The work presented herein describes our methods and results for predicting, measuring and correcting geometric distortions in a 3 T clinical magnetic resonance (MR) scanner for the purpose of image guidance in radiation treatment planning. Geometric inaccuracies due to both inhomogeneities in the background field and nonlinearities in the applied gradients were easily visualized on the MR images of a regularly structured three-dimensional (3D) grid phantom. From a computed tomography scan, the locations of just under 10 000 control points within the phantom were accurately determined in three dimensions using a MATLAB-based computer program. MR distortion was then determined by measuring the corresponding locations of the control points when the phantom was imaged using the MR scanner. Using a reversed gradient method, distortions due to gradient nonlinearities were separated from distortions due to inhomogeneities in the background B0 field. Because the various sources of machine-related distortions can be individually characterized, distortions present in other imaging sequences (for which 3D distortion cannot accurately be measured using phantom methods) can be predicted negating the need for individual distortion calculation for a variety of other imaging sequences. Distortions were found to be primarily caused by gradient nonlinearities and maximum image distortions were reported to be less than those previously found by other researchers at 1.5 T. Finally, the image slices were corrected for distortion in order to provide geometrically accurate phantom images.

The aim of this work is to demonstrate a complete, robust, and time-efficient method for distortion correction of magnetic resonance (MR) images. It is well known that MR images suffer from both machine-related spatial distortions [gradient nonlinearity and main field (B0) inhomogeneity] and patient-related spatial distortions (susceptibility and chemical shift artifacts), and growing interest in the area of MR-based radiotherapy treatment planning has put new requirements on the geometric accuracy of such images. The authors present a two-step method that combines a phantom-based reverse gradient technique for measurement of gradient nonlinearities and a patient-based phase difference mapping technique for measurement of B0 inhomogeneities, susceptibility, and chemical shift distortions. The phase difference mapping technique adds only minutes to the total patient scan time and can be used to correct a variety of images of the same patient and anatomy. The technique was tested on several different phantoms, each designed to isolate one type of distortion. The mean distortion was reduced to 0.2 +/- 0.1 mm in both gradient echo and spin echo images of a grid phantom. For the more difficult case of a highly distorted echo planar image, residual distortion was reduced to subvoxel dimensions. As a final step, the technique was implemented on patient images. The current technique is effective, time efficient, and robust and provides promise for preparing distortion-rectified MR images for use in MR-based treatment planning.

A recombinant strain HCV1 (hepatitis C virus [HCV] genotype 1a) gpE1/ gpE2 (E1E2) vaccine candidate was previously shown by our group to protect chimpanzees and generate broad cross-neutralizing antibodies in animals and humans. In addition, recent independent studies have highlighted the importance of conserved neutralizing epitopes in HCV vaccine development that map to antigenic clusters in E2 or the E1E2 heterodimer. E1E2 can be purified using Galanthis nivalis lectin agarose (GNA), but this technique is suboptimal for global production. Our goal was to investigate a high-affinity and scalable method for isolating E1E2. We generated an Fc tag-derived (Fc-d) E1E2 that was selectively captured by protein G Sepharose, with the tag being removed subsequently using PreScission protease. Surprisingly, despite the presence of the large Fc tag, Fc-d E1E2 formed heterodimers similar to those formed by GNA-purified wild-type (WT) E1E2 and exhibited nearly identical binding profiles to HCV monoclonal antibodies that target conserved neutralizing epitopes in E2 (HC33.4, HC84.26, and AR3B) and the E1E2 heterodimer (AR4A and AR5A). Antisera from immunized mice showed that Fc-d E1E2 elicited anti-E2 antibody titers and neutralization of HCV pseudotype viruses similar to those with WT E1E2. Competition enzyme-linked immunosorbent assays (ELISAs) showed that antisera from immunized mice inhibited monoclonal antibody binding to neutralizing epitopes. Antisera from Fc-d E1E2-immunized mice exhibited stronger competition for AR3B and AR5A than the WT, whereas the levels of competition for HC84.26 and AR4A were similar. We anticipate that Fc-d E1E2 will provide a scalable purification and manufacturing process using protein A/G-based chromatography.IMPORTANCE A prophylactic HCV vaccine is still needed to control this global disease despite the availability of direct-acting antivirals. Previously, we demonstrated that a recombinant envelope glycoprotein (E1E2) vaccine (genotype 1a) elicited cross-neutralizing antibodies from human volunteers. A challenge for isolating the E1E2 antigen is the reliance on GNA, which is unsuitable for large scale-up and global vaccine delivery. We have generated a novel Fc domain-tagged E1E2 antigen that forms functional heterodimers similar to those with native E1E2. Affinity purification and removal of the Fc tag from E1E2 resulted in an antigen with a nearly identical profile of cross-neutralizing epitopes. This antigen elicited anti-HCV antibodies that targeted conserved neutralizing epitopes of E1E2. Owing to the high selectivity and cost-effective binding capacity of affinity resins for capture of the Fctagged rE1E2, we anticipate that our method will provide a means for large-scale production of this HCV vaccine candidate.

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Because of the excellent soft‐tissue detail provided by MR images, it is the optimum imaging modality for treatment planning target delineation. While the structure of a tumor can be seen in great detail on MR images, the geometric accuracy of the images is limited by the homogeneity of the background field, the linearity of the applied gradients, and the magnetic susceptibility of the imaged tissues. As such, MR images cannot be used alone for novel treatment planning purposes (i.e. MR simulation), or in conjunction with CT because of geometric distortion. Our research seeks to quantify the amount of distortion in 3T MR images due to both background inhomogeneities and gradient nonlinearities on a sequence by sequence basis by using a specialized grid phantom and an in‐house developed software program. The matlab‐based program accurately determines the 3D coordinates of over 9000 control points distributed throughout the phantom's volume. Three dimensional distortion maps can be generated by comparing the control point coordinates determined from an MR scan to the control point coordinates determined from a CT scan. Control point locations can be determined to an accuracy of 0.2 mm and distortions as large as 13 mm have been measured. With appropriate post‐processing correction factors derived from the 3D distortion maps, MR images can be undistorted and either combined or used individually for new treatment planning methods that benefit from the superior soft‐tissue information that magnetic resonance techniques provide.