Figure 5

Fiducial marker locations identified from the CT data (red) compared with the locations specified by the phantom manufacturer (blue). The locations that we found were in close agreement with the vendor specifications.

© 2015 Mayo Foundation for Medical Education and Research

Brian McCollough1,2, Paul Weavers, Ph.D.3, Joshua Trzasko, Ph.D.3, Shengzhen Tao2, Matt A. Bernstein, Ph.D.3

1Bethel University, St. Paul, MN; 2Biomedical Engineering and Physiology Graduate Program, Mayo Graduate School, Rochester, MN; 3Department of Radiology, Mayo Clinic, Rochester, MN

•Locations of the fiducial markers in the CT data are consistent with phantom schematics•Increasing distortion is observed within the MRI data near the edges of the FOV •GNL have a significant effect on MRI data and require corrections

Conclusions

•Complete the MRI code, and find the isocenter of the magnetic field and the center of the phantom.•Compare CT and MRI data with the phantom specifications and find the differences. •Compare collected CT and MRI data and directly calculate the distortions between the two data sets. •Feed the results into an iterative corrective scheme.

Future Work

McRobbie, D.W., Moore, E.A., Graves, M.J., & Prince, M.R. (2007). MRI From Picture to Proton: Cambridge: University Press, Cambridge.

Bushberg, J.T., Seibert, J.A., Leidholdt, E.M., & Boone, J.M. (2012). The Essential Physics of Medical Imaging. Philadelphia: Lippincott Williams & Wilkins

J.D. Trzasko, S. Tao, J. Gunter, Y. Shu, J. Huston III, and M.A. Bernstein, in ISMRM Annual Meeting (Toronto, 2015), p. 3735.

S. Tao, J.D. Trzasko, Y. Shu, J. Huston III, and M.A. Bernstein, Magn. Reson. Med. Early View. (2014).

References

Magnetic resonance imaging (MRI) is made possible by subjecting protons (in water) to a static magnetic field, exciting these protons with radio-frequency (RF) energy, and monitoring the resulting RF signal.  Additional dynamic magnetic fields called gradients are applied to spatially encode this RF signal. These gradients are ideally linear, but can suffer from gradient nonlinearity, especially at the edges of the field-of-view (FOV). This work utilized a fiducial phantom with computed tomography (CT) reference data to map the effect of the gradient non-linearity (GNL). A set of CT and MRI data were analyzed to find the positions of several thousand fiducial markers across a large FOV, and the differences in position between the reference standard (CT) and the MRI images were used to characterize the GNL. The resulting information can be used to generate an improved GNL correction.

In this work, software tools were developed to automatically determine the locations of 5229 fiducial markers in the CT and MRI data. Analysis of the CT data demonstrated that the correct number of points was found, with a root mean squared error of 1.73 mm. Most fiducial markers were located in the MRI data, and the distortions from the GNL were evident.

Abstract

1. Locate and identify all 5229 fiducial points in both the CT and MRI images sets.

2. Create a list of all points in the CT and MRI data3. Determine the geometric distortions of the MRI data using the

CT data as the reference truth

Objectives

Develop software tools to evaluate and quantitatively analyze the geometric distortions found in an Magnetic Resonance Imaging (MRI) machine.

Study Aim

CT and MRI images of the same 5229-sphere fiducial phantom were analyzed to determine the image distortion properties of the GE MR750w MRI scanner (GE Healthcare, Waukesha, Wisconsin).  The CT data (1.2695 mm [left/right (L/R)] by 1.2695 mm [anterior/posterior (A/P)] by 0.6 mm [superior/inferior (S/I)] voxel sizes) were taken as the reference to which the MRI data were compared (1.1992 mm (L/R) by 1.1992 mm (A/P) by 1.2 mm voxel sizes).  

The MRI data were corrected with the vendor’s standard gradient non-linearity correction.  

The positions of the fiducial markers were determined from CT and MRI data using a Hough transform. A Hough transform locates the bright circular centers of the fiducial markers. First, a template image was applied that contained all of the markers of interest to exclude any points outside the desired region. Then, using a distance function and the X and Y coordinates, the circles belonging to a specific marker were linked together. Finally, the centers of each marker were calculated by taking the mean location from the top and bottom locations of the circles for a given marker.

Methods

Using a Fiducial Phantom To Track Geometric Distortion in Large Field of View MRI Imaging: Comparison Against CT Reference

Figure 2

Original CT Image CT Image After TransformPoints found by the Hough transform are displayed as blue circles. X

Z

Y

Figure 4Image data included multiple views of each fiducial marker. To determine the physical location of the center of a specific fiducial marker, individual images were compared to adjacent images by calculating the distance between circles in adjacent axial images. The Z-axis location was determined by taking the mean position from the top and bottom positions of a fiducial marker.

CT view of images (blue) with fiducial centers (red)X

Z

Y

A.CT axial view of the circles found by the Hough transform

B.MRI axial view of the circles displaying the outward-radiating distortion caused by the gradient non-linearity.

C.MRI sagittal view of half of the circles displaying the bulging distortion of the magnet.

Figure 3

A.

C.

B.

Z

Y Y

X

Y

X

•The developed software tools were able to automatically determine the locations of all the fiducial markers in the CT data and the locations of most of the fiducial markers in the MRI data•The determined positions in the CT data differed from the manufacturer specifications by a root mean squared error of 1.73 mm

Results

Figure 1

Fiducial Phantom set up inside CT scanner