MR Elastography: Practical Questions, From the <i>AJR</i> Special Series on Imaging of Fibrosis

Yin, Meng; Ehman, Richard L.

doi:10.2214/ajr.23.29437

Cited by 6 publications

(2 citation statements)

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“…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: Background and Methodsmentioning

confidence: 99%

“…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

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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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Section: Background and Methodsmentioning

confidence: 99%