Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data

3D Printing in Cranioplasty for Giant Cranial Deformity with Multi-Dimensional Nuclear Magnetic Simulation

This article is based on 3D printing technology, through multi-dimensional nuclear magnetic stimulation to the in-depth study of the application of plastic surgery in patients with giant cranial deformity cranial reduction, first of all, patients with CT scan of the brain, based on CT data for 3D reconstruction, 3D geometric modeling, using 3D printing Prepare multiple skull 1:1 scale, solid models, perform surgical planning and drills, determine the surgical plan (related parameters such as surgical time, cranial cavity volume, frontal plane ratio, anterior-posterior diameter, left-right diameter, head-to-height ratio, etc.), it can increase the patient’s speed and stride, and complete a variety of material tests. The 3D printing group had lower pain VAS scores at 1 h and 24 h after surgery than the traditional data group. The same data observed from different dimensions may yield different results, but also enable people to understand the nature of things more comprehensively and clearly. It was statistically significant (P < 0.05). The postoperative swelling of the 3D printing group was less than that of the customary group, and the difference was statistically significant (P < 0.05). Through 12 months of follow up observation, the power of 3D printing is higher than that of the habitual group, and the difference is statistically significant (P < 0.05). This technology has an important guiding significance in future related treatment technology.

Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data

Rapid, accurate, and automatic quantitation of two-dimensional nuclear magnetic resonance(2D-NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz-McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one-dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz-McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D-NMR data in terms of speed, accuracy, and automation-especially when compared in these respects to the previously published analysis techniques for 2D-NMR data.