Standard clinical magnetic resonance imaging (MRI) is acquired in two-dimensions where the in-plane resolution is higher than the slice select direction. These acquisitions include axial, coronal, and sagittal planes. To date, there have been few attempts to combine the information of these three orthogonal orientations. This paper aims to take advantage of the different in-plane resolution acquired from each plane orientation and combine them into one volume in order to attain a higher resolution image. This combination of MRI data will allow the detection of smaller areas that would otherwise be missed using only one slice orientation. A comparison of slice thicknesses along with image registration is performed. The mean-squared error and peak signal-to-noise were computed for quantitative assessment. MRI and phantom scans and joint histograms were used for qualitative assessment.

Despite promising advantages such as low cost and portability of functional near-infrared spectroscopy (fNIRS), it has yet to be widely implemented outside of basic research. Specifically, fNIRS has yet to be proven as a standalone tool within a clinical setting. The objective of this study was to assess hemodynamic concentration changes at the primary and premotor motor cortices as a result of simple whole-hand grasping and sequential finger-opposition (tapping) tasks. These tasks were repeated over 3 days in a randomized manner. Ten healthy young adults ($23.8±4.8years$) participated in the study. Quantitatively, no statistically significant differences were discovered between the levels of activation for the two motor tasks ($p>0.05$). Overall, the signals were consistent across all 3 days. The findings show that both finger-opposition and hand grasping can be used interchangeably in fNIRS for assessment of motor function which would be useful in further advancing techniques for clinical implementation.

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.