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Zhu Z, Xue X, Tang T, Luo C, Li Y, Chen J, Xu B, Lin Z, Zhang X, Wang Z, Chen J, Lu J, Zhang W, Li X, Chen Q, Jiang Z, Wang J, Hu Q, Haller S, Li M, Yan C, Zhang B. Improving Image Quality and Decreasing SAR With High Dielectric Constant Pads in 3 T Fetal MRI. J Magn Reson Imaging 2025; 61:2505-2515. [PMID: 39835472 PMCID: PMC12063766 DOI: 10.1002/jmri.29677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 11/26/2024] [Accepted: 11/27/2024] [Indexed: 01/22/2025] Open
Abstract
BACKGROUND At high magnetic fields, degraded image quality due to dielectric artifacts and elevated specific absorption rate (SAR) are two technical challenges in fetal MRI. PURPOSE To assess the potential of high dielectric constant (HDC) pad in increasing image quality and decreasing SAR for 3 T fetal MRI. STUDY TYPE Prospective. FIELD STRENGTH/SEQUENCE 3 T. Balanced steady-state free precession (bSSFP) and single-shot fast spin-echo (SSFSE). POPULATION One hundred twenty-eight participants (maternal-age 29.0 ± 3.6, range 20-40; gestational-age 30.3 ± 3.5 weeks, range 22-37 weeks) undertook bSSFP and 40 participants (maternal-age 29.5 ± 3.8, range 19-40; gestational-age 30.4 ± 3.5 weeks, range 23-37 weeks) undertook SSFSE. ASSESSMENT Patient clinical characteristics were recorded, such as gestational-age, amniotic-fluid-index, abdominal-circumference, body-mass-index, and fetal-presentation. Quantitative Image-quality analysis included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed by three radiologists with four-point scale to evaluate overall image quality, dielectric artifact, and diagnostic confidence. Whole-body total SAR was obtained from the vendor workstation. STATISTICAL TESTING Paired rank sum test was used to analyze the differences in SNR, CNR, overall image quality, dielectric artifact, diagnostic confidence, and SAR with and without HDC pad. Spearman correlation test was used to detect correlations between image quality variable changes and patient clinical characteristics. P values <0.05 were set as statistical significance. RESULTS With HDC pad, SNR and CNR was significantly higher (41.45% increase in SNR, 54.05% increase in CNR on bSSFP; 258.76% increase in SNR, 459.55% increase in CNR on SSFSE). Overall qualitative image quality, dielectric artifact and diagnostic confidence improved significantly. Adding HDC pad significantly reduced Whole-body total SAR (32.60% on bSSFP; 15.40% on SSFSE). There was no significant correlation between image quality variable changes and participant clinical characteristics (P-values ranging from 0.072 to 0.992). DATA CONCLUSION In the clinical setting, adding a HDC pad might increase image quality while reducing dielectric artifact and SAR. PLAN LANGUAGE SUMMARY Dielectric artifacts and elevated SAR are two technical problems in 3T fetal MRI. In a prospective analysis of 168 pregnant participants undertaking 3.0T fetal MRI scanning, high dielectric constant (HDC) pad increased SNR by 41.45%, CNR by 54.05% on bSSFP, and SNR by 258.76%, CNR by 459.55% on SSFSE. Overall image quality, dielectric artifact reduction, and diagnostic confidence assessed by three radiologists was improved. Whole-body total SAR decreased by 32.60% on bSSFP and by 15.40% on SSFSE. These findings suggested that the HDC pad can enhance fetal MRI safety and quality, making it a promising tool for clinical practice. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Xunwen Xue
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Tang Tang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Chao Luo
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- Shenzhen Key Laboratory for MRIShenzhenChina
| | - Ye Li
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- Shenzhen Key Laboratory for MRIShenzhenChina
| | - Jing Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Biyun Xu
- Medical Statistics and Analysis Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
| | - Zengping Lin
- Central Research InstituteUnited Imaging Healthcare Group Co., LtdShanghaiChina
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Zhengge Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Jun Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Jiaming Lu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Wen Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Qian Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Zhuoru Jiang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Junxia Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Qing Hu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | | | - Ming Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Chenchen Yan
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjingJiangsuChina
- Medical Imaging Center, Affiliated Drum Tower HospitalMedical School of Nanjing UniversityNanjingJiangsuChina
- Nanjing University Institute of Medical Imaging and Artificial IntelligenceNanjing UniversityNanjingJiangsuChina
- Jiangsu Key Laboratory of Molecular MedicineNanjingJiangsuChina
- Institute of Brain ScienceNanjing UniversityNanjingJiangsuChina
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Wu Q, Gong P, Liu S, Li Y, Liang D, Zheng H, Wu Y. B 1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T. Magn Reson Med 2024; 92:532-542. [PMID: 38650080 DOI: 10.1002/mrm.30112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/23/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. METHODS Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach. RESULTS B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices. CONCLUSION The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.
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Affiliation(s)
- Qiting Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Pengcheng Gong
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Shengping Liu
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Ye Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Schüre JR, Weinmüller S, Kamm L, Herz K, Zaiss M. Sidebands in CEST MR-How to recognize and avoid them. Magn Reson Med 2024; 91:2391-2402. [PMID: 38317286 DOI: 10.1002/mrm.30011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/04/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
PURPOSE Clinical scanners require pulsed CEST sequences to maintain amplifier and specific absorption rate limits. During off-resonant RF irradiation and interpulse delay, the magnetization can accumulate specific relative phases within the pulse train. In this work, we show that these phases are important to consider, as they can lead to unexpected artifacts when no interpulse gradient spoiling is performed during the saturation train. METHODS We investigated sideband artifacts using a CEST-3D snapshot gradient-echo sequence at 3 T. Initially, Bloch-McConnell simulations were carried out with Pulseq-CEST, while measurements were performed in vitro and in vivo. RESULTS Sidebands can be hidden in Z-spectra, and their structure becomes clearly visible only at high sampling. Sidebands are further influenced by B0 inhomogeneities and the RF phase cycling within the pulse train. In vivo, sidebands are mostly visible in liquid compartments such as CSF. Multi-pulse sidebands can be suppressed by interpulse gradient spoiling. CONCLUSION We provide new insights into sidebands occurring in pulsed CEST experiments and show that, similar as in imaging sequences, gradient and RF spoiling play an important role. Gradient spoiling avoids misinterpretations of sidebands as CEST effects especially in liquid environments including pathological tissue or for CEST resonances close to water. It is recommended to simulate pulsed CEST sequences in advance to avoid artifacts.
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Affiliation(s)
- Jan-Rüdiger Schüre
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Simon Weinmüller
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lukas Kamm
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Kai Herz
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Yong X, Lu S, Hsu YC, Fu C, Sun Y, Zhang Y. Numerical fitting of Extrapolated semisolid Magnetization transfer Reference signals: Improved detection of ischemic stroke. Magn Reson Med 2023; 90:722-736. [PMID: 37052377 DOI: 10.1002/mrm.29660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/09/2023] [Accepted: 03/18/2023] [Indexed: 04/14/2023]
Abstract
PURPOSE To propose a novel Numerical fitting method of the Extrapolated semisolid Magnetization transfer Reference (NEMR) signal for quantifying the CEST effect. THEORY AND METHODS Modified two-pool Bloch-McConnell equations were used to numerically fit the magnetization transfer (MT) and direct water saturation (DS) signals at far off-resonance frequencies, which was subsequently extrapolated into the frequency range of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) pools. Then the subtraction of the fitted two-pool z-spectrum and the experimentally acquired z-spectrum yielded APT# and NOE# signals mostly free of MT and DS contamination. Several strategies were used to accelerate the NEMR fitting. Furthermore, the proposed NEMR method was compared with the conventional extrapolated semisolid magnetization transfer reference (EMR) and magnetization transfer ratio asymmetry (MTRasym ) methods in simulations and stroke patients. RESULTS The combination of RF downsampling, MT lineshape look-up table, and conversion of MATLAB code to C code accelerated the NEMR fitting by over 2700-fold. Monte-Carlo simulations showed that NEMR had higher accuracy than EMR and eliminated the requirement of the steady-state condition. In ischemic stroke patients, the NEMR maps at 1 μT removed hypointense artifacts seen on EMR and MTRasym images, and better depicted stroke lesions than EMR. For NEMR, NOE# yielded significantly (p < 0.05) stronger signal contrast between stroke and normal tissues than APT# at 1 μT. CONCLUSION The proposed NEMR method is suitable for arbitrary saturation settings and can remove MT and DS contamination from the CEST signal for improved detection of ischemic stroke.
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Affiliation(s)
- Xingwang Yong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shanshan Lu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, China
| | - Caixia Fu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, Guangdong, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
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