Restriction-weighted q-space trajectory imaging (ResQ): Toward mapping diffusion time effects with tensor-valued diffusion encoding in human prostate cancer xenografts

PurposeTensor-valued diffusion encoding employs gradient waveforms that enable characterization of tissue microstructure, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that incorporates diffusion-time effects via the restriction-weighting tensor, and we evaluate it in a longitudinal study of prostate cancer xenografts treated by external radiotherapy. MethodsWe proposed a novel gradient waveform design that yields controlled restriction weighting and applied a set of four waveforms at a 9.4 T pre-clinical MRI system. Mice were inoculated with human prostate cancer cells (LnCaP) and assigned to groups that were untreated controls or treated by external beam irradiation. ResQ produced parameters of diffusivity (D), isotropic diffusional variance (VDi), and microscopic diffusion anisotropy (VDa) as well as their diffusion time dependence ({Delta}D, {Delta}VDi, {Delta}VDa). Analyses were performed to characterize parameters longitudinally and across groups. This included a model that neglects time-dependence to highlight the consequences of ignoring restriction effects. ResultsResQ revealed clear diffusion time dependence across all tumors, with marked longitudinal differences between treated and untreated groups, most prominent in D, {Delta}D, and VDi. The ResQ signal representation could capture the signal dynamics, whereas the diffusion-time-independent formulation did not. Neglecting diffusion-time dependence led to substantial parameter bias, most notably a pronounced overestimation of microscopic diffusion anisotropy. ConclusionDiffusion-time effects are non-negligible in prostate cancer and must be considered when using tensor-valued diffusion encoding. The ResQ framework enables controlled restriction weighting and improves the interpretability of diffusion MRI parameters, providing a principled approach for microstructural imaging and potentially enabling novel imaging biomarkers for oncological diagnostics and beyond.