| Computer # |
| 3706.
 | 1 | A Novel Approach for Simultaneous T1, T2, T2*, and Proton Density Mapping via Longitudinal Magnetization Control MOLED Acquisition Weikun Chen1, Xinyu Guo1, Qing Lin1, Jian Wu1, Simin Li1, Taishan Kang2, Liangjie Lin3, Shuhui Cai1, and Congbo Cai1
1Xiamen University, Xiamen, China, 2Magnetic Resonance Center, Zhongshan Hospital Afflicated to Xiamen University, Xiamen, China, 3Clinical & Technical Solutions, Philips Healthcare, Beijing, China Keywords: Quantitative Imaging, Quantitative Imaging, multiple overlapping-echo detachment, pulse sequence design Motivation: Traditional multi-parameter mapping methods use different sequences to acquire images with varying TE or TI, posing challenges like registration, time consumption, and physiological asynchrony among parameters. Goal(s): To rapid acquire self-registered multi-parametric maps. Approach: A longitudinal magnetization control multiple overlapping-echo detachment (LMC-MOLED) imaging method was proposed. In the first phase of LMC-MOLED, T2-T2*-MOLED was acquired using the blip-up blip-down SE-EPI/GRE-EPI readout. Subsequently, after five T2-MOLED acquisitions were applied with varying delay intervals, MOLED images with distinct T1 weighting were captured. Results: LMC-MOLED can simultaneously acquire five slices of multi-parametric maps, including T1, T2, T2*, PD, ΔB0, and B1, within 5.5 s. Impact: LMC-MOLED is a novel and rapid simultaneous multi-parameter
magnetic resonance quantitative method that may aid in the clinical diagnosis
of diseases such as multiple sclerosis and tumors. |
| 3707.
 | 2 | Transient-state CSF suppression for time-efficient, high-resolution 3T whole-brain 3D relaxometry Hongyan Liu1, Edwin Versteeg1, Miha Fuderer1, Oscar van der Heide1, Cornelis A.T. van den Berg1, and Alessandro Sbrizzi1
1Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands Keywords: Quantitative Imaging, Quantitative Imaging Motivation: Cerebrospinal fluid (CSF) pulsation artifacts using 3D unbalanced gradient-echo sequences hinder the accuracy of whole-brain relaxometry measurements. Goal(s): To develop a CSF-suppressed 3D MR-STAT sequence and eliminate CSF pulsation artifacts, allowing for fast, high-resolution whole-brain relaxometry with enhanced image quality. Approach: The time-varying RF flip-angle train is optimized with an additional CSF suppression constraint to achieve both high quantitative parameter encoding capability and low CSF signal intensity. Results: The new CSF-suppressed 3D MR-STAT sequence has been validated on healthy volunteers, demonstrating effective mitigation of CSF ghosting artifacts, and allowing whole-brain relaxometry acquisition within 5.5-minute scan time. Impact: The proposed MR-STAT sequence with CSF suppression requires a short scan time (less than 6 minutes) and shows robustness in whole-brain 1mm3 isotropic relaxometry, and therefore shows the ability to adopt quantitative imaging in broad neuroscientific and clinical applications. |
| 3708.
 | 3 | T1 and T2 of the Human Brain at 64 mT: Measurement of 20 Participants Stephen E. Ogier1, Kalina V. Jordanova1, Deepansh Srivastava2, Tianrui Luo2, Megan E. Poorman2, and Kathryn E. Keenan1
1Magnetic Imaging Group, NIST, Boulder, CO, United States, 2Hyperfine, Guilford, CT, United States Keywords: Quantitative Imaging, Brain Motivation: To enable quantitative techniques at low-field, it is necessary to establish expected T1 and T2 values within the brain for healthy adults. Goal(s): To measure and compare T1 and T2 of the healthy human brain at 64mT. Approach: 20 healthy volunteers were imaged at 64mT. The resulting images were segmented using SynthSeg, and average values for T1 and T2 were computed over gray matter, white matter, and cerebrospinal fluid regions. Results: Mean T1 and T2 were determined. Partial volume effects may limit the accuracy of the image segmentation and lead to large variations in gray matter and cerebrospinal fluid measurements. Impact: To establish normative values for quantitative MRI at low
field, 20 healthy individuals were scanned at 64 mT. T1 and T2
are reported for gray matter, white matter, and cerebrospinal fluid. |
| 3709.
 | 4 | Fast Spin Echo based T2 Mapping with Point Spread Function Correction Tristhal Parasram1 and Dan Xiao1
1Physics, University of Windsor, Windsor, ON, Canada Keywords: Quantitative Imaging, Relaxometry, FSE, PSF, optimization Motivation: Fast Spin Echo (FSE) based method can provide faster T2 mapping compared to the multi-echo spin echo method. However, the varying Point Spread Functions (PSFs) among different echo time images lead to artifacts in the T2 and proton density ($$$\rho$$$) maps. Goal(s): Developing an algorithm to address PSF artifacts in the FSE-based method that enables faster and more accurate T2 mapping. Approach: An optimization process was designed to determine voxel-wise T2 and $$$\rho$$$ values consistent with the acquired data, especially considering various PSFs. Results: The method was validated through simulations, phantom measurements, and mouse brain scans, resulting in improved T2 and $$$\rho$$$ maps. Impact: The method enabled high-quality, fast T2 mapping without the need for any modifications to the FSE pulse sequence. Therefore, it can be readily applied to quantitative studies on subjects that can be imaged with FSE. |
| 3710.
 | 5 | Spin and gradient echo multiple overlapping echo detachment (SAGE-MOLED) for high-fidelity T2-T2* mapping and simultaneous DSC-DCE imaging Qinqin Yang1, Lu Wang1, Nuowei Ge1, Zejun Wu1, Jianfeng Bao2, Zhigang Wu3, Shuhui Cai1, and Congbo Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China, 3Clinical & Technical Support, Philips Healthcare, Shenzhen, China Keywords: Quantitative Imaging, DSC & DCE Perfusion, Spin and gradient echo, Deep learning Motivation: Conventional spin and gradient echo EPI (SAGE-EPI) sequence has limited spatial resolution and suffers from geometric distortion. Goal(s): Our goal was to develop a high-fidelity simultaneous T2 and T2* mapping technique used for dynamic MR imaging. Approach: We developed a single-shot spin and gradient echo multiple overlapping-echo detachment acquisition (SAGE-MOLED) method with deep learning reconstruction. The proposed method was tested in phantom and in vivo human brains, and a single-dose contrast dynamic imaging was performed on a clinical case. Results: SAGE-MOLED achieves distortion-free high-precision T2 and T2* mapping compared to gold-standard methods and was successfully used in simultaneous DSC-DCE imaging. Impact: Rapid high-fidelity T2 and T2* mapping can be achieved using our proposed SAGE-MOLED technique, which further enables leakage-corrected simultaneous DSC and DCE imaging at 1.7×1.7×4.0 mm3 spatial resolution and 1.9 s temporal resolution. |
| 3711.
 | 6 | Simultaneous and Robust Estimation of Cardiac-Induced 3D Brain Velocity and Diffusion Tensor Fields in the Human Brain Kulam Najmudeen Magdoom1,2,3, Alexandru V. Avram1, Joelle Sarlls4, and Peter J. Basser1
1Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States, 2The Military Traumatic Brain Injury Initiative (MTBI2), Uniformed Services University of the Health Sciences, Bethesda, MD, United States, 3The Henry M Jackson Foundation for the Advancement of Military Medicine (HJF) Inc., Bethesda, MD, United States, 4National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States Keywords: Quantitative Imaging, Neuro Motivation: Cardiac-induced brain pulsation is crucial for brain function, yet its measurement is challenging due to small displacements. Goal(s): The goal of this study is to develop a robust method to simultaneously measure both the cardiac-induced brain velocity vector and the corresponding diffusion tensor fields. Approach: We acquire DWIs with long diffusion times to enhance flow sensitivity and employ an outlier rejection method to eliminate inconsistent phase signals. Velocity vector and diffusion tensor fields were estimated from phase and magnitude images, respectively. Results: The high variability in the velocities across acquisitions was significantly reduced using our approach. The brain appeared still during late diastole. Impact: This study facilitates the measurement of intrinsic brain tissue motion with heartbeat compared to DENSE approaches, providing a new method for studying brain function. |
| 3712.
 | 7 | Test-retest reliability of myelin volume fraction quantified with multicomponent MPnRAGE Jayse Merle Weaver1,2, Steven Kecskemeti2, Jose M Guerrero-Gonzalez1,2, and Douglas C Dean III1,2,3
1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 2Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 3Pediatrics, University of Wisconsin-Madison, Madison, WI, United States Keywords: Quantitative Imaging, Brain, Myelin Motivation: There is a need for rapid and high-resolution myelin water imaging techniques, while high test-retest reliability is necessary for widespread adoption. Goal(s): Evaluate the repeatability of multicomponent relaxometry with MPnRAGE. Approach: Data from 12 adolescent subjects scanned 3 consecutive times were reconstructed with motion-correction and fit to multicomponent MPnRAGE model. Coefficient of variation (CoV) was calculated across the 3 scans for each subject for several white and gray matter regions. Results: Mean CoV was <7% across white matter regions. Myelin volume fraction trends between regions were also consistent with prior T2-based techniques. Impact: A
recently developed T1-based technique for myelin water imaging with MPnRAGE
demonstrates high repeatability across consecutive scans. Multicomponent
relaxometry with MPnRAGE enables high-resolution and motion-corrected myelin
mapping in under 10 minutes of scan time. |
| 3713.
 | 8 | Validation of a High Contrast Technique and T1 Values Obtained by Divided Subtracted Inversion Recovery (dSIR) Mark Bydder1 and Fadil Ali2
1Mātai Medical Research Institute, Gisborne, New Zealand, 2Imaging Institute Cleveland Clinic, Cleveland, OH, United States Keywords: Quantitative Imaging, Quantitative Imaging, Validation Motivation: To validate a 2 point high contrast T1 technique using the National Institute of Standards and Technology (NIST) T1 phantom. Goal(s): Qualitative results produced by the divided subtracted IR (dSIR) images have been shown to give unprecedented T1 contrast in case studies of neuroinflammation [1]. The present study validates the theoretical mechanism of contrast in a standardized commercial T1 phantom. Approach: Experiments were performed at 1.5T to compare the empirical signal response versus the theoretical prediction in a phantom with known T1s. Results: The signal response was found to match the predicted bipolar variation very closely. Impact: Clinicians wishing to employ 2 point T1 estimation techniques can be satisfied that the results are grounded in theory and are quantitatively validated. |
| 3714.
 | 9 | Quantitative multi-parametric mapping of human subcortex at ultrahigh field Kerrin J Pine1, Mikhail Zubkov2, Pierre-Louis Bazin3, Gábor Perlaki4,5, Luke J Edwards1, Anneke Alkemade6, Gilles Vandewalle2, Evgeniya Kirilina1, and Nikolaus Weiskopf1,7,8
1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium, 3Full brain picture Analytics, Leiden, Netherlands, 4HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary, 5Department of Neurology, University of Pécs, Pécs, Hungary, 6Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands, 7Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Leipzig, Germany, 8Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom Keywords: Quantitative Imaging, Quantitative Imaging, Brain, Neuro, Subcortex Motivation: Many neurodegenerative diseases affect subcortical nuclei early. Quantitative MRI (qMRI) offers a unique non-invasive tool for detection of neurodegeneration at its early stage, paving the way for development of potential therapies. Goal(s): Ultra-high resolution multi-modal cartography of human subcortex capable of detecting subtle longitudinal changes in macro- and microstructure of subcortical nuclei. Approach: We combined high resolution multi-parametric mapping using 7T, pTx and advanced reconstruction with automated segmentation of subcortical structures. Performance was tested across two sites. Results: While qMRI repeatability varied strongly by structure, automated parcellation was highly repeatable, making the protocol a promising candidate for further studies. Impact: We present a method for multi-contrast quantification of subcortical microstructure. Our high-resolution MRI acquisition and analysis protocol aims to enable longitudinal multi-center studies to detect subcortical neurodegeneration at its early stage. |
| 3715.
 | 10 | Simultaneous brain susceptibility, T1 and T2 quantification at 7T with phase-cycled balanced steady-state free precession Berk Can Acikgoz1,2,3, Cristina Sainz Martinez4,5, Adele L.C. Mackowiak1,2,6, Nils M.J. Plähn1,2,3, Yasaman Safarkhanlo2,3,7, Gabriele Bonanno1,8,9, Eva S. Peper1,2, João Jorge4,5, and Jessica A.M. Bastiaansen1,2
1Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, 2Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, 3Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland, 4CSEM - Swiss Center for Electronics and Microtechnology, Bern, Switzerland, 5CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 6Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 7Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland, 8Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland, 9Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland Keywords: Quantitative Imaging, Brain, Susceptibility, high-field, bSSFP Motivation: Phase-cycled(PC) balanced steady-state free precession(bSSFP) sequences offer yet-to-be-explored capabilities for quantitative susceptibility mapping(QSM), T1, and T2 mapping, particularly attractive for 7T applications. Goal(s): To determine the potential of PC-bSSFP for simultaneous QSM, T1 and T2 mapping in the brain at 7T. Approach: PC-bSSFP-based off-resonance, tissue phase, T1 and T2 maps are compared with reference maps obtained from ME-GRE and MP2RAGE, in three subjects at 7T. Results: PC-bSSFP-based off-resonance and tissue phase maps agreed with ME-GRE-based references with absolute mean errors of 13.2±3Hz and 8.9±3.7Hz, respectively. PC-bSSFP-based T1 and T2 maps matched the expected brain contrast with high precision. Impact: At
7T, PC-bSSFP enables quantitative
measurements of susceptibility, T1, and T2, within one acquisition, while providing high
quality weighted images with a clear distinction between different brain
structures. |
| 3716.
 | 11 | Ultra-Fast Submillimeter Whole Brain QSM Using Highly Accelerated 3D Echo-Planar Imaging with a Complex Convolutional Neural Network Bryan Quah1, Sreekanth Madhusoodhanan Nair1, Arzu Has Silemek1,2, Brian Renner1, Elaina Gombos1, Mustafa Subhi1, Jin Jin3, Fei Han4, Nader Binesh5, Marcel Maya5, Debiao Li2, Marwa Kaisey1, Nancy Sicotte1, Omar Al-Louzi1, and Pascal Sati1,2
1Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3Siemens Healthineers, Brisbane, Australia, 4Siemens Healthineers, CA, United States, 5Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States Keywords: Quantitative Imaging, Quantitative Susceptibility mapping Motivation: To restore image quality of highly accelerated QSM for data interpretation. Goal(s): To evaluate the feasibility of a denoising convolutional neural network for generating high-quality submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla. Approach: A previously developed network for denoising complex-valued MRI data was applied to accelerated 3D-EPI scans (TA: 2min and 3min). Image quality was evaluated and QSM values obtained with and without denoising were compared against reference non-accelerated non-denoised 3D-EPI (TA: 6min). Results: SNR and structural measures demonstrated improved image quality when denoising the accelerated data. Similar QSM values were observed for both highly accelerated denoised 3D-EPI and reference 3D-EPI. Impact: Our approach for denoising complex-valued 3D-EPI brain images shows the feasibility of producing high-quality, whole-brain, submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla, facilitating its clinical adoption. |
| 3717.
 | 12 | Optimizing and Evaluating Fast 3D MulTiPlex (MTP) Imaging for Infant Brain Imaging Zhuoyang Gu1, Xinyi Cai1, Lixuan Zhu1, Weijia Zhang1, Qing Yang1, Tianli Tao1, Weijun Zhang2, Yongquan Ye3, Dinggang Shen1,4,5, Xiaopeng Zong1, and Han Zhang1,5
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 2United Imaging Healthcare, Shanghai, China, 3United Imaging Healthcare America, Houston, TX, United States, 4United Imaging Intelligence, Shanghai, China, 5Shanghai Clinical Research and Trial Center, Shanghai, China Keywords: Quantitative Imaging, Quantitative Imaging, Multiparametric Imaging, Infant Brain Imaging Motivation: The novel MulTiPlex (MTP) technique can generate high-resolution, multi-contrast, qualitative/quantitative images, but its reliability and potential for infant study have not been investigated. Goal(s): To optimize MTP parameters and evaluate its reliability and validity for infant study. Approach: n this study, we systematically assessed MTP’s test-retest reliability, compared MTP with varied accelerating rates using AI-assisted Compressed Sensing (ACS), followed by a comprehensive appraisal of its applicability in infant brain imaging, with both qualitative and quantitative methods. Results: The findings indicate that MTP exhibits commendable reliability and holds significant promise for building large infant MRI database. Impact: MTP generates assorted images with varied spectrum of contrasts and precious quantitative images that take hours to acquire conventionally. Our established MTP protocol combines these merits with fast acquisition, warranting its significant usefulness in infant development and clinical studies. |
| 3718.
 | 13 | Towards rapid T2 mapping using geometry in bSSFP imaging Yiyun Dong1, Qing-San Xiang2, Yang Yang3, and Michael Hoff3
1Physics, University of Washington, Seattle, WA, United States, 2Radiology, University of British Columbia, Vancouver, BC, Canada, 3Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States Keywords: Quantitative Imaging, Quantitative Imaging, T2 quantitative mapping relaxometry, bSSFP, ellipse, elliptical signal model Motivation: Quantitative T2 mapping is useful for indication of neuropathological states such as multiple sclerosis, but current methods are time consuming and can be challenging at low T2 values needed to indicate pathology. Goal(s): To compute T2 maps efficiently and analytically. Approach: T2 maps are computed by exploiting the geometry of the bSSFP signal. The algorithm is enhanced with added regularization, linearization, and solution combination, and is evaluated in simulations and phantom images. Results: The improved algorithm demonstrates precision in simulations and a phantom, especially for lower T2 values. Reconstructed phantom T2 values were realistic, indicating its promise as a diagnostic tool. Impact: Standard quantitative T2 mapping requires significant scan time for adequate fitting; here an analytical T2 mapping method is demonstrated that doesn’t require additional scan time beyond the associated artifact-free bSSFP images generated, inspiring further exploration. |
| 3719.
 | 14 | Multi-echo phase-based quantitative T2 and T2* mapping - a feasibility study Jocelyn Philippe1, Berkin Bilgic2,3, and Borjan Gagoski1,2
1Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States Keywords: Quantitative Imaging, Relaxometry, Phase-Based T2 mapping, T2* (star) mapping Motivation: To provide strategies for accurate, and high-fidelity quantitative T2 and T2* quantitative imaging. Goal(s): To study the feasibility of simultaneous quantification of T2 and T2* using gradient-echo readouts at 3T. Approach: We employed a multi-GRE-based acquisition with optimized TR and RF phase increments to estimate T2 and T2* maps from the acquired phase and magnitude images, respectively. We demonstrated the accuracy and feasibility of our methods, both in the ISMRM/NIST phantom and in vivo. Results: Phase-based T2 mapping method can be combined with T2* mapping while increasing the accuracy of the T2 estimation. Impact: This work shows that it is feasible quantify simultaneously T2 and T2* using a single multi-echo GRE acquisition. |
| 3720.
 | 15 | SSIMPLE: Scan-SpecIfic parameter MaPping from contrast weighted images with self-supervised LEarning Fatih Dogangun1, Yohan Jun2,3, and Berkin Bilgic2,3
1Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 3Harvard Medical School, Boston, MA, United States Keywords: Quantitative Imaging, Quantitative Imaging, self-supervised learning, parameter mapping Motivation: There is rich and complementary information in clinical images, which may lend itself to the estimation of relaxometry parameters. Goal(s): To develop a self-supervised network that can estimate T1, T2, and PD maps from contrast-weighted images with high fidelity. Approach: We developed a scan-specific self-supervised model (SSIMPLE) that harnesses Bloch equations and estimates parameter maps from multi-contrast images without the need for a training dataset and additional constraints. Results: High-fidelity T1, T2, and PD maps with minor biases 4.5%, 11.76%, and 15.45%, respectively, were obtained using the proposed self-supervised network. Impact: Using the developed scan-specific self-supervised neural network, SSIMPLE, high-fidelity parameter maps can be estimated from clinically routine contrast-weighted images without the need for an external training dataset or additional constraints. |