Multiparametric MR assessment of liver fat, iron, and fibrosis: a concise overview of the liver “Triple Screen”

Low, Gavin; Ferguson, Craig; Locas, Stephanie; Tu, Wendy; Manolea, Florin; Sam, Medica; Wilson, Mitchell P

doi:10.1007/s00261-023-03887-0

Cited by 8 publications

(3 citation statements)

References 87 publications

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“…Magnetic resonance elastography (MRE) of the liver is an imaging technique used to detect liver stiffness in the evaluation of suspected fibrosis or cirrhosis. Measurement of hepatic stiffness can help predict the stage of liver fibrosis (1,2). For an accurate assessment of liver disease, highquality MRE images are required.…”

Section: Introductionmentioning

confidence: 99%

Individualized driver amplitude in liver MR elastography: a linear regression study

Zhai,

Liu,

Wen

et al. 2024

Acta Radiol

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Background Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image. Purpose To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality. Material and Methods Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd−l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal amplitude prediction model. Results The optimal formula for linear regression models: driver amplitude (%) = −16.80 + 78.59 × AMVR − 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd−l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test ( F = 22.455, P <0.001) and R2 value of 0.558. Conclusion The individualized amplitude prediction model based on AMVR, breath-holding status, Dd−l, BMI, and angle α is a valuable tool in liver MRE examination.

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Section: Introductionmentioning

confidence: 99%

Individualized driver amplitude in liver MR elastography: a linear regression study

Zhai,

Liu,

Wen

et al. 2024

Acta Radiol

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“…Die MRE wird seit langem zur nichtinvasiven Beurteilung der Fibrose der Leber eingesetzt, technisch lässt sich die Technik jedoch auch in weiteren Zielstrukturen nutzen, z. B. zur Bestimmung der mechanischen und elastischen Eigenschaften muskulärer Strukturen [28][29][30]. Wenngleich keine tomografische Technik, muss in diesem Zusammenhang auch die sonografisch basierte Elastografie als BC-Methode erwähnt werden, die abermals breite Anwendung in der Leber findet [31].…”

Section: Parameter Der Body Compositionunclassified

“…This data is then processed by a mathematical inversion algorithm to compute an elastogram, which represents the distribution of tissue stiffness. For many years, MRE has been used for the noninvasive evaluation of liver fibrosis, but the technique can also be used in other target regions, e. g., to determine the mechanical and elastic properties of muscular structures [28][29][30]. Ultrasound-based elastography should also be mentioned here as a BC method that is broadly used for the liver [31] although it is not a tomographic technique.…”

Section: Body Composition Parametersmentioning

confidence: 99%

Body composition analysis by radiological imaging – methods, applications, and prospects

Linder,

Denecke,

Busse

2024

Rofo

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Background This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging. Methods The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups. Results and Conclusion Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner – in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation. Key Points Citation Format

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Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future

Huang,

Peng,

Kang

2024

VIEW

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Liver fibrosis is a reparative response triggered by liver injury. Non‐invasive assessment and staging of liver fibrosis in patients with chronic liver disease are of paramount importance, as treatment strategies and prognoses depend significantly on the degree of fibrosis. Although liver fibrosis has traditionally been staged through invasive liver biopsy, this method is prone to sampling errors, particularly when biopsy sizes are inadequate. Consequently, there is an urgent clinical need for an alternative to biopsy, one that ensures precise, sensitive, and non‐invasive diagnosis and staging of liver fibrosis. Non‐invasive imaging assessments have assumed a pivotal role in clinical practice, enjoying growing popularity and acceptance due to their potential for diagnosing, staging, and monitoring liver fibrosis. In this comprehensive review, we first delved into the current landscape of non‐invasive imaging technologies, assessing their accuracy and the transformative impact they have had on the diagnosis and management of liver fibrosis in both clinical practice and animal models. Additionally, we provided an in‐depth exploration of recent advancements in ultrasound imaging, computed tomography imaging, magnetic resonance imaging, nuclear medicine imaging, radiomics, and artificial intelligence within the field of liver fibrosis research. We summarized the key concepts, advantages, limitations, and diagnostic performance of each technique. Finally, we discussed the challenges associated with clinical implementation and offer our perspective on advancing the field, hoping to provide alternative directions for the future research.

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Multiparametric MR assessment of liver fat, iron, and fibrosis: a concise overview of the liver “Triple Screen”

Cited by 8 publications

References 87 publications

Individualized driver amplitude in liver MR elastography: a linear regression study

Individualized driver amplitude in liver MR elastography: a linear regression study

Body composition analysis by radiological imaging – methods, applications, and prospects

Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future

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