Table 1 Representative literature summary of methods in investigations describing paravertebral muscle analysis using magentic resonance imaging (MRI)
Citation | Reliability | MRI Sequence | Slice Selection | Muscles of Interest | ROI Selection | Fat Detection | Measure |
|---|---|---|---|---|---|---|---|
Antony et al., 2016 [52] | No | T2 FRFSE | 1 slice per level at IVD L3-S1 | MF, ES | Semi-Automated interactive Segmentation (intelligent scissors) | Semi-Automated User set pixel intensity threshold | FCSA = (CSAFat)/(CSATotal) |
Battaglia et al. 2014 [53] | 2 raters (n = 25); Qualitative: Intra-rater weighted kappa = 0.71–0.93, Inter-rater kappa = 0.76–0.85; Quantitative: Inter-rater ICC = 0.73–0.90 | T1 | 1 slice per level at IVD L4-5, L5-S1 | MF | Qualitative: visual inspection; Quantitative ImageJ manual trace, exclude fascial boder between MF and ES | Qualitative: Goutallier Classification (0,1,2,3,4); Quantitative: pixel intensity range set by user selected fat ROI within MF | % fat = (# of fat pixels)/(total # of pixels) |
Beneck et al., 2012 [12] | 1 rater; intra-rater: ICC (3,1) = 0.961 for CSA | T1 | All slices spanning L4, L5-S1, and S2-S3 vertebral bodies | MF, ES | Slice-o-matic manual trace | Muscle divided into 6–9 regions. Gray-scale signal threshold semi-automatically determined for each region based on user selected small region of muscle | Muscle volume = total volume – fat volume |
Bhadresha et al., 2016 [19] | 2 raters (n = 20); inter-rater ICC = 0.33–0.76, Cronbach’s alpha = 0.59–0.91 | T2 FSE | I slice per level at IVD L3-4, L4-5, L5-S1 | MF, ES, PS | Visual inspection | 18×27 (1 cm apart) grid applied to image. By visual inspection: # of grid points touching fat vs. muscle counted | Muscle to Fat Ratio= Muscle/#fat pixels |
Crawford et al., 2016 [5] | No | 2-point DIXON (3D fast-field echo T1) whole body | Every 3rd slice L1-L5 (with interpolation to full volume) | MF, ES | Semiautomatic segmentation with linear interpolation (Myrian Intrasense, Paris, France) | Semi-automated ROI from water to fat image | Fat signal fraction (FSF) = (Signalfat/[Signalwater + Signalfat])*100 |
D’Hooge et al., 2012 [10] | No | T1 FSE | 1 slice per level at L3 superior endplate, L4 superior endplate, L4 inferior endplate | MF, ES, PS | ImageJ manual trace | Automated based on pixel intensity gray-scale threshold | MFI = (signal intensity of “lean muscle”)/(signal intensity of user defined fat ROI) |
Fortin et al., 2014 [13] | 1 rater; intra-rater: ICC = 0.90 –0.96) | T2 | 1 slice per level at IVD L3-4 and L5-S1 | MF, ES, MF + ES | ImageJ manual trace following fascial borders | User selected pixel intensity gray-scale threshold selected from 4–6 sample ROIs within visible ‘lean muscle’ | FCSA = (CSAFat)/(CSATotal); Proportion estimate of muscle fat content = Average signal intensity of Total ROI |
Hebert et al., 2014 [18] | 1 rater; intra-rater (n = 30) ICC (3,1) = 0.93, Bias = −0.70, 95% LOA = −8.11–6.72 | T1 SE (0.2 Tesla) | 1 slice per level at L4 and L5 | MF | Manual trace | Custom MatLab script; threshold set from midpoint between histogram peaks for ‘fat and muscle’ | % IMAT = CSAFat/CSATotal (IMAT = intramuscular adipose tissue) |
Hu et al., 2011 [54] | 3 raters (n = 29); intra-rater ICC (3,1) for FCSA = 0.832–0.847, for T2 SI = 0.926–0.957; inter-rater CC for FCSA = 0.858–0.894, for T2 SI = 0.891–0.923 | T2 FSE | 1 slice per level at IVD L3-4, L4-5, L5-S1 | MF, ES | PACS workstation manual trace: lean muscle CSA Drawn avoiding visible fat; Fat% - following outer perimeter | PACS embedded ROI and gray-scale histogram software calculated from mean T2 signal intensity | Lean muscle FCSA = Manually traced Muscle CSA Fat Infiltration = T2 signal intensity |
Kjaer et al., 2007 [8] | 2 raters (n = 50); intra-rater k = 0.86; inter-rater k = 0.58 | T1 SE (0.2 Tesla) | 1 slice per level at IVD L3-4, L4-5, L5-S1 | MF | Visual Inspection | Visual categorization: Normal 0–10%, slight 10–50%, severe > 50% fat | Fat infiltration = (0,1,2) |
Paalanne et al., 2011 [28] | 2 raters (n = 35); intra-rater ICC = 0.86–0.88, inter-rater ICC = 0.85–0.87 | T1 FSPGR (In-Phase and Opposed-Phase) | 1 slice at upper endplate of L4 | MF, ES | neaView Radiology; manual trace | Average Signal Intensity | Relative signal loss = (IP SI – OP SI)/(IP SI)*100% |
Pezolato et al., 2012 [22] | 2 raters (n = 10); intra-rater ICC = 0.90–0.94, inter-rater ICC = 0.86–0.83 | T2 FSE | 2 slices per level at upper and lower endplates of L1-L5 | MF, MF + ES | ImageJ manual trace | Grayscale threshold | Fat infiltrate = TotalCSA – FCSA |
Ranson et al., 2005 [55] | 1 rater (n = 6) × 3: average intra-rater ICC = 0.97 | T2 | 1 slice per level at lower vertebral endplate of L1-L5; upper vertebral end plate of L5-S1 | MF, ES, PS, QL | ImageJ manual trace following fascial borders | Grey scale pixel intensity range for muscle, fat, and bone were determined from histogram of manual ROIs of “lean paraspinal muscle”, inter-muscular fat, and vertebral body for either: method 1: global grey scale range (0–120); Method 2: slice specific grey scale range | FCSA = TotalCSA of pixels within grey-scale range for fat |
Shahidi et al., 2016 [48] | No | T2 | 1 slice at L4 (to standardize CSA across individuals) | MF, ES | Quantitative manual trace using MatLab | Pixels identified as either fat or muscle by fitting a two term Gaussian model to the pixel intensities histogram of from segmented ROIs; identified intersection where pixel values above classified as fat and pixels below classified as muscle. | Cross Sectional Area Fat signal fraction = npixels fat/npixels fat + npixels muscle |
Valentin et al., 2015 [7] | 1 rater (n = 24); intra-rater ICC = 0.88–0.99 | T1 | All slices spanning lower endplate of L5 to upper endplate of L1 (10 mm slice thickness) | MF, ES, PS, RA | Analyze Direct Manual trace | Average SI | MFI = (Muscle SI)/(Subcutaneous Fat SI) |