Sentence examples for motion correction problem from inspiring English sources

Using the observation model we have formulated in the previous section, we handle the imaging and motion correction problem as an optimization problem.

The proposed GIR algorithm conducts motion correction by solving the maximum likelihood problem.

However, for the present study, this did not cause any problems during the motion correction process.

Functional data were motion corrected offline with the Siemens motion correction protocol.

The basic motion correction algorithm uses multi-resolution optimization to alleviate this problem and reach a more global minimum [ 11].

Motion correction using the proposed GIR algorithm avoided the convergence problems that led to data points with unrealistic values, which occurred with uncorrected and FBF corrected PET data.

Images were displayed sequentially and corrected for motion when necessary using motion correction software.

In addition to on-line motion correction (prospective acquisition correction), functional scans were corrected retrospectively for motion using the AFNI algorithm (Cox and Jesmanowicz, 1999).

When necessary, acquired volumes were corrected for motion with a 3D rigid motion correction, based on skull movement.

It was also apparent that the conventional FBF algorithm could lead to subsequent convergence problems in the kinetic fitting, whereas the proposed GIR algorithm avoided such problems, thus eliminating the need to subjectively exclude outliers due to unsuccessful motion correction.

Images were corrected for differences in timing of slice acquisition followed by rigid body motion correction.