Zihan Ning¹, Shuo Chen¹, Zhensen Chen², Hualu Han¹, Huiyu Qiao¹, Rui Shen¹, Peng Wu³, and Xihai Zhao^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine Tsinghua University, Beijing, China, ²Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ³Philips Healthcare, Shanghai, China

We proposed a Saturated Multi-delay Arterial Spin Labeling (SAMURAI) technique with a correspondingly modified kinetic model to achieve simultaneous acquisitions of RBF, aBAT, tBAT and T1 map in kidney with a single scan. SAMURAI provided T1 map with excellent correlation (R²=0.976) compared with IR-SE. Compared with multi-TI FAIR, SAMURAI provided equally reliable ASL and T1 quantification results (ICC: 0.875-0.958) with excellent scan-rescan repeatability (ICC: 0.905-0.992) and significantly reduced scan time (45’ vs 4’6’’ for 9 TIs).

Dan Zhu^1,2 and Qin Qin^1,2
1F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ²The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States

The traditional RF-prepared B₁ mapping technique consists of one scan with an RF preparation module for FA-encoding and a second scan without this module for normalizing. To reduce the T₁-induced k-space filtering effect, this method is limited to 2D FLASH acquisition with a two-parameter method. Based on the point spread function analysis of FLASH readout, a novel 3D RF-prepared three-parameter method for B₁-mapping is proposed to correct the T₁-induced quantification bias. The proposed technique was compared with existing methods in the brain, breast, and abdomen and demonstrated with high accuracy for ultrafast B₁ mapping.

Lu Wang¹, Zhen Xing², Qinqin Yang¹, Congbo Cai¹, Zhong Chen¹, Dairong Cao², Zhigang Wu³, and Shuhui Cai^1
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, ²Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China, ³MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) derived relative cerebral blood volume (rCBV) is a valuable diagnosis biomarker. However, the injection of gadolinium-based contrast agent (GBCA) in DSC-MRI acquisition is prone to cause adverse effects. In this study, a rCBV generation method was proposed based on deep neural network and intravoxel incoherent motion magnetic resonance imaging (IVIM-MRI) data. Consistency analysis shows that the rCBV maps generated from our proposed method are of high consistency with the realistic ones, implying that the proposed method has the potential to obtain DSC-MRI derived rCBV maps without GBCA injection.

Borjan Gagoski^1,2, Jaejin Cho^2,3, Zijing Zhang⁴, Tae Hyung Kim^2,3, Wei-Ching Lo⁵, Daniel Polak⁶, Marcel Warntjes⁷, Stephen Cauley^2,3, Kawin Setsompop^8,9, P. Ellen Grant^1,2, and Berkin Bilgic^2,3
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, ⁴State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China, ⁵Siemens Medical Solutions USA, Inc., Charlestown, MA, United States, ⁶Siemens Healthcare GmbH, Erlangen, Germany, ⁷SyntheticMR AB, Linköping, Sweden, ⁸Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ⁹Department of Radiology, Stanford University, Stanford, CA, United States

Volumetric, high resolution, quantitative mapping of brain tissues’ relaxation properties is hindered by long acquisition times and signal-to-noise (SNR) challenges. This study, for the first time, combines the time-efficient wave-CAIPI readouts with the 3D-Quantification using an interleaved Look-Locker acquisition sequence with T₂ preparation pulse (3D-QALAS) scheme, enabling full brain quantitative T₁, T₂ and proton density (PD) maps at 1.15 mm isotropic voxels in only 3 minutes at R=3x2 acceleration. When tested in both the ISMRM/NIST phantom and 7 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D-QALAS at R_GRAPPA=2.

Yunsong Liu¹, Congyu Liao², Daeun Kim³, Kawin Setsompop², and Justin P. Haldar^3
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, ²Stanford University, Stanford, CA, United States, ³University of Southern California, Los Angeles, CA, United States

Multidimensional relaxation correlation spectroscopic imaging methods have demonstrated powerful capabilities to resolve subvoxel microstructure.  In this work, we perform T1-T2 relaxation correlation spectroscopic imaging using a sequence that combines MR fingerprinting with a ViSTa preparation module to enhance sensitivity to short-T1 components.  We demonstrate theoretically and empirically that this approach has advantages over MR fingerprinting without ViSTa.  Empirical results demonstrate the ability to identify at least 6 anatomically plausible tissue components, including a short-T1 component that was not previously resolved when using MR fingerprinting without ViSTa. A novel generalized ADMM algorithm is also proposed that substantially improves computational efficiency.

Kaibao Sun¹, Zhifeng Chen^2,3, Guangyu Dan^1,4, Qingfei Luo¹, Alessandro Scotti¹, Lirong Yan^5,6, and Xiaohong Joe Zhou^1,4,7
1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ³Department of Radiology, Harvard Medical School, Charlestown, MA, United States, ⁴Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States, ⁵USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁶Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁷Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Geometric distortion is a prevalent image artifact in echo planar imaging (EPI). In a method known as BUDA (blip-up/down acquisition), k-space model-based reconstruction of two EPI datasets acquired separately with opposing phase-encoding polarities has been shown to reduce image distortions. A main disadvantage is that the two-shot acquisition strategy doubles the scan time and increases vulnerability to motion. We herein introduce a novel sequence – 3D echo-shifted EPI with BUDA (esEPI-BUDA) – to address the aforementioned issues by integrating the two acquisitions into one shot. We have successfully applied the 3D esEPI-BUDA technique to functional MRI.

Haoan Xu¹, Wen Shi^1,2, Jiwei Sun¹, Cong Sun³, Guangbin Wang^3,4, Yi Zhang¹, and Dan Wu^1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³Department of Radiology, Cheeloo College of Medicine, Shandong Provincial Hospital, Shandong University, Jinan, China, ⁴Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

Slice-to-volume registration and super-resolution reconstruction is commonly used to generate 3D volumes of the fetal brain from 2D stacks in multiple orientations. The current pipeline requires selecting the stack with minimal motion as a reference for registration. We proposed a motion assessment method that automatically determines the reference stack based on CANDECOMP/PARAFAC decomposition. This method is sensitive to motion across slices compared to other state-of-the-art methods. Combining motion assessment with the existing fetal brain MRI processing pipeline improved the reconstruction quality and the success rate.

Qinqin Yang¹, Lingceng Ma¹, Shuhui Cai¹, Hongjian He², Zhong Chen¹, Jianhui Zhong^2,3, and Congbo Cai^1
1Department of Electronic Science, Xiamen University, Xiamen, China, ²Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

A quantitative magnetic resonance imaging (MRI) sequence GRE-MOLED based on overlapping-echo detachment is implemented to simultaneously achieve distortion-free T₂* mapping and quantitative susceptibility mapping (QSM) in a single shot. The accuracy of quantitative structural MRI results is verified by comparison with the results from multi-shot method in in-vivo experiments. We also evaluate the technique through a statistical analysis of fMRI experiments with vision and motion tasks and demonstrate that it achieves higher BOLD sensitivity than conventional method.

Cihat Eldeniz¹, Paul K. Commean¹, Parna E. Boroojeni¹, Jamal Derakhshan¹, Yan Yan², Udayabhanu Jammalamadaka³, Manu S. Goyal¹, Gary Skolnick⁴, Kamlesh B. Patel⁴, and Hongyu An^5
1Mallinckrodt Institute of Radiology, Washington University in St. Louis, SAINT LOUIS, MO, United States, ²Public Health Sciences, Washington University in St. Louis, SAINT LOUIS, MO, United States, ³Office of Research, Rice University, Houston, TX, United States, ⁴Division of Plastic and Reconstructive Surgery, Washington University in St. Louis, SAINT LOUIS, MO, United States, ⁵Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States

Patient motion is a common problem in MRI. Both prospective and retrospective schemes can be inefficient if they use navigators. Therefore, self-navigation without any data redundancy is preferable. In this study, we propose a simple and robust motion detection scheme that is free from stringent assumptions. This is especially important while imaging the head because the motion patterns are unpredictable. The corrected and uncorrected images were reviewed by two neuroradiologists in terms of osseous distinction (in reference to CT), sharpness and artifact-freeness. The method is able to increase osseous distinction and sharpness without increasing artifacts. This offers substantial clinical utility.

Xinxin Xu¹, Yihuan Wang², Shuheng Zhang³, Xiang Chen³, Yang Li³, Ke Wu³, Jianmin Yuan⁴, and Fuhua Yan^2
1Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ²Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³United Imaging Healthcare, Shanghai, China, ⁴Central Research Institute, United Imaging Healthcare, Shanghai, China

 Hepatic proton density fat fraction (PDFF) measurement plays an important role in the assessment of chronic diffuse liver diseases, while it is always time consuming and lack of good reproducibility and repeatability. To address this problem, we introduced a five-ROI sampling approach based on a convolutional neural network that provided high dice coefficient (DC) of whole liver segmentation, good correlation and quicker compared with manual operation.

Qiangqiang Liu^1,2, Shuheng Zhang³, Jiwen Xu^1,2, Jiachen Zhu³, Yiwen Shen⁴, Wenzhen Chen^1,2, Xiaolai Ye^1,2, Dong Wang³, and Jianmin Yuan^5
1Department of Neurosurgery, Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ²Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³United Imaging Healthcare, Shanghai, China, ⁴Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁵Central Research Institute, United Imaging Healthcare, Shanghai, China

It is essential to avoid small vessels during stereo-electroencephalography (SEEG) electrode implantation. In this study, we proposed a 6-fold Compressed sensing accelerated, 5cm/s Low velocity encoded, 0.75mm Isotropic resolution Phase contrast Magnetic Resonance Angiography (CLIP-MRA). In CLIP-MRA, the compressed sensing based acceleration method was shown to achieve better image quality or shorter scan duration compared to parallel imaging based acceleration. CLIP-MRA was able to display not only cortical arteries and veins simultaneously, but also vessels in the skull. Safety and effectiveness of CLIP-MRA utilized preoperative SEEG planning were evaluated on a small patient cohort.

Yat Lam Wong¹, Hing Chiu Chang², Tian Li¹, and Jing Cai^1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, ²Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong

3D view-sharing sequence has been demonstrated its great promise for abdominal 4D-MRI due to its high temporal resolution and availability in clinical scanners. However, the sub-optimal use of these sequence in free-breathing situation deteriorates the image quality by ghost artifacts induced by respiration. We propose a novel technique, Retrospective Motion Artifacts Suppression by Simulated radial K-space (R-MASSK), to suppress motion artifacts and to increase image quality of the 4D-MRI acquired by the 3D view-sharing sequences, TRICKS and TWIST, through simulated radial k-space resampling.

Lei Hu¹, Caixia Fu², Robert Grimm³, Heinrich von Busch⁴, Thomas Benkert ³, and Jun-gong Zhao^1
1Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China, ²MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China, ³MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ⁴Innovation Owner Artificial Intelligence for Oncology, Siemens Healthcare GmbH, Erlangen, Germany

Prostate cancer (PCa) detection by an automated deep-learning computer-aided diagnosis (DL-CAD) system using advanced zoomed diffusion-weighted imaging (z-DWI) and full-field-of-view DWI (f-DWI) were compared. The DL-CAD system using z-DWI performed significantly better for PCa detection accuracy per patient (AUC: 0.937 vs. 0.871; P=0.002) and had significantly higher PCa lesion detection accuracy per lesion compared to f-DWI (AUC: 0.912 vs. 0.833; P=0.003). Given this, use of z-DWI can improve the performance of the DL-CAD system for PCa detection.

Daniel Polak^1,2, Daniel Nicolas Splitthoff¹, Bryan Clifford³, Lo Wei-Ching³, Azadeh Tabari⁴, Susie Huang⁴, John Conklin⁴, Lawrence L. Wald², and Stephen Cauley^2
1Siemens Healthcare GmbH, Erlangen, Germany, ²A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Siemens Medical Solutions, Malvern, PA, United States, ⁴Department of Radiology, Massachusetts General Hospital, Boston, MA, United States

Distributed sequence reorderings for 3D multi-shot acquisitions ensure overlap with the central k-space in every shot. This improves the stability of navigator-free retrospective motion estimation but often reduces the robustness of the image reconstruction. In this work, we have developed and optimized a novel sampling strategy (Linear⁺) that extends the standard sequential sequence ordering with a small number of additional calibration samples acquired near the k-space center. In simulation and in vivo, Linear⁺ enabled accurate motion parameter estimation and correction across multiple motion patterns while providing improved image homogeneity and spatial resolution compared to a distributed reordering.

Aart van Bochove¹, Katrinus Keijnemans¹, Osman Akdag¹, Pim Borman¹, Martin Fast¹, and Astrid van Lier^1
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands

For MR-guided radiotherapy treatment planning of the thorax, cardiorespiratory triggered T2-weighted MRI images are desired. We investigate the usage of a navigator placed on the left ventricle to do cardiorespiratory triggering, and compare it to images obtained by placing the navigator on the liver-lung interface to do respiratory triggering. We scanned 5 healthy volunteers, making high-resolution scans with both navigators, and cine and 4D-MRI scans for reference. We found that cardiorespiratory triggering based on left ventricular motion is possible, and increases image quality, without increasing scan time and losing geometrical respiratory information.

Pamela Franco^1,2,3, Liliana Ma^4,5, Susanne Schnell⁶, Michael Markl^4,5, Cristobal Bertoglio⁷, and Sergio Uribe^1,2,8
1Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ²Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, ³Electrical Engineering Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ⁴Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, United States, ⁵Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States, ⁶Institut für Physik, Universität Greifswald, Greifswald, Germany, ⁷Bernoulli Institute, University of Groningen, Groningen, Netherlands, ⁸Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

4D flow MRI data may be inaccurate due to phase wrapping when using lower VENC than the maximum actual velocity. Accuracy can also be affected by reduced velocity-to-noise ratio (VNR) when using a too high VENC. Dual-VENC approaches have been proposed to unwrap velocity-aliasing artifacts and increase VNR, providing more reliable measurements and allowing more flexibility in the selection of VENC. Dual-VENC methods enable acquisitions of 4D flow MRI data with high dynamic range and without velocity aliasing. The purpose of this study is to compare the performance of some of these methods.

Björn Fricke¹, Gergely David^1,2, Jan Malte Oeschger¹, Patrick Freund², Lars Ruthotto³, Karsten Tabelow⁴, and Siawoosh Mohammadi^1,5
1Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ²Balgrist University Hospital, University of Zurich, Zürich, Switzerland, ³Department of Mathematics, Emory University, Atlanta, GA, United States, ⁴Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ⁵Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

The ACID-Toolbox is an open-source toolbox for brain and spinal-cord diffusion data. It enables the preprocessing and creation of DTI/DKI, and biophysical parameter-maps in one single pipeline. Preprocessing covers correction for eddy-currents and motion-artefacts, susceptibility distortions and adaptive denoising. For model-fitting, the toolbox offers several algorithms to estimate the diffusion or the kurtosis tensors as well as estimation of biophysical parameters. ACID is integrated into the batch system of the Statistical-Parametric-Mapping (SPM12) software for the analysis of neuroimaging data and thus benefits from associated spatial and statistical processing modules.  Additionally, ACID is BIDS compatible but also applicable to non-BIDS-conform data.