into current diffusion pipelines. The proposed method resulted in negligible differences in multi- compartment indices from the standard voxel-wise empirical correction.