This study was undertaken to register 3D parametric breast images derived from Gd-DTPA MR and F-18-FDG PET/CT
dynamic image series. Nonlinear curve fitting (Levenburg-Marquardt algorithm) based on realistic two-compartment
models was performed voxel-by-voxel separately for MR (Brix) and PET (Patlak). PET dynamic series consists of 50
frames of 1-minute duration. Each consecutive PET image was nonrigidly registered to the first frame using a finite
element method and fiducial skin markers. The 12 post-contrast MR images were nonrigidly registered to the precontrast
frame using a free-form deformation (FFD) method. Parametric MR images were registered to parametric PET
images via CT using FFD because the first PET time frame was acquired immediately after the CT image on a PET/CT
scanner and is considered registered to the CT image. We conclude that nonrigid registration of PET and MR parametric
images using CT data acquired during PET/CT scan and the FFD method resulted in their improved spatial
coregistration. The success of this procedure was limited due to relatively large target registration error, TRE = 15.1±7.7
mm, as compared to spatial resolution of PET (6-7 mm), and swirling image artifacts created in MR parametric images
by the FFD. Further refinement of nonrigid registration of PET and MR parametric images is necessary to enhance
visualization and integration of complex diagnostic information provided by both modalities that will lead to improved
diagnostic performance.
This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series
and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10
mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first
frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs.
time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was
accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X2. We evaluated which
parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak
modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast
tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration
followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric
images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we
established that the total cumulated activity and maximum activity parametric images provide the best delineation of
suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed
images.
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 &mgr;Ci of Ge-68 were taped to each breast. In our PET/PET registration method we
utilized CT data. For corresponding FSMs visible on the 1st and nth 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 1st PET frame) to match the consecutive dynamic PET time frames. Each mesh was then
deformed to match the 1st 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 1st and nth 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.
KEYWORDS: Breast, Magnetic resonance imaging, Finite element methods, Positron emission tomography, Image registration, 3D modeling, Image processing, 3D image processing, Skin, Chemical elements
We implemented an iterative nonrigid registration algorithm to accurately combine functional (PET) and anatomical (MRI) images in 3D. Our method relies on a Finite Element Method (FEM) and a set of fiducial skin markers (FSM) placed on breast surface. The method is applicable if the stress conditions in the imaged breast are virtually the same in PET and MRI. In the first phase, the displacement vectors of the corresponding FSM observed in MRI and PET are determined, then FEM is used to distribute FSM displacements linearly over the entire breast volume. Our FEM model relies on the analogy between each of the orthogonal components of displacement field, and the temperature distribution field in a steady state heat transfer (SSHT) in solids. The problem can thus be solved via standard heat-conduction FEM software, with arbitrary conductivity of surface elements set much higher than that of volume elements. After determining the displacements at all mesh nodes, moving (MRI) breast volume is registered to target (PET) breast volume using an image-warping algorithm. In the second iteration, to correct for any residual surface and volume misregistration, a refinement process is applied to the moving image, which was already grossly aligned with the target image in 3D using FSM. To perform this process we determine a number of corresponding points on each moving and target image surfaces using a nearest-point approach. Then, after estimating the displacement vectors between the corresponding points on the surfaces we apply our SSHT model again. We tested our model on twelve patients with suspicious breast lesions. By using lesions visible in both PET and MRI, we established that the target registration error is below two PET voxels. The surface registration error is comparable to the spatial resolution of PET.
KEYWORDS: Breast, Finite element methods, Magnetic resonance imaging, Mammography, Image segmentation, Receivers, Image registration, Breast cancer, Chemical elements, Skin
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 ANSYSTM 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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