Role of High-Resolution Dynamic Contrast-Enhanced MRI with Golden-Angle Radial Sparse Parallel Reconstruction to Identify the Normal Pituitary Gland in Patients with Macroadenomas.

BACKGROUND AND PURPOSE

Preoperative localization of the pituitary gland with imaging in patients with macroadenomas has been inadequately explored. The pituitary gland enhancing more avidly than a macroadenoma has been described in the literature. Taking advantage of this differential enhancement pattern, our aim was to evaluate the role of high-resolution dynamic MR imaging with golden-angle radial sparse parallel reconstruction in localizing the pituitary gland in patients undergoing trans-sphenoidal resection of a macroadenoma.

MATERIALS AND METHODS

A retrospective study was performed in 17 patients who underwent trans-sphenoidal surgery for pituitary macroadenoma. Radial volumetric interpolated brain examination sequences with golden-angle radial sparse parallel technique were obtained. Using an ROI-based method to obtain signal-time curves and permeability measures, 3 separate readers identified the normal pituitary gland distinct from the macroadenoma. The readers' localizations were then compared with the intraoperative location of the gland. Statistical analyses were performed to assess the interobserver agreement and correlation with operative findings.

RESULTS

The normal pituitary gland was found to have steeper enhancement-time curves as well as higher peak enhancement values compared with the macroadenoma ( < .001). Interobserver agreement was almost perfect in all 3 planes (κ = 0.89). In the 14 cases in which the gland was clearly identified intraoperatively, the correlation between the readers' localization and the true location derived from surgery was also nearly perfect (κ = 0.95).

CONCLUSIONS

This study confirms our ability to consistently and accurately identify the normal pituitary gland in patients with macroadenomas with the golden-angle radial sparse parallel technique with quantitative permeability measurements and enhancement-time curves.