APOE ε4/ε4 homozygotes with early Alzheimer's disease show accelerated hippocampal atrophy and cortical thinning that correlates with cognitive decline.

INTRODUCTION: Hippocampal volume (HV) and cortical thickness are commonly used imaging biomarkers in Alzheimer's disease (AD) trials, and may have utility as selection criteria for enrichment strategies. Atrophy rates of these measures, in the high-risk apolipoprotein E (APOE) ε4/ε4 homozygous AD subjects are unknown. METHODS: Data from Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and a tramiprosate trial were analyzed in APOE ε4/ε4 and APOE ε3/ε3 subjects with mild cognitive impairment (MCI) or mild AD. Magnetic resonance imaging (MRI) data were centrally processed using FreeSurfer; total HV and composite average cortical thickness were derived and adjusted for age, head size, and education. Volumetric changes from baseline were assessed using Boundary Shift Integral, and correlated with cognitive changes. RESULTS: APOE ε4/ε4 MCI subjects showed significantly higher % HV atrophy and cortical thinning at 12 months (4.4%, 3.1%, n = 29) compared to APOE ε3/ε3 subjects (2.8%, 1.8%, n = 93) and similarly in mild AD (7.4%, 4.7% n = 21 vs 5.4%, 3.3% n = 29). Differences were all significant at 24 months. Over 24 months, HV atrophy and cortical thinning correlated significantly with Alzheimer's Disease Assessment Scale-Cognitive subscale worsening in APOE ε4/ε4 MCI subjects, but not in mild AD. DISCUSSION: Correlation of volumetric measures to cognitive change in APOE ε4/ε4 subjects with early AD supports their role as efficacy biomarkers. If confirmed in a Phase 3 trial with ALZ-801 (pro-drug of tramiprosate) in APOE ε4/ε4 early AD subjects, it may allow their use as surrogate outcomes in future treatment or prevention trials in AD.

An image-based method to synchronize cone-beam CT and optical surface tracking.

The integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.

Meshless deformable models for 3D cardiac motion and strain analysis from tagged MRI.

Tagged magnetic resonance imaging (TMRI) provides a direct and noninvasive way to visualize the in-wall deformation of the myocardium. Due to the through-plane motion, the tracking of 3D trajectories of the material points and the computation of 3D strain field call for the necessity of building 3D cardiac deformable models. The intersections of three stacks of orthogonal tagging planes are material points in the myocardium. With these intersections as control points, 3D motion can be reconstructed with a novel meshless deformable model (MDM). Volumetric MDMs describe an object as point cloud inside the object boundary and the coordinate of each point can be written in parametric functions. A generic heart mesh is registered on the TMRI with polar decomposition. A 3D MDM is generated and deformed with MR image tagging lines. Volumetric MDMs are deformed by calculating the dynamics function and minimizing the local Laplacian coordinates. The similarity transformation of each point is computed by assuming its neighboring points are making the same transformation. The deformation is computed iteratively until the control points match the target positions in the consecutive image frame. The 3D strain field is computed from the 3D displacement field with moving least squares. We demonstrate that MDMs outperformed the finite element method and the spline method with a numerical phantom. Meshless deformable models can track the trajectory of any material point in the myocardium and compute the 3D strain field of any particular area. The experimental results on in vivo healthy and patient heart MRI show that the MDM can fully recover the myocardium motion in three dimensions.

Tumor tracking method based on a deformable 4D CT breathing motion model driven by an external surface surrogate.

PURPOSE: To develop a tumor tracking method based on a surrogate-driven motion model, which provides noninvasive dynamic localization of extracranial targets for the compensation of respiration-induced intrafraction motion in high-precision radiation therapy. METHODS AND MATERIALS: The proposed approach is based on a patient-specific breathing motion model, derived a priori from 4-dimensional planning computed tomography (CT) images. Model parameters (respiratory baseline, amplitude, and phase) are retrieved and updated at each treatment fraction according to in-room radiography acquisition and optical surface imaging. The baseline parameter is adapted to the interfraction variations obtained from the daily cone beam (CB) CT scan. The respiratory amplitude and phase are extracted from an external breathing surrogate, estimated from the displacement of the patient thoracoabdominal surface, acquired with a noninvasive surface imaging device. The developed method was tested on a database of 7 lung cancer patients, including the synchronized information on internal and external respiratory motion during a CBCT scan. RESULTS: About 30 seconds of simultaneous acquisition of CBCT and optical surface images were analyzed for each patient. The tumor trajectories identified in CBCT projections were used as reference and compared with the target trajectories estimated from surface displacement with the a priori motion model. The resulting absolute differences between the reference and estimated tumor motion along the 2 image dimensions ranged between 0.7 and 2.4 mm; the measured phase shifts did not exceed 7% of the breathing cycle length. CONCLUSIONS: We investigated a tumor tracking method that integrates breathing motion information provided by the 4-dimensional planning CT with surface imaging at the time of treatment, representing an alternative approach to point-based external-internal correlation models. Although an in-room radiograph-based assessment of the reliability of the motion model is envisaged, the developed technique does not involve the estimation and continuous update of correlation parameters, thus requiring a less intense use of invasive imaging.

Is abdominal compression useful in lung stereotactic body radiation therapy? A 4DCT and dosimetric lobe-dependent study.

PURPOSE: To determine the usefulness of abdominal compression in lung stereotactic body radiation therapy (SBRT) depending on lobe tumor location. MATERIALS AND METHODS: Twenty-seven non-small cell lung cancer patients were immobilized in the Stereotactic Body Frame™ (Elekta). Eighteen tumors were located in an upper lobe, one in the middle lobe and nine in a lower lobe (one patient had two lesions). All patients underwent two four-dimensional computed tomography (4DCT) scans, with and without abdominal compression. Three-dimensional tumor motion amplitude was determined using manual landmark annotation. We also determined the internal target volume (ITV) and the influence of abdominal compression on lung dose-volume histograms. RESULTS: The mean reduction of tumor motion amplitude was 3.5 mm (p = 0.009) for lower lobe tumors and 0.8 mm (p = 0.026) for upper/middle lobe locations. Compression increased tumor motion in 5 cases. Mean ITV reduction was 3.6 cm(3) (p = 0.039) for lower lobe and 0.2 cm(3) (p = 0.048) for upper/middle lobe lesions. Dosimetric gain of the compression for lung sparing was not clinically relevant. CONCLUSIONS: The most significant impact of abdominal compression was obtained in patients with lower lobe tumors. However, minor or negative effects of compression were reported for other patients and lung sparing was not substantially improved. At our institute, patients with upper or middle lobe lesions are now systematically treated without compression and the usefulness of compression for lower lobe tumors is evaluated on an individual basis.

Multi-dimensional respiratory motion tracking from markerless optical surface imaging based on deformable mesh registration.

Real-time optical surface imaging systems offer a non-invasive way to monitor intra-fraction motion of a patient's thorax surface during radiotherapy treatments. Due to lack of point correspondence in dynamic surface acquisition, such systems cannot currently provide 3D motion tracking at specific surface landmarks, as available in optical technologies based on passive markers. We propose to apply deformable mesh registration to extract surface point trajectories from markerless optical imaging, thus yielding multi-dimensional breathing traces. The investigated approach is based on a non-rigid extension of the iterative closest point algorithm, using a locally affine regularization. The accuracy in tracking breathing motion was quantified in a group of healthy volunteers, by pair-wise registering the thoraco-abdominal surfaces acquired at three different respiratory phases using a clinically available optical system. The motion tracking accuracy proved to be maximal in the abdominal region, where breathing motion mostly occurs, with average errors of 1.09 mm. The results demonstrate the feasibility of recovering multi-dimensional breathing motion from markerless optical surface acquisitions by using the implemented deformable registration algorithm. The approach can potentially improve respiratory motion management in radiation therapy, including motion artefact reduction or tumour motion compensation by means of internal/external correlation models.

B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation.

A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images.

A graph-based approach for automatic cardiac tractography.

A new automatic algorithm for assessing fiber-bundle organization in the human heart using diffusion-tensor magnetic resonance imaging is presented. The proposed approach distinguishes from the locally "greedy" paradigm, which uses voxel-wise seed initialization intrinsic to conventional tracking algorithms. It formulates the fiber tracking problem as the global problem of computing paths in a boolean-weighted undirected graph, where each voxel is a vertex and each pair of neighboring voxels is connected with an edge. This leads to a global optimization task that can be solved by iterated conditional modes-like algorithms or Metropolis-type annealing. A new deterministic optimization strategy, namely iterated conditional modes with α-relaxation using (t(2))- and (t(4))-moves, is also proposed; it has similar performance to annealing but offers a substantial computational gain. This approach offers some important benefits. The global nature of our tractography method reduces sensitivity to noise and modeling errors. The discrete framework allows an optimal balance between the density of fiber bundles and the amount of available data. Besides, seed points are no longer needed; fibers are predicted in one shot for the whole diffusion-tensor magnetic resonance imaging volume, in a completely automatic way.

A dynamic elastic model for segmentation and tracking of the heart in MR image sequences.

Strong prior models are a prerequisite for reliable spatio-temporal cardiac image analysis. While several cardiac models have been presented in the past, many of them are either too complex for their parameters to be estimated on the sole basis of MR Images, or overly simplified. In this paper, we present a novel dynamic model, based on the equation of dynamics for elastic materials and on Fourier filtering. The explicit use of dynamics allows us to enforce periodicity and temporal smoothness constraints. We propose an algorithm to solve the continuous dynamical problem associated to numerically adapting the model to the image sequence. Using a simple 1D example, we show how temporal filtering can help removing noise while ensuring the periodicity and smoothness of solutions. The proposed dynamic model is quantitatively evaluated on a database of 15 patients which shows its performance and limitations. Also, the ability of the model to capture cardiac motion is demonstrated on synthetic cardiac sequences. Moreover, existence, uniqueness of the solution and numerical convergence of the algorithm can be demonstrated.

Towards production-level cardiac image analysis with grids.

Production exploitation of cardiac image analysis tools is hampered by the lack of proper IT infrastructure in health institutions, the non trivial integration of heterogeneous codes in coherent analysis procedures, and the need to achieve complete automation of these methods. HealthGrids are promising technologies to address these difficulties. This paper details how they can be complemented by high level problem solving environments such as workflow managers to improve the performance of applications both in terms of execution time and robustness of results. Two of the most important important cardiac image analysis tasks are considered, namely myocardium segmentation and motion estimation in a 4D sequence. Results are shown on the corresponding pipelines, using two different execution environments on the EGEE grid production infrastructure.

A new dynamic elastic model for cardiac image analysis.

We believe that strong prior models are a pre-requisite for reliable spatio-temporal cardiac image analysis. While several cardiac models have been presented in the past, many of them are either too complex for their parameters to be estimated on the sole basis of MR Images, or overly simplified. In this paper, we present a novel bio-inspired dynamic model, based on the equation of dynamics for elastic materials. The explicit use of dynamics allows us to enforce periodicity and temporal smoothness constraints. We study two different methods for solving the resulting equations, and show them to be equivalent. We show how temporal filtering can help to remove noise and ensure the periodicity and smoothness of solutions. Finally, we show some results in 1D and on a synthetic model to illustrate the benefits of our new dynamic model and to show how it can be used to analyze cardiac MR images.