Background Inconsistent descriptions of Lumbar multifidus (LM) morphology were previously identified, especially in research applying ultrasonography (US), hampering its clinical applicability with regard to diagnosis and therapy. The aim of this study is to determine the LM-sonoanatomy by comparing high-resolution reconstructions from a 3-D digital spine compared to standard LM-ultrasonography. Methods An observational study was carried out. From three deeply frozen human tissue blocks of the lumbosacral spine, a large series of consecutive photographs at 78 µm interval were acquired and reformatted into 3-D blocks. This enabled the reconstruction of (semi-)oblique cross-sections that could match US-images obtained from a healthy volunteer. Transverse and oblique short-axis views were compared from the most caudal insertion of LM to L1. Results Based on the anatomical reconstructions, we could distinguish the LM from the adjacent erector spinae (ES) in the standard US imaging of the lower spine. At the lumbosacral junction, LM is the only dorsal muscle facing the surface. From L5 upwards, the ES progresses from lateral to medial. A clear distinction between deep and superficial LM could not be discerned. We were only able to identify five separate bands between every lumbar spinous processes and the dorsal part of the sacrum in the caudal anatomical cross-sections, but not in the standard US images. Conclusion The detailed cross-sectional LM-sonoanatomy and reconstructions facilitate the interpretations of standard LM US-imaging, the position of the separate LM-bands, the details of deep interspinal muscles, and demarcation of the LM versus the ES. Guidelines for electrode positioning in EMG studies should be refined to establish reliable and verifiable findings. For clinical practice, this study can serve as a guide for a better characterisation of LM compared to ES and for a more reliable placement of US-probe in biofeedback.
MULTIFILE
CC-BY 4.0 Background: Since the contribution of the lumbar multifidus(LM) is not well understood in relation to nonspecific low back pain(LBP), this may limit physiotherapists in choosing the most appropriate treatment strategy.Objectives: This study aims to compare clinical characteristics, in terms of LM function and morphology, between subacute and chronic LBP patients from a large clinical practice cohort compared to healthy controls.Design: Multicenter case control study.Method: Subacute and chronic LBP patients and healthy controls between 18 and 65 years of age were included.Several clinical tests were performed: primary outcomes were the LM thickness from ultrasound measurements, trunk range of motion(ROM) from 3D kinematic tests, and median frequency and root mean square values of LM by electromyography measurements. The secondary outcomes Numeric Rating Scale for Pain(NRS) and the Oswestry Disability Index(ODI) were administered. Comparisons between groups were made with ANOVA, pvalues< 0.05, with Tukey’s HSD post-hoc test were considered significant.Results: A total of 161 participants were included, 50 healthy controls, 59 chronic LBP patients, and 52 subacute LBP patients. Trunk ROM and LM thickness were significantly larger in healthy controls compared to all LBP patients(p < 0.01). A lower LM thickness was found between subacute and chronic LBP patients although not significant(p = 0.11–0.97). All between-group comparisons showed no statistically significant differences in electromyography outcomes (p = 0.10–0.32). NRS showed no significant differences between LBP subgroups(p = 0.21). Chronic LBP patients showed a significant higher ODI score compared to subacute LBP patients(p = 0.03).Conclusions: Trunk ROM and LM thickness show differences between LBP patients and healthy controls.
MULTIFILE
Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
