W Song

Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT

PURPOSE Compressed sensing theory has enabled an accurate, low-dose cone-beam computed tomography (CBCT) reconstruction using a minimal number of noisy projections. However, the reconstruction time remains a significant challenge for practical implementation in the clinic. In this work, we propose a novel gradient projection algorithm, based on the Gradient-Projection-Barzilai-Borwein formulation (GP-BB), that handles the total variation (TV)-norm regularization-based least squares problem for the CBCT reconstruction in a highly efficient manner, with speed acceptable for routine use in the clinic. METHODS CBCT is reconstructed by minimizing an energy function consisting of a data fidelity term and a TV-norm regularization term. Both terms are simultaneously minimized by calculating the gradient projection of the energy function with the step size determined using an approximate Hessian calculation at each iteration, based on the Barzilai-Borwein formulation. To speed up the process, a multiresolution optimization is used. In addition, the entire algorithm was designed to run with a single graphics processing unit (GPU) card. To evaluate the performance, the Shepp-Logan numerical phantom, the CatPhan 600 physical phantom, and a clinically-treated head-and-neck patient were acquired from the TrueBeam™ system (Varian Medical Systems, Palo Alto, CA). For each scan, in total, 364 projections were acquired in a 200° rotation. The imager has 1024 × 768 pixels with 0.388 × 0.388-mm resolution. This was down-sampled to 512 × 384 pixels with 0.776 × 0.776-mm resolution for reconstruction. Evenly spaced angles were subsampled and used for varying the number of projections for the image reconstruction. To assess the performance of our GP-BB algorithm, we have implemented and compared with three compressed sensing-type algorithms, the two of which are popular and published (forward-backward splitting techniques), and the other one with a basic line-search technique. In addition, the conventional Feldkamp-Davis-Kress (FDK) reconstruction of the clinical patient data is compared as well. RESULTS In comparison with the other compressed sensing-type algorithms, our algorithm showed convergence in ≤30 iterations whereas other published algorithms need at least 50 iterations in order to reconstruct the Shepp-Logan phantom image. With the CatPhan phantom, the GP-BB algorithm achieved a clinically-reasonable image with 40 projections in 12 iterations, in less than 12.6 s. This is at least an order of magnitude faster in reconstruction time compared with the most recent reports utilizing GPU technology given the same input projections. For the head-and-neck clinical scan, clinically-reasonable images were obtained from 120 projections in 34-78 s converging in 12-30 iterations. In this reconstruction range (i.e., 120 projections) the image quality is visually similar to or better than the conventional FDK reconstructed images using 364 projections. This represents a dose reduction of nearly 67% (120∕364 projections) while maintaining a reasonable speed in clinical implementation. CONCLUSIONS In this paper, we proposed a novel, fast, low-dose CBCT reconstruction algorithm using the Barzilai-Borwein step-size calculation. A clinically viable head-and-neck image can be obtained within ∼34-78 s while simultaneously cutting the dose by approximately 67%. This makes our GP-BB algorithm potentially useful in an on-line image-guided radiation therapy (IGRT).

Markerless lung tumor tracking and trajectory reconstruction using rotational cone-beam projections: a feasibility study

Algorithms for direct tumor tracking in rotational cone-beam projections and for reconstruction of phase-binned 3D tumor trajectories were developed. The feasibility of the algorithm was demonstrated on a digital phantom, a physical phantom and two patients. Tracking results were obtained by comparing reference templates generated from 4DCT to rotational cone-beam projections. The 95th percentile absolute errors (e(95)) in phantom tracking results did not exceed 1.7 mm in either imager dimension, while e(95) in the patients was 3.3 mm or less. Accurate phase-binned trajectories were reconstructed in each case, with 3D maximum errors of no more than 1.0 mm in the phantoms and 2.0 mm in the patients. This work shows the feasibility of a direct tumor tracking technique for rotational images, and demonstrates that an accurate 3D tumor trajectory can be reconstructed from relatively less accurate tracking results. The ability to reconstruct the tumors average trajectory from a 3D cone-beam CT scan on the day of treatment could allow for better patient setup and quality assurance, while direct tumor tracking in rotational projections could be clinically useful for rotational therapy such as volumetric modulated arc therapy (VMAT).

Liver motion during cone beam computed tomography guided stereotactic body radiation therapy

PURPOSE Understanding motion characteristics of liver such as, interfractional and intrafractional motion variability, difference in motion within different locations in the organ, and their complex relationship with the breathing cycles are particularly important for image-guided liver SBRT. The purpose of this study was to investigate such motion characteristics based on fiducial markers tracked with the x-ray projections of the CBCT scans, taken immediately prior to the treatments. METHODS Twenty liver SBRT patients were analyzed. Each patient had three fiducial markers (2 × 5-mm gold) percutaneously implanted around the gross tumor. The prescription ranged from 2 to 8 fractions per patient. The CBCT projections data for each fraction (∼650 projections∕scan), for each patient, were analyzed and the 2D positions of the markers were extracted using an in-house algorithm. In total, >55 000 x-ray projections were analyzed from 85 CBCT scans. From the 2D extracted positions, a 3D motion trajectory of the markers was constructed, from each CBCT scans, resulting in left-right (LR), anterior-posterior (AP), and cranio-caudal (CC) location information of the markers with >55 000 data points. The authors then analyzed the interfraction and intrafraction liver motion variability, within different locations in the organ, and as a function of the breathing cycle. The authors also compared the motion characteristics against the planning 4DCT and the RPM™ (Varian Medical Systems, Palo Alto, CA) breathing traces. Variations in the appropriate gating window (defined as the percent of the maximum range at which 50% of the marker positions are contained), between fractions were calculated as well. RESULTS The range of motion for the 20 patients were 3.0 ± 2.0 mm, 5.1 ± 3.1 mm, and 17.9 ± 5.1 mm in the planning 4DCT, and 2.8 ± 1.6 mm, 5.3 ± 3.1 mm, and 16.5 ± 5.7 mm in the treatment CBCT, for LR, AP, and CC directions, respectively. The range of respiratory period was 3.9 ± 0.7 and 4.2 ± 0.8 s during the 4DCT simulation and the CBCT scans, respectively. The authors found that breathing-induced AP and CC motions are highly correlated. That is, all markers moved cranially also moved posteriorly and vice versa, irrespective of the location. The LR motion had a more variable relationship with the AP∕CC motions, and appeared random with respect to the location. That is, when the markers moved toward cranial-posterior direction, 58% of the markers moved to the patient-right, 22% of the markers moved to the patient-left, and 20% of the markers had minimal∕none motion. The absolute difference in the motion magnitude between the markers, in different locations within the liver, had a positive correlation with the absolute distance between the markers (R(2) = 0.69, linear-fit). The interfractional gating window varied significantly for some patients, with the largest having 29.4%-56.4% range between fractions. CONCLUSIONS This study analyzed the liver motion characteristics of 20 patients undergoing SBRT. A large variation in motion was observed, interfractionally and intrafractionally, and that as the distance between the markers increased, the difference in the absolute range of motion also increased. This suggests that marker(s) in closest proximity to the target be used.

Locoregional and distant failure following image-guided stereotactic body radiation for early-stage primary lung cancer

PURPOSE To report our institutional experience using image-guided stereotactic body radiation therapy (SBRT) for early stage lung cancer, including an analysis into factors associated with nodal and distant failures (NF, DF). METHODS Forty-eight patients with early-stage primary lung cancer were treated with image-guided SBRT between 2007 and 2009. Median prescription dose was 48 Gy in 4 fractions. Toxicity was graded according to the NCI CTCAE v3.0 scale. RESULTS Local failure was detected in two lesions and actuarial 24-month local control was 95%. At 24 months, the cumulative incidence of NF was 6%, and DF was 29%. Larger lesions (>3 cm) and younger age (<70 years) were the only factors found to be significantly correlated with increased DF (p=0.005 and p=0.015, respectively). A single grade ≥ 3 toxicity was observed. After adjusting for age and lesion size, distant failure was significantly associated with a poorer OS (Cox regression, p=0.0059). CONCLUSION Image-guided SBRT can produce excellent LC rates with minimal toxicity. Distant failure was a major determinant of OS and the most common pattern of failure, indicating a potential role for systemic therapy in younger patients with large lesions.

GPU-based fast low-dose cone beam CT reconstruction via total variation

X-ray imaging dose from serial Cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. The goal of this paper is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We develop a GPU-friendly version of a forward-backward splitting algorithm to solve this problem. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital phantom and a head-and-neck patient case. The performance under low mAs is also validated using physical phantoms. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for clinical purposes. Phantom experiments indicate that CBCT images can be successfully reconstructed under 0.1 mAs/projection. Comparing with the widely used head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, an overall 36 times dose reduction has been achieved. The reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ∼ 100 times faster than similar regularized iterative reconstruction approaches.

Patient-specific motion artifacts in 4DCT

PURPOSE Four-dimensional computed tomography (4DCT) has enhanced images of the thorax and upper abdomen during respiration, but intraphase residual motion artifacts will persist in cine-mode scanning. In this study, the source and magnitude of projection artifacts due to intraphase target motion is investigated. METHODS A theoretical model of geometric uncertainty due to partial projection artifacts in cine-mode 4DCT was derived based on ideal periodic motion. Predicted artifacts were compared to measured errors with a rigid lung phantom attached to a programmable motion platform. Ideal periodic motion and actual patient breathing patterns were used as input for phantom motion. Reconstructed target dimensions were measured along the direction of motion and compared to the actual, known dimensions. RESULTS Artifacts due to intraphase residual motion in cine-mode 4DCT range from a few mm up to a few cm on a given scanner, and can be predicted based on target motion and CT gantry rotation time. Errors in ITV and GTV dimensions were accurately characterized by the theoretical uncertainty at all phases when sinusoidal motion was considered, and in 96% of 300 measurements when patient breathing patterns were used as motion input. When peak-to-peak motion of 1.5 cm is combined with a breathing period of 4 s and gantry rotation time of 1 s, errors due to partial projection artifacts can be greater than 1 cm near midventilation and are a few mm in the inhale and exhale phases. Incorporation of such uncertainty into margin design should be considered in addition to other uncertainties. CONCLUSIONS Artifacts due to intraphase residual motion exist in 4DCT, even for ideal breathing motions (e.g., sine waves). It was determined that these motion artifacts depend on patient-specific tumor motion and CT gantry rotation speed. Thus, if the patient-specific motion parameters are known (i.e., amplitude and period), a patient-specific margin can and should be designed to compensate for this uncertainty.

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

PURPOSE To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy. METHODS Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. RESULTS For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s. CONCLUSIONS Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection.

Four-dimensional cone-beam computed tomography and digital tomosynthesis reconstructions using respiratory signals extracted from transcutaneously inserted metal markers for liver SBRT.

PURPOSE Respiration-induced intrafraction target motion is a concern in liver cancer radiotherapy, especially in stereotactic body radiotherapy (SBRT), and therefore, verification of its motion is necessary. An effective means to localize the liver cancer is to insert metal fiducial markers to or near the tumor with simultaneous imaging using cone-beam computed tomography (CBCT). Utilizing the fiducial markers, the authors have demonstrated a method to generate breath-induced motion signal of liver for reconstructing 4D digital tomosynthesis (4DDTS) and 4DCBCT images based on phasewise and/or amplitudewise sorting of projection data. METHODS The marker extraction algorithm is based on template matching of a prior known marker image and has been coded to optimally extract marker positions in CBCT projections from the On-Board Imager (Varian Medical Systems, Palo Alto, CA). To validate the algorithm, multiple projection images of moving thorax phantom and five patient cases were examined. Upon extraction of the motion signals from the markers, 4D image sorting and image reconstructions were subsequently performed. In the case of incomplete signals due to projections with missing markers, the authors have implemented signal profiling to replace the missing portion. RESULTS The proposed marker extraction algorithm was shown to be very robust and accurate in the phantom and patient cases examined. The maximum discrepancy of the algorithm predicted marker location versus operator selected location was < 1.2 mm, with the overall average of 0.51 +/- 0.15 mm, for 500 projections. The resulting 4DDTS and 4DCBCT images showed clear reduction in motion-induced blur of the markers and the anatomy for an effective image guidance. The signal profiling method was useful in replacing missing signals. CONCLUSIONS The authors have successfully demonstrated that motion tracking of fiducial markers and the subsequent 4D reconstruction of CBCT and DTS are possible. Due to the significant reduction in motion-induced image blur, it is anticipated that such technology will be useful in image-guided liver SBRT treatments.

Ultra-Fast Digital Tomosynthesis Reconstruction Using General-Purpose GPU Programming for Image-Guided Radiation Therapy

The purpose of this work is to demonstrate an ultra-fast reconstruction technique for digital tomosynthesis (DTS) imaging based on the algorithm proposed by Feldkamp, Davis, and Kress (FDK) using standard general-purpose graphics processing unit (GPGPU) programming interface. To this end, the FDK-based DTS algorithm was programmed “in-house” with C language with utilization of 1) GPU and 2) central processing unit (CPU) cards. The GPU card consisted of 480 processing cores (2 × 240 dual chip) with 1,242 MHz processing clock speed and 1,792 MB memory space. In terms of CPU hardware, we used 2.68 GHz clock speed, 12.0 GB DDR3 RAM, on a 64-bit OS. The performance of proposed algorithm was tested on twenty-five patient cases (5 lung, 5 liver, 10 prostate, and 5 head-and-neck) scanned either with a full-fan or half-fan mode on our cone-beam computed tomography (CBCT) system. For the full-fan scans, the projections from 157.5°–202.5° (45°-scan) were used to reconstruct coronal DTS slices, whereas for the half-fan scans, the projections from both 157.5°–202.5° and 337.5°–22.5° (2 × 45°-scan) were used to reconstruct larger FOV coronal DTS slices. For this study, we chose 45°-scan angle that contained ~80 projections for the full-fan and ~160 projections with 2 × 45°-scan angle for the half-fan mode, each with 1024 × 768 pixels with 32-bit precision. Absolute pixel value differences, profiles, and contrast-to-noise ratio (CNR) calculations were performed to compare and evaluate the images reconstructed using GPU- and CPU-based implementations. The time dependence on the reconstruction volume was also tested with (512 × 512) × 16, 32, 64, 128, and 256 slices. In the end, the GPU-based implementation achieved, at most, 1.3 and 2.5 seconds to complete full reconstruction of 512 × 512 × 256 volume, for the full-fan and half-fan modes, respectively. In turn, this meant that our implementation can process > 13 projections-per-second (pps) and > 18 pps for the full-fan and half-fan modes, respectively. Since commercial CBCT system nominally acquires 11 pps (with 1 gantry-revolution-per-minute), our GPU-based implementation is sufficient to handle the incoming projections data as they are acquired and reconstruct the entire volume immediately after completing the scan. In addition, on increasing the number of slices (hence volume) to be reconstructed from 16 to 256, only minimal increases in reconstruction time were observed for the GPU-based implementation where from 0.73 to 1.27 seconds and 1.42 to 2.47 seconds increase were observed for the full-fan and half-fan modes, respectively. This resulted in speed improvement of up to 87 times compared with the CPU-based implementation (for 256 slices case), with visually identical images and small pixel-value discrepancies (< 6.3%), and CNR differences (< 2.3%). With this achievement, we have shown that time allocation for DTS image reconstruction is virtually eliminated and that clinical implementation of this approach has become quite appealing. In addition, with the speed achievement, further image processing and real-time applications that was prohibited prior due to time restrictions can now be tempered with.

Normal tissue complication probability analysis of acute gastrointestinal toxicity in cervical cancer patients undergoing intensity modulated radiation therapy and concurrent cisplatin.

PURPOSE To test the hypothesis that increased bowel radiation dose is associated with acute gastrointestinal (GI) toxicity in cervical cancer patients undergoing concurrent chemotherapy and intensity-modulated radiation therapy (IMRT), using a previously derived normal tissue complication probability (NTCP) model. METHODS Fifty patients with Stage I-III cervical cancer undergoing IMRT and concurrent weekly cisplatin were analyzed. Acute GI toxicity was graded using the Radiation Therapy Oncology Group scale, excluding upper GI events. A logistic model was used to test correlations between acute GI toxicity and bowel dosimetric parameters. The primary objective was to test the association between Grade ≥2 GI toxicity and the volume of bowel receiving ≥45 Gy (V(45)) using the logistic model. RESULTS Twenty-three patients (46%) had Grade ≥2 GI toxicity. The mean (SD) V(45) was 143 mL (99). The mean V(45) values for patients with and without Grade ≥2 GI toxicity were 176 vs. 115 mL, respectively. Twenty patients (40%) had V(45) >150 mL. The proportion of patients with Grade ≥2 GI toxicity with and without V(45) >150 mL was 65% vs. 33% (p = 0.03). Logistic model parameter estimates V50 and γ were 161 mL (95% confidence interval [CI] 60-399) and 0.31 (95% CI 0.04-0.63), respectively. On multivariable logistic regression, increased V(45) was associated with an increased odds of Grade ≥2 GI toxicity (odds ratio 2.19 per 100 mL, 95% CI 1.04-4.63, p = 0.04). CONCLUSIONS Our results support the hypothesis that increasing bowel V(45) is correlated with increased GI toxicity in cervical cancer patients undergoing IMRT and concurrent cisplatin. Reducing bowel V(45) could reduce the risk of Grade ≥2 GI toxicity by approximately 50% per 100 mL of bowel spared.