ArticleBMC medical imaging2026
Photon-counting CT for paraspinal muscle fat quantification compared with MRI proton density fat fraction.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTo evaluate photon-counting CT (PCCT) derived 70 keV attenuation values and virtual noncontrast fat fraction (VNC FF) in quantifying paraspinal muscle fat infiltration, using MRI proton density fat fraction (PDFF) as the reference standard.
methodsIn this prospective study, 76 adults with low back pain underwent same-day lumbar PCCT and 6-echo q-Dixon MRI within a 2-hour interval. The cohort consisted of 76 participants (38 men, 38 women), with a mean age of 47.7 ± 14.0 years and a mean body mass index (BMI) of 24.6 ± 3.3 kg/m². VNC FF represents a material decomposition-based fat fraction obtained from PCCT. Regions of interest (ROI) were bilaterally drawn in the multifidus, erector spinae, and psoas major at four intervertebral disc levels (L2/3-L5/S1). Correlation analysis, linear mixed-effects regression, Bland-Altman analysis, and receiver operating characteristic analysis were performed.
resultsThe 70 keV CT values showed a strong correlation with MRI PDFF at the ROI level (r = - 0.931), outperforming VNC FF (r = 0.876). At the subject level, correlations were consistently strong across intervertebral disc levels (r range, - 0.964 to - 0.975 for CT values; 0.766 to 0.896 for VNC FF) and muscle groups (r range, - 0.881 to - 0.984 for CT values; 0.827 to 0.966 for VNC FF). Regression modeling enabled derivation of an internally calibrated CT fat fraction (CTFF), which closely approximated MRI PDFF within the study cohort. Agreement in categorical fat infiltration grading (< 10%, 10-30%, 30-50%, > 50%) was moderate (κ = 0.623). For binary classification at the 30% threshold, CTFF demonstrated excellent diagnostic performance (AUC = 0.993, 95% CI: 0.990-0.997).
conclusionPCCT-derived 70 keV CT values showed strong agreement with MRI PDFF and enabled internal regression-based estimation of paraspinal muscle fat, with excellent diagnostic performance for fat infiltration classification.
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