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Jieying Zhang¹, Simin Liu¹, Yishi Wang², and Hua Guo^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ²Philips Healthcare, Beijing, China

Keywords: Image Reconstruction, Diffusion Tensor Imaging

3D simultaneous multi-slab imaging (SMSlab) can achieve high-resolution DWI with high SNR efficiency. Recently, we integrated SMSlab DWI with blipped-CAIPI gradients (blipped-SMSlab) and proposed a hybrid-space reconstruction algorithm, REACH. In this study, REACH is extended for distortion correction, which is called DC-REACH. It can correct the image distortions and the phase interferences introduced by the blipped-CAIPI gradients simultaneously. It also distinctly reduces the g-factor penalty via the joint reconstruction of the blip-up/down data.

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Eun Ji Lim¹, Hyunkyung Maeng¹, and Jaeseok Park^1
1Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, Republic of

Keywords: Image Reconstruction, Diffusion Tensor Imaging

To jointly resolve inter-slice leakages and in-plane aliasing for combined in-plane- and slice-accelerated diffusion MRI data, we proposed a novel, one-step solution for SMS-reconstruction optimally exploiting self-calibrating data from generalized 3D Fourier encoding perspective. To this end, we propose a generalized SMS forward signal model with an extended controlled aliasing and an extended self-calibration.  Aliasing artifacts are jointly resolved in k_y-and k_z-directions by balancing null space consistency with a low rank prior while enforcing data fidelity in 3D k-space.  We demonstrated the proposed method outperforms competing methods for diffusion MRI at SMS=3, R=2.

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Ziyu Li¹, Xi Chen¹, Mark Chiew^1,2,3, Karla L. Miller¹, and Wenchuan Wu^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ²Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada, ³Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

Keywords: Image Reconstruction, Diffusion/other diffusion imaging techniques, Diffusion Acquisition & Reconstruction

3D multi-shot multi-slab imaging can provide superior SNR efficiency for high-resolution dMRI but suffers from motion-induced shot-to-shot phase variations. We propose a highly-efficient, self-navigated method to correct for phase variations in 3D multi-slab dMRI. The sampling of each shot is designed to intersect with the kz=0 plane. These intersections are used to reconstruct a 2D phase map for each shot using a structured low-rank reconstruction that leverages the redundancy in shot and coil dimensions. The phase maps are used to eliminate the phase inconsistency in the final 3D multi-shot reconstruction. We demonstrate the method’s efficacy using highly realistic simulations.

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Matthew J. Middione¹, Michael Loecher¹, Xiaozhi Cao¹, Kawin Setsompop^1,2, and Daniel B. Ennis^1,3
1Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ³Cardiovascular Institute, Stanford University, Stanford, CA, United States

Keywords: Pulse Sequence Design, Diffusion/other diffusion imaging techniques, Eddy Currents

Diffusion encoding gradients produce eddy currents that cause image distortions in DWI. Twice refocused spin-echo (TRSE) and eddy current-nulled convex optimized diffusion encoding (ENCODE) mitigate eddy current-induced image distortions in DWI, but at the expense of extending the TE. Herein, we revise the original ENCODE method by playing additional pre-excitation gradient lobes (Pre-ENCODE). Using simulations, phantom experiments, and in vivo imaging we demonstrate that Pre-ENCODE mitigates eddy current-induced image distortions in DWI with a shorter TE than TRSE and ENCODE.

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Ulrich Katscher¹, Jakob Meineke¹, and Jochen Keupp^1
1Philips Research Europe, Hamburg, Germany

Keywords: Pulse Sequence Design, Diffusion/other diffusion imaging techniques

Double-Echo Steady-State (DESS) sequences are a promising candidate for diffusion weighted imaging (DWI) free of geometric distortions. While diffusion weighted DESS (dwDESS) sequences were originally introduced with a unipolar diffusion weighting gradient G_D, a bipolar G_D is required to obtain a motion robust fully balanced sequence. The inevitable banding artefacts occurring for bipolar G_D can be handled via different techniques like gradient spoiling (thus deviating from the fully balanced sequence) or phase cycling. This study compares these techniques to optimize SNR for a given scan time and given diffusion weighting.

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Zhigang Wu¹, Yajing Zhang², Guillaume Gilbert³, Wengu Su⁴, Yan Zhao⁵, and Jiazheng Wang^6
1Philips Healthcare, Shenzhen, Ltd., Shenzhen, China, ²Philips Health Technology, Suzhou, China, ³MR Clinical Science, Philips Healthcare, Mississauga, ON, Canada, ⁴BU MR Application, Philips Health Technology, Suzhou, China, ⁵BU MR R&D, Philips Health Technology, Suzhou, China, ⁶Philips Healthcare, Beijing, China

Keywords: Pulse Sequence Design, Diffusion/other diffusion imaging techniques, Diffusion, Motion compensated diffusion gradients, Reduced FOV imaging, Multi-shot DWI

Reduced FOV imaging (rFOV) and multi-shot DWI both are very useful techniques to improve spatial resolution for detection of pancreatic lesion. However, respiratory and cardiovascular motion will introduce severe artifacts and ADC bias. Motion compensated diffusion gradients (MOCO) could be used to improve the image quality. In this study, a new sequence named MOCO-rFOV IRIS was developed, which combines the advantages of rFOV, MOCO and image reconstruction using image-space sampling function based multi-shot DWI (IRIS). Results from in vivo data demonstrated that the proposed method could be used to realize motion robust and high resolution DWI for pancreas

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Christian Ewert¹, David Kügler¹, Anastasia Yendiki^2,3, and Martin Reuter^1,2,3
1AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States

Keywords: Image Reconstruction, Diffusion/other diffusion imaging techniques, q-space, denoising

DISCUS addresses two challenges currently limiting the analysis potential of diffusion MRI: sparsity of measurements and variability in q-space sampling schemes. Our method combines the advantages of model-fit approaches with continuous sampling (spherical harmonics, SHORE) and rigid, discrete learning-based methods. DISCUS can be initialized from any acquisition scheme and permits signal prediction for an arbitrary q-vector. Despite the added flexibility, DISCUS performs on par with other, far less flexible learning methods, while outperforming model-fit methods. DISCUS-derived signals translate to higher-quality FA estimates promising accurate analyses even from very short acquisitions.

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Qinfeng Zhu¹, Haotian Li¹, Yi-Cheng Hsu², Yi Sun², and Dan Wu^1
1zhejiang University, Hangzhou, China, ²Siemens Healthcare China, Shanghai, China

Keywords: Pulse Sequence Design, Diffusion/other diffusion imaging techniques

Oscillatory gradient dMRI is used to access restricted diffusion at short diffusion times (t_d). But its clinical application is limited due to the limited gradient strength, leading low b-value and low resolution, and thus is subject to contamination from microcirculation and CSF partial volume. Here we proposed an orthogonal diffusion encoding gradient (ODEG) sequence to improve t_d–dependency measurements in human brain, by applying a pulse gradient orthogonal to the oscillating gradient to suppress the fast diffusion from microcirculation and free water. The results showed that t_d-dependency was significantly improved by the ODEG sequence in the hippocampus and cortical gray matter.

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Lars Mueller¹, Sam Coveney¹, Maryam Afzali^1,2, Irvin Teh¹, Fabrizio Fasano^3,4, Filip Szczepankiewicz⁵, Erica Dall’Armellina¹, Christopher Nguyen⁶, Derek K Jones², and Jurgen E Schneider^1
1Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, ²Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, ³Siemens Healthcare Ltd, Camberly, United Kingdom, ⁴Siemens Healthcare GmbH, Erlangen, Germany, ⁵Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden, ⁶Cleveland Clinic, Cleveland, OH, United States

Keywords: Pulse Sequence Design, Diffusion Tensor Imaging, Spiral

Using spiral trajectories instead of EPI can reduce the echo time in diffusion weighted MRI. The feasibility of spiral trajectories for cardiac DTI has recently been demonstrated, but the larger object size (i.e torso vs. for example skull) may require an inner volume excitation to keep the readout at an useable duration. Here we examine the use of an undersampled spiral with SENSE reconstruction and standard slice selective pulses. We show that MD and FA derived from the slice selective and inner volume excitation yield comparable values with the MD being higher than the one measured with EPI.

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Zhe Wu¹, Alexander Jaffray², Lars Kasper¹, and Kamil Uludag^1,3
1Techna Institute, University Health Network, Toronto, ON, Canada, ²University of British Columbia, Vancouver, BC, Canada, ³Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

Keywords: Pulse Sequence Design, Diffusion/other diffusion imaging techniques, Spiral Imaging, field inhomogeneity correction, gradient correction

We propose a short-TE signal-to-noise ratio (SNR) enhanced diffusion imaging method using simultaneous multi-slice (SMS) accelerated spiral acquisition. The correction of field inhomogeneity and gradient waveforms are introduced in the reconstruction without any assistance of external hardware (e.g. field camera). Results showing the feasibility of this method on both phantom and human subjects, and the corrections of B0 and gradient waveforms are essential to improve the image quality.

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Yunwei Chen¹ and Xinyuan Zhang^1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Keywords: Machine Learning/Artificial Intelligence, Diffusion Tensor Imaging

Deep learning methods have been demonstrated state-of-the-art performance in Diffusion Magnetic Resonance Imaging (dMRI) denoising and parameter estimation. However, existing deep learning methods for dMRI are limited to the specific acquisition scheme. To solve the limitation, we proposed to use spherical harmonic coefficients as the deep learning network’s input. Our results have shown that the proposed method has a high performance in denoising and parameter estimation for DTI with a strong generalization ability.

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Uzair Hussain¹ and Ali Khan^1,2,3
1Robarts Research Institute, Centre for Functional and Metabolic Mapping, London, ON, Canada, ²Department of Medical Biophysics, Western University, London, ON, Canada, ³Western Institute for Neuroscience, Western University, London, ON, Canada

Keywords: Machine Learning/Artificial Intelligence, Diffusion Tensor Imaging

One shortcoming of diffusion MRI (dMRI) is long scan times as numerous images have to be acquired to achieve a reliable angular resolution of diffusion gradient directions. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers that overcome the challenges associated with the dMRI signal being acquired on a sphere instead of a rectangular grid. We apply this method to upsample angular resolution to predict diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. Additionally, gCNNs are able to train with fewer subjects and are general enough to be applied to other dMRI related problems.

Zhongbiao Xu¹, Rongli Zhang², Wei Huang¹, Junying Cheng³, Yingjie Mei⁴, Yihao Guo⁵, Hengwen Sun¹, Yaohui Wang⁶, and Zhifeng Chen^7
1Department of Radiotherapy, Guangdong Provincial People's Hospital, Guangzhou, China, ²Department of Imaging and Interventional radiology, The Chinese University of Hong Kong, HongKong, China, ³Department of MRI, The first Affiliated Hospital of Zhengzhou University, zhengzhou, China, ⁴School of Biomedical Engineering, Southern Medical University, guangzhou, China, ⁵Hainan General Hospital, hainan, China, ⁶Institute of Electrical Engineering, Chinese Academy of Sciences, beijing, China, ⁷Monash Biomedical Imaging, Department of Data Science and AI, Monash University, Clayton, Australia

Keywords: Image Reconstruction, Diffusion Tensor Imaging

DTI is challenged by the prolonged scan time in frontier studies and clinical applications. Parallel imaging can reduce the scan time, but with the SNR loss and the limitation of acceleration factor. In this work, we combined SENSE with self-supervised BM4D reconstruction model to improve image quality. The in vivo experiments demonstrated that the proposed method can obtain greatly improved image quality even with high acceleration factor of 5, compared to conventional methods.

Qiqi Tong¹, Jinsong Li^1,2, Jianhui Zhong^3,4, and Hongjian He^4,5
1Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China, ²College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, ⁴Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ⁵School of physics, Zhejiang University, Hangzhou, China

Keywords: Data Analysis, Diffusion Tensor Imaging, multicenter

A multicenter diffusion magnetic resonance imaging (dMRI) study was designed with different b-table schemes, non-diffusion b0 number, and varied echo time (TE) in two 3T scanners of different vendors. Global sensitivity analysis of 6 traveling subjects was conducted to evaluate the impact of imaging protocol setting on the observed cross-scan variability of diffusion metrics.

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Tobias Goodwin-Allcock¹, Guglielmo Genovese^2,3,4, Belen Zaid^3,5, Stéphane Lehericy^2,3, Charlotte Rosso^3,5, Ting Gong¹, Robert Gray⁶, Parashkev Nachev⁶, Marco Palombo^7,8, and Hui Zhang^1
1Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom, ²Centre de NeuroImagerie de Recherche - CENIR, Paris Brain Institute - ICM, Paris, France, ³UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Sorbonne Université, Paris, France, ⁴Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, ⁵Paris Brain Institute - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France, ⁶University College London Queen Square Institute of Neurology, London, United Kingdom, ⁷Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁸School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Keywords: Data Processing, Diffusion Tensor Imaging, Machine Learning

This work evaluates the clinical viability of Patch-CNN for estimating diffusion MRI (dMRI) parameters from only 6 diffusion-weighted images (DWIs). Machine learning (ML) has been proposed to improve fitting from 6-directional DWIs. However, directional measures, e.g. primary fibre orientation, have only been estimated using CNNs. CNNs have not yet been validated on pathology that is not contained within the training dataset. As pathological diversity is difficult to capture in typical applications, ML methods are clinically viable only if they can generalise to unseen pathology. We show that Patch-CNN may generalise to unseen pathology and estimate directional measures.

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Abdallah Zaid Alkilani^1,2, Tolga Çukur^1,2,3, and Emine Ulku Saritas^1,2,3
1Deparment of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, ²National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, ³Neuroscience Graduate Program, Bilkent University, Ankara, Turkey

Keywords: Data Analysis, Diffusion Tensor Imaging, Susceptibility, Machine Learning/Artificial Intelligence, Brain, Artifacts

Diffusion weighted imaging (DWI) requires correction of susceptibility artifacts before conducting quantitative analyses. Correction is typically performed by acquiring DWI images in reversed phase-encode directions, which are used to estimate and correct for the effects of susceptibility-induced field. In this work, we propose a Forward-Distortion Network (FD-Net) for correcting susceptibility artifacts at multiple b-values. We evaluate the quality of the corrected DWI images and Diffusion Tensor Imaging (DTI) metrics, using FSL’s TOPUP as a reference classical method. In addition to rapid execution times, FD-Net exhibits high-fidelity performance for both DWI images and DTI metrics.

 

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Zhangxuan Hu¹, Xiaocheng Wei¹, Jie Lu², and Bing Wu^1
1GE Healthcare, Beijing, China, ²Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China

Keywords: Machine Learning/Artificial Intelligence, Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) is a well-established tool for providing insights into brain structural connectivity and detecting brain microstructure. High spatial resolution diffusion MRI can provide improved resolvability of fibers with high-curvature (u-fibers). Segmented k-space methods such as Multiplexed sensitivity-encoding (MUSE) are often used to achieve high resolution diffusion images, however the shortcomings, such as prolonged scan time and low signal-noise-ratio (SNR), still exist. In this study, we aim to further improve the image quality of high-resolution diffusion images acquired with MUSE by combing with a deep learning based reconstruction method and thus to improve the sub-cortical fiber tracking accuracy.

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Jens Johansson¹, Kerstin Lagerstrand^2,3, Hanna Hebelka^1,4, and Stephan E Maier^1,5
1Radiology, Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, ²Medical Radiation Sciences, Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, ³Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden, ⁴Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden, ⁵Radiology, Brigham and women's hospital, Boston, MA, United States

Keywords: Data Acquisition, Diffusion/other diffusion imaging techniques, Motion correction, 3D imaging

Routine clinical diffusion imaging is generally performed with 2D echo planar sequences. A single thick-slab 3D approach could offer higher signal-to-noise ratio and better slice resolution, but has not been adopted due to the difficulty to avoid motion-induced phase errors that interfere with multi-shot spatial encoding. A new approach to enable 3D DWI is introduced here: rather than relying on navigator echoes for phase correction, first and second order motion-compensated diffusion encoding gradients are used to minimize phase variations at the source. 

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Kaibao Sun¹, Guangyu Dan^1,2, Qingfei Luo¹, and Xiaohong Joe Zhou^1,2,3
1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States, ³Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Keywords: Data Acquisition, Diffusion/other diffusion imaging techniques

Overestimation of perfusion volume fraction has been reported in conventional IVIM imaging. In a model known as extended T2-IVIM, compartmentalized relaxation times are incorporated to improve quantification of perfusion volume fraction, which requires acquisition of additional images at different TEs. A main challenge is that the scan time is lengthened, decreasing the efficiency while increasing vulnerability to motion. We herein introduce a novel sequence for time-efficient, relaxation-incorporated (TERI) IVIM imaging to address the aforementioned issues. TERI IVIM imaging uses multiple EPI readouts at different TEs in a single shot. The proposed technique has been demonstrated in the human brain.

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Alix Plumley¹, Mara Cercignani¹, Álvaro Planchuelo-Gómez^1,2, James Gholam¹, and Derek K Jones^1
1Cardiff University, Cardiff, United Kingdom, ²University of Valladolid, Valladolid, Spain

Keywords: Software Tools, Diffusion Tensor Imaging, Low-field

A super-resolution approach was used to create 2mm isotropic diffusion tensor images (DTI) from diffusion-weighted imaging data acquired on a low field, portable system. Mean diffusivity, fractional anisotropy and principal eigenvector orientation maps are shown. This work extends the very recently implemented capability of performing DTI on a 64mT system, and shows substantial improvement due to the increased through-plane resolution achieved with super-resolution.