WHOLE-BODY MRI-BASED ASSESSMENT OF ADIPOSE TISSUE: RECOMMENDATIONS FOR DATA ACQUISITION AND PROCESSING.
|Druh||Článek v odborném periodiku|
|Časopis / Zdroj||Anthropologie|
|Fakulta / Pracoviště MU|
|Klíčová slova||Whole-body magnetic resonance imaging; Internal adipose tissue; Subcutaneous adipose tissue; Fat segmentation; Participant positioning|
|Popis||Detailed knowledge of the body composition, especially the amount and distribution of adipose tissue, is critical for cardiovascular and metabolic diseases risk management. Magnetic resonance imaging allows detailed localization and quantification of adipose tissue. From the available protocols, full-body scanning provides the most accurate and complex information. It is, however, also a challenging one. The extent of the entire body and the huge amount of data place high demands on scanning devices and data processing. At the same time, the automated algorithms struggle with the different body types and body compositions. In this study, a protocol for whole-body MRI-based adipose tissue assessment is provided. We hypothesize that the proposed positioning and fixation of the subject and the use of an adaptive data processing workflow will allow effective whole-body data acquisition that is beneficial for both the subject and the examiner. A pilot sample of 11 individuals was scanned on a 3T Siemens Magnetom Prisma device. First, a novel scanning protocol for whole-body scanning was proposed with an emphasis on proper participant positioning. Its greatest benefit lies in adding the system of fillers, pads, barriers, and harnessing providing fixation of the participant for greater comfort and ensuring that the largest possible body volume fits to the Field of View (FOV). Moreover, better feasibility of data processing was ensured due to the provided distancing of body parts. Subsequently, an adaptable, user-friendly, and reliable processing workflow combining automatic compilation of data processing steps with manual adjustments, allowing for detailed whole-body adipose tissue segmentation, was proposed in Avizo software.|