Supratik Bose

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.

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

Large patient anatomies and limited imaging fleld-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. 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. 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%. 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.

A fast and high-quality cone beam reconstruction pipeline using the GPU

Cone beam scanners have evolved rapidly in the past years. Increasing sampling resolution of the projection images and the desire to reconstruct high resolution output volumes increases both the memory consumption and the processing time considerably. In order to keep the processing time down new strategies for memory management are required as well as new algorithmic implementations of the reconstruction pipeline. In this paper, we present a fast and high-quality cone beam reconstruction pipeline using the Graphics Processing Unit (GPU). This pipeline includes the backprojection process and also pre-filtering and post-filtering stages. In particular, we focus on a subset of five stages, but more stages can be integrated easily. In the pre-filtering stage, we first reduce the amount of noise in the acquired projection images by a non-linear curvature-based smoothing algorithm. Then, we apply a high-pass filter as required by the inverse Radon transform. Next, the backprojection pass reconstructs a raw 3D volume. In post-processing, we first filter the volume by a ring artifact removal. Then, we remove cupping artifacts by our novel uniformity correction algorithm. We present the algorithm in detail. In order to execute the pipeline as quickly as possible we take advantage of GPUs that have proven to be very fast parallel processors for numerical problems. Unfortunately, both the projection images and the reconstruction volume are too large to fit into 512 MB of GPU memory. Therefore, we present an efficient memory management strategy that minimizes the bus transfer between main memory and GPU memory. Our results show a 4 times performance gain over a highly optimized CPU implementation using SSE2/3 commands. At the same time, the image quality is comparable to the CPU results with an average per pixel difference of 10-5.

Beam-centric algorithm for pretreatment patient position correction in external beam radiation therapy

PURPOSE In current image guided pretreatment patient position adjustment methods, image registration is used to determine alignment parameters. Since most positioning hardware lacks the full six degrees of freedom (DOF), accuracy is compromised. The authors show that such compromises are often unnecessary when one models the planned treatment beams as part of the adjustment calculation process. The authors present a flexible algorithm for determining optimal realizable adjustments for both step-and-shoot and arc delivery methods. METHODS The beam shape model is based on the polygonal intersection of each beam segment with the plane in pretreatment image volume that passes through machine isocenter perpendicular to the central axis of the beam. Under a virtual six-DOF correction, ideal positions of these polygon vertices are computed. The proposed method determines the couch, gantry, and collimator adjustments that minimize the total mismatch of all vertices over all segments with respect to their ideal positions. Using this geometric error metric as a function of the number of available DOF, the user may select the most desirable correction regime. RESULTS For a simulated treatment plan consisting of three equally weighted coplanar fixed beams, the authors achieve a 7% residual geometric error (with respect to the ideal correction, considered 0% error) by applying gantry rotation as well as translation and isocentric rotation of the couch. For a clinical head-and-neck intensity modulated radiotherapy plan with seven beams and five segments per beam, the corresponding error is 6%. Correction involving only couch translation (typical clinical practice) leads to a much larger 18% mismatch. Clinically significant consequences of more accurate adjustment are apparent in the dose volume histograms of target and critical structures. CONCLUSIONS The algorithm achieves improvements in delivery accuracy using standard delivery hardware without significantly increasing total treatment session duration. It encourages parsimonious utilization of all available DOF. Finally, in certain cases, it obviates the need of a robotic couch having six DOF for the correction of patient displacement and rotations.

TH‐D‐VaIB‐03: Unified Algorithm for KV and MV Scatter and Beam‐Hardening Correction Using the Convolution‐Superposition Method

Purpose: Quantitative cone beam CT(CBCT) is essential for advanced radiation oncology (RO) applications such as portal image‐based 3D dose reconstruction. Quantitative CT requires accurate modeling of scatter, beam‐hardening and detector response. Scatter correction methods are typically semi‐empirical in nature and are designed to reduce visible artifacts while incurring low computational cost. In contrast, Monte Carlo(MC) methods are accurate but impractically slow. Convolution‐superposition (CS) scatter models offer a good balance between accuracy and computational complexity. We show how CS can be employed to implement a unified correction method that enables quantitative kV and MV imaging.Method and Materials: (1) We perform detailed MC modeling of the kV and MV cone beam imaging systems. (2) Using MC, we generate calibration data that map intensities recorded on the flat panel imagers to water‐equivalent thicknesses (WETs). (3) The MC models are used to generate pencil beam kernels for water cylinders of varying thickness. (4) Scattergrams are generated from acquired projection images via the CS method using these kernels indexed by the WET at each pixel. (5) Scattergrams are iteratively refined using a multiplicative correction formula that ensures that the estimated primary image remains non‐negative even when scatter‐to‐primary ratios are very high. (6) The FDK reconstruction algorithm is applied directly to the thickness maps corresponding to the estimated primary images.Results: The algorithm is able to reduce maximum non‐uniformity in the reconstruction of a 16cm cylindrical homogeneous tissue equivalent phantom from 11.7% to 1.5%. When applied to a challenging 35cm × 22.5cm oblong water phantom, a non‐uniformity reduction in from 36% to 2.5% is achieved. A dataset of 200 1024×1024 projections can be processed in 25 seconds. Conclusions: CS methods can be used at both kV and MV energies to enable reconstruction of quantitative CBCTimages.Conflict of Interest: Supported by Siemens.

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 imagingdose of 3.5 cGy per study. False positives occurred in three separate instances, but only when datasets were obtained at imagingdoses 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.

Automatic co-registration 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 (seeds). Before the delivery of each treatment fraction, seed positions can be determined via volumetric imaging. By registering these seed locations with the corresponding locations in the previously acquired treatment planning CT, it is possible to adjust the patient position or the treatment plan so that seed displacement is accommodated. We present an automatic algorithm that identifies seeds in both planning and pretreatment images and subsequently determines the geometric transformation between the two sets. The algorithm is applied to the imaging series of 10 prostate cancer patients. Each series is comprised of a single multislice planning CT and several megavoltage conebeam CT images obtained immediately prior to a subsequent treatment session. Seed locations were determined for 164 images to within 1 mm with an accuracy of 98 plusmn 6.3%.

TU‐FF‐A4‐05: Physical Performance and Image Quality of Megavoltage Cone‐Beam CT

Purpose: To evaluate the physical performance of Megavoltage Cone‐Beam CT (MVCBCT) and to optimize system and reconstruction settings for image quality. Methods and Materials: Several system parameters were varied to quantify their impact on image quality including the exposure (2.7, 4.5, 9.0, 18.0 and 54.0 MU), the cranio‐caudal field‐size (2, 5, 15, 27.4 cm), the voxel size (0.5, 1, 2 mm)and the slice thickness (1, 3, 5 mm). For the reconstruction algorithm, we investigated binning, averaging and diffusion of raw projections as well as four different backprojection filters. Two CT♯ normalization factors were compared. A head size water cylinder with different configurations of CT inserts was used to measure contrast‐to‐noise ratio (CNR) and uniformity. The point‐spread function (PSF) was obtained using a brass wire and an iterative edge blurring technique. The current MVCBCT product settings were used as the performance baseline for comparison. Results: Beam intensity variations per projection of up to 35.4% were observed for a 2.7 MU MVCBCT acquisition. Such variations were mostly captured in the system MU reading per frame and did not affect the CNR. The non‐uniformity was reduced from 18.8% to 14.2% by closing the Y‐jaws for imaging. An optimized reconstruction protocol was developed and showed an improvement of 60% in CNR with a penalty of only 8%for the PSF and an increase of 1 to 2 minutes in reconstruction time. The application of diffusion filtering for 9 MU reconstructions resulted in similar CNR improvement to using 5 times more dose with the current reconstruction protocol. Using reconstructions with smaller voxels and thicker slices can further improve the CNR.Conclusion: The image quality stability of MVCBCT over a 4‐month period was excellent. Soft‐tissue visualization with MVCBCT can be substantially improved with proper system settings. Conflict of Interest: Research sponsored by Siemens OCS.

SU‐FF‐I‐53: The Impact of Portal Imager Shifts and the Assumption of Rigid Isocentricity On Megavoltage Cone Beam CT Images

Purpose: Linear accelerators and the current flat panel positioners do not represent a rigid isocentric imaging system. To correct for this effect in the reconstruction process of Megavoltage Cone Beam CT, the group uses projection matrices that are obtained from geometric calibration. This project studies the effect of deviations in detector position from the calibrated geometry which may occur over time. Method and Materials: To simulate the effect of portal imager shifts, we translated projection images before performing the reconstruction. Synthetic projection matrices that assume perfect isocentricity were also produced to study the utility of our geometric calibration. To accentuate the observed effects, we first used noise-free simulated projections of a CT phantom as well as reconstructions of small, high-contrast ball bearings. We also acquired anatomical images of a Rando head with an acceptable clinical dose (8 MU) to verify our capacity to identify the previously observed artifacts. Results: A pure flat panel shift of 2 mm along the longitudinal direction causes the same shift in the reconstruction volume. A 2 mm shift in the lateral direction however, greatly degrades the image quality with streaking and half-moon shadow artifacts. The orientation of those artifacts depends on the start and end angle of the acquisition. Since most of the flat panel flex was previously measured to be in the longitudinal direction, the use of synthetic projection matrices caused a blurring of the image in the longitudinal direction. Conclusion: The calibrated projection matrices play an important role at conserving the image quality around high contrast objects. A 2 mm lateral shift away from the calibration will likely be detectable in high contrast regions of anatomical images. Longitudinal shifts will not degrade the image but will cause a positioning error. Conflict of Interest: This research is supported by Siemens OCS.