Citations

PurposeTo improve calibrationless parallel imaging using pre‐learned subspaces of coil sensitivity functions.Theory and MethodsA subspace‐based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.ResultsWith no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state‐of‐the‐art methods including JSENSE, DeepSENSE, P‐LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.ConclusionA subspace‐based method named JSENSE‐Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Abstract The recent identification of activating fibroblast growth factor receptor 2 (FGFR2) mutations in endometrial cancer has generated an opportunity for a novel target-based therapy. Here, we explore the therapeutic potential of 2 FGFR inhibitors, the multikinase inhibitor dovitinib (TKI258) and the more selective FGFR inhibitor NVP-BGJ398 for the treatment of endometrial cancer. We examined the effects of both inhibitors on tumor cell growth, FGFR2 signaling, cell cycle, and apoptosis using a panel of 20 molecularly characterized human endometrial cancer cell lines. Anchorage-independent growth was studied using soft agar assays. In vivo studies were conducted using endometrial cancer xenograft models. Cell lines with activating FGFR2 mutations (S252W, N550K) were more sensitive to dovitinib or NVP-BGJ398 when compared with their FGFR2 wild-type counterparts (P = 0.073 and P = 0.021, respectively). Both agents inhibited FGFR2 signaling, induced cell-cycle arrest, and significantly increased apoptosis in FGFR2-mutant lines. In vitro, dovitinib and NVP-BGJ398 were both potent at inhibiting cell growth of FGFR2-mutant endometrial cancer cells, but the activity of dovitinib was less restricted to FGFR2-mutant lines when compared with NVP-BGJ398. In vivo, dovitinib and NVP-BGJ398 significantly inhibited the growth of FGFR2-mutated endometrial cancer xenograft models. In addition, dovitinib showed significant antitumor activity in FGFR2 wild-type endometrial cancer xenograft models including complete tumor regressions in a long-term in vivo study. Dovitinib and NVP-BGJ398 warrant further clinical evaluation in patients with FGFR2-mutated endometrial cancer. Dovitinib may have antitumor activity in endometrial cancer beyond FGFR2-mutated cases and may permit greater flexibility in patient selection. Mol Cancer Ther; 12(5); 632–42. ©2013 AACR.