Keywords:MR-to-PET nonrigid breast-image registration FEM-based soft tissue multimodality nonrigid image registration a b s t r a c tWe have developed and tested a new simple computerized finite element method (FEM) approach to MR-to-PET nonrigid breast-image registration. The method requires five-nine fiducial skin markers (FSMs) visible in MRI and PET that need to be located in the same spots on the breast and two on the flanks during both scans. Patients need to be similarly positioned prone during MRI and PET scans. This is accomplished by means of a low gamma-ray attenuation breast coil replica used as the breast support during the PET scan. We demonstrate that, under such conditions, the observed FSM displacement vectors between MR and PET images, distributed piecewise linearly over the breast volume, produce a deformed FEM mesh that reasonably approximates nonrigid deformation of the breast tissue between the MRI and PET scans. This method, which does not require a biomechanical breast tissue model, is robust and fast. Contrary to other approaches utilizing voxel intensity-based similarity measures or surface matching, our method works for matching MR with pure molecular images (i.e. PET or SPECT only). Our method does not require a good initialization and would not be trapped by local minima during registration process. All processing including FSMs detection and matching, and mesh generation can be fully automated. We tested our method on MR and PET breast images acquired for 15 subjects. The procedure yielded good quality images with an average target registration error below 4 mm (i.e. well below PET spatial resolution of 6-7 mm). Based on the results obtained for 15 subjects studied to date, we conclude that this is a very fast and a well-performing method for MR-to-PET breast-image nonrigid registration. Therefore, it is a promising approach in clinical practice. This method can be easily applied to nonrigid registration of MRI or CT of any type of soft-tissue images to their molecular counterparts such as obtained using PET and SPECT.

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The objectives of this investigation are to improve quality of subtraction MR breast images and improve accuracy of time-signal intensity curves (TSIC) related to local contrast-agent concentration in dynamic MR mammography. The patients, with up to nine fiducial skin markers (FSMs) taped to each breast, were prone with both breasts suspended into a single well that housed the receiver coil. After a preliminary scan, paramagnetic contrast agent gadopentate digmeglumine (Gd) was delivered intravenously, followed by physiological saline. The field of view was centered over the breasts. We used a gradient recalled echo (GRE) technique for pre-Gd baseline, and five more measurements at 60s intervals. Centroids were determined for corresponding FSMs visible on pre-Gd and any post-Gd images. This was followed by segmentation of breast surfaces in all dynamic-series images, and meshing of all post-Gd breast images. Tetrahedral volume and triangular surface elements were used to construct a finite element method (FEM) model. We used ANSYS™ software and an analogy between orthogonal components of the displacement field and the temperature differences in steady-state heat transfer (SSHT) in solids. The floating images were warped to a fixed image using an appropriate shape function for interpolation from mesh nodes to voxels. To reduce any residual misregistration, we performed surface matching between the previously warped floating image and the target image. Our method of motion correction via nonrigid coregistration yielded excellent differential-image series that clearly revealed lesions not visible in unregistered differential-image series. Further, it produced clinically useful maximum intensity projection (MIP) 3D images.

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This study was undertaken to correct for motion artifacts in dynamic breast F-18-FDG PET/CT images, to improve differential-image quality, and to increase accuracy of time-activity curves. Dynamic PET studies, with subjects prone, and breast suspended freely employed a protocol with 50 frames, each 1-minute long. A 30 s long CT scan was acquired immediately before the first PET frame. F-18-FDG was administered during the first PET time frame. Fiducial skin markers (FSMs) each containing ~0.5 µCi of Ge-68 were taped to each breast. In our PET/PET registration method we utilized CT data. For corresponding FSMs visible on the 1 st and n th frames, the geometrical centroids of FSMs were found and their displacement vectors were estimated and used to deform the finite element method (FEM) mesh of the CT image (registered with 1 st PET frame) to match the consecutive dynamic PET time frames. Each mesh was then deformed to match the 1 st PET frame using known FSM displacement vectors as FEM loads, and the warped PET timeframe volume was created. All PET time frames were thus nonrigidly registered with the first frame. An analogy between orthogonal components of the displacement field and the temperature distribution in steady-state heat transfer in solids is used, via standard heat-conduction FEM software with "conductivity" of surface elements set arbitrarily significantly higher than that of volume elements. Consequently, the surface reaches steady state before the volume. This prevents creation of concentrated FEM loads at the locations of FSMs and reaching incorrect FEM solution. We observe improved similarity between the 1 st and n th frames. The contrast and the spatial definition of metabolically hyperactive regions are superior in the registered 3D images compared to unregistered 3D images. Additional work is needed to eliminate small image artifacts due to FSMs.