Phantom-less volumetric bone mineral density (vBMD) measurement using computed tomography (CT) presents a cost-effective alternative to conventional phantom-based approaches, yet faces accuracy challenges across varying tube voltages. Current deep learning-based phantom-less solutions frequently overlook the critical role of frequency variance-a crucial factor for precise BMD measurement and cross-voltage generalization. We present a lightweight CT-based phantom-free vBMD measurement framework that addresses critical limitations in cross-voltage generalization. Core innovations include: (1) Frequency-balancing feature modulation with multi-band fusion, preserving spectral measurement cues; (2) A dual-branch architecture combining domain-specific convolutions with cross-frequency interaction; and (3) Asymmetric channel attention, which allocates attention weights based on frequency characteristics, enabling adaptive emphasis on critical low- and high-frequency components. Comprehensive evaluations across 80, 100, and 120 kVp tube voltages demonstrate the proposed method’s superior measurement accuracy and reliability, achieving overall mean absolute errors of 5.990 \(\text {mg/cm}^3\) and 7.175 \(\text {mg/cm}^3\) on internal (1,614 images) and external (2,245 images) testing sets from two centers, respectively. These results suggest that our method offers a promising solution for clinical PL vBMD measurement across varying CT protocols.