Elimination of motion and pulsation artifacts using BLADE sequences in knee MR imaging

The purpose of this study is to evaluate the ability of proton density (PD)-BLADE sequences in reducing or even eliminating motion and pulsatile flow artifacts in knee magnetic resonance imaging examinations. Eighty consecutive patients, who had been routinely scanned for knee examination, participated in the study. The following pairs of sequences with and without BLADE were compared: (a) PD turbo spin echo (TSE) sagittal (SAG) fat saturation (FS) in 35 patients, (b) PD TSE coronal (COR) FS in 19 patients, (c) T2 TSE axial in 13 patients and (d) PD TSE SAG in 13 patients. Both qualitative and quantitative analyses were performed based on the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and relative contrast (ReCon) measures of normal anatomic structures. The qualitative analysis was performed by experienced radiologists. Also, the presence of image motion and pulsation artifacts was evaluated. Based on the results of the SNR, CRN and ReCon for the different sequences and anatomical structures, the BLADE sequences were significantly superior in 19 cases, whereas the corresponding conventional sequences were significantly superior in only 6 cases. BLADE sequences eliminated motion artifacts in all the cases. However, motion artifacts were shown in (a) six PD TSE SAG FS, (b) three PD TSE COR FS, (c) three PD TSE SAG and (d) two T2 TSE axial conventional sequences. In our results, it was found that, in PD FS sequences (sagittal and coronal), the differences between the BLADE and conventional sequences regarding the elimination of motion and pulsatile flow artifacts were statistically significant. In all the comparisons, the PD FS BLADE sequences (coronal and sagittal) were significantly superior to the corresponding conventional sequences regarding the classification of their image quality. In conclusion, this technique appears to be capable to potentially eliminate motion and pulsatile flow artifacts in MR images.     

The role of optimization in some recent advances in data-driven decision-making

Mathematical Programming - In this paper, we present a new framework of bi-level unconstrained minimization for development of accelerated methods in Convex Programming. These methods use...