A study to quantify the effect of patient motion and develop methods to detect and correct for motion during myocardial perfusion imaging on a CZT solid-state dedicated cardiac camera.

BACKGROUND: Due to differences in the design and acquisition parameters on the solid-state CZT cardiac camera the effect of patient motion may vary compared to Anger cameras. This study evaluates the effect of motion, two new methods of three-dimensional (3D) motion detection and a method of motion correction. METHOD: Phantom acquisitions were offset in the X, Y, and Z directions and combined to simulate different types of motion. Motion artifacts were identified using the total perfusion defect and blinded visual interpretation. Motion was detected by registering planar and reconstructed 30 second images, and corrected by summing the aligned reconstructed images. Validation was performed on phantom data. These techniques were then applied to 40 patient studies. RESULTS: Motion ≥10 mm and ≥60 seconds in duration introduced significant artifacts. There was no significant difference (P = .258) between the two methods of motion detection. Motion correction removed artifacts from 9/10 phantom simulations. Superior-inferior motion ≥8 mm was measured on 10% of patient studies, and 5% were affected by motion. Motion in the lateral and anterior-posterior directions was <8 mm. CONCLUSION: Superior-inferior patient motion artifacts have been identified on myocardial perfusion images acquired on a CZT camera. Routine QC to identify studies with significant motion is recommended.

Derivation of aortic distensibility and pulse wave velocity by image registration with a physics-based regularisation term.

Analysis of the cardiovascular system represents a classical problem in which the solid and fluid phases interact intimately, and so is a rich field of application for state-of-the-art fluid-solid interaction (FSI) analyses. In this paper, we focus on the human aorta. Solution of the full FSI problem requires knowledge of the material properties of the wall and information on vessel support. We show that variation of distensibility along the aorta can be obtained from four-dimensional image data using image registration. If pressure data at one point in the vessel are available, these can be converted to absolute values. Alternatively, values of pulse wave velocity along the vessel can be obtained. The quality of the extracted data is improved by the incorporation into the registration of a regularisation term based on the one-dimensional wave equation. The method has been validated using simulated data. For idealised vessels, the accuracy with which the distensibility and wave velocity can be extracted is high (1%-2%). The method is applied to six clinical datasets from patients with mild coarctation, for which it is shown that wave velocity along the aorta is relatively constant.

Using a registration-based motion correction algorithm to correct for respiratory motion during myocardial perfusion imaging.

OBJECTIVE: The aim of the study was to develop and evaluate a registration-based motion correction algorithm as a method of reducing respiratory motion artefacts in myocardial perfusion imaging. MATERIALS AND METHODS: The NCAT software was used to build nine male and nine female computer simulations of myocardial perfusion imaging data, with different respiratory motions and left ventricular ejection fractions. Imaging data were generated at various time points throughout each cardiac cycle. The data were summed over each cardiac cycle, forward projected, normalized, noise added and reconstructed with and without motion correction. Motion correction was performed using an algorithm that aligns images within a projection using nonlinear registrations. A standard simulation with no respiratory motion was also generated for comparison. The algorithm was applied to the standard to determine its effect on images with no respiratory motion. RESULTS: The median difference in mean segmental counts compared with the standard was calculated for each simulation. The mean (range) of these values was 3% (1-6%), 14% (12-16%) and 28% (28-29%) for displacements of 1, 2 and 3 cm, respectively. The largest changes occurred inferiorly and anteriorly. Motion correction reduced these differences to 2% (0-4%), 5% (2-7%) and 7% (7-7%), respectively. The process of correcting for motion reduced the mean counts in all segments by 3% (1-5%). CONCLUSION: Artefacts resulting from respiratory motion are improved using our algorithm when motion is 2 cm or greater.

An accurate, fast and robust method to generate patient-specific cubic Hermite meshes.

In-silico continuum simulations of organ and tissue scale physiology often require a discretisation or mesh of the solution domain. Cubic Hermite meshes provide a smooth representation of anatomy that is well-suited for simulating large deformation mechanics. Models of organ mechanics and deformation have demonstrated significant potential for clinical application. However, the production of a personalised mesh from patient's anatomy using medical images remains a major bottleneck in simulation workflows. To address this issue, we have developed an accurate, fast and automatic method for deriving patient-specific cubic Hermite meshes. The proposed solution customises a predefined template with a fast binary image registration step and a novel cubic Hermite mesh warping constructed using a variational technique. Image registration is used to retrieve the mapping field between the template mesh and the patient images. The variational warping technique then finds a smooth and accurate projection of this field into the basis functions of the mesh. Applying this methodology, cubic Hermite meshes are fitted to the binary description of shape with sub-voxel accuracy and within a few minutes, which is a significant advance over the existing state of the art methods. To demonstrate its clinical utility, a generic cubic Hermite heart biventricular model is personalised to the anatomy of four patients, and the resulting mechanical stability of these customised meshes is successfully demonstrated.

Nonrigid image registration for head and neck cancer radiotherapy treatment planning with PET/CT.

PURPOSE: Head and neck radiotherapy planning with positron emission tomography/computed tomography (PET/CT) requires the images to be reliably registered with treatment planning CT. Acquiring PET/CT in treatment position is problematic, and in practice for some patients it may be beneficial to use diagnostic PET/CT for radiotherapy planning. Therefore, the aim of this study was first to quantify the image registration accuracy of PET/CT to radiotherapy CT and, second, to assess whether PET/CT acquired in diagnostic position can be registered to planning CT. METHODS AND MATERIALS: Positron emission tomography/CT acquired in diagnostic and treatment position for five patients with head and neck cancer was registered to radiotherapy planning CT using both rigid and nonrigid image registration. The root mean squared error for each method was calculated from a set of anatomic landmarks marked by four independent observers. RESULTS: Nonrigid and rigid registration errors for treatment position PET/CT to planning CT were 2.77 +/- 0.80 mm and 4.96 +/- 2.38 mm, respectively, p = 0.001. Applying the nonrigid registration to diagnostic position PET/CT produced a more accurate match to the planning CT than rigid registration of treatment position PET/CT (3.20 +/- 1.22 mm and 4.96 +/- 2.38 mm, respectively, p = 0.012). CONCLUSIONS: Nonrigid registration provides a more accurate registration of head and neck PET/CT to treatment planning CT than rigid registration. In addition, nonrigid registration of PET/CT acquired with patients in a standardized, diagnostic position can provide images registered to planning CT with greater accuracy than a rigid registration of PET/CT images acquired in treatment position. This may allow greater flexibility in the timing of PET/CT for head and neck cancer patients due to undergo radiotherapy.

Automated gamma knife radiosurgery treatment planning with image registration, data-mining, and Nelder-Mead simplex optimization.

Gamma knife treatments are usually planned manually, requiring much expertise and time. We describe a new, fully automatic method of treatment planning. The treatment volume to be planned is first compared with a database of past treatments to find volumes closely matching in size and shape. The treatment parameters of the closest matches are used as starting points for the new treatment plan. Further optimization is performed with the Nelder-Mead simplex method: the coordinates and weight of the isocenters are allowed to vary until a maximally conformal plan specific to the new treatment volume is found. The method was tested on a randomly selected set of 10 acoustic neuromas and 10 meningiomas. Typically, matching a new volume took under 30 seconds. The time for simplex optimization, on a 3 GHz Xeon processor, ranged from under a minute for small volumes (<1000 cubic mm, 2-3 isocenters), to several tens of hours for large volumes (>30,000 cubic mm, >20 isocenters). In 8/10 acoustic neuromas and 8/10 meningiomas, the automatic method found plans with conformation number equal or better than that of the manual plan. In 4/10 acoustic neuromas and 5/10 meningiomas, both overtreatment and undertreatment ratios were equal or better in automated plans. In conclusion, data-mining of past treatments can be used to derive starting parameters for treatment planning. These parameters can then be computer optimized to give good plans automatically.

Piecewise-quadrilateral registration by optical flow--applications in contrast-enhanced MR imaging of the breast.

In this paper we propose a method for the nonrigid registration of contrast-enhanced dynamic sequences of magnetic resonance(MR) images. The algorithm has been developed with accuracy in mind, but also has a clinically viable execution time (i.e. a few minutes) as a goal. The algorithm is driven by multiresolution optical flow with the brightness consistency assumption relaxed, subject to a regularized best-fit within a family of transforms. The particular family of transforms we have employed uses a grid of control points and trilinear interpolation. We present validation results from a study simulating non-rigid deformation by a biomechanical model of the breast, with simulated uptake of a contrast agent. We further present results from applying the algorithm as part of a routine breast cancer screening protocol.