Review and consensus recommendations on clinical APT‐weighted imaging approaches at 3T: Application to brain tumors

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Abstract

Amide proton transfer‐weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed‐on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi‐center trials in larger patient cohorts and, ultimately, routine clinical use.

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Most cited references 195

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SENSE: Sensitivity encoding for fast MRI

Klaas Pruessmann, Markus Weiger, Markus Scheidegger … (1999)

New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two-coil array in brain imaging. With an array of five coils double-oblique heart images were obtained in one-third of conventional scan time. Magn Reson Med 42:952-962, 1999. Copyright 1999 Wiley-Liss, Inc.

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Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Mark Griswold, Peter Jakob, Robin Heidemann … (2002)

In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique. Copyright 2002 Wiley-Liss, Inc.

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Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI.

Jinyuan Zhou, Jean-Francois Payen, David A H Wilson … (2003)

In the past decade, it has become possible to use the nuclear (proton, 1H) signal of the hydrogen atoms in water for noninvasive assessment of functional and physiological parameters with magnetic resonance imaging (MRI). Here we show that it is possible to produce pH-sensitive MRI contrast by exploiting the exchange between the hydrogen atoms of water and the amide hydrogen atoms of endogenous mobile cellular proteins and peptides. Although amide proton concentrations are in the millimolar range, we achieved a detection sensitivity of several percent on the water signal (molar concentration). The pH dependence of the signal was calibrated in situ, using phosphorus spectroscopy to determine pH, and proton exchange spectroscopy to measure the amide proton transfer rate. To show the potential of amide proton transfer (APT) contrast for detecting acute stroke, pH effects were noninvasively imaged in ischemic rat brain. This observation opens the possibility of using intrinsic pH contrast, as well as protein- and/or peptide-content contrast, as diagnostic tools in clinical imaging.

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Author and article information

Contributors

Jinyuan Zhou:

ORCID: https://orcid.org/0000-0002-6684-7920

jzhou2@jhmi.edu

Journal

Journal ID (nlm-ta): Magn Reson Med

Journal ID (iso-abbrev): Magn Reson Med

Journal ID (doi): 10.1002/(ISSN)1522-2594

Journal ID (publisher-id): MRM

Title: Magnetic Resonance in Medicine

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 0740-3194

ISSN (Electronic): 1522-2594

Publication date (Electronic): 22 April 2022

Publication date (Print): August 2022

Volume: 88

Issue: 2 ( doiID: 10.1002/mrm.v88.2 )

Pages: 546-574

Affiliations

[ ¹ ] Division of MR Research, Department of Radiology Johns Hopkins University School of Medicine Baltimore Maryland USA

[ ² ] Magnetic Resonance Center Max Planck Institute for Biological Cybernetics Tübingen Germany

[ ³ ] Institute of Neuroradiology, University Hospital Erlangen Friedrich‐Alexander Universität Erlangen‐Nürnberg Erlangen Germany

[ ⁴ ] Department of Medical Radiation Physics Lund University Lund Sweden

[ ⁵ ] F.M. Kirby Research Center for Functional Brain Imaging Hugo W. Moser Research Institute at Kennedy Krieger Baltimore Maryland USA

[ ⁶ ] Yerkes Imaging Center, Yerkes National Primate Research Center Emory University Atlanta Georgia USA

[ ⁷ ] Department of Radiology and Research Institute of Radiological Science Yonsei University College of Medicine Seoul South Korea

[ ⁸ ] Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy

[ ⁹ ] Department of Medical Physics in Radiology German Cancer Research Center Heidelberg Germany

[ ¹⁰ ] Faculty of Physics and Astronomy University of Heidelberg Heidelberg Germany

[ ¹¹ ] Department of Neurology Johns Hopkins University School of Medicine Baltimore Maryland USA

[ ¹² ] Department of Radiology University of Illinois at Chicago Chicago Illinois USA

[ ¹³ ] Mental Health and Clinical Neurosciences and Sir Peter Mansfield Imaging Centre, School of Medicine University of Nottingham Nottingham UK

[ ¹⁴ ] Nottingham Biomedical Research Centre, Queen's Medical Centre University of Nottingham Nottingham UK

[ ¹⁵ ] Department of Radiology, Beijing Hospital National Center of Gerontology Beijing China

[ ¹⁶ ] Vanderbilt University Institute of Imaging Science (VUIIS) Vanderbilt University Medical Center Nashville Tennessee USA

[ ¹⁷ ] Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville Tennessee USA

[ ¹⁸ ] Department of Physics Vanderbilt University Nashville Tennessee USA

[ ¹⁹ ] Department of Radiology University of Pittsburgh Pittsburgh Pennsylvania USA

[ ²⁰ ] Center for Neuroscience Imaging Research, Institute for Basic Science and Department of Biomedical Engineering Sungkyunkwan University Suwon South Korea

[ ²¹ ] Hugo W. Moser Research Institute at Kennedy Krieger Baltimore Maryland USA

[ ²² ] Department of Radiology German Cancer Research Center Heidelberg Germany

[ ²³ ] Clinic for Neuroradiology University Hospital Bonn Bonn Germany

[ ²⁴ ] Department of Cancer Systems Imaging The University of Texas MD Anderson Cancer Center Houston Texas USA

[ ²⁵ ] Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center Seoul South Korea

[ ²⁶ ] Center for Advance Metabolic Imaging in Precision Medicine, Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA

[ ²⁷ ] Department of Diagnostic Imaging and Nuclear Medicine Kyoto University Graduate School of Medicine Kyoto Japan

[ ²⁸ ] Institute of Radiology Kantonsspital Winterthur Winterthur Switzerland

[ ²⁹ ] Advanced Imaging Research Center and Department of Radiology University of Texas Southwestern Medical Center Dallas Texas USA

[ ³⁰ ] Department of Chemistry and Biochemistry University of Texas at Dallas Richardson Texas USA

[ ³¹ ] Department of Biomedical Engineering Vanderbilt University Nashville Tennessee USA

[ ³² ] Department of Medical Biophysics University of Toronto Toronto Ontario Canada

[ ³³ ] Department of Diagnostic Radiology/Clinical Sciences Lund Lund University Lund Sweden

[ ³⁴ ] Lund University Bioimaging Center Lund University Lund Sweden

[ ³⁵ ] Department of Medical Imaging and Physiology Skåne University Hospital, Lund University Lund Sweden

[ ³⁶ ] Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences Kyushu University Fukuoka Japan

[ ³⁷ ] Department of Bioengineering U.C. Berkeley Berkeley California USA

[ ³⁸ ] Department of Radiology, Zhujiang Hospital Southern Medical University Guangzhou Guangdong China

[ ³⁹ ] Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

[ ⁴⁰ ] Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou Zhejiang China

[ ⁴¹ ] Department of Radiology, Tongji Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China

Author notes

[*] [* ] Correspondence

Jinyuan Zhou, Division of MR Research, Department of Radiology, Johns Hopkins University, 600 N. Wolfe Street, Park 306G, Baltimore, MD 21287, USA.

Email: jzhou2@ 123456jhmi.edu

Author information

Jinyuan Zhou https://orcid.org/0000-0002-6684-7920

Linda Knutsson https://orcid.org/0000-0002-4263-113X

Phillip Zhe Sun https://orcid.org/0000-0003-4872-1192

Steffen Goerke https://orcid.org/0000-0002-0684-2423

Hye‐Young Heo https://orcid.org/0000-0002-7297-2015

Tao Jin https://orcid.org/0000-0003-2912-3517

Daniel Paech https://orcid.org/0000-0001-5755-6833

Mark D. Pagel https://orcid.org/0000-0002-8109-3995

Moriel Vandsburger https://orcid.org/0000-0003-4052-205X

Yin Wu https://orcid.org/0000-0002-4323-5083

Yi Zhang https://orcid.org/0000-0001-8738-1851

Zhongliang Zu https://orcid.org/0000-0001-7361-7480

Article

Publisher ID: MRM29241

DOI: 10.1002/mrm.29241

PMC ID: 9321891

PubMed ID: 35452155

SO-VID: 6275cb3a-6253-4dd9-af12-b7f524017cf1

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

History

Date revision received : 26 February 2022

Date received : 31 December 2021

Date accepted : 02 March 2022

Page count

Figures: 10, Tables: 5, Pages: 29, Words: 15483

Funding

Funded by: German Research Foundation (DFG)

Award ID: 445704496

Funded by: National Research Foundation of Korea (NRF)

Award ID: 2014R1A1A1002716

Funded by: National Institutes of Health , doi 10.13039/100000002;

Award ID: P41EB015909

Award ID: P41EB029460

Award ID: R01AG069179

Award ID: R01CA228188

Award ID: R01EB015032

Award ID: R37CA248077

Funded by: Swedish Cancer Society , doi 10.13039/501100002794;

Award ID: CAN 2015/251

Award ID: CAN 2018/468

Award ID: CAN 2018/550

Funded by: Swedish Research Council , doi 10.13039/501100004359;

Award ID: 2015‐04170

Award ID: 2019‐01162

Custom metadata

source-schema-version-number 2.0

cover-date August 2022

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:26.07.2022

ScienceOpen disciplines: Radiology & Imaging

Keywords: aptw standardization,apt‐weighted imaging,brain tumor,cest imaging

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: aptw standardization, apt‐weighted imaging, brain tumor, cest imaging

Review and consensus recommendations on clinical APT‐weighted imaging approaches at 3T: Application to brain tumors

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Abstract

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Probing cerebral hemodynamics with BOLD fMRI

Most cited references 195

SENSE: Sensitivity encoding for fast MRI

Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI.

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