The Dixon technique for MRI of the bone marrow

Vucht, Niels van; Santiago, Rodney; Lottmann, Bianca; Pressney, Ian; Harder, Dorothee; Sheikh, Adnan; Saifuddin, Asif

doi:10.1007/s00256-019-03271-4

Cited by 58 publications

(38 citation statements)

References 84 publications

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“…2 A large number of methods have been developed to suppress fat signal, or separate water and fat images for MR imaging and spectroscopy, including short TI inversion recovery (STIR) technique 3,4 that takes advantage of the shorter longitudinal relaxation time T 1 of fat, chemical shift selectivity (CHESS) fat saturation technique, 5 water excitation imaging, 6 and chemical shift-based Dixon water-fat separation techniques. [7][8][9][10] For some diagnostic purposes, such as bone marrow disease, 11 adrenal adenomas, 12 and fatty liver, 13 quantitative information on fat content and the ratio of water and fat can provide some auxiliary diagnosticinformation for doctors, resulting in the widespread application of Dixon methods 7,9 that can simultaneously obtain water-only and fat-only images. Severe B 0 field inhomogeneity presented a challenge for many early separation methods.…”

Section: Introductionmentioning

confidence: 99%

Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE

Liu

Zhao

et al. 2020

Magnetic Resonance in Med

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Purpose To design a new deep learning network for fast and accurate water–fat separation by exploring the correlations between multiple echoes in multi‐echo gradient‐recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions. Methods A new multi‐echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water–fat separation module that processes the feature information extracted from the feature extraction module. The multi‐layer feature fusion (MLFF) mechanism and residual structure were adopted in the water–fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images. Results The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions. Conclusion The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.

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Section: Introductionmentioning

confidence: 99%

Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE

Liu

Zhao

et al. 2020

Magnetic Resonance in Med

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“…More recently, Kim et al and Schmeel et al evaluated a six-echo 3D gradient echo–modified Dixon sequence and reached an AUC of 0.98 in both studies for the differentiation of benign and malignant fractures, using a threshold of FF < 5.3% and ≤ 9% for malignancy, respectively [ 14 , 24 ]. The use of these gradient echo sequences however requires their acquisition in addition to spin echo–based sequences that form the basis for the morphological assessment of bone marrow with MRI [ 22 , 25 , 26 ], whereas a FSE Dixon sequence can provide both the morphological information and reliable quantitative assessment of the fat fraction.…”

Section: Discussionmentioning

confidence: 99%

Differentiation between benign and malignant vertebral compression fractures using qualitative and quantitative analysis of a single fast spin echo T2-weighted Dixon sequence

et al. 2021

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Objectives To determine and compare the qualitative and quantitative diagnostic performance of a single sagittal fast spin echo (FSE) T2-weighted Dixon sequence in differentiating benign and malignant vertebral compression fractures (VCF), using multiple readers and different quantitative methods. Methods From July 2014 to June 2020, 95 consecutive patients with spine MRI performed prior to cementoplasty for acute VCFs were retrospectively included. VCFs were categorized as benign (n = 63, mean age = 76 ± 12 years) or malignant (n = 32, mean age = 63 ± 12 years) with a best valuable comparator as a reference. Qualitative analysis was independently performed by four radiologists by categorizing each VCF as either benign or malignant using only the image sets provided by FSE T2-weighted Dixon sequences. Quantitative analysis was performed using two different regions of interest (ROI1-2) and three methods (signal drop, fat fraction (FF) from ROIs, FF maps). Diagnostic performance was compared using ROC curves analyses. Interobserver agreement was assessed using kappa statistics and intraclass correlation coefficients (ICC). Results The qualitative diagnostic performance ranged from area under the curve (AUC) = 0.97 (95% CI: 0.91–1.00) to AUC = 0.99 (95% CI: 0.95–1.0). The quantitative diagnostic performance ranged from AUC = 0.82 (95% CI: 0.73–0.89) to AUC = 0.97 (95% CI: 0.91–0.99). Pairwise comparisons showed no statistical difference in diagnostic performance (all p > 0.0013, Bonferroni-corrected p < 0.0011). All five cases with disagreement among the readers were correctly diagnosed at quantitative analysis using ROI2. Interobserver agreement was excellent for both qualitative and quantitative analyses. Conclusions A single FSE T2-weighted Dixon sequence can be used to differentiate benign and malignant VCF with high diagnostic performance using both qualitative and quantitative analyses, which can provide complementary information. Key Points • Qualitative analysis of a single FSE T2-weighted Dixon sequence yields high diagnostic performance and excellent observer agreement for differentiating benign and malignant compression fractures. • The same FSE T2-weighted Dixon sequence allows quantitative assessment with high diagnostic performance. • Quantitative data can readily be extracted from the FSE T2-weighted Dixon sequence and may provide complementary information to the qualitative analysis, which may be useful in doubtful cases.

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“… 25 This chemical shift is useful for detecting small fatty components in lesions and has been used clinically in adrenal gland 26 and bone and soft tissue lesions. 27 However, it can cause chemical shift artifacts in clinical imaging in MRI that can become a major problem at higher static field strengths. 28 The chemical shifts of these artifacts are inversely proportional to the sampling bandwidth.…”

Section: Physical Characteristics Of Low-field Magnetic Resonance Imaging Systemsmentioning

confidence: 99%

Low-Field Magnetic Resonance Imaging

et al. 2021

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Low-field magnetic resonance imaging (MRI) systems have seen a renaissance recently due to improvements in technology (both hardware and software). Originally, the performance of low-field MRI systems was rated lower than their actual clinical usefulness, and they were viewed as low-cost but poorly performing systems. However, various applications similar to high-field MRI systems (1.5 T and 3 T) have gradually become possible, culminating with high-performance low-field MRI systems and their adaptations now being proposed that have unique advantages over high-field MRI systems in various aspects. This review article describes the physical characteristics of low-field MRI systems and presents both their advantages and disadvantages for clinical use (past to present), along with their cutting-edge clinical applications.

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The Dixon technique for MRI of the bone marrow

Cited by 58 publications

References 84 publications

Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE

Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE

Differentiation between benign and malignant vertebral compression fractures using qualitative and quantitative analysis of a single fast spin echo T2-weighted Dixon sequence

Low-Field Magnetic Resonance Imaging

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