Fixed gantry tomosynthesis system for radiation therapy image guidance based on a multiple source x-ray tube with carbon nanotube cathodes.

The authors present the design and simulation of an imaging system that employs a compact multiple source x-ray tube to produce a tomosynthesis image from a set of projections obtained at a single tube position. The electron sources within the tube are realized using cold cathode carbon nanotube technology. The primary intended application is tomosynthesis-based 3D image guidance during external beam radiation therapy. The tube, which is attached to the gantry of a medical linear accelerator (linac) immediately below the multileaf collimator, operates within the voltage range of 80-160 kVp and contains a total of 52 sources that are arranged in a rectilinear array. This configuration allows for the acquisition of tomographic projections from multiple angles without any need to rotate the linac gantry. The x-ray images are captured by the same amorphous silicon flat panel detector employed for portal imaging on contemporary linacs. The field of view (FOV) of the system corresponds to that part of the volume that is sampled by rays from all sources. The present tube and detector configuration provides an 8 x 8 cm2 FOV in the plane of the linac isocenter when the 40.96 x 40.96 cm2 imaging detector is placed 40 cm from the isocenter. Since this tomosynthesis application utilizes the extremities of the detector to record image detail relating to structures near the isocenter, simultaneous treatment and imaging is possible for most clinical cases, where the treated target is a small region close to the linac isocenter. The tomosynthesis images are reconstructed using the simultaneous iterative reconstruction technique, which is accelerated using a graphic processing unit. The authors present details of the system design as well as simulated performance of the imaging system based on reprojections of patient CT images.

Algorithm for X-ray scatter, beam-hardening, and beam profile correction in diagnostic (kilovoltage) and treatment (megavoltage) cone beam CT.

Quantitative reconstruction of cone beam X-ray computed tomography (CT) datasets requires accurate modeling of scatter, beam-hardening, beam profile, and detector response. Typically, commercial imaging systems use fast empirical corrections that are designed to reduce visible artifacts due to incomplete modeling of the image formation process. In contrast, Monte Carlo (MC) methods are much more accurate but are relatively slow. Scatter kernel superposition (SKS) methods offer a balance between accuracy and computational practicality. We show how a single SKS algorithm can be employed to correct both kilovoltage (kV) energy (diagnostic) and megavoltage (MV) energy (treatment) X-ray images. Using MC models of kV and MV imaging systems, we map intensities recorded on an amorphous silicon flat panel detector to water-equivalent thicknesses (WETs). Scattergrams are derived from acquired projection images using scatter kernels indexed by the local WET values and are then iteratively refined using a scatter magnitude bounding scheme that allows the algorithm to accommodate the very high scatter-to-primary ratios encountered in kV imaging. The algorithm recovers radiological thicknesses to within 9% of the true value at both kV and megavolt energies. Nonuniformity in CT reconstructions of homogeneous phantoms is reduced by an average of 76% over a wide range of beam energies and phantom geometries.

Automatic coregistration of volumetric images based on implanted fiducial markers.

The accurate delivery of external beam radiation therapy is often facilitated through the implantation of radio-opaque fiducial markers (gold seeds). Before the delivery of each treatment fraction, seed positions can be determined via low dose volumetric imaging. By registering these seed locations with the corresponding locations in the previously acquired treatment planning computed tomographic (CT) scan, it is possible to adjust the patient position so that seed displacement is accommodated. The authors present an unsupervised automatic algorithm that identifies seeds in both planning and pretreatment images and subsequently determines a rigid geometric transformation between the two sets. The algorithm is applied to the imaging series of ten prostate cancer patients. Each test series is comprised of a single multislice planning CT and multiple megavoltage conebeam (MVCB) images. Each MVCB dataset is obtained immediately prior to a subsequent treatment session. Seed locations were determined to within 1 mm with an accuracy of 97 +/- 6.1% for datasets obtained by application of a mean imaging dose of 3.5 cGy per study. False positives occurred in three separate instances, but only when datasets were obtained at imaging doses too low to enable fiducial resolution by a human operator, or when the prostate gland had undergone large displacement or significant deformation. The registration procedure requires under nine seconds of computation time on a typical contemporary computer workstation.

Focused beam-stop array for the measurement of scatter in megavoltage portal and cone beam CT imaging.

We describe a focused beam-stop array (BSA) for the measurement of object scatter in imaging systems that utilize x-ray beams in the megavoltage (MV) energy range. The BSA consists of 64 doubly truncated tungsten cone elements of 0.5 cm maximum diameter that are arranged in a regular array on an acrylic slab. The BSA is placed in the accessory tray of a medical linear accelerator at a distance of approximately 50 cm from the focal spot. We derive an expression that allows us to estimate the scatter in an image taken without the array present, given image values in a second image with the array in place. The presence of the array reduces fluence incident on the imaged object. This leads to an object-dependent underestimation bias in the scatter measurements. We apply corrections in order to address this issue. We compare estimates of the flat panel detector response to scatter obtained using the BSA to those derived from Monte Carlo simulations. We find that the two estimates agree to within 10% in terms of RMS error for 30 cm x 30 cm water slabs in the thickness range of 10-30 cm. Larger errors in the scatter estimates are encountered for thinner objects, probably owing to extrafocal radiation sources. However, RMS errors in the estimates of primary images are no more than 5% for water slab thicknesses in the range of 1-30 cm. The BSA scatter estimates are also used to correct cone beam tomographic projections. Maximum deviations of central profiles of uniform water phantoms are reduced from 193 to 19 HU after application of corrections for scatter, beam hardening, and lateral truncation that are based on the BSA-derived scatter estimate. The same corrections remove the typical cupping artifact from both phantom and patient images. The BSA proves to be a useful tool for quantifying and removing image scatter, as well as for validating models of MV imaging systems.

CT truncation artifact removal using water-equivalent thicknesses derived from truncated projection data.

PURPOSE: Large patient anatomies and limited imaging field-of-view (FOV) lead to truncation of CT projections. Truncation introduces serious artifacts into reconstructed images, including central cupping and bright external rings. FOV may be increased using laterally offset detectors, but this requires sophisticated imaging hardware and full angular scanning. We propose a novel method to complete truncated projections based on the observation that the thickness of the patient may be estimated along the projection rays by calculating water-equivalent thicknesses (WET). These values are not at all affected by truncation and thus constitute valuable auxiliary information. METHODS: We parameterize pairs of points along each ray that intersects the unknown object boundary. These points are separated by the measured WET value (obtained from projections that have been corrected for scatter and beam-hardening). We assume, for all large body parts, that the patient outline may be roughly approximated as an ellipse. Using a deterministic optimization algorithm, we simultaneously estimate the point positions and ellipse parameters by minimizing the distance between point sets and the ellipse boundary. The optimal ellipse is used to complete the truncated projections. Reconstruction then ensues. We apply the algorithm to a severely truncated CT dataset of a typical abdomen. RESULTS: The RMS error between complete data and truncated reconstructions (corrected using an empirical extrapolation approach) is 20.4% for an abdominal dataset. The new algorithm reduces this error to 1.0%. CONCLUSION: Even thought the algorithm assumes an elliptical patient cross-section, truly impressive increases in quantitative image quality are observed. The presence of pelvic bone in the image does not appreciably bias the ellipse position even though it does bias the thickness estimates for some rays. The algorithm incurs low computational cost and is suitable for on-line clinical workflows.