Noise-referenced multi-shell diffusion-weighted imaging for regional cerebrospinal fluid thermometry

We developed a multi-shell diffusion-weighted imaging approach for separate ventricular and subarachnoid cerebrospinal fluid thermometry. The approach uses measured image noise to assess agreement among temperature estimates. In 12 participants, temperature was estimated from four non-zero diffusion weightings. For each voxel, disagreement between these estimates was compared with that expected from image noise, accounting for their shared reference image. Voxels meeting this consistency criterion were retained for regional analysis. Mean diffusion-derived temperature ({+/-} SD) was 38.4 {+/-} 0.4 {degrees}C in the lateral ventricles and 36.0 {+/-} 1.1 {degrees}C in the subarachnoid space. Ventricular estimates were consistently higher than subarachnoid estimates across all participants, with a mean difference of 2.4 {+/-} 1.2 {degrees}C (95% confidence interval 1.6 -3.1 {degrees}C). This difference persisted under alternative noise models and temperature estimators. Subarachnoid estimates increased with retained voxel count; in a post hoc analysis of the six participants with the largest retained counts, ventricular estimates remained higher by 1.6 {+/-} 0.7 {degrees}C. Absolute temperature accuracy and the physiological origin of the regional difference require independent validation. This approach provides an internal consistency check for CSF thermometry and supports separate assessment of ventricular and subarachnoid temperatures.