3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution mapping
Zhifeng Chen
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
Search for more papers by this authorCorresponding Author
Congyu Liao
Department of Radiology, Stanford University, Stanford, California, USA
Congyu Liao and Yanqiu Feng contributed equally to this work.
Correspondence
Yanqiu Feng, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Email: [email protected]
Congyu Liao, Department of Radiology, Stanford University, Stanford, California, USA.
Email: [email protected]
Search for more papers by this authorXiaozhi Cao
Department of Radiology, Stanford University, Stanford, California, USA
Search for more papers by this authorBenedikt A. Poser
Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, University of Maastricht, Maastricht, The Netherlands
Search for more papers by this authorZhongbiao Xu
Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Science, Guangzhou, China
Search for more papers by this authorWei-Ching Lo
Siemens Medical Solutions, Boston, Massachusetts, USA
Search for more papers by this authorManyi Wen
Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
Search for more papers by this authorJaejin Cho
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Search for more papers by this authorQiyuan Tian
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Search for more papers by this authorYaohui Wang
Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Yanqiu Feng
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
Congyu Liao and Yanqiu Feng contributed equally to this work.
Correspondence
Yanqiu Feng, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Email: [email protected]
Congyu Liao, Department of Radiology, Stanford University, Stanford, California, USA.
Email: [email protected]
Search for more papers by this authorLing Xia
Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
Search for more papers by this authorWufan Chen
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Search for more papers by this authorFeng Liu
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorBerkin Bilgic
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Search for more papers by this authorZhifeng Chen
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
Search for more papers by this authorCorresponding Author
Congyu Liao
Department of Radiology, Stanford University, Stanford, California, USA
Congyu Liao and Yanqiu Feng contributed equally to this work.
Correspondence
Yanqiu Feng, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Email: [email protected]
Congyu Liao, Department of Radiology, Stanford University, Stanford, California, USA.
Email: [email protected]
Search for more papers by this authorXiaozhi Cao
Department of Radiology, Stanford University, Stanford, California, USA
Search for more papers by this authorBenedikt A. Poser
Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, University of Maastricht, Maastricht, The Netherlands
Search for more papers by this authorZhongbiao Xu
Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Science, Guangzhou, China
Search for more papers by this authorWei-Ching Lo
Siemens Medical Solutions, Boston, Massachusetts, USA
Search for more papers by this authorManyi Wen
Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
Search for more papers by this authorJaejin Cho
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Search for more papers by this authorQiyuan Tian
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Search for more papers by this authorYaohui Wang
Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Yanqiu Feng
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
Congyu Liao and Yanqiu Feng contributed equally to this work.
Correspondence
Yanqiu Feng, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Email: [email protected]
Congyu Liao, Department of Radiology, Stanford University, Stanford, California, USA.
Email: [email protected]
Search for more papers by this authorLing Xia
Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
Search for more papers by this authorWufan Chen
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
Search for more papers by this authorFeng Liu
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorBerkin Bilgic
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Search for more papers by this authorClick here for author-reader discussions
Funding information: China Postdoc Council, Grant/Award Number: OCPC-20190089; China Postdoctoral Science Foundation, Grant/Award Number: 2018M633073; Guangdong Medical Scientific Research Foundation, Grant/Award Number: A2019041; Major Scientific Project of Zhejiang Laboratory, Grant/Award Number: 2020ND8AD01; National Natural Science Foundation of China, Grant/Award Numbers: 61801205, U21A6005; NIBIB, Grant/Award Numbers: P41 EB030006, R01 EB019437, R01 EB028797, R01 EB032378, R03 EB031175, U01 EB025162, U01 EB026996; NIMH, Grant/Award Number: R01 MH116173; Nvidia; Science and Technology Program of Guangdong, Grant/Award Number: 2018B030333001
Abstract
Purpose
This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative mapping.
Methods
3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction.
Results
Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. For mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland–Altman analysis.
Conclusions
The proposed technique enables rapid 3D distortion-free high-resolution imaging and mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.
CONFLICT OF INTEREST
Wei-Ching Lo is a Staff Scientist at Siemens Healthineers, USA.
Open Research
DATA AVAILABILITY STATEMENT
All the sample data were performed according to procedures approved by the local Internal Review Board after obtaining informed suitable written consents. All the 3T data, as well as the 7T data, will be made available on request, via a request to the corresponding author. Two exemplar 3D-BUDA data are made available at https://zenodo.org/record/7412718#.Y7OMSOxByX2.
The main image reconstruction code that supports the findings is available at https://github.com/zjuczf168/zjuczf168.
Supporting Information
| Filename | Description |
|---|---|
| mrm29578-sup-0001-Figures.docxWord 2007 document , 1.6 MB | Figure S1. The flow chart of standard 3D-BUDA image reconstruction. Figure S2. The phase images of 3D-BUDA image reconstruction for the two-shot GRE-EPI BUDA dataset from a 3T scanner. Figure S3. Comparison of different reconstructions (SENSE, TOPUP, Hybrid-space SENSE, and 3D-BUDA) on image quality and distortion-correction effect for the same two-shot GRE-EPI BUDA dataset from the 7T Magnetom scanner. First column: Blip-up EPI SENSE results. Second column: Blip-down EPI SENSE results. Third column: TOPUP results. Fourth column: Hybrid-space SENSE results. Last column: The 3D-BUDA image reconstruction results. Three rows are the three planes of 3D imaging. In this dataset, Rinplane × Rz = 5 × 1. The total acquisition time of blip-up EPI, blip-down EPI, TOPUP, Hybrid-space SENSE and 3D-BUDA are 9, 9, 16, 16, and 16 s, respectively. A 2-s FOV-matched FLASH low-resolution pre-scan for coil sensitivity map is counted in these acquisitions. Figure S4. Comparison of field maps and error maps of different time-matched acquisition schemes (Rinplane = 8). (a) Reference field map generated by fully-sampled 3D-BUDA data. (b) Field map from four-shot Rz = 1 3D-BUDA data (two blip-up shots and two blip-down shots). (c) Field map from eight-shot Rz = 2 3D-BUDA data (four blip-up shots and four blip-down shots). (d): Corresponding error map of (b). (e): Corresponding error map of (c). |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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