Head movement during scanning introduces artificial signal changes and impedes activation detection in fMRI studies. The head motion in fMRI acquired using slice-based Echo Planar Imaging (EPI) sequence can be estimated and compensated by aligning the images onto a reference volume through image registration. Registering EPI images volume by volume fails to consider head motion between slices, leading to biased head motion estimates. Slice-to-volume registration is used to estimate motion parameters for each slice by more accurately representing the image acquisition sequence. However, it is prone to image noise and geometric distortion, resulting in high variance estimates. In this work, we propose a Gaussian particle filter based head motion tracking algorithm to reduce the image misregistration errors. The algorithm models head motion by using a dynamic state space model (SSM) to model continuous slice acquisition thereby providing more accurate motion estimates and voxel position estimates. We demonstrate significant performance improvement of the proposed approach as compared to previous registration-only methods of head motion estimation.

We propose a method of using a relatively low degree of freedom (DOF) warping to accurately measure the interval change of lesions having homogeneous contrast. The setting presented here presupposes the use of interval computed tomography (CT) liver exams. After a 3 x 24 DOF warping of the later examination to match the liver's pose in the earlier exam of the interval pair is performed, the lesion's volume change is estimated using the computed difference volume of the two data sets via a novel method that counts partial volume contributions and is insensitive to slight misregistration. A mathematically generated phantom is used to quantify accuracy in the presence of noise. We also quantify the accuracy of our CT liver registrations using microcoils implanted for chemotherapy. A probabilistic liver atlas is used to support automatic masking and liver-focused registration.

There have been significant efforts to build a probabilistic atlas of the brain and to use it for many common applications, such as segmentation and registration. Though the work related to brain atlases can be applied to nonbrain organs, less attention has been paid to actually building an atlas for organs other than the brain. Motivated by the automatic identification of normal organs for applications in radiation therapy treatment planning, we present a method to construct a probabilistic atlas of an abdomen consisting of four organs (i.e., liver, kidneys, and spinal cord). Using 32 noncontrast abdominal computed tomography (CT) scans, 31 were mapped onto one individual scan using thin plate spline as the warping transform and mutual information (MI) as the similarity measure. Except for an initial coarse placement of four control points by the operators, the MI-based registration was automatic. Additionally, the four organs in each of the 32 CT data sets were manually segmented. The manual segmentations were warped onto the "standard" patient space using the same transform computed from their gray scale CT data set and a probabilistic atlas was calculated. Then, the atlas was used to aid the segmentation of low-contrast organs in an additional 20 CT data sets not included in the atlas. By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.

Medical imaging data sets are often corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The authors describe an automatic technique that not only improves the worst situations, such as those encountered with magnetic resonance imaging (MRI) surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets, such as MRI head scans, without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects, such as rf coil design, and patient dependent effects, such as attenuation of overlying tissue experienced both in high field MRI and X-ray computed tomography (CT). The authors show results for several MRI imaging situations including thorax, head, and breast. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation in overlying tissues.

The authors have extended point-based registration to include simultaneous registration of points, lines, and planes, to permit accurate and easily implemented three-dimensional (3-D) registration of multimodal data sets for fusion of clinical anatomic and functional imaging modalities. Constructive geometry is used to define user-identified features where each feature's role in the reconstruction is weighted based on its relative statistical quality, i.e., variance. The algorithm employs singular value decomposition (SVD) and optimization techniques to find the minimum weighted least mean square error (LMSE) affine solution. The new method is generally more accurate due to the availability of more features to register. Notably the error surface contains only one minimum. Different subclasses of affine solutions can be obtained based on appropriateness and sufficiency of the number and type of input features. Preliminary results indicate that this method is useful in multimodal diagnostic image fusion.

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step method was developed to model and then reduce the effect. Using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two polynomial. In the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated data set. The compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. Acting as a preprocessor, the proposed technique can enhance the role of statistical segmentation algorithms in body MRI data sets.

An optimal deformable contour approximation algorithm using a cardinal-form piecewise cubic spline (PCS) curve representation is presented. The approximation is optimal in the sense of least square errors in both location and orientation. The knots are set automatically at high curvature positions. The sample data are generated by a robust edge fragment detection algorithm which is optimal in the sense of a weighted absolute error. An initial contour placement algorithm uses a penalized maximum likelihood algorithm to group the edge fragments together for an initial contour. A controlled deformable contour algorithm refines the initial contour to cover meaningful edge features.>

A multidimensional edge model is established and a first-order estimation for multidimensional edge profiles is proposed. An optimal edge location detection algorithm is developed. The advantages of the algorithm are that (1) it has little dependence on assumptions of edge models, noise models, or smoothing filters, (2) it has better abilities for detecting very weak edges and making less edge orientation errors than other edge detectors, (3) it can handle corners and complicated multidimensional image structures, and (4) it detects different edge types at the same time.>

An algorithm for the optimal edge contour estimation in medical images without a priori shape models is described. The proposed method is an intermediate-level approach compared with the usual low-level edge-detection operators. An optimal and robust contour estimator is derived by the minimization of a risk function which measures the error from both an inappropriate choice of edge contour and an inappropriate choice of the noise model in the image. The result includes Huber's function. If a parametric statistical noise model and the Neyman-Pearson criterion are used, the result is an extension of maximum-likelihood function. A recursive formulation can be implemented by assuming an independent random field and a Markov path model. The assumption of independent statistics can be satisfied by the use of an autoregressive moving-average preprocessor. The problem of varying edge strength is lessened using an adaptive trimmed mean. The robust algorithm is implemented using a priority-tree (stack) structure. The system's performance is illustrated by estimation of lesion boundaries in medical images.>