Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

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

Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. 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 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. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 seconds, 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 seconds on the same 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 seconds

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

open5siThe 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.Fassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, GuidoFassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, Guid

Application of a spring-dashpot system to clinical lung tumor motion data

A spring-dashpot system based on the Voigt model was developed to model the correlation between abdominal respiratory motion and tumor motion during lung radiotherapy. The model was applied to clinical data comprising 52 treatment beams from 10 patients, treated on the Mitsubishi Real-Time Radiation Therapy system, Sapporo, Japan. In Stage 1, model parameters were optimized for individual patients and beams to determine reference values and to investigate how well the model can describe the data. In Stage 2, for each patient the optimal parameters determined for a single beam were applied to data from other beams to investigate whether a beam-specific set of model parameters is sufficient to model tumor motion over a course of treatment. In Stage 1 the baseline root mean square (RMS) residual error for all individually-optimized beam data was 0.90 plus or minus 0.40 mm. In Stage 2, patient-specific model parameters based on a single beam were found to model the tumor position closely, even for irregular beam data, with a mean increase with respect to Stage 1 values in RMS error of 0.37 mm. On average the obtained model output for the tumor position was 95% of the time within an absolute bound of 2.0 mm and 2.6 mm in Stage 1 and 2, respectively. The model was capable of dealing with baseline, amplitude and frequency variations of the input data, as well as phase shifts between the input tumor and output abdominal signals. These results indicate that it may be feasible to collect patient-specific model parameters during or prior to the first treatment, and then retain these for the rest of the treatment period. The model has potential for clinical application during radiotherapy treatment of lung tumors

Verifying 4D gated radiotherapy using time-integrated electronic portal imaging: a phantom and clinical study

<p>Abstract</p> <p>Background</p> <p>Respiration-gated radiotherapy (RGRT) can decrease treatment toxicity by allowing for smaller treatment volumes for mobile tumors. RGRT is commonly performed using external surrogates of tumor motion. We describe the use of time-integrated electronic portal imaging (TI-EPI) to verify the position of internal structures during RGRT delivery</p> <p>Methods</p> <p>TI-EPI portals were generated by continuously collecting exit dose data (aSi500 EPID, Portal vision, Varian Medical Systems) when a respiratory motion phantom was irradiated during expiration, inspiration and free breathing phases. RGRT was delivered using the Varian RPM system, and grey value profile plots over a fixed trajectory were used to study object positions. Time-related positional information was derived by subtracting grey values from TI-EPI portals sharing the pixel matrix. TI-EPI portals were also collected in 2 patients undergoing RPM-triggered RGRT for a lung and hepatic tumor (with fiducial markers), and corresponding planning 4-dimensional CT (4DCT) scans were analyzed for motion amplitude.</p> <p>Results</p> <p>Integral grey values of phantom TI-EPI portals correlated well with mean object position in all respiratory phases. Cranio-caudal motion of internal structures ranged from 17.5–20.0 mm on planning 4DCT scans. TI-EPI of bronchial images reproduced with a mean value of 5.3 mm (1 SD 3.0 mm) located cranial to planned position. Mean hepatic fiducial markers reproduced with 3.2 mm (SD 2.2 mm) caudal to planned position. After bony alignment to exclude set-up errors, mean displacement in the two structures was 2.8 mm and 1.4 mm, respectively, and corresponding reproducibility in anatomy improved to 1.6 mm (1 SD).</p> <p>Conclusion</p> <p>TI-EPI appears to be a promising method for verifying delivery of RGRT. The RPM system was a good indirect surrogate of internal anatomy, but use of TI-EPI allowed for a direct link between anatomy and breathing patterns.</p

A Measure of Voxel Similarity for Improving the Image-based Quantification of Tissue Structure and Function

Therapeutic response assessment is a key component in adaptive image-guided radiotherapy. Conventional anatomic measures of response offer little information about the spatial distribution of tumor change. Recently developed voxel-wise response assessment methods operating on functional and biological imaging are better capable of evaluating the heterogeneity of response within the tumor, and thus may yield greater sensitivity than conventional approaches. However, voxel-wise analyses are limited by local registration uncertainties inherent to longitudinal imaging of tumors with changing morphology. A multi-resolution local histogram (LH) moment-based measure of voxel similarity was developed for the purpose of assessing the strength of correspondence between voxels of serial tumor images. This measure was first benchmarked through a series of experiments designed to establish robustness to image intensity variation and sensitivity to alterations in tissue structure through application of simulated deformations. The LH similarity method was subsequently developed as a means of mapping the spatial extent of structural change in tumors through the incorporation of an estimate of image complexity. The change maps were applied to a voxel-wise analysis of diffusion-weighted magnetic resonance imaging of patients with glioblastoma, acquired pre- and post-chemoradiotherapy. The sensitivity of the voxel-wise analysis in differentiating responding/stable patients from non-responding/progressing patients was improved by stratifying the analysis voxels according to regions of interest (ROI) based on the LH similarity-based estimate of tumor change. Meaningful correspondence relationships between evaluated voxels are essential for accurate image-based quantification of tumor structure and function with voxel-wise analysis techniques. The LH similarity methods developed here can robustly evaluate the quality of spatial and temporal voxel correspondence relationships and provide an automated tool for ROI selection and voxel change stratification. It is readily extendable to the analysis of the wide array of anatomic, functional and biological imaging currently used to characterize tumors, guide therapy and assess response.Ph

Concentration of oil-in-water emulsions by cross-flow ultrafiltration

The concentration of waste cutting oils by ultrafiltration was studied in a thin channel crossflow unit using regenerated cellulose membranes having molecular weight cutoffs (MWCOs) of 10 000 and 30 000. Experiments were carried out to determine the effects of emulsion concentration (1-5% w/v), transmembrane pressure (0.103-1.241 MPa), and temperature (25-45$sp circ$C) on the permeation rate of clean water and on the oil content of the water, using a model cutting oil emulsion having a mean droplet size of 2.8 um.The emulsion concentration had no effect on the permeate flux. Increasing temperature or pressure increased the flux. The concentration of oil in the permeate was directly proportional to the emulsion feed concentration, yielding rejections of oil greater than 0.995. At feed concentrations above 5% w/v, the membrane fouled. The permeate fluxes were approximately 1.5 L/min m$sp2$ for the 10 000 MWCO membrane, and 5.0 /min L/min m2 for the 30 000 MWCO membrane. A statistical model was developed relating ie permeate flux to the temperature and pressure.The fluxes were compared to a gel polarization model which was based on shear-driven diffusion. The experimental fluxes were somewhat higher than the predicted fluxes, with better agreement for the 50 000 MWCO membrane