Evaluation of two-point Dixon water-fat separation for liver specific contrast-enhanced assessment of liver maximum capacity

Haimerl, Michael; Probst, Ute; Poelsterl, Stefanie; Fellner, Claudia; Nickel, Dominik; Weigand, Kilian; Brunner, Stefan M.; Zeman, Florian; Stroszczynski, Christian; Wiggermann, Philipp

doi:10.1038/s41598-018-32207-6

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Water 1 H R 1 is multiexponential, particularly with sequences where macromolecule-associated fast-relaxing water contributes to the measurement. Other substances in the liver may also contribute to the 1 H signal, such as glycogen [87] or triglyceride [76,131]. Inflowing blood [110,132], physiologic motion [71], magnetization transfer, and iron affect the measured R 1 in ways which depend both on the sequence and on the analysis employed.…”

Section: Discussionmentioning

confidence: 99%

Survey of water proton longitudinal relaxation in liver in vivo

Waterton

2021

Magn Reson Mater Phy

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Objective To determine the variability, and preferred values, for normal liver longitudinal water proton relaxation rate R1 in the published literature. Methods Values of mean R1 and between-subject variance were obtained from literature searching. Weighted means were fitted to a heuristic and to a model. Results After exclusions, 116 publications (143 studies) remained, representing apparently normal liver in 3392 humans, 99 mice and 249 rats. Seventeen field strengths were included between 0.04 T and 9.4 T. Older studies tended to report higher between-subject coefficients of variation (CoV), but for studies published since 1992, the median between-subject CoV was 7.4%, and in half of those studies, measured R1 deviated from model by 8.0% or less. Discussion The within-study between-subject CoV incorporates repeatability error and true between-subject variation. Between-study variation also incorporates between-population variation, together with bias from interactions between methodology and physiology. While quantitative relaxometry ultimately requires validation with phantoms and analysis of propagation of errors, this survey allows investigators to compare their own R1 and variability values with the range of existing literature.

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Section: Discussionmentioning

confidence: 99%

Survey of water proton longitudinal relaxation in liver in vivo

Waterton

2021

Magn Reson Mater Phy

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“…The other limitation of this study is that we investigated the proposed method on T1-weighted MRI without fat saturation, which is not commonly used as diagnostic MRI in the abdominal area. Instead, fat suppression techniques have been routinely used for upper abdomen (Reeder and Sirlin 2010, Beddy et al 2011, Brandão et al 2013, Li et al 2014b, Haimerl et al 2018. For instance, fat suppression can be utilized to improve visualization of the uptake of contrast material through suppressing the signal from normal adipose tissue.…”

Section: Discussionmentioning

confidence: 99%

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

et al. 2020

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Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.

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Influence of Gadoxetate disodium to the hepatic proton density fat fraction quantified with the Dixon sequences in a rabbit model

Wang,

Zhang,

Huang

et al. 2024

Abdom Radiol

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Objective To study the impact of Gx on quantification of hepatic fat contents under metabolic dysfunction-associated steatotic liver disease (MASLD) imaged on VIBE Dixon in hepatobiliary specific phase. Methods Forty-two rabbits were randomly divided into control group (n = 10) and high-fat diet group (n = 32). Imaging was performed before enhancement (Pre-Gx) and at the 13th (Post-Gx13) and 17th (Post-Gx17) min after Gx enhancement with 2E- and 6E-VIBE Dixon to determine hepatic proton density fat fractions (PDFF). PDFFs were compared with vacuole percentage (VP) measured under histopathology. Results 33 animals were evaluated and including control group (n = 11) and MASLD group (n = 22). Pre-Gx, Post-Gx13, Post-Gx17 PDFFs under 6E-VIBE Dixon had strong correlations with VPs (r2 = 0.8208—0.8536). PDFFs under 2E-VIBE Dixon were reduced significantly (P < 0.001) after enhancement (r2 = 0.7991/0.8014) compared with that before enhancement (r2 = 0.7643). There was no significant difference between PDFFs of Post-Gx13 and Post-Gx17 (P = 0.123) for which the highest consistency being found with 6E-VIBE Dixon before enhancement (r2 = 0.8536). The signal intensity of the precontrast compared with the postcontrast, water image under 2E-VIBE Dixon increased significantly (P < 0.001), fat image showed no significant difference (P = 0.754). Conclusion 2E- and 6E-VIBE Dixon can obtain accurate PDFFs in the hepatobiliary specific phase from 13 to 17th min after Gx enhancement. On 2E-VIBE Dixon (FA = 10°), effective minimization of T1 Bias by the Gx administration markedly improved the accuracy of the hepatic PDFF quantification. Graphical abstract

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Evaluation of two-point Dixon water-fat separation for liver specific contrast-enhanced assessment of liver maximum capacity

Cited by 6 publications

References 32 publications

Survey of water proton longitudinal relaxation in liver in vivo

Survey of water proton longitudinal relaxation in liver in vivo

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

Influence of Gadoxetate disodium to the hepatic proton density fat fraction quantified with the Dixon sequences in a rabbit model

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