Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical model. Several parameters used in SMS reconstruction impact the quality of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure that SMS acceleration does not significantly degrade resulting image quality.
Gradient-echo echo planar imaging (EPI) data were acquired with a range of SMS acceleration factors from volunteers. Images were collected using two head coils (a 48-channel array and a smaller 32-channel array). Data from these coils were reconstructed offline using a range of coil compression factors and reconstruction kernel parameters. A hybrid space (ky-x), externally-calibrated coil-by-coil slice unaliasing approach was used for image reconstruction. The resulting SMS images were compared with identical EPI data acquired without SMS. A functional MRI (fMRI) experiment was also performed and group analysis results were compared between data sets reconstructed with different coil compression levels.
The 32-channel coil with smaller dimensions outperformed the larger 48-channel coil in our experiments. Generally, a large calibration region and small kernel sizes in ky direction improved image quality. Use of regularization in the kernel fitting procedure had a notable impact. With optimal selection of other hyperparameters in the hybrid space SMS unaliasing algorithm, coil compression caused small reduction in quality in SMS accelerated images. Similarly, group analysis of fMRI results did not show a significant influence of coil compression on resulting image quality.
Hyperparameters used in SMS reconstruction need to be fine-tuned once the experimental factors such as the RF receive coil and SMS factor have been determined.
Purpose: To develop a general framework for Parallel Imaging (PI) with the use of Maxwell regularization for the estimation of the sensitivity maps (SMs) and constrained optimization for the parameter-free image reconstruction. Theory and Methods: Certain characteristics of both the SMs and the images are routinely used to regularize the otherwise ill-posed optimization-based joint reconstruction from highly accelerated PI data. In this paper we rely on a fundamental property of SMs--they are solutions of Maxwell equations-- we construct the subspace of all possible SM distributions supported …
Purpose: To develop a single-shot multi-slice T1 mapping method by combing simultaneous multi-slice (SMS) excitations, single-shot inversion-recovery (IR) radial fast low-angle shot (FLASH) and a nonlinear model-based reconstruction method. Methods: SMS excitations are combined with a single-shot IR radial FLASH sequence for data acquisition. A previously developed single-slice calibrationless model-based reconstruction is extended to SMS, formulating the estimation of parameter maps and coil sensitivities from all slices as a single nonlinear inverse problem. Joint-sparsity constraints are fu…
Purpose: To introduce a novel reconstruction method for simultaneous multi-slice (SMS) accelerated multi shot diffusion weighted imaging (ms-DWI). Methods: SMS acceleration using blipped CAIPI schemes have been proposed to speed up the acquisition of ms-DWIs. The reconstruction of the above data requires (i) phase compensation to combine data from different shots and (ii) slice unfolding to separate the data of different slices. The traditional approach is to first estimate the phase maps corresponding to each shot from a navigator or from the slice-aliased individual shot data. The phase maps…
Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. A radiofrequency (RF)-encoded multi-slab spin-echo EPI acquisition with multiple echo times (TEs) was developed to obtain high SNR efficiency with reduced repetition time (TR). This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-sh…
Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T
Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London（转化心血管成像中心，心血管科学研究所，伦敦大学学院）；Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital（临床生理学，临床科学系，伦德大学，斯德哥尔摩大学医院）；Department of Biomedical Engineering, Faculty of Engineering, Lund University（生物医学工程系，工程学院，伦德大学）；Research & Collaborations GBI, Siemens Healthcare Ltd（研究与合作GBI，西门子医疗有限公司）
针对实时同步多层 bSSFP 心脏磁共振成像中采集和重建时间长的问题，提出基于 3D U-Net 的深度伪影抑制方法，实现快速在线重建，显著缩短采集和重建时间，同时保持诊断图像质量。
Split-slice augmentation for simultaneous multi-slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance.
Increasing imaging speed is of utmost importance in in-vivo magnetic resonance imaging (MRI). With simultaneous multi-slice (SMS) MRI we can simultaneously acquire several slices of an object, which allows for higher undersampling factors compared to single- or conventional multi-slice measurements by exploiting axial coil sensitivity information. In this thesis, we give a short introduction to the physical principles of MRI, cover the basics of a FLASH based SMS MRI sequence and perform tests to verify its accuracy: We check the fidelity of the slice distance as well as the flip angle and con…
University of Minnesota（明尼苏达大学）；Center for Magnetic Resonance Research（磁共振研究中心）
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging has emerged as a powerful strategy, becoming a part of large-scale studies, such as the Human Connectome Project. However, when SMS imaging is combined with in-plane acceleration for higher acceleration rates, conventional SMS reconstruction methods may suffer from noise amplification and other artifacts. Recently, deep learning (DL) techniques have gained interest…
Purpose: The development of a calibrationless parallel imaging method for accelerated simultaneous multi-slice (SMS) MRI based on Regularized Nonlinear Inversion (NLINV), evaluated using Cartesian and radial FLASH. Theory and Methods: NLINV is a parallel imaging method that jointly estimates image content and coil sensitivities using a Newton-type method with regularization. Here, NLINV is extended to SMS-NLINV for reconstruction and separation of all simultaneously acquired slices. The performance of the extended method is evaluated for different sampling schemes using phantom and in-vivo exp…
Spin-echo functional MRI (SE-fMRI) has the potential to improve spatial specificity when compared to gradient-echo fMRI. However, high spatiotemporal resolution SE-fMRI with large slice-coverage is challenging as SE-fMRI requires a long echo time (TE) to generate blood oxygenation level-dependent (BOLD) contrast, leading to long repetition times (TR). The aim of this work is to develop an acquisition method that enhances the slice-coverage of SE-fMRI at high spatiotemporal resolution. An acquisition scheme was developed entitled Multisection Excitation by Simultaneous Spin-echo Interleaving (M…
College of Health Science and Environmental Engineering, Shenzhen Technology University（深圳科技大学健康科学与环境工程学院）；University Medical Center Göttingen（哥廷根大学医学中心）；Guangdong-Hongkong-Macau CNS Regeneration Institute, Key Laboratory of CNS Regeneration (Jinan University)-Ministry of Education, Jinan University（广东-港澳-澳门神经科学再生研究所，神经科学再生重点实验室（暨南大学）-教育部，暨南大学）；Department of Radiology, The First Hospital of Tsinghua University（清华大学第一医院放射科）；Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University（清华大学生物医学成像研究中心，生物医学工程系，医学院）；Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University（教育部生物医学工程重点实验室，生物医学工程与仪器科学学院，浙江大学）；Department of Endocrinology and Metabolism, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院邵逸夫医院内分泌科）；Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen (Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine（分子成像与医学智能实验室，放射科，深圳龙岗中心医院（深圳临床医学院，广州中医药大学；龙岗临床学院，汕头大学医学院））；Longgang Clinical Institute of Shantou University Medical College)
Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所北京市射线成像技术与装备工程研究中心）；School of Nuclear Science and Technology, University of Chinese Academy of Sciences（中国科学院大学核科学与技术学院）；Department of Electrical and Computer Engineering, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校电气与计算机工程系）
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible, we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience. More specifically, we propose to learn an initializer for the conju…
