Body composition analysis based on the characterization of different tissue compartments is currently experiencing increasing attention by a broad range of medical disciplines for both clinical and research questions. However, body composition profiling (BCP) can be performed utilizing different modalities, which all come along with several technical and diagnostic strengths and limitations, respectively. Magnetic resonance imaging (MRI) demonstrates good soft tissue resolution, high contrast between fat and water, and is free from ionizing radiation. This review article represents an overview of imaging techniques for body composition assessment, focussing on qualitative and quantitative methods of assessing adipose tissue and muscles in MRI.

Background: CT artifacts induced by orthopedic implants can limit image quality and diagnostic yield. As a number of different strategies to reduce artifact extent exist, the aim of this study was to systematically compare ex vivo the impact of different CT metal artifact reduction (MAR) strategies on spine implants made of either standard titanium or carbon-fiber-reinforced-polyetheretherketone (CFR-PEEK). Methods: Spine surgeons fluoroscopically-guided prepared six sheep spine cadavers with pedicle screws and rods of either titanium or CFR-PEEK. Samples were subjected to single- and dual-energy (DE) CT-imaging. Different tube voltages (80, DE mixed, 120 and tin-filtered 150 kVp) at comparable radiation dose and iterative reconstruction versus monoenergetic extrapolation (ME) techniques were compared. Also, the influence of image reconstruction kernels (soft vs. bone tissue) was investigated. Qualitative (Likert scores) and quantitative parameters (attenuation changes induced by implant artifact, implant diameter and image noise) were evaluated by two independent radiologists. Artifact degree of different MAR-strategies and implant materials were compared by multiple ANOVA analysis. Results: CFR-PEEK implants induced markedly less artifacts than standard titanium implants (p < .001). This effect was substantially larger than any other tested MAR technique. Reconstruction algorithms had small impact in CFR-PEEK implants and differed significantly in MAR efficiency (p < .001) with best MAR performance for DECT ME 130 keV (bone kernel). Significant differences in image noise between reconstruction kernels were seen (p < .001) with minor impact on artifact degree. Conclusions: CFR-PEEK spine implants induce significantly less artifacts than standard titanium compositions with higher MAR efficiency than any alternate scanning or image reconstruction strategy. DECT ME 130 keV image reconstructions showed least metal artifacts. Reconstruction kernels primarily modulate image noise with minor impact on artifact degree.

To compare the diagnostic performance of texture analysis (TA) against visual qualitative assessment in the differentiation of spondyloarthritis (SpA) from degenerative changes in the sacroiliac joints (SIJ). Ninety patients referred for suspected inflammatory lower back pain from the rheumatology department were retrospectively included at our university hospital institution. MRI at 3 T of the lumbar spine and SIJ was performed with oblique coronal T1-weighted (w), fluid-sensitive fat-saturated (fs) TIRM and fsT1w intravenously contrast-enhanced (CE) images. Subjects were divided into three age- and gender-matched groups (30 each) based on definite clinical diagnosis serving as clinical reference standard with either degenerative, inflammatory (SpA) or no changes of the SIJ. SIJ were rated qualitatively by two independent radiologists and quantitatively by region-of-interest-based TA with 304 features subjected to machine learning logistic regression with randomized ten-fold selection of training and validation data. Qualitative and quantitative results were evaluated for diagnostic performance and compared against clinical reference standard. Agreement of radiologist's diagnose with clinical reference was fair for both readers (κ = 0.32 and 0.44). ROC statistics revealed significant outperformance of TA compared to qualitative ratings for differentiation of SpA from remainder (AUC = 0.89 vs. 0.75), SpA from degenerative (AUC = 0.91 vs. 0.67) and TIRM-positive SpA (i.e. with bone marrow edema) from remainder cases (AUC = 0.95 vs. 0.76). T1w-CE images were the most important discriminator for detection of SpA. TA is superior to qualitative assessment for the differentiation of inflammatory from degenerative changes of the SIJ. Intravenous CE-images increase diagnostic yield in quantitative TA.

Objectives: To develop, test, and validate a body composition profiling algorithm for automated segmentation of body compartments in whole-body magnetic resonance imaging (wbMRI) and to investigate the influence of different acquisition parameters on performance and robustness. Materials and Methods: A segmentation algorithm for subcutaneous and visceral adipose tissue (SCAT and VAT) and total muscle mass (TMM) was designed using a deep learning U-net architecture convolutional neuronal network. Twenty clinical wbMRI scans were manually segmented and used as training, validation, and test datasets. Segmentation performance was then tested on different data, including different magnetic resonance imaging protocols and scanners with and without use of contrast media. Test-retest reliability on 2 consecutive scans of 16 healthy volunteers each as well as impact of parameters slice thickness, matrix resolution, and different coil settings were investigated. Sorensen-Dice coefficient (DSC) was used to measure the algorithms' performance with manual segmentations as reference standards. Test-retest reliability and parameter effects were investigated comparing respective compartment volumes. Abdominal volumes were compared with published normative values. Results: Algorithm performance measured by DSC was 0.93 (SCAT) to 0.77 (VAT) using the test dataset. Dependent from the respective compartment, similar or slightly reduced performance was seen for other scanners and scan protocols (DSC ranging from 0.69–0.72 for VAT to 0.83–0.91 for SCAT). No significant differences in body composition profiling was seen on repetitive volunteer scans (P = 0.88–1) or after variation of protocol parameters (P = 0.07–1). Conclusions: Body composition profiling from wbMRI by using a deep learning–based convolutional neuronal network algorithm for automated segmentation of body compartments is generally possible. First results indicate that robust and reproducible segmentations equally accurate to a manual expert may be expected also for a range of different acquisition parameters.